NCO User Guide

\input texinfo @c -*-texinfo-*- $Header$ Purpose: TeXInfo documentation for netCDF Operators (NCO) URL: http://nco.sf.net/nco.texi Copyright (C) 1995--present Charlie Zender Permission is granted to copy, distribute and/or modify this document under the terms of the GNU Free Documentation License, Version 1.3 or any later version published by the Free Software Foundation; with no Invariant Sections, no Front-Cover Texts, and no Back-Cover Texts. The license is available online at http://www.gnu.org/copyleft/fdl.html The original author of this software, Charlie Zender, seeks to improve it with your suggestions, contributions, bug-reports, and patches. Charlie Zender <surname at uci dot edu> (yes, my surname is zender) Department of Earth System Science 3200 Croul Hall University of California, Irvine Irvine, CA 92697-3100 After editing any hyperlink locations, Emacs users update indices with C-c C-u C-a texinfo-all-menus-update C-c C-u C-e texitnfo-every-node-update Multiple files: M-x texinfo-multiple-files-update Usage: export TEX='tex --src-specials' cd ~/nco/doc;texi2dvi nco.texi;makeinfo --html --ifinfo --no-split --output=nco.html nco.texi;makeinfo nco.texi;dvips -o nco.ps nco.dvi;texi2dvi --pdf nco.texi;makeinfo --xml --ifinfo --no-split --output=nco.xml nco.texi;makeinfo --no-headers --output=nco.txt nco.texi cd ~/nco/doc;/usr/bin/scp index.shtml nco_news.shtml ChangeLog TODO VERSION nco.dvi nco.html nco.info* nco.pdf nco.ps nco.texi nco.txt nco.xml ../README ../data/ncap.in ../data/ncap2.in zender,nco@web.sf.net:/home/project-web/nco/htdocs;cd - cd ~/nco/doc;/usr/bin/scp index.shtml nco_news.shtml ChangeLog TODO VERSION nco.dvi nco.html nco.info* nco.pdf nco.ps nco.texi nco.txt nco.xml ../README ../data/ncap.in ../data/ncap2.in dust.ess.uci.edu:Sites/nco;cd - dvips -o nco.ps nco.dvi dvips -Ppdf -G0 -o nco.ps nco.dvi makeinfo --html --ifinfo --no-split --output=nco.html nco.texi makeinfo --no-split --output=nco.info nco.texi makeinfo --no-headers --no-split --output=nco.txt nco.texi makeinfo --xml --no-split --output=nco.xml nco.texi makeinfo --docbook --no-split --output=nco.xml nco.texi pdftotext nco.pdf nco.txt ps2pdf -dMaxSubsetPct=100 -dCompatibilityLevel=1.2 -dSubsetFonts=true -dEmbedAllFonts=true nco.ps nco.pdf texi2dvi --output=nco.dvi nco.texi texi2dvi --pdf --output=nco.pdf nco.texi texi2html -monolithic -verbose nco.texi texi2html -l2h -l2h_tmp=./l2h_tmp -monolithic -verbose nco.texi # Invoke latex2html # 20130806 Copy CMIP5 images (takes additional time) cd ~/nco/doc/xmp;/usr/bin/scp fgr*.png fgr*.eps zender,nco@web.sf.net:/home/project-web/nco/htdocs/xmp;cd - # 20120203 Copy all nco.html PNG images (takes additional time) cd ~/nco/doc;/usr/bin/scp nco_*.png zender,nco@web.sf.net:/home/project-web/nco/htdocs;cd - # 20130801 Copy nco.texi to Ubuntu machine for quick build-tests scp ~/nco/doc/nco.texi givre.ess.uci.edu:nco/doc NB: @cindex references in footnotes propagate to PDF file, not to HTML files, bug-report sent to makeinfo people 20040229 Producing HTML, makeinfo vs. texi2html: makeinfo: Better format overall Uses node names for cross references and index Acronyms look better Excludes @ifinfo sections by default (override with --ifinfo) texi2html: Index sub-divided by first character More fancy options (e.g., latex2html), though none very useful Misprints title Includes @ifinfo sections by default Official TeXInfo documentation: http://www.gnu.org/software/texinfo/manual/texinfo/texinfo.html Legend (defined in "highlighting" section of TeXInfo manual): @b{}: Selects bold face @i{}: Selects an italic font @r{}: Selects a Roman font (usefule in example-like environments to have comments in Roman) @t{}: Selects the fixed-width font used by @code{} @sansserif{}: Selects a sanserif font @slanted{}: Selects a slanted font @caption{}: Caption of floating figures @code{}: Program text, e.g., @code{if(foo) x=y;} @command{}: Commands, e.g., @command{ncra} @dfn{}: Define use of term, e.g., @dfn{supercalifragilisticexpialidocious} @email{}: E-mail address, e.g., @email{surname at uci dot edu} @emph{}: Emphasize text, e.g., @emph{important} @env{}: Environment variable, e.g., @env{HOME} @file{}: Filename, e.g., @file{in.nc} @float: Environment for TeX floating figures (e.g., @float Figure,fgr:glb) @image{drc/nm,sz} Directory, filename (w/o suffix) and figure horizontal size @html: Text until @end html passed without translation @ifhtml: Text until @end ifhtml passed with translation @kbd{}: Keyboard input, e.g., @kbd{ncra in.nc out.nc} @key{}: Key name, e.g., @key{ESC} (rarely needed) @option{}: Command-line option, e.g., @option{--dbg} @samp{}: Extended commands, character sequences, e.g., @samp{ncra in.nc out.nc} @uref{}: URL with optional alternate text, e.g., @uref{http://nco.sf.net,NCO homepage} @url{}: URL, synonym for @uref @var{}: Metasyntactic variable, e.g., @var{fl_in} @verbatim: Anything goes inside environment (no @'s needed to protect special characters like braces) @verbatiminclude: Insert contents of file here, e.g., @verbatiminclude{nco.sh} @example: Quoted environment (@'s needed to protect special characters like braces) @w{}: Unbreakable text, e.g., @w{of 1} Use '@*' to force hard carriage return Use '@:', after periods, questions marks, exclamation marks, or colons that do not end sentences, e.g., 'vs.@:' Use '@.', '@!', and `@?' to end sentences that end with single capital letters (e.g., initials) Outline Hierarchy: @section @subsection @unnumberedsubsec Resources: Octave TeXInfo manual shows clean TeXInfo structure /usr/share/doc/octave-2.1.34/interpreter/octave.texi nco.info 5.2.9-alpha02 5.2.9-alpha02 1995--2024 2024 4 October 2024 October 2024 NCO 5.2.9-alpha02 User Guide node @set mybibrefnode \node\ @value{mybibrefnode} node @set mybibtable true @ifset mybiblist @clear mybiblist @end ifset @mybibsetrefnode{\node\} node @set mybiblist true @ifset mybibtable @clear mybibtable @end ifset @mybibsetrefnode{\node\} ref @ifclear mybibrefnode @mybibmakeref{mybibsetrefnode was not used, \ref\} @end ifclear @c %**else if @ifset mybibrefnode @mybibmakeref{@mybibnode{}, \ref\} @end ifset noderef (See item [\ref\] in @ref{\node\, \ref\}.) ref @ifclear mybiblist @ifclear mybibtable @set mybiblist true @end ifclear @end ifclear @ifset mybiblist @item @anchor{\ref\}[\ref\] @end ifset @c %**else if @ifset mybibtable @item @anchor{\ref\}[\ref\] @end ifset true References i v u x T % Define TeX macros to roughly correspond to LaTeX style files % Use \gdef instead of \def to make definition persistent across TeX blocks % These should be consistent with any TeXInfo macros % 0. Alphabet \gdef\ddd{d} % [ltr] d \gdef\iii{i} % [ltr] i \gdef\jjj{j} % [ltr] j \gdef\kkk{k} % [ltr] k \gdef\qqq{q} % [ltr] q \gdef\sss{s} % [ltr] s \gdef\vvv{v} % [ltr] v \gdef\xxx{x} % [ltr] x \gdef\yyy{y} % [ltr] y % 1. Primary commands \gdef\cod{{\rm CO}_{2}} % [frc] Math pi \gdef\dfr{{\rm d}} % [frc] Math differential fxm: upright \gdef\dmnidx{n} % [idx] Dimension index \gdef\clmnbr{I} % [nbr] Column number \gdef\rownbr{J} % [nbr] Row number \gdef\dmnnbr{N} % [nbr] Dimension number \gdef\dmn{D} % [dmn] Variable dimension \gdef\frcdst{f} % [flg] Destination grid fraction \gdef\frcsgs{s} % [flg] Sub-gridscale fraction \gdef\idx{i} % [idx] Index \gdef\lmnidx{i} % [idx] Element index \gdef\lmnnbr{N} % [nbr] Number of elements in input hyperslab \gdef\me{{\rm e}} % [frc] Math e fxm: upright \gdef\mi{{\rm i}} % [frc] Math i fxm: upright \gdef\mpi{\pi} % [frc] Math pi \gdef\mpp{\cal{M}} % [map] Map \gdef\mskflg{m} % [flg] Mask flag \gdef\mssflg{\mu} % [flg] Missing value flag \gdef\outnbr{J} % [nbr] Number of elements in output hyperslab \gdef\prmsbs{\prime} % [sbs] Prime subscript \gdef\psl{\epsilon} % [frc] epsilon \gdef\rdridx{r} % [idx] Re-order index \gdef\rdrnbr{R} % [nbr] Re-order number \gdef\rdr{R} % [dmn] Re-order dimension \gdef\shridx{s} % [idx] Share index \gdef\shrnbr{S} % [nbr] Share number \gdef\shr{S} % [dmn] Share dimension \gdef\tllnbr{M} % [nbr] Tally (number of valid elements in input hyperslab) \gdef\tm{t} % [s] Time \gdef\wgt{w} % [frc] Weight \gdef\wndmrd{v} % [m s-1] Meridional wind speed \gdef\wndznl{u} % [m s-1] Zonal wind speed % 2. Derived commands \gdef\pslavg{\bar{\epsilon}} % [frc] Mean error \gdef\pslmax{\epsilon_{\rm max}} % [frc] Maximum error \gdef\pslmin{\epsilon_{\rm min}} % [frc] Minimum error \gdef\pslmabs{\epsilon_{\rm mabs}^{+}} % [frc] Maximum absolute error \gdef\pslmibs{\epsilon_{\rm mibs}^{+}} % [frc] Minimum absolute error \gdef\pslmebs{\bar{\epsilon}^{+}} % [frc] Mean absolute error \gdef\dmnvct{{\bf \dmn}} % [vct] Dimension vector \gdef\dmnprm{\dmn^{\prmsbs}} % [vct] Dimension prime \gdef\dmnsubnnn{\dmn_{\dmnidx}} % [dmn] Dimension sub nnn \gdef\shrsubnnn{\shr_{\dmnidx}} % [dmn] Share dimension sub nnn \gdef\shrsubsss{\shr_{\shridx}} % [dmn] Share dimension sub sss \gdef\dmnsubnnnprm{\dmn_{\dmnidx}^{\prmsbs}} % [vct] Dimension prime sub nnn \gdef\dmnvctprm{{\bf \dmn}^{\prmsbs}} % [vct] Dimension vector prime \gdef\rdrvct{{\bf \rdr}} % [vct] Re-order vector \gdef\shrvct{{\bf \shr}} % [vct] Share vector \gdef\xxxprm{\xxx^{\prmsbs}} % [ltr] x prime % 3. Doubly derived commands netCDF * NCO::

User Guide for the netCDF Operator suite

% fxm: Try to get thumbnails working with texinfo --pdf -generated PDF files % \input thumbpdf.sty % Experiment with smaller amounts of whitespace between chapters and sections \global\chapheadingskip = 15pt plus 4pt minus 2pt \global\secheadingskip = 12pt plus 3pt minus 2pt \global\subsecheadingskip = 9pt plus 2pt minus 2pt \global\parskip 6pt plus 1pt This file documents NCO, a collection of utilities to manipulate and analyze netCDF files. Copyright ©right; 1995&textndash;2024 Charlie Zender This is the first edition of the NCO User Guide,&linebreak; and is consistent with version 2 of texinfo.tex. Permission is granted to copy, distribute and/or modify this document under the terms of the GNU Free Documentation License, Version 1.3 or any later version published by the Free Software Foundation; with no Invariant Sections, no Front-Cover Texts, and no Back-Cover Texts. The license is available online at http://www.gnu.org/copyleft/fdl.html The original author of this software, Charlie Zender, wants to improve it with the help of your suggestions, improvements, bug-reports, and patches.&linebreak; Charlie Zender <surname at uci dot edu> (yes, my surname is zender)&linebreak; 3200 Croul Hall&linebreak; Department of Earth System Science&linebreak; University of California, Irvine&linebreak; Irvine, CA 92697-3100&linebreak; Permission is granted to process this file through TeX and print the results, provided the printed document carries copying permission notice identical to this one except for the removal of this paragraph (this paragraph not being relevant to the printed manual). <meta name="Author" content="Charlie Zender"> <meta name="Keywords" content="NCO documentation, NCO User Guide, netCDF, operator, GCM, CCM, scientific data, ncbo, ncchecker, ncclimo, ncfe, ncecat, ncflint, ncks, ncra, ncrcat, ncremap, ncrename, ncwa"> <body text="#000000" link="#0000EF" vlink="#008080" alink="#FF0000"> NCO User Guide A suite of netCDF operators Edition 5.2.9-alpha02, for NCO Version 5.2.9-alpha02 October 2024 by Charlie Zender Departments of Earth System Science and Computer Science University of California, Irvine WWW readers: Having trouble finding the section you want? Search for keywords in the <a href="#index">(hyper) index</a> at the end 0pt plus 1filll Copyright ©right; 1995&textndash;2024 Charlie Zender. This is the first edition of the NCO User Guide,&linebreak; and is consistent with version 2 of texinfo.tex. Published by Charlie Zender&linebreak; Department of Earth System Science&linebreak; 3200 Croul Hall&linebreak; University of California, Irvine&linebreak; Irvine, CA 92697-3100 USA&linebreak; Permission is granted to copy, distribute and/or modify this document under the terms of the GNU Free Documentation License, Version 1.3 or any later version published by the Free Software Foundation; with no Invariant Sections, no Front-Cover Texts, and no Back-Cover Texts. The license is available online at http://www.gnu.org/copyleft/fdl.html We gratefully acknowledge support for NCO development and maintenance provided by these institutions and programs: DOE ACME DE-SC0012998, LLNL-B625903, LLNL-B632442, NASA ACCESS NNX12AF48A and NNX14AH55A, and NSF SEI IIS-0431203, AGS-1541031, and OAC-2004993. This research was supported as part of the Energy Exascale Earth System Model (E3SM) project, formerly known as Accelerated Climate Modeling for Energy (ACME), funded by the U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research. This material is based upon work supported by the National Science Foundation. The original author of this software, Charlie Zender, wants to improve it with the help of your suggestions, improvements, bug-reports, and patches.&linebreak; Charlie Zender <surname at uci dot edu> (yes, my surname is zender)&linebreak; Department of Earth System Science&linebreak; 3200 Croul Hall&linebreak; University of California, Irvine&linebreak; Irvine, CA 92697-3100&linebreak; TopForeword(dir)(dir) NCO User Guide Note to readers of the NCO User Guide in Info format: The ./nco.pdfNCO User Guide in PDF format (also on http://nco.sf.net/nco.pdfSourceForge) contains the complete NCO documentation. This Info documentation is equivalent except it refers you to the printed (i.e., DVI, PostScript, and PDF) documentation for description of complex mathematical expressions. The netCDF Operators, or NCO, are a suite of programs known as operators. The operators facilitate manipulation and analysis of data stored in the self-describing netCDF format, available from (http://www.unidata.ucar.edu/software/netcdf). Each NCO operator (e.g., ncks) takes netCDF input file(s), performs an operation (e.g., averaging, hyperslabbing, or renaming), and outputs a processed netCDF file. Although most users of netCDF data are involved in scientific research, these data formats, and thus NCO, are generic and are equally useful in fields from agriculture to zoology. The NCO User Guide illustrates NCO use with examples from the field of climate modeling and analysis. The NCO homepage is http://nco.sf.net, and the source code is maintained at http://github.com/nco/nco. This documentation is for NCO version 5.2.9-alpha02. It was last updated 4 October 2024. Corrections, additions, and rewrites of this documentation are gratefully welcome. Enjoy,&linebreak; Charlie Zender * Foreword::

* Summary::

* Introduction::

* Strategies::

* Shared features::

* Reference Manual::

* Contributing::

* Quick Start::

* CMIP5 Example::

* Parallel::

* CCSM Example::

* mybibnode::

* General Index::

<a name="fwd"></a>  ForewordSummaryTopTop Foreword foreword Charlie Zender NCO is the result of software needs that arose while I worked on projects funded by NCAR, NASA, and ARM. Thinking they might prove useful as tools or templates to others, it is my pleasure to provide them freely to the scientific community. Many users (most of whom I have never met) have encouraged the development of NCO. Thanks espcially to Jan Polcher, Keith Lindsay, Arlindo da Silva, John Sheldon, and William Weibel for stimulating suggestions and correspondence. Your encouragment motivated me to complete the NCO User Guide. So if you like NCO, send me a note! I should mention that NCO is not connected to or officially endorsed by Unidata, ACD, ASP, CGD, or Nike.&linebreak; Charlie Zender&linebreak; May 1997&linebreak; Boulder, Colorado&linebreak; Major feature improvements entitle me to write another Foreword. In the last five years a lot of work has been done to refine NCO. open source NCO is now an open source project and appears to be much healthier for it. The list of illustrious institutions that do not endorse NCO continues to grow, and now includes UCI.&linebreak; Charlie Zender&linebreak; October 2000&linebreak; Irvine, California&linebreak; The most remarkable advances in NCO capabilities in the last few years are due to contributions from the Open Source community. Especially noteworthy are the contributions of Henry Butowsky and Rorik Peterson.&linebreak; Charlie Zender&linebreak; January 2003&linebreak; Irvine, California&linebreak; NCO was generously supported from 2004&textndash;2008 by US National Science Foundation (NSF) grant http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=0431203IIS-0431203. This support allowed me to maintain and extend core NCO code, and others to advance NCO in new directions: Gayathri Venkitachalam helped implement MPI; Harry Mangalam improved regression testing and benchmarking; Daniel Wang developed the server-side capability, SWAMP; and Henry Butowsky, a long-time contributor, developed ncap2. This support also led NCO to debut in professional journals and meetings. The personal and professional contacts made during this evolution have been immensely rewarding.&linebreak; Charlie Zender&linebreak; March 2008&linebreak; Grenoble, France&linebreak; The end of the NSF SEI grant in August, 2008 curtailed NCO development. Fortunately we could justify supporting Henry Butowsky on other research grants until May, 2010 while he developed the key ncap2 features used in our climate research. And recently the NASA ACCESS program commenced funding us to support netCDF4 group functionality. Thus NCO will grow and evade bit-rot for the foreseeable future. I continue to receive with gratitude the thanks of NCO users at nearly every scientific meeting I attend. People introduce themselves, shake my hand and extol NCO, often effusively, while I grin in stupid embarassment. These exchanges lighten me like anti-gravity. Sometimes I daydream how many hours NCO has turned from grunt work to productive research for researchers world-wide, or from research into early happy-hours. It&textrsquo;s a cool feeling. Charlie Zender&linebreak; April, 2012&linebreak; Irvine, California&linebreak; The NASA ACCESS 2011 program generously supported (Cooperative Agreement NNX12AF48A) NCO from 2012&textndash;2014. This allowed us to produce the first iteration of a Group-oriented Data Analysis and Distribution (GODAD) software ecosystem. Shifting more geoscience data analysis to GODAD is a long-term plan. Then the NASA ACCESS 2013 program agreed to support (Cooperative Agreement NNX14AH55A) NCO from 2014&textndash;2016. This support permits us to implement support for Swath-like Data (SLD). Most recently, the DOE has funded me to implement NCO re-gridding and parallelization in support of their ACME program. After many years of crafting NCO as an after-hours hobby, I finally have the cushion necessary to give it some real attention. And I&textrsquo;m looking forward to this next, and most intense yet, phase of NCO development. Charlie Zender&linebreak; June, 2015&linebreak; Irvine, California&linebreak; The DOE Energy Exascale Earth System Model (E3SM) project (formerly ACME) has generously supported NCO development for the past four years. Supporting NCO for a mission-driven, high-performance climate model development effort has brought unprecedented challenges and opportunities. After so many years of staid progress, the recent development speed has been both exhilirating and terrifying. Charlie Zender&linebreak; May, 2019&linebreak; Laguna Beach, California&linebreak; The DOE E3SM project has supported NCO development and maintenance since 2015. This is an eternity in the world of research funding! Their reliable support has enabled us to add cutting-edge features including quantization, vertical interpolation, and support for multiple regridding weight-generators. Recently NSF supported us to enable user-friendly support for modern compression algorithms that can make geoscience data analysis greener by reducing dataset size, and thereby storage, power, and associated greenhouse gas emissions. I am grateful for this this agency support that inspires me to create new features that help my amazing colleagues pursue their scientific ideas. Charlie Zender&linebreak; July, 2022&linebreak; Laguna Beach, California&linebreak; <a name="smr"></a>  SummaryIntroductionForewordTop Summary operators summary This manual describes NCO, which stands for netCDF Operators. NCO is a suite of programs known as operators. Each operator is a standalone, command line program executed at the shell-level like, e.g., ls or mkdir. The operators take netCDF files (including HDF5 files constructed using the netCDF API) as input, perform an operation (e.g., averaging or hyperslabbing), and produce a netCDF file as output. The operators are primarily designed to aid manipulation and analysis of data. The examples in this documentation are typical applications of the operators for processing climate model output. This stems from their origin, though the operators are as general as netCDF itself. <a name="ntr"></a>  IntroductionStrategiesSummaryTop Introduction introduction * Availability::

* How to Use This guide::

* Compatability::

* Symbolic Links::

* Libraries::

* netCDF2/3/4 and HDF4/5 Support::

* Help Requests and Bug Reports::

AvailabilityHow to Use This guideIntroductionIntroduction

Availability NCO availability source code The complete NCO source distribution is currently distributed as a compressed tarfile from http://sf.net/projects/nco and from http://dust.ess.uci.edu/nco/nco.tar.gz. The compressed tarfile must be uncompressed and untarred before building NCO. Uncompress the file with gunzip nco.tar.gz. Extract the source files from the resulting tarfile with tar -xvf nco.tar. GNU tar lets you perform both operations in one step with tar -xvzf nco.tar.gz. documentation WWW documentation on-line documentation HTML &tex;info Info User Guide NCO User Guide The documentation for NCO is called the NCO User Guide. The User Guide is available in PDF, Postscript, HTML, DVI, &tex;info, and Info formats. These formats are included in the source distribution in the files nco.pdf, nco.ps, nco.html, nco.dvi, nco.texi, and nco.info*, respectively. All the documentation descends from a single source file, nco.texi To produce these formats, nco.texi was simply run through the freely available programs texi2dvi, dvips, texi2html, and makeinfo. Due to a bug in &tex;, the resulting Postscript file, nco.ps, contains the Table of Contents as the final pages. Thus if you print nco.ps, remember to insert the Table of Contents after the cover sheet before you staple the manual. . Hence the documentation in every format is very similar. However, some of the complex mathematical expressions needed to describe ncwa can only be displayed in DVI, Postscript, and PDF formats. publications presentations A complete list of papers and publications on/about NCO is available on the NCO homepage. Most of these are freely available. The primary refereed publications are ZeM06 and Zen08. These contain copyright restrictions which limit their redistribution, but they are freely available in preprint form from the NCO. NCO homepage If you want to quickly see what the latest improvements in NCO are (without downloading the entire source distribution), visit the NCO homepage at http://nco.sf.net. The HTML version of the User Guide is also available online through the World Wide Web at URL http://nco.sf.net/nco.html. netCDF To build and use NCO, you must have netCDF installed. The netCDF homepage is http://www.unidata.ucar.edu/software/netcdf. New NCO releases are announced on the netCDF list and on the nco-announce mailing list http://lists.sf.net/mailman/listinfo/nco-announce. This tests incorporates an image using the @code{@@image} command. @image{/data/zender/ps/odxc,6in,}

How to Use This guideCompatabilityAvailabilityIntroduction

How to Use This Guide contents introduction Detailed instructions about http://nco.sf.net/#Sourcehow to download the newest version, and http://nco.sf.net/#bldhow to complie source code, as well as a http://nco.sf.net/#FAQFAQ and descriptions of http://nco.sf.net/#bugKnown Problems etc. are on our homepage (http://nco.sf.net/). There are twelve operators in the current version (5.2.9-alpha02). The function of each is explained in Reference ManualReference Manual. Many of the tasks that NCO can accomplish are described during the explanation of common NCO Features (Shared features). More specific use examples for each operator can be seen by visiting the operator-specific examples in the Reference Manual. These can be found directly by prepending the operator name with the xmp_ tag, e.g., http://nco.sf.net/nco.html#xmp_ncks. Also, users can type the operator name on the shell command line to see all the available options, or type, e.g., man ncks to see a help man-page. NCO is a command-line language. You may either use an operator after the prompt (e.g., $ here), like,

$ operator  input [output]

or write all commands lines into a shell script, as in the CMIP5 Example (CMIP5 Example). If you are new to NCO, the Quick Start (Quick Start) shows simple examples about how to use NCO on different kinds of data files. More detailed &textldquo;real-world&textrdquo; examples are in the CMIP5 ExampleCMIP5 Example. The General IndexIndex is presents multiple keyword entries for the same subject. If these resources do not help enough, please Help Requests and Bug Reports.

CompatabilitySymbolic LinksHow to Use This guideIntroduction

Operating systems compatible with NCO OS IBM NEC SGI HP DEC PGI Cray Digital Sun Intel Comeau Compaq Macintosh Microsoft Windows PathScale QLogic compatability portability installation In its time on Earth, NCO has been successfully ported and tested on so many 32- and 64-bit platforms that if we did not write them down here we would forget their names: IBM AIX 4.x, 5.x, FreeBSD 4.x, GNU/Linux 2.x, LinuxPPC, LinuxAlpha, LinuxARM, LinuxSparc64, LinuxAMD64, SGI IRIX 5.x and 6.x, MacOS X 10.x, DEC OSF, NEC Super-UX 10.x, Sun SunOS 4.1.x, Solaris 2.x, Cray UNICOS 8.x&textndash;10.x, and Microsoft Windows (95, 98, NT, 2000, XP, Vista, 7, 8, 10). If you port the code to a new operating system, please send me a note and any patches you required. UNIX Unidata UDUnits The major prerequisite for installing NCO on a particular platform is the successful, prior installation of the netCDF library (and, as of 2003, the UDUnits library). Unidata has shown a commitment to maintaining netCDF and UDUnits on all popular UNIX platforms, and is moving towards full support for the Microsoft Windows operating system (OS). Given this, the only difficulty in implementing NCO on a particular platform is standardization of various C-language API system calls. NCO code is tested for ANSI compliance by compiling with C99 compilers including those from CC c++ cc clang como cxx gcc g++ icc MVS pgcc pgCC pathcc pathCC xlC xlc GNU (gcc -std=c99 -pedantic -D_BSD_SOURCE -D_POSIX_SOURCE -Wall) The _BSD_SOURCE token is required on some Linux platforms where gcc dislikes the network header files like netinet/in.h)., Comeau Computing (como --c99), Cray (cc), HP/Compaq/DEC (cc), IBM (xlc -c -qlanglvl=extc99), Intel (icc -std=c99), LLVM LLVM (clang), NEC (cc), PathScale (QLogic) (pathcc -std=c99), PGI (pgcc -c9x), SGI (cc -c99), and Sun (cc). C++ ISO libnco NCO (all commands and the libnco library) and the C++ interface to netCDF (called libnco_c++) comply with the ISO C++ standards as implemented by Comeau Computing (como), Cray (CC), GNU (g++ -Wall), HP/Compaq/DEC (cxx), IBM (xlC), Intel (icc), Microsoft (MVS), NEC (c++), PathScale (Qlogic) (pathCC), PGI (pgCC), SGI (CC -LANG:std), and Sun (CC -LANG:std). Makefile See nco/bld/Makefile and nco/src/nco_c++/Makefile.old for more details and exact settings. ANSI C89 printf Until recently (and not even yet), ANSI-compliant has meant compliance with the 1989 ISO C-standard, usually called C89 (with minor revisions made in 1994 and 1995). C89 lacks variable-size arrays, restricted pointers, some useful printf formats, and many mathematical special functions. C99 These are valuable features of C99, the 1999 ISO C-standard. NCO is C99-compliant where possible and C89-compliant where necessary. Certain branches in the code are required to satisfy the native SGI and SunOS C compilers, which are strictly ANSI C89 compliant, and cannot benefit from C99 features. However, C99 features are fully supported by modern AIX, GNU, Intel, NEC, Solaris, and UNICOS compilers. NCO requires a C99-compliant compiler as of NCO version 2.9.8, released in August, 2004. The most time-intensive portion of NCO execution is spent in arithmetic operations, e.g., multiplication, averaging, subtraction. These operations were performed in Fortran by default until August, 1999. This was a design decision based on the relative speed of Fortran-based object code vs.&noeos; C-based object code in late 1994. C compiler vectorization capabilities have dramatically improved since 1994. We have accordingly replaced all Fortran subroutines with C functions. This greatly simplifies the task of building NCO on nominally unsupported platforms. C language As of August 1999, NCO built entirely in C by default. This allowed NCO to compile on any machine with an ANSI C compiler. C99 C89 restrict In August 2004, the first C99 feature, the restrict type qualifier, entered NCO in version 2.9.8. C compilers can obtain better performance with C99 restricted pointers since they inform the compiler when it may make Fortran-like assumptions regarding pointer contents alteration. Subsequently, NCO requires a C99 compiler to build correctly NCO may still build with an ANSI or ISO C89 or C94/95-compliant compiler if the C pre-processor undefines the restrict type qualifier, e.g., by invoking the compiler with -Drestrict=''.. GSL ncap2 In January 2009, NCO version 3.9.6 was the first to link to the GNU Scientific Library (GSL). GSL must be version 1.4 or later. NCO, in particular ncap2, uses the GSL special function library to evaluate geoscience-relevant mathematics such as Bessel functions, Legendre polynomials, and incomplete gamma functions (GSL special functions). gamma In June 2005, NCO version 3.0.1 began to take advantage of C99 mathematical special functions. These include the standarized gamma function (called tgamma() for &textldquo;true gamma&textrdquo;). automagic NCO automagically takes advantage of some GNU Compiler Collection (GCC) extensions to ANSI C. As of July 2000 and NCO version 1.2, NCO no longer performs arithmetic operations in Fortran. We decided to sacrifice executable speed for code maintainability. Since no objective statistics were ever performed to quantify the difference in speed between the Fortran and C code, the performance penalty incurred by this decision is unknown. Supporting Fortran involves maintaining two sets of routines for every arithmetic operation. The USE_FORTRAN_ARITHMETIC flag is still retained in the Makefile. The file containing the Fortran code, nco_fortran.F, has been deprecated but a volunteer (Dr.&noeos; Frankenstein?) could resurrect it. If you would like to volunteer to maintain nco_fortran.F please contact me. It is still possible to request Fortran routines to perform arithmetic operations, however. @cindex preprocessor tokens @cindex @code{USE_FORTRAN_ARITHMETIC} This can be accomplished by defining the preprocessor token @code{USE_FORTRAN_ARITHMETIC} and rebuilding @acronym{NCO}. @cindex performance As its name suggests, the @code{USE_FORTRAN_ARITHMETIC} token instructs @acronym{NCO} to attempts to interface the @w{C routines} with Fortran arithmetic. Although using Fortran calls instead @w{of C} reduces the portability and and increases the maintenance of the @acronym{NCO} operators, it may also increase the performance of the numeric operators. Presumably this will depend on your machine type, the quality of @w{the C} and Fortran compilers, and the size of the data files @footnote{If you decide to test the efficiency of the averagers compiled with @code{USE_FORTRAN_ARITHMETIC} versus the default @w{C averagers} I would be most interested to hear the results. Please E-mail me the results including the size of the datasets, the platform, and the change in the wallclock time for execution.}. * Windows Operating System::

<a name="wnd"></a>  <a name="windows"></a>  <a name="qt"></a>  Windows Operating SystemCompatabilityCompatability Compiling NCO for Microsoft Windows OS Windows Microsoft XP (Microsoft operating system) NT (Microsoft operating system) Vista (Microsoft operating system) MVS Microsoft Visual Studio NCO has been successfully ported and tested on most Microsoft Windows operating systems including: XP SP2/Vista/7/10. Support is provided for compiling either native Windows executables, using the Microsoft Visual Studio Compiler (MVSC), or with Cygwin, the UNIX-emulating compatibility layer with the GNU toolchain. The switches necessary to accomplish both are included in the standard distribution of NCO. C99 CMake <a name="CMake"></a>  <a name="cmake"></a>  <a name="cmk"></a>  With Microsoft Visual Studio compiler, one must build NCO with C++ since MVSC does not support C99. Support for Qt, a convenient integrated development environment, was deprecated in 2017. As of NCO version 4.6.9 (September, 2017) please build native Windows executables with CMake: cd ~/nco/cmake cmake .. -DCMAKE_INSTALL_PREFIX=${HOME} make install The file nco/cmake/build.bat shows how deal with various path issues. Anaconda Conda <a name="anaconda"></a>  <a name="cnd"></a>  <a name="conda"></a>  As of NCO version 4.7.1 (December, 2017) the Conda package for NCO is available from the conda-forge channel on all three smithies: Linux, MacOS, and Windows. # Recommended install with Conda conda config --add channels conda-forge # Permananently add conda-forge conda install nco # Or, specify conda-forge explicitly as a one-off: conda install -c conda-forge nco Using the freely available Cygwin (formerly gnu-win32) development environment The Cygwin package is available from&linebreak; http://sourceware.redhat.com/cygwin&linebreak; gcc g++ Currently, Cygwin 20.x comes with the GNU C/C++ compilers (gcc, g++. These GNU compilers may be used to build the netCDF distribution itself., the compilation process is very similar to installing NCO on a UNIX system. preprocessor tokens Cygwin gnu-win32 WIN32 GNUmakefile Makefile f90 Set the PVM_ARCH preprocessor token to WIN32. Note that defining WIN32 has the side effect of disabling Internet features of NCO (see below). NCO should now build like it does on UNIX. UNIX getuid gethostname <arpa/nameser.h> <resolv.h> The least portable section of the code is the use of standard UNIX and Internet protocols (e.g., ftp, rcp, scp, sftp, getuid, gethostname, and header files <arpa/nameser.h> and <resolv.h>). ftp sftp rcp scp SSH remote files Fortunately, these UNIX-y calls are only invoked by the single NCO subroutine which is responsible for retrieving files stored on remote systems (Remote storage). In order to support NCO on the Microsoft Windows platforms, this single feature was disabled (on Windows OS only). This was required by Cygwin 18.x&textmdash;newer versions of Cygwin may support these protocols (let me know if this is the case). The NCO operators should behave identically on Windows and UNIX platforms in all other respects. <a name="sym"></a>

Symbolic LinksLibrariesCompatabilityIntroduction

Symbolic Links symbolic links NCO relies on a common set of underlying algorithms. To minimize duplication of source code, multiple operators sometimes share the same underlying source. This is accomplished by symbolic links from a single underlying executable program to one or more invoked executable names. For example, nces and ncrcat are symbolically linked to the ncra executable. The ncra executable behaves slightly differently based on its invocation name (i.e., argv[0]), which can be nces, ncra, or ncrcat. Logically, these are three different operators that happen to share the same executable. Cygwin synonym pseudonym --pseudonym For historical reasons, and to be more user friendly, multiple synonyms (or pseudonyms) may refer to the same operator invoked with different switches. For example, ncdiff is the same as ncbo and ncpack is the same as ncpdq. We implement the symbolic links and synonyms by the executing the following UNIX commands in the directory where the NCO executables are installed.

ln -s -f ncbo ncdiff    # ncbo --op_typ='-'
ln -s -f ncra nces      # ncra --pseudonym='nces'
ln -s -f ncra ncrcat    # ncra --pseudonym='ncrcat'
ln -s -f ncbo ncadd     # ncbo --op_typ='+'
ln -s -f ncbo ncsubtract # ncbo --op_typ='-'
ln -s -f ncbo ncmultiply # ncbo --op_typ='*'
ln -s -f ncbo ncdivide   # ncbo --op_typ='/'
ln -s -f ncpdq ncpack    # ncpdq
ln -s -f ncpdq ncunpack  # ncpdq --unpack
# NB: Windows/Cygwin executable/link names have '.exe' suffix, e.g.,
ln -s -f ncbo.exe ncdiff.exe
...

The imputed command called by the link is given after the comment. As can be seen, some these links impute the passing of a command line argument to further modify the behavior of the underlying executable. For example, ncdivide is a pseudonym for ncbo --op_typ='/'. <a name="lbr"></a>

LibrariesnetCDF2/3/4 and HDF4/5 SupportSymbolic LinksIntroduction

Libraries libraries LD_LIBRARY_PATH dynamic linking static linking Like all executables, the NCO operators can be built using dynamic linking. performance operator speed speed execution time This reduces the size of the executable and can result in significant performance enhancements on multiuser systems. Unfortunately, if your library search path (usually the LD_LIBRARY_PATH environment variable) is not set correctly, or if the system libraries have been moved, renamed, or deleted since NCO was installed, it is possible NCO operators will fail with a message that they cannot find a dynamically loaded (aka shared object or .so) library. This will produce a distinctive error message, such as ld.so.1:&hyphenbreak; /usr/local/bin/nces:&hyphenbreak; fatal:&hyphenbreak; libsunmath.&hyphenbreak;so.1:&hyphenbreak; can't open&hyphenbreak; file:&hyphenbreak; errno&hyphenbreak;=2. If you received an error message like this, ask your system administrator to diagnose whether the library is truly missing The ldd command, if it is available on your system, will tell you where the executable is looking for each dynamically loaded library. Use, e.g., ldd `which nces`., or whether you simply need to alter your library search path. As a final remedy, you may re-compile and install NCO with all operators statically linked.

netCDF2/3/4 and HDF4/5 SupportHelp Requests and Bug ReportsLibrariesIntroduction

netCDF2/3/4 and HDF4/5 Support netCDF2 netCDF3 netCDF version 2 was released in 1993. NCO (specifically ncks) began soon after this in 1994. netCDF 3.0 was released in 1996, and we were not exactly eager to convert all code to the newer, less tested netCDF implementation. One netCDF3 interface call (nc_inq_libvers) was added to NCO in January, 1998, to aid in maintainance and debugging. In March, 2001, the final NCO conversion to netCDF3 was completed (coincidentally on the same day netCDF 3.5 was released). NCO versions 2.0 and higher are built with the -DNO_NETCDF_2 flag to ensure no netCDF2 interface calls are used. NO_NETCDF_2 HDF Hierarchical Data Format Mike Folk However, the ability to compile NCO with only netCDF2 calls is worth maintaining because HDF version 4, aka HDF4 or simply HDF, The Hierarchical Data Format, or HDF, is another self-describing data format similar to, but more elaborate than, netCDF. HDF comes in two flavors, HDF4 and HDF5. Often people use the shorthand HDF to refer to the older format HDF4. People almost always use HDF5 to refer to HDF5. (available from http://hdfgroup.orgHDF) supports only the netCDF2 library calls (see http://hdfgroup.org/UG41r3_html/SDS_SD.fm12.html#47784). There are two versions of HDF. Currently HDF version 4.x supports the full netCDF2 API and thus NCO version 1.2.x. If NCO version 1.2.x (or earlier) is built with only netCDF2 calls then all NCO operators should work with HDF4 files as well as netCDF files One must link the NCO code to the HDF4 MFHDF library instead of the usual netCDF library. Apparently MF stands for Multi-file not for Mike Folk. In any case, until about 2007 the MFHDF library only supported netCDF2 calls. Most people will never again install NCO 1.2.x and so will never use NCO to write HDF4 files. It is simply too much trouble.. NETCDF2_ONLY The preprocessor token NETCDF2_ONLY exists in NCO version 1.2.x to eliminate all netCDF3 calls. Only versions of NCO numbered 1.2.x and earlier have this capability. Unidata NCSA netCDF4 HDF5 HDF version 5 became available in 1999, but did not support netCDF (or, for that matter, Fortran) as of December 1999. By early 2001, HDF5 did support Fortran90. Thanks to an NSF-funded &textldquo;harmonization&textrdquo; partnership, HDF began to fully support the netCDF3 read interface (which is employed by NCO 2.x and later). In 2004, Unidata and THG began a project to implement the HDF5 features necessary to support the netCDF API. NCO version 3.0.3 added support for reading/writing netCDF4-formatted HDF5 files in October, 2005. See File Formats and Conversion for more details. HDF support for netCDF was completed with HDF5 version version 1.8 in 2007. The netCDF front-end that uses this HDF5 back-end was completed and released soon after as netCDF version 4. Download it from the http://my.unidata.ucar.edu/content/software/netcdf/netcdf-4netCDF4 website. <a name="nco4"></a>  NCO version 3.9.0, released in May, 2007, added support for all netCDF4 atomic data types except NC_STRING. Support for NC_STRING, including ragged arrays of strings, was finally added in version 3.9.9, released in June, 2009. Support for additional netCDF4 features has been incremental. We add one netCDF4 feature at a time. You must build NCO with netCDF4 to obtain this support. NC_UBYTE NC_USHORT NC_UINT NC_INT64 NC_UINT64 NCO supports many netCDF4 features including atomic data types, Lempel-Ziv compression (deflation), chunking, and groups. The new atomic data types are NC_UBYTE, NC_USHORT, NC_UINT, NC_INT64, and NC_UINT64. Eight-byte integer support is an especially useful improvement from netCDF3. All NCO operators support these types, e.g., ncks copies and prints them, ncra averages them, and ncap2 processes algebraic scripts with them. ncks prints compression information, if any, to screen. deflation NCO version 3.9.1 (June, 2007) added support for netCDF4 Lempel-Ziv deflation. Lempel-Ziv deflation is a lossless compression technique. See Deflation for more details. chunking NCO version 3.9.9 (June, 2009) added support for netCDF4 chunking in ncks and ncecat. NCO version 4.0.4 (September, 2010) completed support for netCDF4 chunking in the remaining operators. See Chunking for more details. groups NCO version 4.2.2 (October, 2012) added support for netCDF4 groups in ncks and ncecat. Group support for these operators was complete (e.g., regular expressions to select groups and Group Path Editing) as of NCO version 4.2.6 (March, 2013). See Group Path Editing for more details. Group support for all other operators was finished in the NCO version 4.3.x series completed in December, 2013. broadcasting groups Support for netCDF4 in the first arithmetic operator, ncbo, was introduced in NCO version 4.3.0 (March, 2013). NCO version 4.3.1 (May, 2013) completed this support and introduced the first example of automatic group broadcasting. See ncbo netCDF Binary Operator for more details. HDF5 -4 -3 netCDF4-enabled NCO handles netCDF3 files without change. In addition, it automagically handles netCDF4 (HDF5) files: If you feed NCO netCDF3 files, it produces netCDF3 output. If you feed NCO netCDF4 files, it produces netCDF4 output. Use the handy-dandy -4 switch to request netCDF4 output from netCDF3 input, i.e., to convert netCDF3 to netCDF4. See File Formats and Conversion for more details. <a name="hdf4"></a>  <a name="HDF4"></a>  HDF4 --hdf4 When linked to a netCDF library that was built with HDF4 support The procedure for doing this is documented at http://www.unidata.ucar.edu/software/netcdf/docs/build_hdf4.html., NCO automatically supports reading HDF4 files and writing them as netCDF3/netCDF4/HDF5 files. NCO can only write through the netCDF API, which can only write netCDF3/netCDF4/HDF5 files. So NCO can read HDF4 files, perform manipulations and calculations, and then it must write the results in netCDF format. NCO support for HDF4 has been quite functional since December, 2013. For best results install NCO versions 4.4.0 or later on top of netCDF versions 4.3.1 or later. Getting to this point has been an iterative effort where Unidata improved netCDF library capabilities in response to our requests. NCO versions 4.3.6 and earlier do not explicitly support HDF4, yet should work with HDF4 if compiled with a version of netCDF (4.3.2 or later?) that does not unexpectedly die when probing HDF4 files with standard netCDF calls. NCO versions 4.3.7&textndash;4.3.9 (October&textndash;December, 2013) use a special flag to circumvent netCDF HDF4 issues. The user must tell these versions of NCO that an input file is HDF4 format by using the --hdf4 switch. HDF4_UNKNOWN When compiled with netCDF version 4.3.1 (20140116) or later, NCO versions 4.4.0 (January, 2014) and later more gracefully handle HDF4 files. In particular, the --hdf4 switch is obsolete. Current versions of NCO use netCDF to determine automatically whether the underlying file is HDF4, and then take appropriate precautions to avoid netCDF4 API calls that fail when applied to HDF4 files (e.g., nc_inq_var_chunking(), nc_inq_var_deflate()). When compiled with netCDF version 4.3.2 (20140423) or earlier, NCO will report that chunking and deflation properties of HDF4 files as HDF4_UNKNOWN, because determining those properties was impossible. When compiled with netCDF version 4.3.3-rc2 (20140925) or later, NCO versions 4.4.6 (October, 2014) and later fully support chunking and deflation features of HDF4 files. Unfortunately, netCDF version 4.7.4 (20200327) introduced a regression that breaks this functionality for all NCO versions until we first noticed the regression a year later and implemented a workaround to restore this functionality as of 4.9.9-alpha02 (20210327). The --hdf4 switch is supported (for backwards compatibility) yet redundant (i.e., does no harm) with current versions of NCO and netCDF. Converting HDF4 files to netCDF: Since NCO reads HDF4 files natively, it is now easy to convert HDF4 files to netCDF files directly, e.g.,

ncks        fl.hdf fl.nc # Convert HDF4->netCDF4 (NCO 4.4.0+, netCDF 4.3.1+)
ncks --hdf4 fl.hdf fl.nc # Convert HDF4->netCDF4 (NCO 4.3.7-4.3.9)

The most efficient and accurate way to convert HDF4 data to netCDF format is to convert to netCDF4 using NCO as above. Many HDF4 producers (NASA!) love to use netCDF4 types, e.g., unsigned bytes, so this procedure is the most typical. Conversion of HDF4 to netCDF4 as above suffices when the data will only be processed by NCO and other netCDF4-aware tools. ncl_convert2nc nc3tonc4 However, many tools are not fully netCDF4-aware, and so conversion to netCDF3 may be desirable. Obtaining any netCDF file from an HDF4 is easy:

ncks -3 fl.hdf fl.nc      # HDF4->netCDF3 (NCO 4.4.0+, netCDF 4.3.1+)
ncks -4 fl.hdf fl.nc      # HDF4->netCDF4 (NCO 4.4.0+, netCDF 4.3.1+)
ncks -6 fl.hdf fl.nc      # HDF4->netCDF3 64-bit  (NCO 4.4.0+, ...)
ncks -7 -L 1 fl.hdf fl.nc # HDF4->netCDF4 classic (NCO 4.4.0+, ...)
ncks --hdf4 -3 fl.hdf fl.nc # HDF4->netCDF3 (netCDF 4.3.0-)
ncks --hdf4 -4 fl.hdf fl.nc # HDF4->netCDF4 (netCDF 4.3.0-)
ncks --hdf4 -6 fl.hdf fl.nc # HDF4->netCDF3 64-bit  (netCDF 4.3.0-)
ncks --hdf4 -7 fl.hdf fl.nc # HDF4->netCDF4 classic (netCDF 4.3.0-)

As of NCO version 4.4.0 (January, 2014), these commands work even when the HDF4 file contains netCDF4 atomic types (e.g., unsigned bytes, 64-bit integers) because NCO can autoconvert everything to atomic types supported by netCDF3 Prior to NCO version 4.4.0 (January, 2014), we recommended the ncl_convert2nc tool to convert HDF to netCDF3 when both these are true: 1. You must have netCDF3 and 2. the HDF file contains netCDF4 atomic types. More recent versions of NCO handle this problem fine, and include other advantages so we no longer recommend ncl_convert2nc because ncks is faster and more space-efficient. Both automatically convert netCDF4 types to netCDF3 types, yet ncl_convert2nc cannot produce full netCDF4 files. In contrast, ncks will happily convert HDF straight to netCDF4 files with netCDF4 types. Hence ncks can and does preserve the variable types. Unsigned bytes stay unsigned bytes. 64-bit integers stay 64-bit integers. Strings stay strings. Hence, ncks conversions often result in smaller files than ncl_convert2nc conversions. Another tool useful for converting netCDF3 to netCDF4 files, and whose functionality is, we think, also matched or exceeded by ncks, is the Python script nc3tonc4 by Jeff Whitaker.. hdf_name illegal names As of NCO version 4.4.4 (May, 2014) both ncl_convert2nc and NCO have built-in, automatic workarounds to handle element names that contain characters that are legal in HDF though are illegal in netCDF. For example, slashes and leading special characters are are legal in HDF and illegal in netCDF element (i.e., group, variable, dimension, and attribute) names. NCO converts these forbidden characters to underscores, and retains the original names of variables in automatically produced attributes named hdf_name Two real-world examples: NCO translates the NASA CERES dimension (FOV) Footprints to _FOV_ Footprints, and Cloud & Aerosol, Cloud Only, Clear Sky w/Aerosol, and Clear Sky (yes, the dimension name includes whitespace and special characters) to Cloud & Aerosol, Cloud Only, Clear Sky w_Aerosol, and Clear Sky ncl_convert2nc makes the element name netCDF-safe in a slightly different manner, and also stores the original name in the hdf_name attribute.. H4CF h4tonccf Finally, in February 2014, we learned that the HDF group has a project called H4CF (described http://hdfeos.org/software/h4cflib.phphere) whose goal is to make HDF4 files accessible to CF tools and conventions. Their project includes a tool named h4tonccf that converts HDF4 files to netCDF3 or netCDF4 files. We are not yet sure what advantages or features h4tonccf has that are not in NCO, though we suspect both methods have their own advantages. Corrections welcome. RPM Debian As of 2012, netCDF4 is relatively stable software. Problems with netCDF4 and HDF libraries have mainly been fixed. Binary NCO distributions shipped as RPMs and as debs have used the netCDF4 library since 2010 and 2011, respectively. NETCDF4_ROOT One must often build NCO from source to obtain netCDF4 support. Typically, one specifies the root of the netCDF4 installation directory. Do this with the NETCDF4_ROOT variable. Then use your preferred NCO build mechanism, e.g.,

export NETCDF4_ROOT=/usr/local/netcdf4 # Set netCDF4 location
cd ~/nco;./configure --enable-netcdf4  # Configure mechanism -or-
cd ~/nco/bld;./make NETCDF4=Y allinone # Old Makefile mechanism

We carefully track the netCDF4 releases, and keep the netCDF4 atomic type support and other features working. Our long term goal is to utilize more of the extensive new netCDF4 feature set. The next major netCDF4 feature we are likely to utilize is parallel I/O. We will enable this in the MPI netCDF operators. <a name="help"></a>  <a name="hlp"></a>  <a name="bug"></a>

Help Requests and Bug ReportsnetCDF2/3/4 and HDF4/5 SupportIntroduction

Help Requests and Bug Reports reporting bugs bugs, reporting core dump help features, requesting We generally receive three categories of mail from users: help requests, bug reports, and feature requests. Notes saying the equivalent of &textldquo;Hey, NCO continues to work great and it saves me more time everyday than it took to write this note&textrdquo; are a distant fourth. There is a different protocol for each type of request. The preferred etiquette for all communications is via NCO Project Forums. Do not contact project members via personal e-mail unless your request comes with money or you have damaging information about our personal lives. Please use the Forums&textmdash;they preserve a record of the questions and answers so that others can learn from our exchange. Also, since NCO is both volunteer-driven and government-funded, this record helps us provide program officers with information they need to evaluate our project. Before posting to the NCO forums described below, you might first https://sf.net/account/register.phpregister your name and email address with SourceForge.net or else all of your postings will be attributed to nobody. Once registered you may choose to monitor any forum and to receive (or not) email when there are any postings including responses to your questions. We usually reply to the forum message, not to the original poster. If you want us to include a new feature in NCO, please consider implementing the feature yourself and sending us the patch. If that is beyond your ken, then send a note to the http://sf.net/p/nco/discussion/9829NCO Discussion forum. Read the manual before reporting a bug or posting a help request. Sending questions whose answers are not in the manual is the best way to motivate us to write more documentation. We would also like to accentuate the contrapositive of this statement. If you think you have found a real bug the most helpful thing you can do is simplify the problem to a manageable size and then report it. The first thing to do is to make sure you are running the latest publicly released version of NCO. Once you have read the manual, if you are still unable to get NCO to perform a documented function, submit a help request. Follow the same procedure as described below for reporting bugs (after all, it might be a bug). debugging -r -D That is, describe what you are trying to do, and include the complete commands (run with -D 5), error messages, and version of NCO (with -r). Some commands behave differently depending on the exact order and rank of dimensions in the pertinent variables. In such cases we need you to provide that metadata, e.g., the text results of ncks -m on your input and/or output files. Post your help request to the http://sf.net/p/nco/discussion/9830NCO Help forum. If you think you used the right command when NCO misbehaves, then you might have found a bug. Incorrect numerical answers are the highest priority. We usually fix those within one or two days. Core dumps and sementation violations receive lower priority. They are always fixed, eventually. How do you simplify a problem that reveal a bug? Cut out extraneous variables, dimensions, and metadata from the offending files and re-run the command until it no longer breaks. Then back up one step and report the problem. Usually the file(s) will be very small, i.e., one variable with one or two small dimensions ought to suffice. <a name="dbg"></a>  <a name="-D"></a>  -r --revision --version --vrs -D debug-level --debug-level debug-level --dbg_lvl debug-level debug-level dbg_lvl Run the operator with -r and then run the command with -D 5 to increase the verbosity of the debugging output. It is very important that your report contain the exact error messages and compile-time environment. Include a copy of your sample input file, or place one on a publicly accessible location, of the file(s). If you are sure it is a bug, post the full report to the http://sf.net/p/nco/bugsNCO Project buglist. Otherwise post all the information to http://sf.net/p/nco/discussion/9830NCO Help forum. installation autoconf nco.configure.${GNU_TRP}.foo nco.config.log.${GNU_TRP}.foo nco.make.${GNU_TRP}.foo config.guess configure.eg Build failures count as bugs. Our limited machine access means we cannot fix all build failures. The information we need to diagnose, and often fix, build failures are the three files output by GNU build tools, nco.config.log.${GNU_TRP}.foo, nco.configure.${GNU_TRP}.foo, and nco.make.${GNU_TRP}.foo. The file configure.eg shows how to produce these files. Here ${GNU_TRP} is the &textldquo;GNU architecture triplet&textrdquo;, the chip-vendor-OS string returned by config.guess. Please send us your improvements to the examples supplied in configure.eg. regressions archive The regressions archive at http://dust.ess.uci.edu/nco/rgr contains the build output from our standard test systems. You may find you can solve the build problem yourself by examining the differences between these files and your own. <a name="str"></a>

StrategiesShared featuresIntroductionTop Operator Strategies * Philosophy::

* Climate Model Paradigm::

* Temporary Output Files::

* Appending Variables::

* Simple Arithmetic and Interpolation::

* Statistics vs Concatenation::

* Large Numbers of Files::

* Large Datasets::

* Memory Requirements::

* Performance::

<a name="phl"></a>  PhilosophyClimate Model ParadigmStrategiesStrategies

Philosophy philosophy climate model The main design goal is command line operators which perform useful, scriptable operations on netCDF files. Many scientists work with models and observations which produce too much data to analyze in tabular format. Thus, it is often natural to reduce and massage this raw or primary level data into summary, or second level data, e.g., temporal or spatial averages. These second level data may become the inputs to graphical and statistical packages, and are often more suitable for archival and dissemination to the scientific community. NCO performs a suite of operations useful in manipulating data from the primary to the second level state. IDL Matlab NCL Perl Yorick Higher level interpretive languages (e.g., IDL, Yorick, Matlab, NCL, Perl, Python), and lower level compiled languages (e.g., C, Fortran) can always perform any task performed by NCO, but often with more overhead. NCO, on the other hand, is limited to a much smaller set of arithmetic and metadata operations than these full blown languages. command line switches Another goal has been to implement enough command line switches so that frequently used sequences of these operators can be executed from a shell script or batch file. Finally, NCO was written to consume the absolute minimum amount of system memory required to perform a given job. The arithmetic operators are extremely efficient; their exact memory usage is detailed in Memory Requirements. <a name="clm_mdl"></a>

Climate Model ParadigmTemporary Output FilesPhilosophyStrategies

Climate Model Paradigm climate model NCAR GCM NCO was developed at NCAR to aid analysis and manipulation of datasets produced by General Circulation Models (GCMs). GCM datasets share many features with other gridded scientific datasets and so provide a useful paradigm for the explication of the NCO operator set. Examples in this manual use a GCM paradigm because latitude, longitude, time, temperature and other fields related to our natural environment are as easy to visualize for the layman as the expert. <a name="out"></a>

Temporary Output FilesAppending VariablesClimate Model ParadigmStrategies

Temporary Output Files data safety error tolerance safeguards temporary output files temporary files NCO operators are designed to be reasonably fault tolerant, so that a system failure or user-abort of the operation (e.g., with C-c) does not cause loss of data. The user-specified output-file is only created upon successful completion of the operation The ncrename and ncatted operators are exceptions to this rule. ncrename netCDF Renamer.. This is accomplished by performing all operations in a temporary copy of output-file. The name of the temporary output file is constructed by appending .pid<process ID>.<operator name>.tmp to the user-specified output-file name. When the operator completes its task with no fatal errors, the temporary output file is moved to the user-specified output-file. This imbues the process with fault-tolerance since fatal error (e.g., disk space fills up) affect only the temporary output file, leaving the final output file not created if it did not already exist. Note the construction of a temporary output file uses more disk space than just overwriting existing files &textldquo;in place&textrdquo; (because there may be two copies of the same file on disk until the NCO operation successfully concludes and the temporary output file overwrites the existing output-file). performance operator speed speed execution time Also, note this feature increases the execution time of the operator by approximately the time it takes to copy the output-file The OS-specific system move command is used. This is mv for UNIX, and move for Windows.. Finally, note this fault-tolerant feature allows the output-file to be the same as the input-file without any danger of &textldquo;overlap&textrdquo;. <a name="tmp_fl"></a>  <a name="no_tmp_fl"></a>  <a name="wrt_tmp_fl"></a>  --no_tmp_fl --wrt_tmp_fl --write_tmp_fl --create_ram --open_ram RAM disks RAM files Over time many &textldquo;power users&textrdquo; have requested a way to turn-off the fault-tolerance safety feature that automatically creates a temporary file. Often these users build and execute production data analysis scripts that are repeated frequently on large datasets. Obviating an extra file write can then conserve significant disk space and time. For this purpose NCO has, since version 4.2.1 in August, 2012, made configurable the controls over temporary file creation. The --wrt_tmp_fl and equivalent --write_tmp_fl switches ensure NCO writes output to an intermediate temporary file. This is and has always been the default behavior so there is currently no need to specify these switches. However, the default may change some day, especially since writing to RAM disks (RAM disks) may some day become the default. The --no_tmp_fl switch causes NCO to write directly to the final output file instead of to an intermediate temporary file. &textldquo;Power users&textrdquo; may wish to invoke this switch to increase performance (i.e., reduce wallclock time) when manipulating large files. When eschewing temporary files, users may forsake the ability to have the same name for both output-file and input-file since, as described above, the temporary file prevented overlap issues. However, if the user creates the output file in RAM (RAM disks) then it is still possible to have the same name for both output-file and input-file.

ncks in.nc out.nc # Default: create out.pid.tmp.nc then move to out.nc
ncks --wrt_tmp_fl in.nc out.nc # Same as default
ncks --no_tmp_fl in.nc out.nc # Create out.nc directly on disk
ncks --no_tmp_fl in.nc in.nc # ERROR-prone! Overwrite in.nc with itself
ncks --create_ram --no_tmp_fl in.nc in.nc # Create in RAM, write to disk
ncks --open_ram --no_tmp_fl in.nc in.nc # Read into RAM, write to disk

There is no reason to expect the fourth example to work. The behavior of overwriting a file while reading from the same file is undefined, much as is the shell command cat foo > foo. Although it may &textldquo;work&textrdquo; in some cases, it is unreliable. One way around this is to use --create_ram so that the output file is not written to disk until the input file is closed, RAM disks. However, as of 20130328, the behavior of the --create_ram and --open_ram examples has not been thoroughly tested. The NCO authors have seen compelling use cases for utilizing the RAM switches, though not (yet) for combining them with --no_tmp_fl. NCO implements both options because they are largely independent of eachother. It is up to &textldquo;power users&textrdquo; to discover which best fit their needs. We welcome accounts of your experiences posted to the forums. <a name="-A"></a>  <a name="-O"></a>  -A -O --apn --append --ovr --overwrite overwriting files appending variables appending to files Other safeguards exist to protect the user from inadvertently overwriting data. If the output-file specified for a command is a pre-existing file, then the operator will prompt the user whether to overwrite (erase) the existing output-file, attempt to append to it, or abort the operation. However, in processing large amounts of data, too many interactive questions slows productivity. Therefore NCO also implements two ways to override its own safety features, the -O and -A switches. Specifying -O tells the operator to overwrite any existing output-file without prompting the user interactively. Specifying -A tells the operator to attempt to append to any existing output-file without prompting the user interactively. These switches are useful in batch environments because they suppress interactive keyboard input. <a name="apn"></a>  <a name="append"></a>

Appending VariablesSimple Arithmetic and InterpolationTemporary Output FilesStrategies

Appending Variables Adding variables from one file to another is often desirable. concatenation appending variables merging files pasting variables This is referred to as appending, although some prefer the terminology merging The terminology merging is reserved for an (unwritten) operator which replaces hyperslabs of a variable in one file with hyperslabs of the same variable from another file or pasting. Appending is often confused with what NCO calls concatenation. record dimension In NCO, concatenation refers to splicing a variable along the record dimension. The length along the record dimension of the output is the sum of the lengths of the input files. Appending, on the other hand, refers to copying a variable from one file to another file which may or may not already contain the variable Yes, the terminology is confusing. By all means mail me if you think of a better nomenclature. Should NCO use paste instead of append? . NCO can append or concatenate just one variable, or all the variables in a file at the same time. In this sense, ncks can append variables from one file to another file. This capability is invoked by naming two files on the command line, input-file and output-file. When output-file already exists, the user is prompted whether to overwrite, append/replace, or exit from the command. Selecting overwrite tells the operator to erase the existing output-file and replace it with the results of the operation. Selecting exit causes the operator to exit&textmdash;the output-file will not be touched in this case. Selecting append/replace causes the operator to attempt to place the results of the operation in the existing output-file, ncks netCDF Kitchen Sink. <a name="unn"></a>  union of files disjoint files The simplest way to create the union of two files is

ncks -A fl_1.nc fl_2.nc

This puts the contents of fl_1.nc into fl_2.nc. The -A is optional. On output, fl_2.nc is the union of the input files, regardless of whether they share dimensions and variables, or are completely disjoint. The append fails if the input files have differently named record dimensions (since netCDF supports only one), or have dimensions of the same name but different sizes. <a name="bnr"></a>

Simple Arithmetic and InterpolationStatistics vs ConcatenationAppending VariablesStrategies

Simple Arithmetic and Interpolation Users comfortable with NCO semantics may find it easier to perform some simple mathematical operations in NCO rather than higher level languages. ncbo (ncbo netCDF Binary Operator) does file addition, subtraction, multiplication, division, and broadcasting. It even does group broadcasting. ncflint (ncflint netCDF File Interpolator) does file addition, subtraction, multiplication and interpolation. Sequences of these commands can accomplish simple yet powerful operations from the command line. <a name="statisticians"></a>  <a name="averagers"></a>

Statistics vs ConcatenationLarge Numbers of FilesSimple Arithmetic and InterpolationStrategies

Statistics vs Concatenation <a name="sym_ncea"></a>  <a name="sym_nces"></a>  <a name="sym_ncrcat"></a>  symbolic links The most frequently used operators of NCO are probably the statisticians (i.e., tools that do statistics) and concatenators. Because there are so many types of statistics like averaging (e.g., across files, within a file, over the record dimension, over other dimensions, with or without weights and masks) and of concatenating (across files, along the record dimension, along other dimensions), there are currently no fewer than five operators which tackle these two purposes: ncra, nces, ncwa, ncrcat, and ncecat. These operators do share many capabilities Currently nces and ncrcat are symbolically linked to the ncra executable, which behaves slightly differently based on its invocation name (i.e., argv[0]). These three operators share the same source code, and merely have different inner loops., though each has its unique specialty. Two of these operators, ncrcat and ncecat, concatenate hyperslabs across files. The other two operators, ncra and nces, compute statistics across (and/or within) files The third averaging operator, ncwa, is the most sophisticated averager in NCO. However, ncwa is in a different class than ncra and nces because it operates on a single file per invocation (as opposed to multiple files). On that single file, however, ncwa provides a richer set of averaging options&textmdash;including weighting, masking, and broadcasting.. First, let&textrsquo;s describe the concatenators, then the statistics tools. * Concatenation::

* Averaging::

* Interpolating::

<a name="cnc"></a>  ConcatenationAveragingStatistics vs ConcatenationStatistics vs Concatenation Concatenators ncrcat and ncecat ncecat ncrcat Joining together independent files along a common record dimension is called concatenation. ncrcat is designed for concatenating record variables, while ncecat is designed for concatenating fixed length variables. Consider five files, 85.nc, 86.nc, &dots; 89.nc each containing a year&textrsquo;s worth of data. Say you wish to create from them a single file, 8589.nc containing all the data, i.e., spanning all five years. If the annual files make use of the same record variable, then ncrcat will do the job nicely with, e.g., ncrcat 8?.nc 8589.nc. The number of records in the input files is arbitrary and can vary from file to file. ncrcat netCDF Record Concatenator, for a complete description of ncrcat. However, suppose the annual files have no record variable, and thus their data are all fixed length. ensemble climate model For example, the files may not be conceptually sequential, but rather members of the same group, or ensemble. Members of an ensemble may have no reason to contain a record dimension. ncecat will create a new record dimension (named record by default) with which to glue together the individual files into the single ensemble file. If ncecat is used on files which contain an existing record dimension, that record dimension is converted to a fixed-length dimension of the same name and a new record dimension (named record) is created. Consider five realizations, 85a.nc, 85b.nc, &dots; 85e.nc of 1985 predictions from the same climate model. Then ncecat 85?.nc 85_ens.nc glues together the individual realizations into the single file, 85_ens.nc. If an input variable was dimensioned [lat,lon], it will have dimensions [record,lat,lon] in the output file. A restriction of ncecat is that the hyperslabs of the processed variables must be the same from file to file. Normally this means all the input files are the same size, and contain data on different realizations of the same variables. ncecat netCDF Ensemble Concatenator, for a complete description of ncecat. ncpdq <a name="dmn_cat"></a>  ncpdq makes it possible to concatenate files along any dimension, not just the record dimension. First, use ncpdq to convert the dimension to be concatenated (i.e., extended with data from other files) into the record dimension. Second, use ncrcat to concatenate these files. Finally, if desirable, use ncpdq to revert to the original dimensionality. As a concrete example, say that files x_01.nc, x_02.nc, &dots; x_10.nc contain time-evolving datasets from spatially adjacent regions. The time and spatial coordinates are time and x, respectively. Initially the record dimension is time. Our goal is to create a single file that contains joins all the spatially adjacent regions into one single time-evolving dataset. for idx in 01 02 03 04 05 06 07 08 09 10; do # Bourne Shell ncpdq -a x,time x_${idx}.nc foo_${idx}.nc # Make x record dimension done ncrcat foo_??.nc out.nc # Concatenate along x ncpdq -a time,x out.nc out.nc # Revert to time as record dimension Note that ncrcat will not concatenate fixed-length variables, whereas ncecat concatenates both fixed-length and record variables along a new record variable. To conserve system memory, use ncrcat where possible. <a name="avg"></a>  AveragingInterpolatingConcatenationStatistics vs Concatenation Averagers nces, ncra, and ncwa nces ncra ncwa The differences between the averagers ncra and nces are analogous to the differences between the concatenators. ncra is designed for averaging record variables from at least one file, while nces is designed for averaging fixed length variables from multiple files. ncra performs a simple arithmetic average over the record dimension of all the input files, with each record having an equal weight in the average. nces performs a simple arithmetic average of all the input files, with each file having an equal weight in the average. Note that ncra cannot average fixed-length variables, but nces can average both fixed-length and record variables. To conserve system memory, use ncra rather than nces where possible (e.g., if each input-file is one record long). The file output from nces will have the same dimensions (meaning dimension names as well as sizes) as the input hyperslabs (nces netCDF Ensemble Statistics, for a complete description of nces). The file output from ncra will have the same dimensions as the input hyperslabs except for the record dimension, which will have a size of 1 (ncra netCDF Record Averager, for a complete description of ncra). <a name="ntp"></a>  InterpolatingAveragingStatistics vs Concatenation Interpolator ncflint ncflint ncflint can interpolate data between or two files. Since no other operators have this ability, the description of interpolation is given fully on the ncflint reference page (ncflint netCDF File Interpolator). Note that this capability also allows ncflint to linearly rescale any data in a netCDF file, e.g., to convert between differing units. <a name="lrg"></a>  <a name="input_files"></a>

Large Numbers of FilesLarge DatasetsStatistics vs ConcatenationStrategies

Large Numbers of Files files, numerous input -n loop Occasionally one desires to digest (i.e., concatenate or average) hundreds or thousands of input files. automagic NASA EOSDIS Unfortunately, data archives (e.g., NASA EOSDIS) may not name netCDF files in a format understood by the -n loop switch (Specifying Input Files) that automagically generates arbitrary numbers of input filenames. The -n loop switch has the virtue of being concise, and of minimizing the command line. This helps keeps output file small since the command line is stored as metadata in the history attribute (History Attribute). However, the -n loop switch is useless when there is no simple, arithmetic pattern to the input filenames (e.g., h00001.nc, h00002.nc, &dots; h90210.nc). Moreover, filename globbing does not work when the input files are too numerous or their names are too lengthy (when strung together as a single argument) to be passed by the calling shell to the NCO operator The exact length which exceeds the operating system internal limit for command line lengths varies across OSs and shells. GNU bash may not have any arbitrary fixed limits to the size of command line arguments. Many OSs cannot handle command line arguments (including results of file globbing) exceeding 4096 characters.. When this occurs, the ANSI C-standard argc-argv method of passing arguments from the calling shell to a C-program (i.e., an NCO operator) breaks down. There are (at least) three alternative methods of specifying the input filenames to NCO in environment-limited situations. <a name="stdin"></a>  standard input provenance stdin The recommended method for sending very large numbers (hundreds or more, typically) of input filenames to the multi-file operators is to pass the filenames with the UNIX standard input feature, aka stdin: # Pipe large numbers of filenames to stdin /bin/ls | grep ${CASEID}_'......'.nc | ncecat -o foo.nc This method avoids all constraints on command line size imposed by the operating system. A drawback to this method is that the history attribute (History Attribute) does not record the name of any input files since the names were not passed as positional arguments on the command line. This makes it difficult later to determine the data provenance. nco_input_file_number nco_input_file_list global attributes attributes, global To remedy this situation, operators store the number of input files in the nco_input_file_number global attribute and the input file list itself in the nco_input_file_list global attribute (File List Attributes). Although this does not preserve the exact command used to generate the file, it does retains all the information required to reconstruct the command and determine the data provenance. As of NCO version 5.1.1, released in November, 2022, all operators support specifying input files via stdin (Specifying Input Files), and also store such input filenames in the File List Attributes). globbing shell extended regular expressions regular expressions pattern matching xargs UNIX A second option is to use the UNIX xargs command. This simple example selects as input to xargs all the filenames in the current directory that match a given pattern. For illustration, consider a user trying to average millions of files which each have a six character filename. If the shell buffer cannot hold the results of the corresponding globbing operator, ??????.nc, then the filename globbing technique will fail. Instead we express the filename pattern as an extended regular expression, ......\.nc (Subsetting Files). We use grep to filter the directory listing for this pattern and to pipe the results to xargs which, in turn, passes the matching filenames to an NCO multi-file operator, e.g., ncecat. # Use xargs to transfer filenames on the command line /bin/ls | grep ${CASEID}_'......'.nc | xargs -x ncecat -o foo.nc pipes The single quotes protect the only sensitive parts of the extended regular expression (the grep argument), and allow shell interpolation (the ${CASEID} variable substitution) to proceed unhindered on the rest of the command. xargs uses the UNIX pipe feature to append the suitably filtered input file list to the end of the ncecat command options. output file input files -o fl_out The -o foo.nc switch ensures that the input files supplied by xargs are not confused with the output file name. xargs does, unfortunately, have its own limit (usually about 20,000 characters) on the size of command lines it can pass. Give xargs the -x switch to ensure it dies if it reaches this internal limit. When this occurs, use either the stdin method above, or the symbolic link presented next. symbolic links Perl Even when its internal limits have not been reached, the xargs technique may not be flexible enough to handle all situations. A full scripting language like Perl or Python can handle any level of complexity of filtering input filenames, and any number of filenames. The technique of last resort is to write a script that creates symbolic links between the irregular input filenames and a set of regular, arithmetic filenames that the -n loop switch understands. Perl For example, the following Perl script creates a monotonically enumerated symbolic link to up to one million .nc files in a directory. If there are 999,999 netCDF files present, the links are named 000001.nc to 999999.nc: -n loop # Create enumerated symbolic links /bin/ls | grep \.nc | perl -e \ '$idx=1;while(<STDIN>){chop;symlink $_,sprintf("%06d.nc",$idx++);}' ncecat -n 999999,6,1 000001.nc foo.nc # Remove symbolic links when finished /bin/rm ??????.nc The -n loop option tells the NCO operator to automatically generate the filnames of the symbolic links. This circumvents any OS and shell limits on command-line size. The symbolic links are easily removed once NCO is finished. history provenance One drawback to this method is that the history attribute (History Attribute) retains the filename list of the symbolic links, rather than the data files themselves. This makes it difficult to determine the data provenance at a later date.

Large DatasetsMemory RequirementsLarge Numbers of FilesStrategies

Large Datasets large datasets LFS Large File Support Large datasets are those files that are comparable in size to the amount of random access memory (RAM) in your computer. Many users of NCO work with files larger than 100 MB. Files this large not only push the current edge of storage technology, they present special problems for programs which attempt to access the entire file at once, such as nces and ncecat. swap space If you work with a 300 MB files on a machine with only 32 MB of memory then you will need large amounts of swap space (virtual memory on disk) and NCO will work slowly, or even fail. There is no easy solution for this. The best strategy is to work on a machine with sufficient amounts of memory and swap space. Since about 2004, many users have begun to produce or analyze files exceeding 2 GB in size. These users should familiarize themselves with NCO&textrsquo;s Large File Support (LFS) capabilities (Large File Support). The next section will increase your familiarity with NCO&textrsquo;s memory requirements. With this knowledge you may re-design your data reduction approach to divide the problem into pieces solvable in memory-limited situations. <a name="ulimit"></a>  server UNICOS Cray GNU/Linux ulimit core dump If your local machine has problems working with large files, try running NCO from a more powerful machine, such as a network server. If you get a memory-related core dump (e.g., Error exit (core dumped)) on a GNU/Linux system, or the operation ends before the entire output file is written, try increasing the process-available memory with ulimit:

ulimit -f unlimited

This may solve constraints on clusters where sufficient hardware resources exist yet where system administrators felt it wise to prevent any individual user from consuming too much of resource. Certain machine architectures, e.g., Cray UNICOS, have special commands which allow one to increase the amount of interactive memory. ilimit On Cray systems, try to increase the available memory with the ilimit command. speed The speed of the NCO operators also depends on file size. When processing large files the operators may appear to hang, or do nothing, for large periods of time. In order to see what the operator is actually doing, it is useful to activate a more verbose output mode. This is accomplished by supplying a number greater than 0 to the -D debug-level (or --debug-level, or --dbg_lvl) switch. -D debug-level --debug-level debug-level --dbg_lvl debug-level debug-level dbg_lvl debugging When the debug-level is non-zero, the operators report their current status to the terminal through the stderr facility. Using -D does not slow the operators down. Choose a debug-level between 1 and 3 for most situations, e.g., nces -D 2 85.nc 86.nc 8586.nc. A full description of how to estimate the actual amount of memory the multi-file NCO operators consume is given in Memory Requirements. <a name="mmr"></a>

Memory RequirementsPerformanceLarge DatasetsStrategies

Memory Requirements memory requirements memory available RAM swap space peak memory usage --ram_all --open_ram --diskless_all Many people use NCO on gargantuan files which dwarf the memory available (free RAM plus swap space) even on today&textrsquo;s powerful machines. These users want NCO to consume the least memory possible so that their scripts do not have to tediously cut files into smaller pieces that fit into memory. We commend these greedy users for pushing NCO to its limits! threads OpenMP shared memory machines This section describes the memory NCO requires during operation. The required memory depends on the underlying algorithms, datatypes, and compression, if any. The description below is the memory usage per thread. Users with shared memory machines may use the threaded NCO operators (OpenMP Threading). The peak and sustained memory usage will scale accordingly, i.e., by the number of threads. In all cases the memory use refers to the uncompressed size of the data. The netCDF4 library automatically decompresses variables during reads. The filesize can easily belie the true size of the uncompressed data. In other words, the usage below can be taken at face value for netCDF3 datasets only. Chunking will also affect memory usage on netCDF4 operations. Memory consumption patterns of all operators are similar, with the exception of ncap2. * Single and Multi-file Operators::

* Memory for ncap2::

Single and Multi-file OperatorsMemory for ncap2Memory RequirementsMemory Requirements Single and Multi-file Operators multi-file operators The multi-file operators currently comprise the record operators, ncra and ncrcat, and the ensemble operators, nces and ncecat. The record operators require much less memory than the ensemble operators. This is because the record operators operate on one single record (i.e., time-slice) at a time, whereas the ensemble operators retrieve the entire variable into memory. Let

MS

be the peak sustained memory demand of an operator,

FT

be the memory required to store the entire contents of all the variables to be processed in an input file,

FR

be the memory required to store the entire contents of a single record of each of the variables to be processed in an input file,

VR

be the memory required to store a single record of the largest record variable to be processed in an input file,

VT

be the memory required to store the largest variable to be processed in an input file,

VI

be the memory required to store the largest variable which is not processed, but is copied from the initial file to the output file. All operators require

MI = VI

during the initial copying of variables from the first input file to the output file. This is the initial (and transient) memory demand. The sustained memory demand is that memory required by the operators during the processing (i.e., averaging, concatenation) phase which lasts until all the input files have been processed. The operators have the following memory requirements: ncrcat requires

MS <= VR

. ncecat requires

MS <= VT

. ncra requires

MS = 2FR + VR

. nces requires

MS = 2FT + VT

. ncbo requires

MS <= 3VT

(both input variables and the output variable). ncflint requires

MS <= 3VT

(both input variables and the output variable). ncpdq requires

MS <= 2VT

(one input variable and the output variable). ncwa requires

MS <= 8VT

(see below). Note that only variables that are processed, e.g., averaged, concatenated, or differenced, contribute to

MS

. Variables that do not appear in the output file (Subsetting Files) are never read and contribute nothing to the memory requirements. Further note that some operators perform internal type-promotion on some variables prior to arithmetic (Type Conversion). For example, ncra, nces, and ncwa all promote integer types to double-precision floating-point prior to arithmetic, then perform the arithmetic, then demote back to the original integer type after arithmetic. This preserves the on-disk storage type while obtaining the precision advantages of double-precision floating-point arithmetic. Since version 4.3.6 (released in September, 2013), NCO also by default converts single-precision floating-point to double-precision prior to arithmetic, which incurs the same RAM penalty. Hence, the sustained memory required for integer variables and single-precision floats are two or four-times their on-disk, uncompressed, unpacked sizes if they meet the rules for automatic internal promotion. Put another way, disabling auto-promotion of single-precision variables (with --flt) considerably reduces the RAM footprint of arithmetic operators. The --open_ram switch (and switches that invoke it like --ram_all and --diskless_all) incurs a RAM penalty. These switches cause each input file to be copied to RAM upon opening. Hence any operator invoking these switches utilizes an additional

FT

of RAM (i.e.,

MS += FT

). See RAM disks for further details. <a name="mmr_ncwa"></a>  ncwa consumes between two and eight times the memory of an NC_DOUBLE variable in order to process it. Peak consumption occurs when storing simultaneously in memory one input variable, one tally array, one input weight, one conformed/working weight, one weight tally, one input mask, one conformed/working mask, and one output variable. NCO&textrsquo;s tally arrays are of type C-type long, whose size is eight-bytes on all modern computers, the same as NC_DOUBLE By contrast NC_INT and its deprecated synonym NC_LONG are only four-bytes. Perhaps this is one reason why the NC_LONG token is deprecated.. When invoked, the weighting and masking features contribute up to three-eighths and two-eighths of these requirements apiece. If weights and masks are not specified (i.e., no -w or -a options) then ncwa requirements drop to

MS <= 3VT

(one input variable, one tally array, and the output variable). The output variable is the same size as the input variable when averaging only over a degenerate dimension. However, normally the output variable is much smaller than the input, and is often a simple scalar, in which case the memory requirements drop by

1VT

since the output array requires essentially no memory. All of this is subject to the type promotion rules mentioned above. For example, ncwa averaging a variable of type NC_FLOAT requires

MS <= 16VT

(rather than

MS <= 8VT

) since all arrays are (at least temporarily) composed of eight-byte elements, twice the size of the values on disk. Without mask or weights, the requirements for NC_FLOAT are

MS <= 6VT

(rather than

MS <= 3VT

as for NC_DOUBLE) due to temporary internal promotion of both the input variable and the output variable to type NC_DOUBLE. The --flt option that suppresses promotion reduces this to

MS <= 4VT

(the tally elements do not change size), and to

MS <= 3VT

when the output array is a scalar. OpenMP threads The above memory requirements must be multiplied by the number of threads thr_nbr (OpenMP Threading). -t thr_nbr If this causes problems then reduce (with -t thr_nbr) the number of threads. Memory for ncap2Single and Multi-file OperatorsMemory Requirements Memory for ncap2 ncap2 binary operations unary operations memory leaks left hand casting ncap2 has unique memory requirements due its ability to process arbitrarily long scripts of any complexity. All scripts acceptable to ncap2 are ultimately processed as a sequence of binary or unary operations. ncap2 requires

MS <= 2VT

under most conditions. An exception to this is when left hand casting (Left hand casting) is used to stretch the size of derived variables beyond the size of any input variables. Let

VC

be the memory required to store the largest variable defined by left hand casting. In this case,

MS <= 2VC

. malloc() ncap2 scripts are complete dynamic and may be of arbitrary length. A script that contains many thousands of operations, may uncover a slow memory leak even though each single operation consumes little additional memory. Memory leaks are usually identifiable by their memory usage signature. Leaks cause peak memory usage to increase monotonically with time regardless of script complexity. Slow leaks are very difficult to find. Sometimes a malloc() (or new[]) failure is the only noticeable clue to their existence. If you have good reasons to believe that a memory allocation failure is ultimately due to an NCO memory leak (rather than inadequate RAM on your system), then we would be very interested in receiving a detailed bug report.

PerformanceMemory RequirementsStrategies

Performance papers overview An overview of NCO capabilities as of about 2006 is in Zender, C. S. (2008), &textldquo;Analysis of Self-describing Gridded Geoscience Data with netCDF Operators (NCO)&textrdquo;, Environ. Modell. Softw., doi:10.1016/j.envsoft.2008.03.004. This paper is also available at http://dust.ess.uci.edu/ppr/ppr_Zen08.pdf. scaling performance NCO performance and scaling for arithmetic operations is described in Zender, C. S., and H. J. Mangalam (2007), &textldquo;Scaling Properties of Common Statistical Operators for Gridded Datasets&textrdquo;, Int. J. High Perform. Comput. Appl., 21(4), 485-498, doi:10.1177/1094342007083802. This paper is also available at http://dust.ess.uci.edu/ppr/ppr_ZeM07.pdf. It is helpful to be aware of the aspects of NCO design that can limit its performance: buffering No data buffering is performed during nc_get_var and nc_put_var operations. performance operator speed speed execution time Hyperslabs too large to hold in core memory will suffer substantial performance penalties because of this. monotonic coordinates Since coordinate variables are assumed to be monotonic, the search for bracketing the user-specified limits should employ a quicker algorithm, like bisection, than the two-sided incremental search currently implemented. C_format FORTRAN_format signedness scale_format add_offset C_format, FORTRAN_format, signedness, scale_format and add_offset attributes are ignored by ncks when printing variables to screen. Yorick In the late 1990s it was discovered that some random access operations on large files on certain architectures (e.g., UNICOS) were much slower with NCO than with similar operations performed using languages that bypass the netCDF interface (e.g., Yorick). This may have been a penalty of unnecessary byte-swapping in the netCDF interface. It is unclear whether such problems exist in present day (2007) netCDF/NCO environments, where unnecessary byte-swapping has been reduced or eliminated. <a name="ftr"></a>

Shared featuresReference ManualStrategiesTop Shared Features Many features have been implemented in more than one operator and are described here for brevity. The description of each feature is preceded by a box listing the operators for which the feature is implemented. command line switches Command line switches for a given feature are consistent across all operators wherever possible. If no &textldquo;key switches&textrdquo; are listed for a feature, then that particular feature is automatic and cannot be controlled by the user. * Internationalization::

* Metadata Optimization::

* OpenMP Threading::

* Command Line Options::

* Sanitization of Input::

* Specifying Input Files::

* Specifying Output Files::

* Remote storage::

* Retaining Retrieved Files::

* File Formats and Conversion::

* Zarr and NCZarr::

* Large File Support::

* Subsetting Files::

* Subsetting Coordinate Variables::

* Group Path Editing::

* C and Fortran Index Conventions::

* Hyperslabs::

* Stride::

* Record Appending::

* Subcycle::

* Interleave::

* Multislabs::

* Wrapped Coordinates::

* Auxiliary Coordinates::

* Grid Generation::

* Regridding::

* Climatology and Bounds Support::

* UDUnits Support::

* Rebasing Time Coordinate::

* Multiple Record Dimensions::

* Missing Values::

* Chunking::

* Quantization Algorithms::

* Compression::

* Deflation::

* MD5 digests::

* Buffer sizes::

* RAM disks::

* Unbuffered I/O::

* Packed data::

* Operation Types::

* Type Conversion::

* Batch Mode::

* Global Attribute Addition::

* History Attribute::

* File List Attributes::

* CF Conventions::

* ARM Conventions::

* Operator Version::

<a name="i18n"></a>  InternationalizationMetadata OptimizationShared featuresShared features

Internationalization Internationalization I18N Availability: All operators&linebreak; L10N NCO support for internationalization of textual input and output (e.g., Warning messages) is nascent. We introduced the first foreign language string catalogues (French and Spanish) in 2004, yet did not activate these in distributions because the catalogues were nearly empty. We seek volunteers to populate our templates with translations for their favorite languages. <a name="hdr"></a>  <a name="hdr_pad"></a>

Metadata OptimizationOpenMP ThreadingInternationalizationShared features

Metadata Optimization metadata optimization performance operator speed speed execution time nc__enddef() --hdr_pad hdr_pad --header_pad hdr_pad Availability: All operators&linebreak; Short options: None&linebreak; Long options: --hdr_pad, --header_pad&linebreak; NCO supports padding headers to improve the speed of future metadata operations. Use the --hdr_pad and --header_pad switches to request that hdr_pad bytes be inserted into the metadata section of the output file. There is little downside to padding a header with kilobyte of space, since subsequent manipulation of the file will annotate the history attribute with all commands, let alone any explicit metadata additions with ncatted.

ncks --hdr_pad=1000  in.nc out.nc # Pad header with  1 kB space
ncks --hdr_pad=10000 in.nc out.nc # Pad header with 10 kB space

Future metadata expansions will not incur the netCDF3 performance penalty of copying the entire output file unless the expansion exceeds the amount of header padding. This can be beneficial when it is known that some metadata will be added at a future date. The operators that benefit most from judicious use of header padding are ncatted and ncrename, since they only alter metadata. This optimization exploits the netCDF library nc__enddef() function. This function behaves differently with different storage formats. It will improve speed of future metadata expansion with CLASSIC and 64bit netCDF files, though not necessarily with NETCDF4 files, i.e., those created by the netCDF interface to the HDF5 library (File Formats and Conversion). netCDF3 formats use a simple sequential ordering that requires copying the file if the size of new metadata exceeds the available padding. netCDF4 files use internal file pointers that allow flexibility at inserting and removing data without necessitating copying the whole file. <a name="omp"></a>  <a name="openmp"></a>

OpenMP ThreadingCommand Line OptionsMetadata OptimizationShared features

OpenMP Threading OpenMP threads SMP shared memory parallelism parallelism nco_openmp_thread_number --thr_nbr thr_nbr --threads thr_nbr --omp_num_threads thr_nbr -t thr_nbr Availability: ncclimo, ncks, ncremap&linebreak; Short options: -t&linebreak; Long options: --thr_nbr, --threads, --omp_num_threads&linebreak; NCO supports shared memory parallelism (SMP) when compiled with an OpenMP-enabled compiler. Threads requests and allocations occur in two stages. First, users may request a specific number of threads thr_nbr with the -t switch (or its long option equivalents, --thr_nbr, --threads, and --omp_num_threads). If not user-specified, OpenMP obtains thr_nbr from the OMP_NUM_THREADS environment variable, if present, or from the OS, if not. Caveat: Unfortunately, threading does not improve NCO throughput (i.e., wallclock time) because nearly all NCO operations are I/O-bound. This means that NCO spends negligible time doing anything compared to reading and writing. The only exception is regridding with ncremap which uses ncks under-the-hood. As of 2017, threading works only for regridding, thus this section is relevant only to ncclimo, ncks, and ncremap. We have seen some and can imagine other use cases where ncwa, ncpdq, and ncap2 (with long scripts) will complete faster due to threading. The main benefits of threading so far have been to isolate the serial from parallel portions of code. This parallelism is now exploited by OpenMP but then runs into the I/O bottleneck during output. The bottleneck will be ameliorated for large files by the use of MPI-enabled calls in the netCDF4 library when the underlying filesystem is parallel (e.g., PVFS or JFS). Implementation of the parallel output calls in NCO is not a goal of our current funding and would require new volunteers or funding. thr_nbr OMP_NUM_THREADS ncrcat ncwa ncap2 ncpdq large datasets NCO may modify thr_nbr according to its own internal settings before it requests any threads from the system. Certain operators contain hard-code limits to the number of threads they request. We base these limits on our experience and common sense, and to reduce potentially wasteful system usage by inexperienced users. For example, ncrcat is extremely I/O-intensive so we restrict

thr_nbr <= 2 for ncrcat. This is based on the notion that the best performance that can be expected from an operator which does no arithmetic is to have one thread reading and one thread writing simultaneously. In the future (perhaps with netCDF4), we hope to demonstrate significant threading improvements with operators like ncrcat by performing multiple simultaneous writes. Compute-intensive operators (ncremap) benefit most from threading. The greatest increases in throughput due to threading occur on large datasets where each thread performs millions, at least, of floating-point operations. Otherwise, the system overhead of setting up threads probably outweighs the speed enhancements due to SMP parallelism. However, we have not yet demonstrated that the SMP parallelism scales beyond four threads for these operators. Hence we restrict

thr_nbr <= 4 for all operators. We encourage users to play with these limits (edit file nco_omp.c) and send us their feedback. debugging dbg_lvl Once the initial thr_nbr has been modified for any operator-specific limits, NCO requests the system to allocate a team of thr_nbr threads for the body of the code. The operating system then decides how many threads to allocate based on this request. Users may keep track of this information by running the operator with

dbg_lvl > 0. By default, threaded operators attach one global attribute, nco_openmp_thread_number, to any file they create or modify. This attribute contains the number of threads the operator used to process the input files. This information helps to verify that the answers with threaded and non-threaded operators are equal to within machine precision. benchmarks This information is also useful for benchmarking. <a name="cmd_ln"></a>

Command Line OptionsSanitization of InputOpenMP ThreadingShared features

Command Line Options command line options Availability: All operators&linebreak; POSIX UNIX GNU switches NCO achieves flexibility by using command line options. These options are implemented in all traditional UNIX commands as single letter switches, e.g., ls -l. For many years NCO used only single letter option names. In late 2002, we implemented GNU/POSIX extended or long option names for all options. This was done in a backward compatible way such that the full functionality of NCO is still available through the familiar single letter options. Many features of NCO introduced since 2002 now require the use of long options, simply because we have nearly run out of single letter options. More importantly, mnemonics for single letter options are often non-intuitive so that long options provide a more natural way of expressing intent. long options Extended options, also called long options, are implemented using the system-supplied getopt.h header file, if possible. BSD getopt getopt_long getopt.h This provides the getopt_long function to NCO If a getopt_long function cannot be found on the system, NCO will use the getopt_long from the my_getopt package by Benjamin Sittler bsittler&arobase;iname.com. This is BSD-licensed software available from http://www.geocities.com/ResearchTriangle/Node/9405/#my_getopt.. -D debug-level --dbg_lvl debug-level The syntax of short options (single letter options) is -key value (dash-key-space-value). Here, key is the single letter option name, e.g., -D 2. The syntax of long options (multi-letter options) is --long_name value (dash-dash-key-space-value), e.g., --dbg_lvl 2 or --long_name=value (dash-dash-key-equal-value), e.g., --dbg_lvl=2. Thus the following are all valid for the -D (short version) or --dbg_lvl (long version) command line option.

ncks -D 3 in.nc        # Short option, preferred form
ncks -D3 in.nc         # Short option, alternate form
ncks --dbg_lvl=3 in.nc # Long option, preferred form
ncks --dbg_lvl 3 in.nc # Long option, alternate form

The third example is preferred for two reasons. First, --dbg_lvl is more specific and less ambiguous than -D. The long option format makes scripts more self documenting and less error-prone. Often long options are named after the source code variable whose value they carry. Second, the equals sign = joins the key (i.e., long_name) to the value in an uninterruptible text block. Experience shows that users are less likely to mis-parse commands when restricted to this form. * Truncating Long Options::

* Multi-arguments::

Truncating Long OptionsMulti-argumentsCommand Line OptionsCommand Line Options Truncating Long Options Truncating options Options, truncating GNU implements a superset of the POSIX standard. Their superset accepts any unambiguous truncation of a valid option:

ncks -D 3 in.nc        # Short option
ncks --dbg_lvl=3 in.nc # Long option, full form
ncks --dbg=3 in.nc     # Long option, OK unambiguous truncation
ncks --db=3 in.nc      # Long option, OK unambiguous truncation
ncks --d=3 in.nc       # Long option, ERROR ambiguous truncation

The first four examples are equivalent and will work as expected. The final example will exit with an error since ncks cannot disambiguate whether --d is intended as a truncation of --dbg_lvl, of --dimension, or of some other long option. NCO provides many long options for common switches. For example, the debugging level may be set in all operators with any of the switches -D, --debug-level, or --dbg_lvl. This flexibility allows users to choose their favorite mnemonic. For some, it will be --debug (an unambiguous truncation of --debug-level, and other will prefer --dbg. Interactive users usually prefer the minimal amount of typing, i.e., -D. We recommend that re-usable scripts employ long options to facilitate self-documentation and maintainability. This manual generally uses the short option syntax in examples. This is for historical reasons and to conserve space in printed output. Users are expected to pick the unambiguous truncation of each option name that most suits their taste. <a name="MTA"></a>  <a name="mta"></a>  <a name="multi-arguments"></a>  <a name="Multi-arguments"></a>  Multi-argumentsTruncating Long OptionsCommand Line Options Multi-arguments Multi-arguments MTA Options, multi-argument --gaa key=val --ppc key=val --qnt key=val --rgr key=val --trr key=val As of NCO version 4.6.2 (November, 2016), NCO accepts multiple key-value pair options for a single feature to be joined together into a single extended argument called a multi-argument, sometimes abbreviated MTA. Only four NCO features accept multiple key-value pairs that can be aggregated into multi-arguments. These features are: Global Attribute Addition options indicated via --gaa (Global Attribute Addition); Image Manipulation indicated via --trr <a name="envi"></a>  <a name="ENVI"></a>  <a name="BIL"></a>  <a name="BSQ"></a>  <a name="BIP"></a>  <a name="bil"></a>  <a name="bsq"></a>  <a name="bip"></a>  ENVI DOE BIL format BSQ format BIP format Terraref --trr NCO supports decoding ENVI images in support of the DOE Terraref project. These options are indicated via the ncks --trr switch, and are otherwise undocumented. Please contact us if more support and documentation of handling of ENVI BIL, BSQ, and BIP images would be helpful, Precision-Preserving Compression options are indicated via --ppc (Precision-Preserving Compression); and Regridding options are indicated via --rgr (Regridding). Arguments to these four indicator options take the form of key-value pairs, e.g., --rgr key=val. As of version 5.2.5 (May 2024), NCO changed to preferring --qnt over --ppc for quantization algorithms. The two are simply synonyms, and backward compatibility is maintained. These features have so many options that making each key its own command line option would pollute the namespace of NCO&textrsquo;s global options. Yet supplying multiple options to each indicator option one-at-a-time can result in command lines overpopulated with indicator switches (e.g., --rgr): ncks --rgr grd_ttl='Title' --rgr grid=grd.nc --rgr latlon=129,256 \ --rgr lat_typ=fv --rgr lon_typ=grn_ctr ... Multi-arguments combine all the indicator options into one option that receives a single argument that comprises all the original arguments glued together by a delimiter, which is, by default, #. Thus the multi-argument version of the above example is ncks --rgr grd_ttl='Title'#grid=grd.nc#latlon=129,256#lat_typ=fv#lon_typ=grn_ctr Note the aggregation of all key=val pairs into a single argument. NCO simply splits this argument at each delimiter, and processes the sub-arguments as if they had been passed with their own indicator option. Multi-arguments produce the same results, and may be mixed with, traditional indicator options supplied one-by-one. As mentioned previously, the multi-argument delimiter string is, by default, the hash-sign #. When any key=val pair contains the default delimiter, the user must specify a custom delimiter string so that options are parsed correctly. The options to change the multi-argument delimiter string are --mta_dlm=delim_string or --dlm_mta=delim_string, where delim_string can be any single or multi-character string that (1) is not contained in any key or val string; and (2) will not confuse the shell. For example, to use multi-arguments to pass a string that includes the hash symbol (the default delimiter is #), one must also change the delimiter so something besides hash, e.g., a colon :: ncks --dlm=":" --gaa foo=bar:foo2=bar2:foo3,foo4="hash # is in value" ncks --dlm=":" --gaa foo=bar:foo2=bar2:foo3,foo4="Thu Sep 15 13\:03\:18 PDT 2016" ncks --dlm="csz" --gaa foo=barcszfoo2=bar2cszfoo3,foo4="Long text" In the second example, the colons that are escaped with the backslash become literal characters. Many characters have special shell meanings and so must be escaped by a single or double backslash or enclosed in single quotes to prevent interpolation. These special characters include :, $, %, *, &arobase;, and &. If val is a long text string that could contain the default delimiter, then delimit with a unique multi-character string such as csz in the third example. As of NCO version 4.6.7 (May, 2017), multi-argument flags no longer need be specified as key-value pairs. By definition a flag sets a boolean value to either True or False. Previously MTA flags had to employ key-value pair syntax, e.g., --rgr infer=Y or --rgr no_cll_msr=anything in order to parse correctly. Now the MTA parser accepts flags in the more intuitive syntax where they are listed by name, i.e., the flag name alone indicates the flag to set, e.g., --rgr infer or --rgr no_cll_msr are valid. A consequence of this is that flags in multi-argument strings appear as straightforward flag names, e.g., --rgr infer#no_cll_msr#latlon=129,256. It is also valid to prefix flags in multi-arument strings with single or double-dashes to make the flags more visible, e.g., --rgr latlon=129,256#--infer#-no_cll_msr. <a name="scr"></a>  <a name="sntz"></a>  <a name="security"></a>  <a name="sanitize"></a>  <a name="whitelist"></a>  <a name="wht_lst"></a>

Sanitization of InputSpecifying Input FilesCommand Line OptionsShared features

Sanitization of Input sanitize security whitelist blacklist Availability: All operators&linebreak; NCO is often installed in system directories (although not with Conda), and on some production machines it may have escalated privileges. Since NCO manipulates files by using system() calls (e.g., to move and copy them with mv and cp) it makes sense to audit it for vulnerabilities and protect it from malicious users trying to exploit security gaps. Securing NCO against malicious attacks is multi-faceted, and involves careful memory management and auditing of user-input. In versions 4.7.3&textndash;4.7.6 (March-September, 2018), NCO implements a whitelist of characters allowed in user-specified filenames. This whitelist proved unpopular mainly because it proscribed certain character combinations that could appear in automatically generated files, and was therefore turned-off in 4.7.7 and following versions. The whitelist is described here for posterity and for possible improvement and re-introduction: The purpose of the whitelist was to prevent malicious users from injecting filename strings that could be used for attacks. The whitelist allowed only these characters: abcdefghijklmnopqrstuvwxyz ABCDEFGHIJKLMNOPQRSTUVWXYZ 1234567890_-.@ :%/ The backslash character \ was also whitelisted for Windows only. This whitelist allows filenames to be URLs, include username prefixes, and standard non-alphabetic characters. The implied blacklist included these characters ;|<>[](),&*?'" This blacklist rules-out strings that may contain dangerous commands and injection attacks. If you would like any of these characters whitelisted, please contact us and include a compelling real-world use-case. The DAP protocol supports accessing files with so-called &textldquo;constraint expressions&textrdquo;. NCO allows access to a wider set of whitelisted characters for files whose names indicate the DAP protocol. This is defined as any filename beginning with the string http://, https://, or dap4://. The whitelist for these files is expanded to include these characters: #=:[];|{}/<> The whitelist method is straightforward, and does not interfere with NCO&textrsquo;s globbing feature. The whitelist applies only to filenames because they are handled by shell commands passed to the system() function. However, the whitelist method is applicable to other user-input such as variable lists, hyperslab arguments, etc. Hence, the whitelist could be applied to other user-input in the future. <a name="fl_in"></a>  <a name="in"></a>  <a name="input"></a>

Specifying Input FilesSpecifying Output FilesSanitization of InputShared features

Specifying Input Files input files globbing regular expressions wildcards NINTAP Processor, CCM CCM Processor standard input stdin -n loop --nintap loop -p input-path --pth input-path --path input-path input-path Availability (-n): nces, ncecat, ncra, ncrcat&linebreak; Availability (-p): All operators&linebreak; Availability (stdin): All operators&linebreak; Short options: -n, -p&linebreak; Long options: --nintap, --pth, --path&linebreak; It is important that users be able to specify multiple input files without typing every filename in full, often a tedious task even by graduate student standards. UNIX There are four different ways of specifying input files to NCO: explicitly typing each, using UNIX shell wildcards, and using the NCO -n and -p switches (or their long option equivalents, --nintap or --pth and --path, respectively). Techniques to augment these methods to specify arbitrary numbers (e.g., thousands) and patterns of filenames are discussed separately (Large Numbers of Files). To illustrate these methods, consider the simple problem of using ncra to average five input files, 85.nc, 86.nc, &dots; 89.nc, and store the results in 8589.nc. Here are the four methods in order. They produce identical answers.

ncra 85.nc 86.nc 87.nc 88.nc 89.nc 8589.nc
ncra 8[56789].nc 8589.nc
ncra 8?.nc 8589.nc
ncra -p input-path 85.nc 86.nc 87.nc 88.nc 89.nc 8589.nc
ncra -n 5,2,1 85.nc 8589.nc

The first method (explicitly specifying all filenames) works by brute force. The second method relies on the operating system shell to glob (expand) the regular expression 8[56789].nc. The shell then passes the valid filenames (those which match the regular expansion) to ncra. In this case ncra never knows that a regular expression was used, because the shell intercepts and expands and matches the regular expression before ncra is actually invoked. The third method is uses globbing with a different regular expression that is less safe (it will also match unwanted files such as 81.nc and 8Z.nc if present). The fourth method uses the -p input-path argument to specify the directory where all the input files reside. NCO prepends input-path (e.g., /data/username/model) to all input-files (though not to output-file). Thus, using -p, the path to any number of input files need only be specified once. Note input-path need not end with /; the / is automatically generated if necessary. The last method passes (with -n) syntax concisely describing the entire set of filenames The -n option is a backward-compatible superset of the NINTAP option from the NCAR CCM Processor. The CCM Processor was custom-written Fortran code maintained for many years by Lawrence Buja at NCAR, and phased-out in the late 1990s. NCO copied some ideas, like NINTAP-functionality, from CCM Processor capabilities.. multi-file operators files, multiple This option is only available with the multi-file operators: ncra, ncrcat, nces, and ncecat. By definition, multi-file operators are able to process an arbitrary number of input-files. This option is very useful for abbreviating lists of filenames representable as alphanumeric_prefix+numeric_suffix+.+filetype where alphanumeric_prefix is a string of arbitrary length and composition, numeric_suffix is a fixed width field of digits, and filetype is a standard filetype indicator. For example, in the file ccm3_h0001.nc, we have alphanumeric_prefix = ccm3_h, numeric_suffix = 0001, and filetype = nc. NCO decodes lists of such filenames encoded using the -n syntax. The simpler (three-argument) -n usage takes the form -n file_number,digit_number,numeric_increment where file_number is the number of files, digit_number is the fixed number of numeric digits comprising the numeric_suffix, and numeric_increment is the constant, integer-valued difference between the numeric_suffix of any two consecutive files. The value of alphanumeric_prefix is taken from the input file, which serves as a template for decoding the filenames. In the example above, the encoding -n 5,2,1 along with the input file name 85.nc tells NCO to construct five (5) filenames identical to the template 85.nc except that the final two (2) digits are a numeric suffix to be incremented by one (1) for each successive file. Currently filetype may be either be empty, nc, h5, cdf, hdf, hd5, or he5. If present, these filetype suffixes (and the preceding .) are ignored by NCO as it uses the -n arguments to locate, evaluate, and compute the numeric_suffix component of filenames. wrapped filenames climate model Recently the -n option has been extended to allow convenient specification of filenames with &textldquo;circular&textrdquo; characteristics. This means it is now possible for NCO to automatically generate filenames which increment regularly until a specified maximum value, and then wrap back to begin again at a specified minimum value. The corresponding -n usage becomes more complex, taking one or two additional arguments for a total of four or five, respectively:

-n
file_number,digit_number,numeric_increment[,numeric_max[,numeric_min]]

where numeric_max, if present, is the maximum integer-value of numeric_suffix and numeric_min, if present, is the minimum integer-value of numeric_suffix. Consider, for example, the problem of specifying non-consecutive input files where the filename suffixes end with the month index. In climate modeling it is common to create summertime and wintertime averages which contain the averages of the months June&textndash;July&textndash;August, and December&textndash;January&textndash;February, respectively:

ncra -n 3,2,1 85_06.nc 85_0608.nc
ncra -n 3,2,1,12 85_12.nc 85_1202.nc
ncra -n 3,2,1,12,1 85_12.nc 85_1202.nc

The first example shows that three arguments to the -n option suffice to specify consecutive months (06, 07, 08) which do not &textldquo;wrap&textrdquo; back to a minimum value. The second example shows how to use the optional fourth and fifth elements of the -n option to specify a wrap value. The fourth argument to -n, when present, specifies the maximum integer value of numeric_suffix. In the example the maximum value is 12, which will be formatted as 12 in the filename string. The fifth argument to -n, when present, specifies the minimum integer value of numeric_suffix. The default minimum filename suffix is 1, which is formatted as 01 in this case. Thus the second and third examples have the same effect, that is, they automatically generate, in order, the filenames 85_12.nc, 85_01.nc, and 85_02.nc as input to NCO. As of NCO version 4.5.2 (September, 2015), NCO supports an optional sixth argument to -n, the month-indicator. The month-indicator affirms to NCO that the right-most digits being manipulated in the generated filenames correspond to month numbers (with January formatted as 01 and December as 12). Moreover, it assumes digits to the left of the month are the year. The full (six-argument) -n usage takes the form -n file_number,digit_number,month_increment,max_month,min_month,yyyymm. The yyyymm string is a clunky way (can you think of a clearer way?) to tell NCO to enumerate files in year-month mode. When present, yyyymm string causes NCO to automatically generate a filename series whose right-most two digits increment from min_month by month_increment up to max_month and then the leftmost digits (i.e., the year) increment by one, and the whole process is repeated until the file_number filenames are generated.

ncrcat -n 3,6,1,12,1         198512.nc 198512_198502.nc
ncrcat -n 3,6,1,12,1,yyyymm  198512.nc 198512_198602.nc
ncrcat -n 3,6,1,12,12,yyyymm 198512.nc 198512_198712.nc

The first command above concatenates three files (198512.nc, 198501.nc, 198502.nc) into the output file. The second command above concatenates three files (198512.nc, 198601.nc, 198602.nc). The yyyymm-indicator causes the left-most digits to increment each time the right-most two digits reach their maximum and then wrap. The first command does not have the indicator so it is always 1985. The third command concatenates three files (198512.nc, 198612.nc, 198712.nc). As of NCO version 5.1.1, released in November, 2022, all operators support specifying input files via stdin. This capability was implemented with NCZarr in mind, though it can also be used with traditional POSIX files. The ncap2, ncks, ncrename, and ncatted operators accept one or two filenames as positional arguments. If the input file for these operators is provided via stdin, then the output file, if any, must be specified with -o out.nc so the operators know whether to check stdin. Multi-file operators (ncra, ncea, ncrcat, ncecat) will continue to identify the last positional argument as the output file unless the -o out.nc form is used. The best best practice is to use -o out.nc to specify output filenames when stdin is used for input filenames: echo in.nc | ncks echo in.nc | ncks -o out.nc echo "in1.nc in2.nc" | ncbo -o out.nc echo "in1.nc in2.nc" | ncflint -o out.nc For the provenance reasons dicussed above (Large Numbers of Files), all filenames input via stdin are stored as global attributes in the File List Attributes). <a name="-o"></a>  <a name="--output"></a>  <a name="fl_out"></a>  <a name="out"></a>  <a name="output"></a>

Specifying Output FilesRemote storageSpecifying Input FilesShared features

Specifying Output Files output file input files positional arguments command line switches -o fl_out --output fl_out --fl_out fl_out Availability: All operators&linebreak; Short options: -o&linebreak; Long options: --fl_out, --output&linebreak; NCO commands produce no more than one output file, fl_out. Traditionally, users specify fl_out as the final argument to the operator, following all input file names. This is the positional argument method of specifying input and ouput file names. The positional argument method works well in most applications. NCO also supports specifying fl_out using the command line switch argument method, -o fl_out. Specifying fl_out with a switch, rather than as a positional argument, allows fl_out to precede input files in the argument list. multi-file operators This is particularly useful with multi-file operators for three reasons. Multi-file operators may be invoked with hundreds (or more) filenames. Visual or automatic location of fl_out in such a list is difficult when the only syntactic distinction between input and output files is their position. xargs input files Second, specification of a long list of input files may be difficult (Large Numbers of Files). Making the input file list the final argument to an operator facilitates using xargs for this purpose. Some alternatives to xargs are heinous and undesirable. Finally, many users are more comfortable specifying output files with -o fl_out near the beginning of an argument list. compilers linkers Compilers and linkers are usually invoked this way. Users should specify fl_out using either (not both) method. If fl_out is specified twice (once with the switch and once as the last positional argument), then the positional argument takes precedence. <a name="rmt"></a>

Remote storageRetaining Retrieved FilesSpecifying Output FilesShared features

Accessing Remote Files rcp scp .rhosts NCAR MSS MSS Mass Store System URL ftp sftp wget remote files synchronous file access asynchronous file access --pth input-path --path input-path --lcl output-path --local output-path -l output-path .netrc history Availability: All operators&linebreak; Short options: -p, -l&linebreak; Long options: --pth, --path, --lcl, --local&linebreak; All NCO operators can retrieve files from remote sites as well as from the local file system. A remote site can be an anonymous FTP server, a machine on which the user has rcp, scp, or sftp privileges, NCAR&textrsquo;s Mass Storage System (MSS), or an OPeNDAP server. Examples of each are given below, following a brief description of the particular access protocol. <a name="ftp"></a>  To access a file via an anonymous FTP server, simply supply the remote file&textrsquo;s URL. Anonymous FTP usually requires no further credentials, e.g., no .netrc file is necessary. FTP is an intrinsically insecure protocol because it transfers passwords in plain text format. Users should access sites using anonymous FTP, or better yet, secure FTP (SFTP, see below) when possible. Some FTP servers require a login/password combination for a valid user account. NCO allows transactions that require additional credentials so long as the required information is stored in the .netrc file. Usually this information is the remote machine name, login, and password, in plain text, separated by those very keywords, e.g.,

machine dust.ess.uci.edu login zender password bushlied

Eschew using valuable passwords for FTP transactions, since .netrc passwords are potentially exposed to eavesdropping software NCO does not implement command line options to specify FTP logins and passwords because copying those data into the history global attribute in the output file (done by default) poses an unacceptable security risk. . <a name="sftp"></a>  SFTP, i.e., secure FTP, uses SSH-based security protocols that solve the security issues associated with plain FTP. NCO supports SFTP protocol access to files specified with a homebrew syntax of the form

sftp://machine.domain.tld:/path/to/filename

Note the second colon following the top-level-domain, tld. This syntax is a hybrid between an FTP URL and standard remote file syntax. <a name="rcp"></a>  <a name="scp"></a>  To access a file using rcp or scp, specify the Internet address of the remote file. Of course in this case you must have rcp or scp privileges which allow transparent (no password entry required) access to the remote machine. This means that ~/.rhosts or ~/ssh/authorized_keys must be set accordingly on both local and remote machines. HPSS hsi msrcp msread nrnet <a name="hpss"></a>  <a name="HPSS"></a>  <a name="hsi"></a>  <a name="HSI"></a>  <a name="msrcp"></a>  <a name="msread"></a>  <a name="nrnet"></a>  To access a file on a High Performance Storage System (HPSS) (such as that at NCAR, ECMWF, LANL, DKRZ, LLNL) specify the full HPSS pathname of the remote file and use the --hpss flag. Then NCO will attempt to detect whether the local machine has direct (synchronous) HPSS access. If so, NCO attempts to use the Hierarchical Storage Interface (HSI) command hsi get The hsi command must be in the user&textrsquo;s path in one of the following directories: /usr/local/bin, /opt/hpss/bin, or /ncar/opt/hpss/hsi. Tell us if the HPSS installation at your site places the hsi command in a different location, and we will add that location to the list of acceptable paths to search for hsi. . The following examples show how one might analyze files stored on remote systems.

ncks -l . ftp://dust.ess.uci.edu/pub/zender/nco/in.nc
ncks -l . sftp://dust.ess.uci.edu:/home/ftp/pub/zender/nco/in.nc
ncks -l . dust.ess.uci.edu:/home/zender/nco/data/in.nc
ncks -l . /ZENDER/nco/in.nc # NCAR (broken old MSS path)
ncks -l . --hpss /home/zender/nco/in.nc # NCAR HPSS
ncks -l . http://thredds-test.ucar.edu/thredds/dodsC/testdods/in.nc

The first example works verbatim if your system is connected to the Internet and is not behind a firewall. The second example works if you have sftp access to the machine dust.ess.uci.edu. The third example works if you have rcp or scp access to the machine dust.ess.uci.edu. The fourth and fifth examples work on NCAR computers with local access to the HPSS hsi get command NCO supported the old NCAR Mass Storage System (MSS) until version 4.0.7 in April, 2011. NCO supported MSS-retrievals via a variety of mechanisms including the msread, msrcp, and nrnet commands invoked either automatically or with sentinels like ncks -p mss:/ZENDER/nco -l . in.nc. Once the MSS was decommissioned in March, 2011, support for these retrieval mechanisms was replaced by support for HPSS. . The sixth command works if your local version of NCO is OPeNDAP-enabled (this is fully described in OPeNDAP), or if the remote file is accessible via wget. The above commands can be rewritten using the -p input-path option as follows: -p input-path input-path -l output-path output-path

ncks -p ftp://dust.ess.uci.edu/pub/zender/nco -l . in.nc
ncks -p sftp://dust.ess.uci.edu:/home/ftp/pub/zender/nco -l . in.nc
ncks -p dust.ess.uci.edu:/home/zender/nco -l . in.nc
ncks -p /ZENDER/nco -l . in.nc
ncks -p /home/zender/nco -l . --hpss in.nc # HPSS
ncks -p http://thredds-test.ucar.edu/thredds/dodsC/testdods \ 
     -l . in.nc

Using -p is recommended because it clearly separates the input-path from the filename itself, sometimes called the stub. stub When input-path is not explicitly specified using -p, NCO internally generates an input-path from the first input filename. The automatically generated input-path is constructed by stripping the input filename of everything following the final / character (i.e., removing the stub). The -l output-path option tells NCO where to store the remotely retrieved file. It has no effect on locally-retrieved files, or on the output file. Often the path to a remotely retrieved file is quite different than the path on the local machine where you would like to store the file. If -l is not specified then NCO internally generates an output-path by simply setting output-path equal to input-path stripped of any machine names. If -l is not specified and the remote file resides on a detected HPSS system, then the leading character of input-path, /, is also stripped from output-path. Specifying output-path as -l ./ tells NCO to store the remotely retrieved file and the output file in the current directory. Note that -l . is equivalent to -l ./ though the latter is syntactically more clear. * OPeNDAP::

<a name="dap"></a>  <a name="DAP"></a>  <a name="DODS"></a>  <a name="OPeNDAP"></a>  <a name="dods"></a>  <a name="opendap"></a>  OPeNDAPRemote storageRemote storage OPeNDAP DAP DODS HTTP protocol DODS_ROOT Distributed Oceanographic Data System oceanography data access protocol Open-source Project for a Network Data Access Protocol OPeNDAP. server client-server The Distributed Oceanographic Data System (DODS) provides useful replacements for common data interface libraries like netCDF. The DODS versions of these libraries implement network transparent access to data via a client-server data access protocol that uses the HTTP protocol for communication. Although DODS-technology originated with oceanography data, it applyies to virtually all scientific data. In recognition of this, the data access protocol underlying DODS (which is what NCO cares about) has been renamed the Open-source Project for a Network Data Access Protocol, OPeNDAP. We use the terms DODS and OPeNDAP interchangeably, and often write OPeNDAP/DODS for now. In the future we will deprecate DODS in favor of DAP or OPeNDAP, as appropriate NVODS National Virtual Ocean Data System open source DODS is being deprecated because it is ambiguous, referring both to a protocol and to a collection of (oceanography) data. It is superceded by two terms. DAP is the discipline-neutral Data Access Protocol at the heart of DODS. The National Virtual Ocean Data System (NVODS) refers to the collection of oceanography data and oceanographic extensions to DAP. In other words, NVODS is implemented with OPeNDAP. OPeNDAP is also the open source project which maintains, develops, and promulgates the DAP standard. OPeNDAP and DAP really are interchangeable. Got it yet?. NCO may be DAP-enabled by linking NCO to the OPeNDAP libraries. Makefile This is described in the OPeNDAP documentation and automagically implemented in NCO build mechanisms Automagic support for DODS version 3.2.x was deprecated in December, 2003 after NCO version 2.8.4. NCO support for OPeNDAP versions 3.4.x commenced in December, 2003, with NCO version 2.8.5. NCO support for OPeNDAP versions 3.5.x commenced in June, 2005, with NCO version 3.0.1. NCO support for OPeNDAP versions 3.6.x commenced in June, 2006, with NCO version 3.1.3. NCO support for OPeNDAP versions 3.7.x commenced in January, 2007, with NCO version 3.1.9.. The ./configure mechanism automatically enables NCO as OPeNDAP clients if it can find the required OPeNDAP libraries. Since about 2010 the netCDF library can be configured (with --enable-dap) to build DAP directly into the netCDF library, which NCO automatically links to, so DAP need not be installed as a third-party library. It has been so many years since NCO has needed to support linking to DAP installed outside of the netCDF library that is is unclear whether this configuration The minimal set of libraries required to build NCO as OPeNDAP clients, where OPeNDAP is supplied as a separate library apart from libnetcdf.a, are, in link order, libnc-dap.a, libdap.a, and libxml2 and libcurl.a.. still works. The $DODS_ROOT environment variable may be used to override the default OPeNDAP library location at NCO compile-time. Building NCO with bld/Makefile and the command make DODS=Y adds the (non-intuitive) commands to link to the OPeNDAP libraries installed in the $DODS_ROOT directory. The file doc/opendap.sh contains a generic script intended to help users install OPeNDAP before building NCO. The documentation at the http://www.opendap.orgOPeNDAP Homepage is voluminous. Check there and on the http://www.unidata.ucar.edu/software/dods/home/mailLists/DODS mail lists. to learn more about the extensive capabilities of OPeNDAP We are most familiar with the OPeNDAP ability to enable network-transparent data access. constraint expressions server-side processing OPeNDAP has many other features, including sophisticated hyperslabbing and server-side processing via constraint expressions. If you know more about this, please consider writing a section on &textldquo;OPeNDAP Capabilities of Interest to NCO Users&textrdquo; for incorporation in the NCO User Guide.. Once NCO is DAP-enabled the operators are OPeNDAP clients. All OPeNDAP clients have network transparent access to any files controlled by a OPeNDAP server. Simply specify the input file path(s) in URL notation and all NCO operations may be performed on remote files made accessible by a OPeNDAP server. This command tests the basic functionality of OPeNDAP-enabled NCO clients:

% ncks -O -o ~/foo.nc -C -H -v one -l /tmp \
  -p http://thredds-test.ucar.edu/thredds/dodsC/testdods in.nc
% ncks -H -v one ~/foo.nc
one = 1

The one = 1 outputs confirm (first) that ncks correctly retrieved data via the OPeNDAP protocol and (second) that ncks created a valid local copy of the subsetted remote file. With minor changes to the above command, netCDF4 can be used as both the input and output file format:

% ncks -4 -O -o ~/foo.nc -C -H -v one -l /tmp \
  -p http://thredds-test.ucar.edu/thredds/dodsC/testdods in_4.nc
% ncks -H -v one ~/foo.nc
one = 1

And, of course, OPeNDAP-enabled NCO clients continue to support orthogonal features such as UDUnits (UDUnits Support):

% ncks -u -C -H -v wvl -d wvl,'0.4 micron','0.7 micron' \
  -p http://thredds-test.ucar.edu/thredds/dodsC/testdods in_4.nc
% wvl[0]=5e-07 meter

The next command is a more advanced example which demonstrates the real power of OPeNDAP-enabled NCO clients. The ncwa client requests an equatorial hyperslab from remotely stored NCEP reanalyses data of the year 1969. The NOAA OPeNDAP server (hopefully!) serves these data. The local ncwa client then computes and stores (locally) the regional mean surface pressure (in Pa).

ncwa -O -C -a lat,lon,time -d lon,-10.,10. -d lat,-10.,10. \
http://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets/ncep.reanalysis.dailyavgs/surface/pres.sfc.1969.nc ~/foo.nc

packing unpacking All with one command! The data in this particular input file also happen to be packed (Methods and functions), although this complication is transparent to the user since NCO automatically unpacks data before attempting arithmetic. NCO obtains remote files from the OPeNDAP server (e.g., www.cdc.noaa.gov) rather than the local machine. Input files are first copied to the local machine, then processed. The OPeNDAP server performs data access, hyperslabbing, and transfer to the local machine. I/O This allows the I/O to appear to NCO as if the input files were local. The local machine performs all arithmetic operations. Only the hyperslabbed output data are transferred over the network (to the local machine) for the number-crunching to begin. The advantages of this are obvious if you are examining small parts of large files stored at remote locations. Natually there are many versions of OPeNDAP servers supplying data and bugs in the server can appear to be bugs in NCO. However, with very few exceptions For example, DAP servers do not like variables with periods (&textldquo;.&textrdquo;) in their names even though this is perfectly legal with netCDF. Such names may cause the DAP service to fail because DAP interprets the period as structure delimiter in an HTTP query string. an NCO command that works on a local file must work across an OPeNDAP connection or else there is a bug in the server. This is because NCO does nothing special to handle files served by OPeNDAP, the whole process is (supposed to be) completely transparent to the client NCO software. Therefore it is often useful to try NCO commands on various OPeNDAP servers in order to isolate whether a problem may be due to a bug in the OPeNDAP server on a particular machine. For this purpose, one might try variations of the following commands that access files on public OPeNDAP servers:

# Strided access to HDF5 file
ncks -v Time -d Time,0,10,2 http://eosdap.hdfgroup.uiuc.edu:8080/opendap/data/NASAFILES/hdf5/BUV-Nimbus04_L3zm_v01-00-2012m0203t144121.h5
# Strided access to netCDF3 file
ncks -O -D 1 -d time,1 -d lev,0 -d lat,0,100,10 -d lon,0,100,10 -v u_velocity http://nomads.ncep.noaa.gov:9090/dods/rtofs/rtofs_global20140303/rtofs_glo_2ds_forecast_daily_prog ~/foo.nc

These servers were operational at the time of writing, March 2014. Unfortunately, administrators often move or rename path directories. Recommendations for additional public OPeNDAP servers on which to test NCO are welcome. <a name="rtn"></a>

Retaining Retrieved FilesFile Formats and ConversionRemote storageShared features

Retaining Retrieved Files file deletion file removal file retention -R --rtn --retain Availability: All operators&linebreak; Short options: -R&linebreak; Long options: --rtn, --retain&linebreak; In order to conserve local file system space, files retrieved from remote locations are automatically deleted from the local file system once they have been processed. Many NCO operators were constructed to work with numerous large (e.g., 200 MB) files. Retrieval of multiple files from remote locations is done serially. Each file is retrieved, processed, then deleted before the cycle repeats. In cases where it is useful to keep the remotely-retrieved files on the local file system after processing, the automatic removal feature may be disabled by specifying -R on the command line. Invoking -R disables the default printing behavior of ncks. This allows ncks to retrieve remote files without automatically trying to print them. See ncks netCDF Kitchen Sink, for more details. FTP SSH msrcp Note that the remote retrieval features of NCO can always be used to retrieve any file, including non-netCDF files, via SSH, anonymous FTP, or msrcp. Often this method is quicker than using a browser, or running an FTP session from a shell window yourself. server For example, say you want to obtain a JPEG file from a weather server.

ncks -R -p ftp://weather.edu/pub/pix/jpeg -l . storm.jpg

In this example, ncks automatically performs an anonymous FTP login to the remote machine and retrieves the specified file. When ncks attempts to read the local copy of storm.jpg as a netCDF file, it fails and exits, leaving storm.jpg in the current directory. DODS server If your NCO is DAP-enabled (OPeNDAP), then you may use NCO to retrieve any files (including netCDF, HDF, etc.) served by an OPeNDAP server to your local machine. For example,

ncks -R -l . -p \
http://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets/ncep.reanalysis.dailyavgs/surface \
  pres.sfc.1969.nc

It may occasionally be useful to use NCO to transfer files when your other preferred methods are not available locally. <a name="conversion"></a>  <a name="fl_fmt"></a>  <a name="hdf"></a>  <a name="cdf5"></a>  <a name="64bit"></a>  <a name="64bit-data"></a>  <a name="64bit-offset"></a>  <a name="netcdf4"></a>

File Formats and ConversionZarr and NCZarrRetaining Retrieved FilesShared features

File Formats and Conversion HDF CDF5 netCDF2 netCDF3 netCDF4 NETCDF4_CLASSIC files NETCDF4 files CLASSIC files 64BIT_OFFSET files 64BIT_DATA files CDF5 files --3 -3 -4 -5 -6 -7 --4 --5 --6 --7 --netcdf4 --fl_fmt --file_format --64bit_offset --64bit_data Availability: ncap2, nces, ncecat, ncflint, ncks, ncpdq, ncra, ncrcat, ncwa&linebreak; Short options: -3, -4, -5, -6, -7&linebreak; Long options: --3, --4, --5, --6, --64bit_offset, --7, --fl_fmt, --netcdf4&linebreak; All NCO operators support (read and write) all three (or four, depending on how one counts) file formats supported by netCDF4. The default output file format for all operators is the input file format. The operators listed under &textldquo;Availability&textrdquo; above allow the user to specify the output file format independent of the input file format. These operators allow the user to convert between the various file formats. (The operators ncatted and ncrename do not support these switches so they always write the output netCDF file in the same format as the input netCDF file.) * File Formats::

* Determining File Format::

* File Conversion::

* Autoconversion::

File FormatsDetermining File FormatFile Formats and ConversionFile Formats and Conversion File Formats netCDF supports five types of files: CLASSIC, 64BIT_OFFSET, 64BIT_DATA, NETCDF4, and NETCDF4_CLASSIC. The CLASSIC (aka CDF1) format is the traditional 32-bit offset written by netCDF2 and netCDF3. As of 2005, nearly all netCDF datasets were in CLASSIC format. The 64BIT_OFFSET (originally called plain old 64BIT) (aka CDF2) format was added in Fall, 2004. As of 2010, many netCDF datasets were in 64BIT_OFFSET format. As of 2013, an increasing number of netCDF datasets were in NETCDF4_CLASSIC format. The 64BIT_DATA (aka CDF5 or PNETCDF) format was added to netCDF in January, 2016 and immediately supported by NCO. Support for Zarr and NCZarr backend storage formats was added to netCDF in 2021 and supported by NCO in 2022. The NETCDF4 format uses HDF5 as the file storage layer. The files are (usually) created, accessed, and manipulated using the traditional netCDF3 API (with numerous extensions). The NETCDF4_CLASSIC format refers to netCDF4 files created with the NC_CLASSIC_MODEL mask. Such files use HDF5 as the back-end storage format (unlike netCDF3), though they incorporate only netCDF3 features. Hence NETCDF4_CLASSIC files are entirely readable by applications that use only the netCDF3 API (though the applications must be linked with the netCDF4 library). NCO must be built with netCDF4 to write files in the new NETCDF4 and NETCDF4_CLASSIC formats, and to read files in these formats. Datasets in the default CLASSIC or the newer 64BIT_OFFSET formats have maximum backwards-compatibility with older applications. NCO has deep support for NETCDF4 formats. If backwards compatibility is important, and your datasets are too large for netCDF3, use NETCDF4_CLASSIC instead of CLASSIC format files. NCO support for the NETCDF4 format is complete and many high-performance disk/RAM efficient workflows utilize this format. As mentioned above, all operators write use the input file format for output files unless told otherwise. Toggling the short option -6 or the long option --6 or --64bit_offset (or their key-value equivalent --fl_fmt=64bit_offset) produces the netCDF3 64-bit offset format named 64BIT_OFFSET. NCO must be built with netCDF 3.6 or higher to produce a 64BIT_OFFSET file. As of NCO version 4.6.9 (September, 2017), toggling the short option -5 or the long options --5, --64bit_data, --cdf5, or --pnetcdf (or their key-value equivalent --fl_fmt=64bit_data) produces the netCDF3 64-bit data format named 64BIT_DATA. This format is widely used by MPI-enabled modeling codes because of its long association with PnetCDF. NCO must be built with netCDF 4.4 or higher to produce a 64BIT_DATA file. Using the -4 switch (or its long option equivalents --4 or --netcdf4), or setting its key-value equivalent --fl_fmt=netcdf4 produces a NETCDF4 file (i.e., with all supported HDF5 features). Using the -7 switch (or its long option equivalent --7 The reason (and mnemonic) for -7 is that NETCDF4_CLASSIC files include great features of both netCDF3 (compatibility) and netCDF4 (compression, chunking) and, well,

3+4=7

., or setting its key-value equivalent --fl_fmt=netcdf4_classic produces a NETCDF4_CLASSIC file (i.e., with all supported HDF5 features like compression and chunking but without groups or new atomic types). Operators given the -3 (or --3) switch without arguments will (attempt to) produce netCDF3 CLASSIC output, even from netCDF4 input files. Note that NETCDF4 and NETCDF4_CLASSIC are the same binary format. The latter simply causes a writing application to fail if it attempts to write a NETCDF4 file that cannot be completely read by the netCDF3 library. Conversely, NETCDF4_CLASSIC indicates to a reading application that all of the file contents are readable with the netCDF3 library. NCO has supported reading/writing basic NETCDF4 and NETCDF4_CLASSIC files since October, 2005. <a name="fmt_inq"></a>  Determining File FormatFile ConversionFile FormatsFile Formats and Conversion Determining File Format Input files often end with the generic .nc suffix that leaves (perhaps by intention) the internal file format ambiguous. There are at least three ways to discover the internal format of a netCDF-supported file. These methods determine whether it is a classic (32-bit offset) or newer 64-bit offset netCDF3 format, or is a netCDF4 format. Each method returns the information using slightly different terminology that becomes easier to understand with practice. First, examine the first line of global metadata output by ncks -M: netCDF3 classic file format netCDF4 classic file format netCDF4 file format 32-bit offset file format 64-bit offset file format 64-bit data file format PnetCDF file format ncks -M

% ncks -M foo_3.nc
Summary of foo_3.nc: filetype = NC_FORMAT_CLASSIC, 0 groups ...
% ncks -M foo_6.nc
Summary of foo_6.nc: filetype = NC_FORMAT_64BIT_OFFSET, 0 groups ...
% ncks -M foo_5.nc
Summary of foo_5.nc: filetype = NC_FORMAT_CDF5, 0 groups ...
% ncks -M foo_7.nc
Summary of foo_7.nc: filetype = NC_FORMAT_NETCDF4_CLASSIC, 0 groups ...
% ncks -M foo_4.nc
Summary of foo_4.nc: filetype = NC_FORMAT_NETCDF4, 0 groups ...

This method requires a netCDF4-enabled NCO version 3.9.0+ (i.e., from 2007 or later). extended file format underlying file format NC_FORMATX_NC3 NC_FORMATX_NC_HDF5 NC_FORMATX_NC_HDF4 NC_FORMATX_PNETCDF NC_FORMATX_DAP2 NC_FORMATX_DAP4 As of NCO version 4.4.0 (January, 2014), ncks will also print the extended or underlying format of the input file. The extended filetype will be one of the six underlying formats that are accessible through the netCDF API. These formats are NC_FORMATX_NC3 (classic and 64-bit versions of netCDF3 formats), NC_FORMATX_NC_HDF5 (classic and extended versions of netCDF4, and &textldquo;pure&textrdquo; HDF5 format), NC_FORMATX_NC_HDF4 (HDF4 format), NC_FORMATX_PNETCDF (PnetCDF format), NC_FORMATX_DAP2 (accessed via DAP2 protocol), and NC_FORMATX_DAP4 (accessed via DAP4 protocol). For example,

% ncks -D 2 -M hdf.hdf
Summary of hdf.hdf: filetype = NC_FORMAT_NETCDF4 (representation of \
  extended/underlying filetype NC_FORMAT_HDF4), 0 groups ...
% ncks -D 2 -M http://thredds-test.ucar.edu/thredds/dodsC/testdods/in.nc
Summary of http://thredds-test.ucar.edu/thredds/dodsC/testdods/in.nc: \
  filetype = NC_FORMAT_CLASSIC (representation of extended/underlying \
  filetype NC_FORMATX_DAP2), 0 groups  
% ncks -D 2 -M foo_4.nc
Summary of foo_4.nc: filetype = NC_FORMAT_NETCDF4 (representation of \
  extended/underlying filetype NC_FORMAT_HDF5), 0 groups

The extended filetype determines some of the capabilities that netCDF has to alter the file. Second, query the file with ncdump -k: ncdump

% ncdump -k foo_3.nc
classic
% ncdump -k foo_6.nc
64-bit offset
% ncdump -k foo_5.nc
cdf5
% ncdump -k foo_7.nc
netCDF-4 classic model
% ncdump -k foo_4.nc
netCDF-4

This method requires a netCDF4-enabled netCDF 3.6.2+ (i.e., from 2007 or later). The third option uses the POSIX-standard od (octal dump) command: od octal dump

% od -An -c -N4 foo_3.nc
   C   D   F 001
% od -An -c -N4 foo_6.nc
   C   D   F 002
% od -An -c -N4 foo_5.nc
   C   D   F 005
% od -An -c -N4 foo_7.nc
 211   H   D   F
% od -An -c -N4 foo_4.nc
 211   H   D   F

This option works without NCO and ncdump. Values of C D F 001 and C D F 002 indicate 32-bit (classic) and 64-bit netCDF3 formats, respectively, while values of 211 H D F indicate either of the newer netCDF4 file formats. <a name="hdf2nc"></a>  <a name="3to4"></a>  <a name="4to3"></a>  File ConversionAutoconversionDetermining File FormatFile Formats and Conversion File Conversion Let us demonstrate converting a file from any netCDF-supported input format into any netCDF output format (subject to limits of the output format). Here the input file in.nc may be in any of these formats: netCDF3 (classic, 64bit_offset, 64bit_data), netCDF4 (classic and extended), HDF4, HDF5, HDF-EOS (version 2 or 5), and DAP. The switch determines the output format written in the comment: The switches -5, --5, and pnetcdf are reserved for PnetCDF files, i.e., NC_FORMAT_CDF5. Such files are similar to netCDF3 classic files, yet also support 64-bit offsets and the additional netCDF4 atomic types.

ncks --fl_fmt=classic in.nc foo_3.nc # netCDF3 classic
ncks --fl_fmt=64bit_offset in.nc foo_6.nc # netCDF3 64bit-offset
ncks --fl_fmt=64bit_data in.nc foo_5.nc # netCDF3 64bit-data
ncks --fl_fmt=cdf5 in.nc foo_5.nc # netCDF3 64bit-data
ncks --fl_fmt=netcdf4_classic in.nc foo_7.nc # netCDF4 classic
ncks --fl_fmt=netcdf4 in.nc foo_4.nc # netCDF4 
ncks -3 in.nc foo_3.nc # netCDF3 classic
ncks --3 in.nc foo_3.nc # netCDF3 classic
ncks -6 in.nc foo_6.nc # netCDF3 64bit-offset
ncks --64 in.nc foo_6.nc # netCDF3 64bit-offset
ncks -5 in.nc foo_5.nc # netCDF3 64bit-data
ncks --5 in.nc foo_5.nc # netCDF3 64bit-data
ncks -4 in.nc foo_4.nc # netCDF4 
ncks --4 in.nc foo_4.nc # netCDF4 
ncks -7 in.nc foo_7.nc # netCDF4 classic
ncks --7 in.nc foo_7.nc # netCDF4 classic

Of course since most operators support these switches, the &textldquo;conversions&textrdquo; can be done at the output stage of arithmetic or metadata processing rather than requiring a separate step. Producing (netCDF3) CLASSIC or 64BIT_OFFSET or 64BIT_DATA files from NETCDF4_CLASSIC files always works. <a name="autoconversion"></a>  <a name="autocnv"></a>  AutoconversionFile ConversionFile Formats and Conversion Autoconversion Because of the dearth of support for netCDF4 amongst tools and user communities (including the CF conventions), it is often useful to convert netCDF4 to netCDF3 for certain applications. Until NCO version 4.4.0 (January, 2014), producing netCDF3 files from netCDF4 files only worked if the input files contained no netCDF4-specific features (e.g., atomic types, multiple record dimensions, or groups). As of NCO version 4.4.0, ncks supports autoconversion of many netCDF4 features to their closest netCDF3-compatible representations. Since converting netCDF4 to netCDF3 results in loss of features, &textldquo;automatic down-conversion&textrdquo; may be a more precise description of what we term autoconversion. NCO employs three algorithms to downconvert netCDF4 to netCDF3: autoconversion Autoconversion of atomic types: Autoconversion automatically promotes NC_UBYTE to NC_SHORT, and NC_USHORT to NC_INT. It automatically demotes the three types NC_UINT, NC_UINT64, and NC_INT64 to NC_INT. And it converts NC_STRING to NC_CHAR. All numeric conversions work for attributes and variables of any rank. Two numeric types (NC_UBYTE and NC_USHORT) are promoted to types with greater range (and greater storage). This extra range is often not used so promotion perhaps conveys the wrong impression. However, promotion never truncates values or loses data (this perhaps justifies the extra storage). Three numeric types (NC_UINT, NC_UINT64 and NC_INT64) are demoted. Since the input range is larger than the output range, demotion can result in numeric truncation and thus loss of data. In such cases, it would possible to convert the data to floating-point values instead. If this feature interests you, please be the squeaky wheel and let us know. String conversions (to NC_CHAR) work for all attributes, but not for variables. This is because attributes are at most one-dimensional and may be of any size whereas variables require gridded dimensions that usually do not fit the ragged sizes of text strings. Hence scalar NC_STRING attributes are correctly converted to and stored as NC_CHAR attributes in the netCDF3 output file, but NC_STRING variables are not correctly converted. If this limitation annoys or enrages you, please let us know by being the squeaky wheel. --fix_rec_dmn all Convert multiple record dimensions to fixed-size dimensions. Many netCDF4 and HDF5 datasets have multiple unlimited dimensions. Since a netCDF3 file may have at most one unlimited dimension, all but possibly one unlimited dimension from the input file must be converted to fixed-length dimensions prior to storing netCDF4 input as netCDF3 output. By invoking --fix_rec_dmn all the user ensures the output file will adhere to netCDF3 conventions and the user need not know the names of the specific record dimensions to fix. See ncks netCDF Kitchen Sink for a description of the --fix_rec_dmn option. flattening Flattening (removal) of groups. Many netCDF4 and HDF5 datasets have group hierarchies. Since a netCDF3 file may not have any groups, groups in the input file must be removed. This is also called &textldquo;flattening&textrdquo; the hierarchical file. See Group Path Editing for a description of the GPE option -G : to flatten files. Putting the three algorithms together, one sees that the recipe to convert netCDF4 to netCDF4 becomes increasingly complex as the netCDF4 features in the input file become more elaborate:

# Convert file with netCDF4 atomic types
ncks -3 in.nc4 out.nc3
# Convert file with multiple record dimensions + netCDF4 atomic types
ncks -3 --fix_rec_dmn=all in.nc4 out.nc3
# Convert file with groups, multiple record dimensions + netCDF4 atomic types
ncks -3 -G : --fix_rec_dmn=all in.nc4 out.nc3

Future versions of NCO may automatically invoke the record dimension fixation and group flattening when converting to netCDF3 (rather than requiring it be specified manually). If this feature would interest you, please let us know. <a name="Zarr"></a>  <a name="NCZarr"></a>  <a name="zarr"></a>  <a name="nczarr"></a>  <a name="conversion"></a>  <a name="zarr"></a>  <a name="ncz2psx"></a>  <a name="fl_fmt"></a>  Zarr NCZarr ncz2psx POSIX S3 stdin

Zarr and NCZarrLarge File SupportFile Formats and ConversionShared features

Zarr and NCZarr Availability: All Operators&linebreak; As of version 5.1.1 (November 2022), all NCO operators support NCZarr I/O. This support is currently limited to the file:// scheme. Support for the S3 scheme is next. All NCO commands should work as expected independent of the back-end storage format of the I/O. Operators can ingest and output POSIX or Zarr backend file formats: in_ncz="file://${HOME}/in_zarr4#mode=nczarr,file" in_psx="${HOME}/in_zarr4.nc" out_ncz="file://${HOME}/foo#mode=nczarr,file" out_psx="${HOME}/foo.nc" ncks ${in_ncz} # Print contents of Zarr file ncks -O -v var ${in_psx} ${out_psx} # POSIX input to POSIX output ncks -O -v var ${in_psx} ${out_ncz} # POSIX input to Zarr output ncks -O -v var ${in_ncz} ${out_psx} # Zarr input to POSIX output ncks -O -v var ${in_ncz} ${out_ncz} # Zarr input to Zarr output ncks -O --cmp='gbr|shf|zst' ${in_psx} ${out_ncz} # Quantize/Compress ncks -O --cmp='gbr|shf|zst' ${in_ncz} ${out_ncz} # Quantize/Compress Note that NCZarr only supports the netCDF4 (not netCDF3) data model. This is because NCZarr needs to know chunking and compression information by default (it is not optional). Hence if the input format is netCDF3, then the user must explicitly specify a netCDF4 format for the output NCZarr storage: in_psx="${HOME}/in_zarr3.nc" # As above, but a netCDF3 input file ncks -O -4 ${in_psx} ${out_ncz} # netCDF3 POSIX input to Zarr output ncks -O -7 ${in_psx} ${out_ncz} # netCDF3 POSIX input to Zarr output Furthermore, the current NCZarr library (version 4.9.2, March 2023) does not yet support record dimensions (this is a significant and high priority netCDF library limitation, not an NCO limitation). All dimensions must be fixed, not record To workaround this limitation, first fix any record dimensions with, e.g., ncks -O --fix_rec_dmn=all ${in_psx} ${in_psx} Commands with Zarr I/O behave mostly as expected. NCO treats Zarr and POSIX files identically once they are opened via the netCDF API. The main difference between Zarr and POSIX, from the viewpoint of NCO, is in handling the filenames. By default NCO performs operations in temporary files that it moves to a final destination once the rest of the command succeeds. Supporting Zarr in NCO requires applying the correct procedures to create, copy, move/rename, and delete files and directories correctly depending on the backend format. Many NCO users rely on POSIX filename globbing for multi-file operations, e.g., ncra in*.nc out.nc. Globbing returns matches in POSIX format (e.g., in1.nc in2.nc in3.nc) which lacks the scheme:// indicator and the #mode=... fragment that the netCDF API needs to open a Zarr store. There is no perfect solution to this. A partial solution is available by judiciously using NCO&textrsquo;s new stdin capabilities for all operators (Specifying Input Files). The procedure uses the ls command (instead of globbing) to identify the desired Zarr stores, and pipes the (POSIX-style) results of that through the newly supplied NCO filter-script ncz2psx that will prepend the desired scheme and append the desired fragment to the matched Zarr stores, and pipe those results onward to an NCO operator: nces in*.nc out.nc # POSIX input files via globbing ls in*.nc | nces out.nc # POSIX input files via stdin ls -d in* | ncz2psx | nces out.nc # Zarr input via stdin ls -d in* | ncz2psx --scheme=file --mode=nczarr,file | nces out.nc <a name="lfs"></a>

Large File SupportSubsetting FilesZarr and NCZarrShared features

Large File Support LFS Large File Support Availability: All operators&linebreak; Short options: none&linebreak; Long options: none&linebreak; NCO has Large File Support (LFS), meaning that NCO can write files larger than 2 GB on some 32-bit operating systems with netCDF libraries earlier than version 3.6. If desired, LFS support must be configured when both netCDF and NCO are installed. netCDF versions 3.6 and higher support 64-bit file addresses as part of the netCDF standard. We recommend that users ignore LFS support which is difficult to configure and is implemented in NCO only to support netCDF versions prior to 3.6. This obviates the need for configuring explicit LFS support in applications (such as NCO) that now support 64-bit files directly through the netCDF interface. See File Formats and Conversion for instructions on accessing the different file formats, including 64-bit files, supported by the modern netCDF interface. If you are still interested in explicit LFS support for netCDF versions prior to 3.6, know that LFS support depends on a complex, interlocking set of operating system Linux and AIX do support LFS. and netCDF support issues. The netCDF LFS http://my.unidata.ucar.edu/content/software/netcdf/faq-lfs.htmlFAQ describes the various file size limitations imposed by different versions of the netCDF standard. NCO and netCDF automatically attempt to configure LFS at build time. <a name="x"></a>  <a name="grp"></a>  <a name="var"></a>  <a name="xcl"></a>  <a name="exclude"></a>  <a name="sbs"></a>  <a name="subset"></a>

Subsetting FilesSubsetting Coordinate VariablesLarge File SupportShared features

Subsetting Files subsetting union intersection exclusion extraction -v var --variable var -g grp --grp grp --group grp -x --exclude --xcl --unn --union --gxvx --grp_xtr_var_xcl Options -g grp&linebreak; Availability: ncbo, nces, ncecat, ncflint, ncks, ncpdq, ncra, ncrcat, ncwa&linebreak; Short options: -g&linebreak; Long options: --grp and --group&linebreak; Options -v var and -x&linebreak; Availability: (ncap2), ncbo, nces, ncecat, ncflint, ncks, ncpdq, ncra, ncrcat, ncwa&linebreak; Short options: -v, -x&linebreak; Long options: --variable, --exclude or --xcl&linebreak; Options --unn&linebreak; Availability: ncbo, nces, ncecat, ncflint, ncks, ncpdq, ncra, ncrcat, ncwa&linebreak; Short options: &linebreak; Long options: --unn and --union&linebreak; Options --grp_xtr_var_xcl&linebreak; Availability: ncks&linebreak; Short options: &linebreak; Long options: --gxvx and --grp_xtr_var_xcl&linebreak; Subsetting variables refers to explicitly specifying variables and groups to be included or excluded from operator actions. Subsetting is controlled by the -v var[,&dots;] and -x options for directly specifying variables. Specifying groups, whether in addition to or instead of variables, is quite similar and is controlled by the -g grp[,&dots;] and -x options. A list of variables or groups to extract is specified following the -v and -g options, e.g., -v time,lat,lon or -g grp1,grp2. Both options may be specified simultaneously and NCO will extract the intersection of the lists, i.e., only variables of the specified names found in groups of the specified names. The --unn option causes NCO to extract the union, rather than the intersection, of the specified groups and variables. Not using the -v or -g option is equivalent to specifying all variables or groupp, respectively. The -x option causes the list of variables specified with -v to be excluded rather than extracted. Thus -x saves typing when you only want to extract fewer than half of the variables in a file. ncks -x -v v1,v2 in.nc out.nc # Extract all variables except v1, v2 ncks -C -x -v lat,lon in.nc out.nc # Extract all except lat, lon The first example above shows the typical use of -x to subset all variables except a few into the output. Note that v1 and v2 will be retained in the output if they are coordinate-like variables (Subsetting Coordinate Variables) associated with any extracted variable. If one wishes to exclude coordinate-like variables despite their being referenced by extracted variables, one must use the -C (or synonym --xcl_ass_var) option as shown in the second example. Variables or groups explicitly specified for extraction with -v var[,&dots;] or -g grp[,&dots;] must be present in the input file or an error will result. Variables explicitly specified for exclusion with -x -v var[,&dots;] need not be present in the input file. To accord with the sophistication of the underlying hierarchy, group subsetting is controlled by a few powerful yet subtle syntactical distinctions. When learning this syntax it is helpful to keep in mind the similarity between group hierarchies and directory structures. <a name="gxvx"></a>  <a name="grp_xtr_var_xcl"></a>  As of NCO 4.4.4 (June, 2014), ncks (alone) supports an option to include specified groups yet exclude specified variables. The --grp_xtr_var_xcl switch (with long option equivalent --gxvx) extracts all contents of groups given as arguments to -g grp[,&dots;], except for variables given as arguments to -v var[,&dots;]. Use this when one or a few variables in hierarchical files are not to be extracted, and all other variables are. This is useful when coercing netCDF4 files into netCDF3 files such as with converting, flattening, or dismembering files (see Flattening Groups).

ncks --grp_xtr_var_xcl -g g1 -v v1 # Extract all of group g1 except v1

mv cp recursion recursive anchor anchoring <a name="rcr"></a>  <a name="recursion"></a>  <a name="recursive"></a>  <a name="ncr"></a>  <a name="anchor"></a>  <a name="anchoring"></a>  Two properties of subsetting, recursion and anchoring, are best illustrated by reminding the user of their UNIX equivalents. The UNIX command mv src dst moves src and all its subdirectories (and all their subdirectories etc.) to dst. In other words mv is, by default, recursive. In contrast, the UNIX command cp src dst moves src, and only src, to dst, If src is a directory, not a file, then that command fails. One must explicitly request to copy directories recursively, i.e., with cp -r src dst. In NCO recursive extraction (and copying) of groups is the default (like with mv, not with cp). Recursion is turned off by appending a trailing slash to the path. These UNIX commands also illustrate a property we call anchoring. The command mv src dst moves (recursively) the source directory src to the destination directory dst. If src begins with the slash character then the specified path is relative to the root directory, otherwise the path is relative to the current working directory. In other words, an initial slash character anchors the subsequent path to the root directory. In NCO an initial slash anchors the path at the root group. Paths that begin and end with slash characters (e.g., //, /g1/, and /g1/g2/) are both anchored and non-recursive. Consider the following commands, all of which may be assumed to end with in.nc out.nc:

ncks -g  g1  # Extract, recursively, all groups with a g1 component
ncks -g  g1/ # Extract, non-recursively, all groups terminating in g1
ncks -g /g1  # Extract, recursively, root group g1
ncks -g /g1/ # Extract, non-recursively root group g1
ncks -g //   # Extract, non-recursively the root group

The first command is probably the most useful and common. It would extract these groups, if present, and all their direct ancestors and children: /g1, /g2/g1, and /g3/g1/g2. In other words, the simplest form of -g grp grabs all groups that (and their direct ancestors and children, recursively) that have grp as a complete component of their path. A simple string match is insufficient, grp must be a complete component (i.e., group name) in the path. The option -g g1 would not extract these groups because g1 is not a complete component of the path: /g12, /fg1, and /g1g1. The second command above shows how a terminating slash character / cancels the recursive copying of groups. An argument to -g which terminates with a slash character extracts the group and its direct ancestors, but none of its children. The third command above shows how an initial slash character / anchors the argument to the root group. The third command would not extract the group /g2/g1 because the g1 group is not at the root level, but it would extract, any group /g1 at the root level and all its children, recursively. The fourth command is the non-recursive version of the third command. The fifth command is a special case of the fourth command. <a name="unn"></a>  <a name="nsx"></a>  <a name="union"></a>  <a name="intersection"></a>  union intersection --unn --union --nsx --intersection As mentioned above, both -v and -g options may be specified simultaneously and NCO will, by default, extract the intersection of the lists, i.e., the specified variables found in the specified groups Intersection-mode can also be explicitly invoked with the --nsx or --intersection switches. These switches are supplied for clarity and consistency and do absolutely nothing since intersection-mode is the default.. The --unn option causes NCO to extract the union, rather than the intersection, of the specified groups and variables. Consider the following commands (which may be assumed to end with in.nc out.nc):

# Intersection-mode subsetting (default)
ncks -g  g1  -v v1 # Yes: /g1/v1, /g2/g1/v1. No: /v1, /g2/v1
ncks -g /g1  -v v1 # Yes: /g1/v1, /g1/g2/v1. No: /v1, /g2/v1, /g2/g1/v1
ncks -g  g1/ -v v1 # Yes: /g1/v1, /g2/g1/v1. No: /v1, /g2/v1, /g1/g2/v1
ncks -v  g1/v1     # Yes: /g1/v1, /g2/g1/v1. No: /v1, /g2/v1, /g1/g2/v1
ncks -g /g1/ -v v1 # Yes: /g1/v1. No: /g2/g1/v1, /v1, /g2/v1 ...
ncks -v /g1/v1     # Yes: /g1/v1. No: /g2/g1/v1, /v1, /g2/v1 ...

# Union-mode subsetting (invoke with --unn or --union)
ncks -g  g1  -v v1 --unn # All variables in  g1 or progeny, or named v1
ncks -g /g1  -v v1 --unn # All variables in /g1 or progeny, or named v1
ncks -g  g1/ -v v1 --unn # All variables in  g1 or named v1
ncks -g /g1/ -v v1 --unn # All variables in /g1 or named v1

The first command (-g g1 -v v1) extracts the variable v1 from any group named g1 or descendent g1. The second command extracts v1 from any root group named g1 and any descendent groups as well. The third and fourth commands are equivalent ways of extracting v1 only from the root group named g1 (not its descendents). The fifth and sixth commands are equivalent ways of extracting the variable v1 only from the root group named g1. Subsetting in union-mode (with --unn) causes all variables to be extracted which meet either one or both of the specifications of the variable and group specifications. Union-mode subsetting is simply the logical &textldquo;OR&textrdquo; of intersection-mode subsetting. As discussed below, the group and variable specifications may be comma separated lists of regular expressions for added control over subsetting. memory requirements Remember, if averaging or concatenating large files stresses your systems memory or disk resources, then the easiest solution is often to subset (with -g and/or -v) to retain only the most important variables (Memory Requirements).

ncks          in.nc out.nc # Extract all groups and variables
ncks -v scl   # Extract variable scl from all groups
ncks -g g1    # Extract group g1 and descendents
ncks -x -g g1 # Extract all groups except g1 and descendents
ncks -g g2,g3 -v scl # Extract scl from groups g2 and g3

Overwriting and appending work as expected:

# Replace scl in group g2 in out.nc with scl from group g2 from in.nc
ncks -A -g g2 -v scl in.nc out.nc

Due to its special capabilities, ncap2 interprets the -v switch differently (ncap2 netCDF Arithmetic Processor). For ncap2, the -v switch takes no arguments and indicates that only user-defined variables should be output. ncap2 neither accepts nor understands the -x and -g switches. <a name="rx"></a>  <a name="wildcarding"></a>  extended regular expressions regular expressions pattern matching wildcards grep -E egrep ncatted GNU Regular expressions the syntax that NCO use pattern-match object names in netCDF file against user requests. The user can select all variables beginning with the string DST from an input file by supplying the regular expression ^DST to the -v switch, i.e., -v '^DST'. The meta-characters used to express pattern matching operations are ^$+?.*[]{}|. If the regular expression pattern matches any part of a variable name then that variable is selected. This capability is also called wildcarding, and is very useful for sub-setting large data files. Extended regular expressions are defined by the POSIX grep -E (aka egrep) command. As of NCO 2.8.1 (August, 2003), variable name arguments to the -v switch may contain extended regular expressions. As of NCO 3.9.6 (January, 2009), variable names arguments to ncatted may contain extended regular expressions. As of NCO 4.2.4 (November, 2012), group name arguments to the -g switch may contain extended regular expressions. POSIX regex Because of its wide availability, NCO uses the POSIX regular expression library regex. Regular expressions of arbitary complexity may be used. Since netCDF variable names are relatively simple constructs, only a few varieties of variable wildcards are likely to be useful. For convenience, we define the most useful pattern matching operators here: . (wildcard character) $ (wildcard character) ^ (wildcard character) ? (filename expansion) * (filename expansion) ^ Matches the beginning of a string $ Matches the end of a string . Matches any single character

The most useful repetition and combination operators are ? (wildcard character) * (wildcard character) + (wildcard character) | (wildcard character) ? The preceding regular expression is optional and matched at most once * The preceding regular expression will be matched zero or more times + The preceding regular expression will be matched one or more times | The preceding regular expression will be joined to the following regular expression. The resulting regular expression matches any string matching either subexpression.

To illustrate the use of these operators in extracting variables and groups, consider file in_grp.nc with groups g0&textndash;g9, and subgroups s0&textndash;s9, in each of those groups, and file in.nc with variables Q, Q01&textndash;Q99, Q100, QAA&textndash;QZZ, Q_H2O, X_H2O, Q_CO2, X_CO2. ncks -v '.+' in.nc # All variables (default) ncks -v 'Q.?' in.nc # Variables that contain Q ncks -v '^Q.?' in.nc # Variables that start with Q ncks -v '^Q+.?.' in.nc # Q, Q0--Q9, Q01--Q99, QAA--QZZ, etc. ncks -v '^Q..' in.nc # Q01--Q99, QAA--QZZ, etc. ncks -v '^Q[0-9][0-9]' in.nc # Q01--Q99, Q100 ncks -v '^Q[[:digit:]]{2}' in.nc # Q01--Q99 ncks -v 'H2O$' in.nc # Q_H2O, X_H2O ncks -v 'H2O$|CO2$' in.nc # Q_H2O, X_H2O, Q_CO2, X_CO2 ncks -v '^Q[0-9][0-9]$' in.nc # Q01--Q99 ncks -v '^Q[0-6][0-9]|7[0-3]' in.nc # Q01--Q73, Q100 ncks -v '(Q[0-6][0-9]|7[0-3])$' in.nc # Q01--Q73 ncks -v '^[a-z]_[a-z]{3}$' in.nc # Q_H2O, X_H2O, Q_CO2, X_CO2 ncks -g 'g.' in_grp.nc # 10 Groups g0-g9 ncks -g 's.' in_grp.nc # 100 sub-groups g0/s0, g0/s1, ... g9/s9 ncks -g 'g.' -v 'v.' in_grp.nc # All variables 'v.' in groups 'g.' Beware&textmdash;two of the most frequently used repetition pattern matching operators, * and ?, are also valid pattern matching operators for filename expansion (globbing) at the shell-level. Confusingly, their meanings in extended regular expressions and in shell-level filename expansion are significantly different. In an extended regular expression, * matches zero or more occurences of the preceding regular expression. Thus Q* selects all variables, and Q+.* selects all variables containing Q (the + ensures the preceding item matches at least once). To match zero or one occurence of the preceding regular expression, use ?. Documentation for the UNIX egrep command details the extended regular expressions which NCO supports. <a name="globbing"></a>  <a name="glb"></a>  globbing shell bash csh quotes One must be careful to protect any special characters in the regular expression specification from being interpreted (globbed) by the shell. This is accomplish by enclosing special characters within single or double quotes

ncra -v Q?? in.nc out.nc   # Error: Shell attempts to glob wildcards
ncra -v '^Q+..' in.nc out.nc # Correct: NCO interprets wildcards
ncra -v '^Q+..' in*.nc out.nc # Correct: NCO interprets, Shell globs

The final example shows that commands may use a combination of variable wildcarding and shell filename expansion (globbing). For globbing, * and ? have nothing to do with the preceding regular expression! In shell-level filename expansion, * matches any string, including the null string and ? matches any single character. Documentation for bash and csh describe the rules of filename expansion (globbing). <a name="no_coord"></a>  <a name="no_crd"></a>  <a name="crd"></a>  <a name="-C"></a>  <a name="-c"></a>  <a name="xcl_ass_var"></a>  <a name="xtr_ass_var"></a>

Subsetting Coordinate VariablesGroup Path EditingSubsetting FilesShared features

Subsetting Coordinate Variables subsetting -C -c --xcl_ass_var --xtr_ass_var --no_coords --no_crd --coords --crd Availability: ncap2, ncbo, nces, ncecat, ncflint, ncks, ncpdq, ncra, ncrcat, ncwa&linebreak; Short options: -C, -c&linebreak; Long options: --no_coords, --no_crd, --xcl_ass_var, --crd, --coords, --xtr_ass_var&linebreak; By default, coordinates variables associated with any variable appearing in the input-file will be placed in the output-file, even if they are not explicitly specified, e.g., with the -v switch. Thus variables with a latitude coordinate lat always carry the values of lat with them into the output-file. This automatic inclusion feature can be disabled with -C, which causes NCO to exclude (or, more precisely, not to automatically include) coordinates and associated variables from the extraction list. However, using -C does not preclude the user from including some coordinates in the output files simply by explicitly selecting the coordinates and associated variables with the -v option. The -c option, on the other hand, is a shorthand way of automatically specifying that all coordinate and associated variables in input-files should appear in output-file. The user can thereby select all coordinate variables without even knowing their names. The meaning of &textldquo;coordinates&textrdquo; in these two options has expanded since about 2009 from simple one dimensional coordinates (per the NUG) definition) to any and all associated variables. This includes multi-dimensional coordinates as well as a menagerie of associated variables defined by the CF metadata conventions: CF conventions As of NCO version 4.4.5 (July, 2014) both -c and -C honor the CF ancillary_variables convention described in CF Conventions. As of NCO version 4.0.8 (April, 2011) both -c and -C honor the CF bounds convention described in CF Conventions. As of NCO version 4.6.4 (January, 2017) both -c and -C honor the CF cell_measures convention described in CF Conventions. As of NCO version 4.4.9 (May, 2015) both -c and -C honor the CF climatology convention described in CF Conventions. As of NCO version 3.9.6 (January, 2009) both -c and -C honor the CF coordinates convention described in CF Conventions. As of NCO version 4.6.4 (January, 2017) both -c and -C honor the CF formula_terms convention described in CF Conventions. As of NCO version 4.6.0 (May, 2016) both -c and -C honor the CF grid_mapping convention described in CF Conventions. The expanded categories of variables controlled by -c and -C justified adding a more descriptive switch. As of NCO version 4.8.0 (May, 2019) the switch --xcl_ass_var, which stands for &textldquo;exclude associated variables&textrdquo;, is synonymous with -C and --xtr_ass_var, which stands for &textldquo;extract associated variables&textrdquo;, is synonymous with -c. <a name="gpe"></a>

Group Path EditingC and Fortran Index ConventionsSubsetting Coordinate VariablesShared features

Group Path Editing -G gpe_dsc --gpe gpe_dsc Options -G gpe_dsc&linebreak; Availability: ncbo, ncecat, nces, ncflint, ncks, ncpdq, ncra, ncrcat, ncwa&linebreak; Short options: -G&linebreak; Long options: --gpe&linebreak; Group Path Editing, or GPE, allows the user to restructure (i.e., add, remove, and rename groups) in the output file relative to the input file based on the instructions they provide. As of NCO 4.2.3 (November, 2012), all operators that accept netCDF4 files with groups accept the -G switch, or its long-option equivalent --gpe. To master GPE one must understand the meaning of the required gpe_dsc structure/argument that specifies the transformation of input-to-output group paths. Each gpe_dsc contains up to three elements (two are optional) in the following order:&linebreak; gpe_dsc = grp_pth:lvl_nbr or grp_pth&arobase;lvl_nbr grp_pth Group Path. group path This (optional) component specifies the output group path that should be appended after any editing (i.e., deletion or truncation) of the input path is performed. lvl_nbr The number of levels to delete (from the head) or truncate (from the tail) of the input path.

If both components of the argument are present, then a single character, either the colon or at-sign (: or &arobase;), must separate them. If only grp_pth is specifed, the separator character may be omitted, e.g., -G g1. If only lvl_nbr is specifed, the separator character is still required to indicate it is a lvl_nbr arugment and not a grp_pth, e.g., -G :-1 or -G &arobase;1. If the at-sign separator character &arobase; is used instead of the colon separator character :, then the following lvl_nbr arugment must be positive and it will be assumed to refer to Truncation-Mode. Hence, -G :-1 is the same as -G &arobase;1. This is simply a way of making the lvl_nbr argument positive-definite. * Flattening Groups::

* Moving Groups::

* Dismembering Files::

* Checking CF-compliance::

Flattening GroupsMoving GroupsGroup Path EditingGroup Path Editing Deletion, Truncation, and Flattening of Groups <a name="flatten"></a>  <a name="delete"></a>  <a name="truncate"></a>  &arobase; (separator character) : (separator character) delete (groups) truncate (groups) flatten (groups) GPE has three editing modes: Delete, Truncate, and Flatten. Select one of GPE&textrsquo;s three editing modes by supplying a lvl_nbr that is positive, negative, or zero for Delete-, Truncate- and Flatten-mode, respectively. In Delete-mode, lvl_nbr is a positive integer which specifies the maximum number of group path components (i.e., groups) that GPE will try to delete from the head of grp_pth. For example

lvl_nbr = 3 changes the input path /g1/g2/g3/g4/g5 to the output path /g4/g5. Input paths with lvl_nbr or fewer components (groups) are completely erased and the output path commences from the root level. In other words, GPE is tolerant of specifying too many group components to delete. It deletes as many as possible, without complaint, and then begins to flatten the file (which fails if namespace conflicts arise). In Truncate-mode, lvl_nbr is a negative integer which specifies the maximum number of group path components (i.e., groups) that GPE will try to truncate from the tail of grp_pth. For example

lvl_nbr = -3 changes the input path /g1/g2/g3/g4/g5 to the output path /g1/g2. Input paths with lvl_nbr or fewer components (groups) are completely erased and the output path commences from the root level. In Flatten-mode, indicated by the separator character alone or with

lvl_nbr = 0, GPE removes the entire group path from the input file and constructs the output path beginning at the root level. For example -G :0 and -G : are identical and change the input path /g1/g2/g3/g4/g5 to the output path / whereas -G g1:0 and -G g1: are identical and result in the output path /g1 for all variables. Subsequent to the alteration of the input path by the specified editing mode, if any, GPE prepends (in Delete Mode) or Appends (in Truncate-mode) any specifed grp_pth to the output path. For example -G g2 changes the input paths / and /g1 to /g2 and /g1/g2, respectively. Likewise, -G g2/g3 changes the input paths / and /g1 to /g2/g3 and /g1/g2/g3, respectively. When grp_pth and lvl_nbr are both specified, the editing actions are taken in sequence so that, e.g., -G g1/g2:2 changes the input paths / and /h1/h2/h3/h4 to /g1/g2 and /g1/g2/h3/h4, respectively. Likewise, -G g1/g2:-2 changes the input paths / and /h1/h2/h3/h4 to /g1/g2 and /h1/h2/g1/g2, respectively. Combining GPE with subsetting (Subsetting Files) yields powerful control over the extracted (or excluded) variables and groups and their placement in the output file as shown by the following commands. All commands below may be assumed to end with in.nc out.nc. # Prepending paths without editing: ncks # /g?/v? -> /g?/v? ncks -v v1 # /g?/v1 -> /g?/v1 ncks -g g1 # /g1/v? -> /g1/v? ncks -G o1 # /g?/v? -> /o1/g?/v? ncks -G o1 -g g1 # /g1/v? -> /o1/g1/v? ncks -g g1 -v v1 # /g1/v1 -> /g1/v1 ncks -G o1 -v v1 # /g?/v1 -> /o1/g?/v1 ncks -G o1 -g g1 -v v1 # /g1/v1 -> /o1/g1/v1 ncks -G g1 -g / -v v1 # /v1 -> /g1/v1 ncks -G g1/g2 -v v1 # /g?/v1 -> /g1/g2/g?/v1 # Delete-mode: Delete from and Prepend to path head # Syntax: -G [ppn]:lvl_nbr = # of levels to delete ncks -G :1 -g g1 -v v1 # /g1/v1 -> /v1 ncks -G :1 -g g1/g1 -v v1 # /g1/g1/v1 -> /g1/v1 ncks -G :2 -g g1/g1 -v v1 # /g1/g1/v1 -> /v1 ncks -G :2 -g g1 -v v1 # /g1/v1 -> /v1 ncks -G g2:1 -g g1 -v v1 # /g1/v1 -> /g2/v1 ncks -G g2:2 -g g1/g1 -v v1 # /g1/g1/v1 -> /g2/v1 ncks -G g2:1 -g / -v v1 # /v1 -> /g2/v1 ncks -G g2:1 -v v1 # /v1 -> /g2/v1 ncks -G g2:1 -g g1/g1 -v v1 # /g1/g1/v1 -> /g2/g1/v1 # Flatten-mode: Remove all input path components # Syntax: -G [apn]: colon without numerical argument ncks -G : -v v1 # /g?/v1 -> /v1 ncks -G : -g g1 -v v1 # /g1/v1 -> /v1 ncks -G : -g g1/g1 -v v1 # /g1/g1/v1 -> /v1 ncks -G g2: -v v1 # /g?/v1 -> /g2/v1 ncks -G g2: # /g?/v? -> /g2/v? ncks -G g2: -g g1/g1 -v v1 # /g1/g1/v1 -> /g2/v1 # Truncate-mode: Truncate from and Append to path tail # Syntax: -G [apn]:-lvl_nbr = # of levels to truncate # NB: -G [apn]:-lvl_nbr is equivalent to -G [apn]@lvl_nbr ncks -G :-1 -g g1 -v v1 # /g1/v1 -> /v1 ncks -G :-1 -g g1/g2 -v v1 # /g1/g2/v1 -> /g1/v1 ncks -G :-2 -g g1/g2 -v v1 # /g1/g2/v1 -> /v1 ncks -G :-2 -g g1 -v v1 # /g1/v1 -> /v1 ncks -G g2:-1 -v v1 # /g?/v1 -> /g2/v1 ncks -G g2:-1 -g g1 -v v1 # /g1/v1 -> /g2/v1 ncks -G g1:-1 -g g1/g2 -v v1 # /g1/g2/v1 -> /g1/g1/v1 <a name="mv"></a>  <a name="move"></a>  Moving GroupsDismembering FilesFlattening GroupsGroup Path Editing Moving Groups move groups groups, moving rename groups groups, renaming Until fall 2013 (netCDF version 4.3.1-pre1), netCDF contained no library function for renaming groups, and therefore ncrename cannot rename groups. However, NCO built on earlier versions of netCDF than 4.3.1 can use a GPE-based workaround mechanism to &textldquo;rename&textrdquo; groups. The GPE mechanism actually moves (i.e., copies to a new location) groups, a more arduous procedure than simply renaming them. GPE applies to all selected groups, so, in the general case, one must move only the desired group to a new file, and then merge that new file with the original to obtain a file where the desired group has been &textldquo;renamed&textrdquo; and all else is unchanged. Here is how to &textldquo;rename&textrdquo; group /g4 to group /f4 with GPE instead of ncrename

ncks -O -G f4:1 -g g4 ~/nco/data/in_grp.nc ~/tmp.nc # Move /g4 to /f4
ncks -O -x -g g4 ~/nco/data/in_grp.nc ~/out.nc # Excise /g4
ncks -A ~/tmp.nc ~/out.nc # Add /f4 to new file

If the original group g4 is not excised from out.nc (step two above), then the final output file would contain both g4 and a copy named f4. Thus GPE can be used to both &textldquo;rename&textrdquo; and copy groups. The recommended way to rename groups when when netCDF version 4.3.1 is availale is to use ncrename (ncrename netCDF Renamer). <a name="xmp_flt"></a>  One may wish to flatten hierarchical group files for many reasons. These include 1. To obtain flat netCDF3 files for use with tools that do not work with netCDF4 files, 2. To split-apart hierarchies to re-assemble into different hierarchies, and 3. To provide a subset of a hierarchical file with the simplest possible storage structure.

ncks -O -G : -g cesm -3 ~/nco/data/cmip5.nc ~/cesm.nc # Extract /cesm to /

The switch Note that the switch should appear after the and switches. This is due to an artifact of the GPE implementation which we wish to remove in the future. specifies the output dataset should be in netCDF3 format, the option flattens all extracted groups, and the option extracts only the cesm group and leaves all other groups (e.g., ecmwf, giss). <a name="dismember"></a>  <a name="disaggregate"></a>  <a name="ncdismember"></a>  Dismembering FilesChecking CF-complianceMoving GroupsGroup Path Editing Dismembering Files disaggregate dismember ncdismember Let us show how to completely disaggregate (or, more memorably) dismember a hierarchical dataset. For now we take this to mean: store each group as a standalone flat dataset in netCDF3 format. This can be accomplished by looping the previous example over all groups. This script ncdismember dismembers the input file fl_in specified in the first argument and places the resulting files in the directory drc_out specified by the second argument: cat > ~/ncdismember << 'EOF' #!/bin/sh # Purpose: Dismember netCDF4/HDF5 hierarchical files. CF-check them. # Place each input file group in separate netCDF3 output file # Described in NCO User Guide at http://nco.sf.net/nco.html#dismember # Requirements: NCO 4.3.x+, UNIX shell utilities awk, grep, sed # Optional: Decker CFchecker https://bitbucket.org/mde_/cfchecker # Usage: # ncdismember <fl_in> <drc_out> [cf_chk] [cf_vrs] [opt] # where fl_in is input file/URL to dismember, drc_out is output directory # CF-compliance check is performed when optional third argument is not '0' # Default checker is Decker's cfchecker installed locally # Specify cf_chk=nerc for smallified uploads to NERC checker # Optional fourth argument cf_vrs is CF version to check # Optional fifth argument opt passes straight-through to ncks # Arguments must not use shell expansion/globbing # NB: ncdismember does not clean-up output directory, so user must # chmod a+x ~/sh/ncdismember # Examples: # ncdismember ~/nco/data/mdl_1.nc /data/zender/tmp # ncdismember http://dust.ess.uci.edu/nco/mdl_1.nc /tmp # ncdismember http://thredds-test.ucar.edu/thredds/dodsC/testdods/foo.nc /tmp # ncdismember ~/nco/data/mdl_1.nc /data/zender/nco/tmp cf # ncdismember ~/nco/data/mdl_1.nc /data/zender/nco/tmp nerc # ncdismember ~/nco/data/mdl_1.nc /data/zender/nco/tmp cf 1.3 # ncdismember ~/nco/data/mdl_1.nc /data/zender/nco/tmp cf 1.5 --fix_rec_dmn=all # Command-line argument defaults fl_in="${HOME}/nco/data/mdl_1.nc" # [sng] Input file to dismember/check drc_out="${DATA}/nco/tmp" # [sng] Output directory cf_chk='0' # [flg] Perform CF-compliance check? Which checker? cf_vrs='1.5' # [sng] Compliance-check this CF version (e.g., '1.5') opt='' # [flg] Additional ncks options (e.g., '--fix_rec_dmn=all') # Use single quotes to pass multiple arguments to opt=${5} # Otherwise arguments would be seen as ${5}, ${6}, ${7} ... # Command-line argument option parsing if [ -n "${1}" ]; then fl_in=${1}; fi if [ -n "${2}" ]; then drc_out=${2}; fi if [ -n "${3}" ]; then cf_chk=${3}; fi if [ -n "${4}" ]; then cf_vrs=${4}; fi if [ -n "${5}" ]; then opt=${5}; fi # Prepare output directory echo "NCO dismembering file ${fl_in}" fl_stb=$(basename ${fl_in}) drc_out=${drc_out}/${fl_stb} mkdir -p ${drc_out} cd ${drc_out} chk_dck='n' chk_nrc='n' if [ ${cf_chk} = 'nerc' ]; then chk_nrc='y' fi # chk_nrc if [ ${cf_chk} != '0' ] && [ ${cf_chk} != 'nerc' ]; then chk_dck='y' hash cfchecker 2>/dev/null || { echo >&2 "Local cfchecker command not found, will smallify and upload to NERC checker instead"; chk_nrc='y'; chk_dck='n'; } fi # !cf_chk # Obtain group list grp_lst=`ncks -m ${fl_in} | grep '// group' | awk '{$1=$2=$3="";sub(/^ */,"",$0);print}'` IFS=$'\n' # Change Internal-Field-Separator from <Space><Tab><Newline> to <Newline> for grp_in in ${grp_lst} ; do # Replace slashes by dots for output group filenames grp_out=`echo ${grp_in} | sed 's/\///' | sed 's/\//./g'` if [ "${grp_out}" = '' ]; then grp_out='root' ; fi # Tell older NCO/netCDF if HDF4 with --hdf4 switch (signified by .hdf/.HDF suffix) hdf4=`echo ${fl_in} | awk '{if(match(tolower($1),".hdf$")) hdf4="--hdf4"; print hdf4}'` # Flatten to netCDF3, anchor, no history, no temporary file, padding, HDF4 flag, options cmd="ncks -O -3 -G : -g ${grp_in}/ -h --no_tmp_fl --hdr_pad=40 ${hdf4} ${opt} ${fl_in} ${drc_out}/${grp_out}.nc" # Use eval in case ${opt} contains multiple arguments separated by whitespace eval ${cmd} if [ ${chk_dck} = 'y' ]; then # Decker checker needs Conventions <= 1.6 no_bck_sls=`echo ${drc_out}/${grp_out} | sed 's/\\\ / /g'` ncatted -h -a Conventions,global,o,c,CF-${cf_vrs} ${no_bck_sls}.nc else # !chk_dck echo ${drc_out}/${grp_out}.nc fi # !chk_dck done if [ ${chk_dck} = 'y' ]; then echo 'Decker CFchecker reports CF-compliance of each group in flat netCDF3 format' cfchecker -c ${cf_vrs} *.nc fi if [ ${chk_nrc} = 'y' ]; then # Smallification and NERC upload from qdcf script by Phil Rasch (PJR) echo 'Using remote CFchecker http://puma.nerc.ac.uk/cgi-bin/cf-checker.pl' cf_lcn='http://puma.nerc.ac.uk/cgi-bin/cf-checker.pl' for fl in ${drc_out}/*.nc ; do fl_sml=${fl} cf_out=${fl%.nc}.html dmns=`ncdump -h ${fl_in} | sed -n -e '/dimensions/,/variables/p' | grep = | sed -e 's/=.*//'` hyp_sml='' for dmn in ${dmns}; do dmn_lc=`echo ${dmn} | tr "[:upper:]" "[:lower:]"` if [ ${dmn_lc} = 'lat' ] || [ ${dmn_lc} = 'latitude' ] || [ ${dmn_lc} = 'lon' ] || [ ${dmn_lc} = 'longitude' ] || [ ${dmn_lc} = 'time' ]; then hyp_sml=`echo ${hyp_sml}" -d ${dmn},0"` fi # !dmn_lc done # Create small version of input file by sampling only first element of lat, lon, time ncks -O ${hyp_sml} ${fl} ${fl_sml} # Send small file to NERC checker curl --form cfversion=1.6 --form upload=@${fl_sml} --form press="Check%20file" ${cf_lcn} -o ${cf_out} # Strip most HTML to improve readability cat ${cf_out} | sed -e "s/<[^>]*>//g" -e "/DOCTYPE/,/\]\]/d" -e "s/CF-Convention//g" -e "s/Output of//g" -e "s/Compliance Checker//g" -e "s/Check another//g" -e "s/CF-Checker follows//g" -e "s/Received//g" -e "s/for NetCDF//g" -e "s/NetCDF format//g" -e "s/against CF version 1//g" -e "s/\.\.\.//g" echo "Full NERC compliance-check log for ${fl} in ${cf_out}" done fi # !nerc EOF chmod 755 ~/ncdismember # Make command executable /bin/mv -f ~/ncdismember ~/sh # Store in location on $PATH, e.g., /usr/local/bin zender@roulee:~$ ncdismember ~/nco/data/mdl_1.nc ${DATA}/nco/tmp NCO dismembering file /home/zender/nco/data/mdl_1.nc /data/zender/nco/tmp/mdl_1.nc/cesm.cesm_01.nc /data/zender/nco/tmp/mdl_1.nc/cesm.cesm_02.nc /data/zender/nco/tmp/mdl_1.nc/cesm.nc /data/zender/nco/tmp/mdl_1.nc/ecmwf.ecmwf_01.nc /data/zender/nco/tmp/mdl_1.nc/ecmwf.ecmwf_02.nc /data/zender/nco/tmp/mdl_1.nc/ecmwf.nc /data/zender/nco/tmp/mdl_1.nc/root.nc A (potentially more portable) binary executable could be written to dismember all groups with a single invocation, yet dismembering without loss of information is possible now with this simple script on all platforms with UNIXy utilities. Note that all dimensions inherited by groups in the input file are correctly placed by ncdismember into the flat files. Moreover, each output file preserves the group metadata of all ancestor groups, including the global metadata from the input file. As written, the script could fail on groups that contain advanced netCDF4 features because the user requests (with the -3 switch) that output be netCDF3 classic format. However, ncks detects many format incompatibilities in advance and works around them. For example, ncks autoconverts netCDF4-only atomic-types (such as NC_STRING and NC_UBYTE) to corresponding netCDF3 atomic types (NC_CHAR and NC_SHORT) when the output format is netCDF3. <a name="cf-compliance"></a>  <a name="nccf"></a>  Checking CF-complianceDismembering FilesGroup Path Editing Checking CF-compliance CF compliance checker cfchecker ncdismember compliance checker Martin Schultz Michael Decker One application of dismembering is to check the CF-compliance of each group in a file. When invoked with the optional third argumnt cf, ncdismember passes each file it generates to freely available compliance checkers, such as cfchecker CFchecker is developed by Michael Decker and Martin Schultz at Forschungszentrum Julich and distributed at https://bitbucket.org/mde_/cfchecker.. zender@roulee:~$ ncdismember ~/nco/data/mdl_1.nc /data/zender/nco/tmp cf NCO dismembering file /home/zender/nco/data/mdl_1.nc CFchecker reports CF-compliance of each group in flat netCDF3 format WARNING: Using the default (non-CF) Udunits database cesm.cesm_01.nc: INFO: INIT: running CFchecker version 1.5.15 INFO: INIT: checking compliance with convention CF-1.5 INFO: INIT: using standard name table version: 25, last modified: 2013-07-05T05:40:30Z INFO: INIT: using area type table version: 2, date: 10 July 2013 INFO: 2.4: no axis information found in dimension variables, not checking dimension order WARNING: 3: variable "tas1" contains neither long_name nor standard_name attribute WARNING: 3: variable "tas2" contains neither long_name nor standard_name attribute INFO: 3.1: variable "tas1" does not contain units attribute INFO: 3.1: variable "tas2" does not contain units attribute -------------------------------------------------- cesm.cesm_02.nc: ... By default the CF version checked is determined automatically by cfchecker. The user can override this default by supplying a supported CF version, e.g., 1.3, as an optional fourth argument to ncdismember. Current valid CF options are 1.0, 1.1, 1.2, 1.3, 1.4, and 1.5. <a name="diwg"></a>  Our development and testing of ncdismember is funded by our involvement in NASA&textrsquo;s Dataset Interoperability Working Group (https://wiki.earthdata.nasa.gov/display/ESDSWG/Dataset+Interoperability+Working+GroupDIWG), though our interest extends beyond NASA datasets. Taken together, NCO&textrsquo;s features (autoconversion to netCDF3 atomic types, fixing multiple record dimensions, autosensing HDF4 input, scoping rules for CF conventions) make ncdismember reliable and friendly for both dismembering hierarchical files and for CF-compliance checks. Most HDF4 and HDF5 datasets can be checked for CF-compliance with a one-line command. Example compliance checks of common NASA datasets are at http://dust.ess.uci.edu/diwg. Our long-term goal is to enrich the hierarchical data model with the expressivity and syntactic power of CF conventions. <a name="daac"></a>  NASA asked the DIWG to prepare a one-page summary of the procedure necessary to check HDF files for CF-compliance: cat > ~/ncdismember.txt << 'EOF' Preparing an RPM-based OS to Test HDF & netCDF Files for CF-Compliance By Charlie Zender, UCI & NASA Dataset Interoperability Working Group (DIWG) Installation Summary: 1. HDF4 [with internal netCDF support _disabled_] 2. HDF5 3. netCDF [with external HDF4 support _enabled_] 4. NCO 5. numpy 6. netcdf4-python 7. python-lxml 8. CFunits-python 9. CFChecker 10. ncdismember All 10 packages can use default installs _except_ HDF4 and netCDF. Following instructions for Fedora Core 20 (FC20), an RPM-based Linux OS Feedback and changes for other Linux-based OS's welcome to zender at uci.edu ${H4DIR}, ${H5DIR}, ${NETCDFDIR}, ${NCODIR}, may all be different For simplicity CZ sets them all to /usr/local # 1. HDF4. Build in non-default manner. Turn-off its own netCDF support. # Per http://www.unidata.ucar.edu/software/netcdf/docs/build_hdf4.html # HDF4 support not necessary though it makes ncdismember more comprehensive wget -c http://www.hdfgroup.org/ftp/HDF/HDF_Current/src/hdf-4.2.9.tar.gz tar xvzf hdf-4.2.9.tar.gz cd hdf-4.2.9 ./configure --enable-shared --disable-netcdf --disable-fortran --prefix=${H4DIR} make && make check && make install # 2. HDF5. Build normally. RPM may work too. Please let me know if so. # HDF5 is a necessary pre-requisite for netCDF4 wget -c ftp://ftp.unidata.ucar.edu/pub/netcdf/netcdf-4/hdf5-1.8.11.tar.gz tar xvzf hdf5-1.8.11.tar.gz cd hdf5-1.8.11 ./configure --enable-shared --prefix=${H5DIR} make && make check && make install # 3. netCDF version 4.3.1 or later. Build in non-default manner with HDF4. # Per http://www.unidata.ucar.edu/software/netcdf/docs/build_hdf4.html # Earlier versions of netCDF may fail checking some HDF4 files wget -c ftp://ftp.unidata.ucar.edu/pub/netcdf/netcdf-4.3.2.tar.gz tar xvzf netcdf-4.3.2.tar.gz cd netcdf-4.3.2 CPPFLAGS="-I${H5DIR}/include -I${H4DIR}/include" \ LDFLAGS="-L${H5DIR}/lib -L${H4DIR}/lib" \ ./configure --enable-hdf4 --enable-hdf4-file-tests make && make check && make install # 4. NCO version 4.4.0 or later. Some RPMs available. Or install by hand. # Later versions of NCO have much better support for ncdismember wget http://nco.sourceforge.net/src/nco-4.4.4.tar.gz . tar xvzf nco-4.4.4.tar.gz cd nco-4.4.4 ./configure --prefix=${NCODIR} make && make install # 5. numpy sudo yum install numpy -y # 6. netcdf4-python sudo yum install netcdf4-python -y # 7. python-lxml sudo yum install python-lxml -y # 8. CFunits-python. No RPM available. Must install by hand. # http://code.google.com/p/cfunits-python/ wget http://cfunits-python.googlecode.com/files/cfunits-0.9.6.tar.gz . tar xvzf cfunits-0.9.6.tar.gz cd cfunits-0.9.6 sudo python setup.py install # 9. CFChecker. No RPM available. Must install by hand. # https://bitbucket.org/mde_/cfchecker wget https://bitbucket.org/mde_/cfchecker/downloads/CFchecker-1.5.15.tar.bz2 . tar xvjf CFchecker-1.5.15.tar.bz2 cd CFchecker sudo python setup.py install # 10. ncdismember. Copy script from http://nco.sf.net/nco.html#ncdismember # Store dismembered files somewhere, e.g., ${DATA}/nco/tmp/hdf mkdir -p ${DATA}/nco/tmp/hdf # Many datasets work with a simpler command... ncdismember ~/nco/data/in.nc ${DATA}/nco/tmp/hdf cf 1.5 ncdismember ~/nco/data/mdl_1.nc ${DATA}/nco/tmp/hdf cf 1.5 ncdismember ${DATA}/hdf/AMSR_E_L2_Rain_V10_200905312326_A.hdf \ ${DATA}/nco/tmp/hdf cf 1.5 ncdismember ${DATA}/hdf/BUV-Nimbus04_L3zm_v01-00-2012m0203t144121.h5 \ ${DATA}/nco/tmp/hdf cf 1.5 ncdismember ${DATA}/hdf/HIRDLS-Aura_L3ZAD_v06-00-00-c02_2005d022-2008d077.he5 ${DATA}/nco/tmp/hdf cf 1.5 # Some datasets, typically .h5, require the --fix_rec_dmn=all argument ncdismember_${DATA}/hdf/GATMO_npp_d20100906_t1935191_e1935505_b00012_c20110707155932065809_noaa_ops.h5 ${DATA}/nco/tmp/hdf cf 1.5 --fix_rec_dmn=all ncdismember ${DATA}/hdf/mabel_l2_20130927t201800_008_1.h5 \ ${DATA}/nco/tmp/hdf cf 1.5 --fix_rec_dmn=all EOF A PDF version of these instructions is available http://dust.ess.uci.edu/diwg/ncdismember.pdfhere. <a name="fortran"></a>  <a name="ftn"></a>  <a name="-F"></a>

C and Fortran Index ConventionsHyperslabsGroup Path EditingShared features

C and Fortran Index conventions index convention Fortran index convention C index convention -F --fortran Availability: ncbo, nces, ncecat, ncflint, ncks, ncpdq, ncra, ncrcat, ncwa&linebreak; Short options: -F&linebreak; Long options: --fortran&linebreak; I/O The -F switch changes NCO to read and write with the Fortran index convention. By default, NCO uses C-style (0-based) indices for all I/O. In C, indices count from 0 (rather than 1), and dimensions are ordered from slowest (inner-most) to fastest (outer-most) varying. In Fortran, indices count from 1 (rather than 0), and dimensions are ordered from fastest (inner-most) to slowest (outer-most) varying. transpose Hence C and Fortran data storage conventions represent mathematical transposes of eachother. record variable Note that record variables contain the record dimension as the most slowly varying dimension. See ncpdq netCDF Permute Dimensions Quickly for techniques to re-order (including transpose) dimensions and to reverse data storage order. record dimension Consider a file 85.nc containing 12 months of data in the record dimension time. The following hyperslab operations produce identical results, a June-July-August average of the data:

ncra -d time,5,7 85.nc 85_JJA.nc
ncra -F -d time,6,8 85.nc 85_JJA.nc

Printing variable three_dmn_var in file in.nc first with the C indexing convention, then with Fortran indexing convention results in the following output formats:

% ncks --trd -v three_dmn_var in.nc
lat[0]=-90 lev[0]=1000 lon[0]=-180 three_dmn_var[0]=0 
...
% ncks --trd -F -v three_dmn_var in.nc
lon(1)=0 lev(1)=100 lat(1)=-90 three_dmn_var(1)=0 
...

HyperslabsStrideC and Fortran Index ConventionsShared features

Hyperslabs hyperslab dimension limits coordinate limits -0 -d dim,[min][,[max][,[stride]]] --dimension dim,[min][,[max][,[stride]]] --dmn dim,[min][,[max][,[stride]]] Availability: ncbo, nces, ncecat, ncflint, ncks, ncpdq, ncra, ncrcat, ncwa&linebreak; Short options: -d dim,[min][,[max][,[stride]]]&linebreak; Long options: --dimension dim,[min][,[max][,[stride]]],&linebreak; --dmn dim,[min][,[max][,[stride]]]&linebreak; A hyperslab is a subset of a variable&textrsquo;s data. The coordinates of a hyperslab are specified with the -d dim,[min][,[max][,[stride]]] short option (or with the same arguments to the --dimension or --dmn long options). At least one hyperslab argument (min, max, or stride) must be present. The bounds of the hyperslab to be extracted are specified by the associated min and max values. A half-open range is specified by omitting either the min or max parameter. The separating comma must be present to indicate the omission of one of these arguments. The unspecified limit is interpreted as the maximum or minimum value in the unspecified direction. A cross-section at a specific coordinate is extracted by specifying only the min limit and omitting a trailing comma. Dimensions not mentioned are passed with no reduction in range. The dimensionality of variables is not reduced (in the case of a cross-section, the size of the constant dimension will be one).

# First and second longitudes
ncks -F -d lon,1,2 in.nc out.nc
# Second and third longitudes
ncks -d lon,1,2 in.nc out.nc

As of version 4.2.1 (August, 2012), NCO allows one to extract the last N elements of a hyperslab. Negative integers as min or max elements of a hyperslab specification indicate offsets from the end (Python also uses this convention). Consistent with this convention, the value -1 (negative one) indicates the last element of a dimension, and negative zero is algebraically equivalent to zero and so indicates the first element of a dimension. Previously, for example, -d time,-2,-1 caused a domain error. Now it means select the penultimate and last timesteps, independent of the size of the time dimension. Select only the first and last timesteps, respectively, with -d time,0 and -d time,-1. Negative integers work for min and max indices, though not for stride.

# Second through penultimate longitudes
ncks -d lon,1,-2 in.nc out.nc
# Second through last longitude
ncks -d lon,1,-1 in.nc out.nc
# Second-to-last to last longitude
ncks -d lon,-3,-1 in.nc out.nc
# Second-to-last to last longitude 
ncks -d lon,-3, in.nc out.nc

The -F argument, if any, applies the Fortran index convention only to indices specified as positive integers:

# First through penultimate longitudes
ncks -F -d lon,1,-2 in.nc out.nc (-F affects only start index)
# First through last longitude
ncks -F -d lon,1,-1 in.nc out.nc
# Second-to-last to penultimate longitude (-F has no effect)
ncks -F -d lon,-3,-1 in.nc out.nc
# Second-to-last to last longitude (-F has no effect)
ncks -F -d lon,-3, in.nc out.nc

stride Coordinate values should be specified using real notation with a decimal point required in the value, whereas dimension indices are specified using integer notation without a decimal point. This convention serves only to differentiate coordinate values from dimension indices. It is independent of the type of any netCDF coordinate variables. In other words, even if coordinates are defined as integers, specify them with decimal points to have the command interpret them as values, rather than indices. For a given dimension, the specified limits must both be coordinate values (with decimal points) or dimension indices (no decimal points). If values of a coordinate-variable are used to specify a range or cross-section, then the coordinate variable must be monotonic (values either increasing or decreasing). In this case, command-line values need not exactly match coordinate values for the specified dimension. Ranges are determined by seeking the first coordinate value to occur in the closed range [min,max] and including all subsequent values until one falls outside the range. The coordinate value for a cross-section is the coordinate-variable value closest to the specified value and must lie within the range or coordinate-variable values. The stride argument, if any, must be a dimension index, not a coordinate value. Stride, for more information on the stride option.

# All longitude values between 1 and 2 degrees
ncks -d lon,1.0,2.0 in.nc out.nc
# All longitude values between 1 and 2 degrees
ncks -F -d lon,1.0,2.0 in.nc out.nc
# Every other longitude value between 0 and 90 degrees
ncks -F -d lon,0.0,90.0,2 in.nc out.nc

As shown, we recommend using a full floating-point suffix of .0 instead of simply . in order to make obvious the selection of hyperslab elements based on coordinate value rather than index. NC_CHAR User-specified coordinate limits are promoted to double-precision values while searching for the indices which bracket the range. Thus, hyperslabs on coordinates of type NC_CHAR are computed numerically rather than lexically, so the results are unpredictable. wrapped coordinates The relative magnitude of min and max indicate to the operator whether to expect a wrapped coordinate (Wrapped Coordinates), such as longitude. If

min > max, the NCO expects the coordinate to be wrapped, and a warning message will be printed. When this occurs, NCO selects all values outside the domain [

max < min], i.e., all the values exclusive of the values which would have been selected if min and max were swapped. If this seems confusing, test your command on just the coordinate variables with ncks, and then examine the output to ensure NCO selected the hyperslab you expected (coordinate wrapping is currently only supported by ncks). Because of the way wrapped coordinates are interpreted, it is very important to make sure you always specify hyperslabs in the monotonically increasing sense, i.e.,

min < max (even if the underlying coordinate variable is monotonically decreasing). The only exception to this is when you are indeed specifying a wrapped coordinate. The distinction is crucial to understand because the points selected by, e.g., -d longitude,50.,340., are exactly the complement of the points selected by -d longitude,340.,50.. Not specifying any hyperslab option is equivalent to specifying full ranges of all dimensions. This option may be specified more than once in a single command (each hyperslabbed dimension requires its own -d option). <a name="srd"></a>  <a name="stride"></a>

StrideRecord AppendingHyperslabsShared features

Stride stride -d dim,[min],[max],stride --dimension dim,[min],[max],stride --dmn dim,[min],[max],stride Availability: ncbo, nces, ncecat, ncflint, ncks, ncpdq, ncra, ncrcat, ncwa&linebreak; Short options: -d dim,[min][,[max][,[stride]]]&linebreak; Long options: --dimension dim,[min][,[max][,[stride]]],&linebreak; --dmn dim,[min][,[max][,[stride]]]&linebreak; All data operators support specifying a stride for any and all dimensions at the same time. The stride is the spacing between consecutive points in a hyperslab. A stride of 1 picks all the elements of the hyperslab, and a stride of 2 skips every other element, etc.&eosperiod; ncks multislabs support strides, and are more powerful than the regular hyperslabs supported by the other operators (Multislabs). Using the stride option for the record dimension with ncra and ncrcat makes it possible, for instance, to average or concatenate regular intervals across multi-file input data sets. The stride is specified as the optional fourth argument to the -d hyperslab specification: -d dim,[min][,[max][,[stride]]]. Specify stride as an integer (i.e., no decimal point) following the third comma in the -d argument. There is no default value for stride. Thus using -d time,,,2 is valid but -d time,,,2.0 and -d time,,, are not. When stride is specified but min is not, there is an ambiguity as to whether the extracted hyperslab should begin with (using C-style, 0-based indexes) element 0 or element stride-1. NCO must resolve this ambiguity and it chooses element 0 as the first element of the hyperslab when min is not specified. Thus -d time,,,stride is syntactically equivalent to -d time,0,,stride. This means, for example, that specifying the operation -d time,,,2 on the array 1,2,3,4,5 selects the hyperslab 1,3,5. To obtain the hyperslab 2,4 instead, simply explicitly specify the starting index as 1, i.e., -d time,1,,2. For example, consider a file 8501_8912.nc which contains 60 consecutive months of data. Say you wish to obtain just the March data from this file. Using 0-based subscripts (C and Fortran Index Conventions) these data are stored in records 2, 14, &dots; 50 so the desired stride is 12. Without the stride option, the procedure is very awkward. One could use ncks five times and then use ncrcat to concatenate the resulting files together: Bourne Shell C Shell for idx in 02 14 26 38 50; do # Bourne Shell ncks -d time,${idx} 8501_8912.nc foo.${idx} done foreach idx (02 14 26 38 50) # C Shell ncks -d time,${idx} 8501_8912.nc foo.${idx} end ncrcat foo.?? 8589_03.nc rm foo.?? With the stride option, ncks performs this hyperslab extraction in one operation:

ncks -d time,2,,12 8501_8912.nc 8589_03.nc

ncks netCDF Kitchen Sink, for more information on ncks. Applying the stride option to the record dimension in ncra and ncrcat makes it possible, for instance, to average or concatenate regular intervals across multi-file input data sets.

ncra -F -d time,3,,12 85.nc 86.nc 87.nc 88.nc 89.nc 8589_03.nc
ncrcat -F -d time,3,,12 85.nc 86.nc 87.nc 88.nc 89.nc 8503_8903.nc

Record AppendingSubcycleStrideShared features

Record Appending record append --rec_apn --record_append Availability: ncra, ncrcat&linebreak; Short options: None&linebreak; Long options: --rec_apn, --record_append&linebreak; As of version 4.2.6 (March, 2013), NCO allows both Multi-File, Multi-Record operators (ncra and ncrcat) to append their output directly to the end of an existing file. This feature may be used to augment a target file, rather than construct it from scratch. This helps, for example, when a timeseries is concatenated from input data that becomes available in stages rather than all at once. In such cases this switch significantly speeds writing. Consider the use case where one wishes to preserve the contents of fl_1.nc, and add to them new records contained in fl_2.nc. Previously the output had to be placed in a third file, fl_3.nc (which could also safely be named fl_2.nc), via

ncrcat -O fl_1.nc fl_2.nc fl_3.nc

Under the hood this operation copies all information in fl_1.nc and fl_2.nc not once but twice. The first copy is performed through the netCDF interface, as all data from fl_1.nc and fl_2.nc are extracted and placed in the output file. The second copy occurs (usually much) more quickly as the (by default) temporary output file is copied (sometimes a quick re-link suffices) to the final output file (Temporary Output Files). All this copying is expensive for large files. The --record_append switch appends all records in fl_2.nc to the end (after the last record) of fl_1.nc:

ncrcat --rec_apn fl_2.nc fl_1.nc

The ordering of the filename arguments may seem non-intuitive. If the record variable represents time in these files, then the values in fl_1.nc precede those in fl_2.nc, so why do the files appear in the reverse order on the command line? fl_1.nc is the last file named because it is the pre-existing output file to which we will append all the other input files listed (in this case only fl_2.nc). The contents of fl_1.nc are completely preserved, and only values in fl_2.nc (and any other input files) are copied. This switch avoids the necessity of copying all of fl_1.nc through the netCDF interface to a new output file. The --rec_apn switch automatically puts NCO into append mode (Appending Variables), so specifying -A is redundant, and simultaneously specifying overwrite mode with -O causes an error. By default, NCO works in an intermediate temporary file. Power users may combine --rec_apn with the --no_tmp_fl switch (Temporary Output Files):

ncrcat --rec_apn --no_tmp_fl fl_2.nc fl_1.nc

This avoids creating an intermediate file, and copies only the minimal amount of data (i.e., all of fl_2.nc). Hence, it is fast. We recommend users try to understand the safety trade-offs involved. One side-effect of --rec_apn to be aware of is how attributes are handled. When appending files, NCO typically overwrites attributes for existing variables in the destination file with the corresponding attributes from the same variable in the source file. The exception to this rule is when --rec_apn is invoked. As of version 4.7.9 (January, 2019), NCO leaves unchanged the attributes for existing variables in the destination file. This is primarily to ensure that calendar attributes (e.g., units, calendar) of the record coordinate, if any, are maintained, so that the data appended to them can be re-based to the existing units. Otherwise rebasing would fail or require rewriting the entire file which is counter to the purpose of --rec_apn. <a name="subcycle"></a>  <a name="ssc"></a>  <a name="duration"></a>  <a name="drn"></a>  <a name="mro"></a>

SubcycleInterleaveRecord AppendingShared features

Subcycle duration sub-cycle subcycle MRO Multi-Record Operator --mro -d dim,[min],[max],[stride],[subcycle] --dimension dim,[min],[max],[stride],[subcycle] --dmn dim,[min],[max],[stride],subcycle] Availability: ncra, ncrcat&linebreak; Short options: -d dim,[min][,[max][,[stride][,[subcycle]]]]&linebreak; Long options: --mro --dimension dim,[min][,[max][,[stride][,[subcycle]]]]&linebreak; --dmn dim,[min][,[max][,[stride][,[subcycle]]]]&linebreak; As of version 4.2.1 (August, 2012), NCO allows both Multi-File, Multi-Record operators, ncra and ncrcat, to extract and operate on multiple groups of records. These groups may be connected to physical sub-cycles of a periodic nature, e.g., months of a year, or hours of a day. Or they may be thought of as groups of a specifed duration. We call this the subcycle feature, sometimes abbreviated SSC When originally released in 2012 this was called the duration feature, and was abbreviated DRN.. The subcycle feature allows processing of groups of records separated by regular intervals of records. It is perhaps best illustrated by an extended example that describes how to solve the same problem both with and without the SSC feature. Creating seasonal cycles is a common task in climate data processing. Suppose a 150-year climate simulation produces 150 output files, each comprising 12 records, each record a monthly mean: 1850.nc, 1851.nc, ... 1999.nc. Our goal is to create a single file that contains the climatological summertime (June, July, and August, aka JJA) mean. Traditionally, we would first compute the climatological monthly mean for each month of summer. Each of these is a 150-year mean, i.e., # Step 1: Create climatological monthly files clm06.nc..clm08.nc for mth in {6..8}; do mm=`printf "%02d" $mth` ncra -O -F -d time,${mm},,12 -n 150,4,1 1850.nc clm${mm}.nc done # Step 2: Average climatological monthly files into summertime mean ncra -O clm06 clm07.nc clm08.nc clm_JJA.nc So far, nothing is unusual and this task can be performed by any NCO version. The SSC feature makes obsolete the need for the shell loop used in Step 1 above. The new SSC option aggregates more than one input record at a time before performing arithmetic operations, and, with an additional switch, allows archival of those results in multiple-record output (MRO) files. This reduces the task of producing the climatological summertime mean to one step:

# Step 1: Compute climatological summertime mean
ncra -O -F -d time,6,,12,3 -n 150,4,1 1850.nc clm_JJA.nc

The SSC option instructs ncra (or ncrcat) to process files in groups of three records. To better understand the meaning of each argument to the -d hyperslab option, read it this way: &textldquo;for the time dimension start with the sixth record, continue without end, repeat the process every twelfth record, and define a sub-cycle as three consecutive records&textrdquo;. A separate option, --mro, instructs ncra to output its results from each sub-group, and to produce a Multi-Record Output (MRO) file rather than a Single-Record Output (SRO) file. Unless Multi-Record-Output is indicated (either with --mro or implicitly, as with interleave-mode), ncra collects together all sub-groups, operates on their ensemble, and produces a single output record. Adding --mro to the above example causes ncra to archive all (150) annual summertime means to one file:

# Step 1: Archive all 150 summertime means in one file
ncra --mro -O -F -d time,6,,12,3 -n 150,4,1 1850.nc 1850_2009_JJA.nc
# ...or all (150) annual means...
ncra --mro -O -d time,,,12,12 -n 150,4,1 1850.nc 1850_2009.nc

These operations generate and require no intermediate files. This contrasts to previous NCO methods, which require generating, averaging, then catenating 150 files. The --mro option only works on ncra and has no effect on (or rather is redundant for) ncrcat, since ncrcat always outputs all selected records. <a name="interleave"></a>  <a name="ilv"></a>

InterleaveMultislabsSubcycleShared features

Interleave interleave --mro -d dim,[min],[max],[stride],[subcycle],[interleave] --dimension dim,[min],[max],[stride],[subcycle],[interleave] --dmn dim,[min],[max],[stride],subcycle],[interleave] Availability: ncra, ncrcat&linebreak; Short options: -d dim,[min][,[max][,[stride][,[subcycle][,[interleave]]]]]&linebreak; Long options: --mro --dimension dim,[min][,[max][,[stride][,[subcycle][,[interleave]]]]]&linebreak; --dmn dim,[min][,[max][,[stride][,[subcycle][,[interleave]]]]]&linebreak; <a name="bug_ilv"></a>  Caveat lector: Unforunately NCO versions from 4.9.4&textndash;5.1.8 (September, 2020 through October, 2023) contained a bug that affected the subcycle and interleave (SSC and ILV) hyperslab features. The bug was triggered by invoking the SSC feature without explicitly providing an ILV parameter. The software failed to initialize an internal flag that indicated whether ILV had been invoked. The resulting behavior was compiler-dependent. Most compilers set the flag to false (as it should have been), but occasionally it was set to true). The bug expressed itself by extracting only a single timestep, rather than the number of timesteps indicated by the SSC parameter. This behavior was fixed in NCO version 5.1.9 (November, 2023). As of version 4.9.4 (September, 2020), NCO allows both Multi-File, Multi-Record operators (ncra and ncrcat) to extract, interleave, and operate on multiple groups of records. Interleaving (or de-interleaving, depending on one&textrsquo;s perspective) means altering the order of records in a group to be processed. Specifically, the interleaving feature (sometimes abbreviated ILV) causes the operator to treat as sequential records those that are separated by multiples of the specified interleave parameter within a group or sub-cycle of records. The interleave feature sequences records with respect to their position relative to the beginning of each sub-cycle. Records an integer multiple of interleave from the sub-cycle start are first extracted (ncrcat) or reduced (ncra), then records offset from these by one, two, et cetera up to

interleave-1. In this manner interleaving extracts an inner (intra-sub-cycle) loop that preserves high-frequency signals relative to the longer stride between sub-cycles. Thus interleaving allows deconvolution of periodic phenomena within a time-series. Processing simple arithmetic sequences is a helpful way to understand what interleaving does. Here are some examples to reify the abstract. Let in1.nc contain the record-array [1..10], in2.nc contain [11..20], and in12.nc contain [1..20]. ncra -O -d time,,,,10,5 ~/in1.nc ~/foo.nc # 3.5, 4.5, 5.5, 6.5, 7.5 ncrcat -O -d time,0,4,,6,2 ~/in1.nc ~/foo.nc # 1, 3, 5, 2, 4, 6 (+WARNING) ncrcat -O -d time,2,,10,4,2 ~/in12.nc ~/foo.nc # 3, 5, 4, 6, 13, 15, 14, 16 ncra -O -d time,2,,10,4,2 ~/in12.nc ~/foo.nc # 4, 5, 14, 15 ncra -O -d time,,,,10,2 ~/in1.nc ~/in2.nc ~/foo.nc # 5, 6, 15, 16 ncra -O -d time,,,,10,2 ~/in12.nc ~/foo.nc # 5, 6, 15, 16 Interleaving is perhaps best illustrated by an extended example that describes how to solve the same problem both with and without the ILV feature. Consider as an example an interannual timeseries archived at a high-enough temporal frequency to resolve the diurnal cycle with tpd timesteps-per-day. Many climate models and re-analyses are archived at hourly, tri-hourly, or six-hourly resolution yielding

tpd = 24, 8, or

6

, respectively. Our goal is to extract a monthly mean diurnal cycle from this timeseries. Suppose a 150-year climate simulation produces 150 output files, each comprising 365 days of hourly data, or 8760 records, each record an hourly mean: 1850.nc, 1851.nc, ... 1999.nc. Our goal is to create a single file that contains the climatological monthly mean diurnal cycle for, say, March, which contains 31 days or 744 hourly records that commence on the 60th day of the 356-day year, with record index 1416. Traditionally, we might first compute the climatological monthly mean for hour of the day, then combine those into a full diurnal cycle: # Step 1: Create climatological hourly files hr00.nc..hr23.nc for hr in {0..23}; do hh=`printf "%02d" $hr` let srt=${hr}+1416 # Alternatively, use UDUnits by setting srt=1850-03-01T00:00:01 ncra -O -d time,${srt},,8760 -n 150,4,1 1850.nc hr${hh}.nc done # Step 2: Concatenate climatological hourly files into diurnal cycle ncrcata -O hr??.nc clm_drn.nc So far, nothing is unusual and this task can be performed by any NCO version. The ILV feature obsoletes the need for the shell loop used in Step 1 above. The new ILV option aggregates more than one input record at a time before performing arithmetic operations, and, with an additional switch, allows archival of those results in multiple-record output (MRO) files. This reduces the task of producing the climatological summertime mean to one step:

# Step 1: Archive all 150 March-mean diurnal cycles in one file
ncra -O -d time,1850-03-01T00:00:01,,8760,744,24 -n 150,4,1 1850.nc clm_drn.nc

The ILV option instructs ncra (or ncrcat) to process files in groups of 31 days (744 hourly records) interleaved with a 24-record cycle. The end result will have 150 sets of 24-timesteps representing the diurnal cycle of March in every year. A given timestep is the mean of the same hour of the day for every day in March of that year. <a name="multislab"></a>  <a name="multislabs"></a>  <a name="msa"></a>  <a name="mlt"></a>

MultislabsWrapped CoordinatesInterleaveShared features

Multislabs multislab multi-hyperslab MSA -d dim,[min][,[max][,[stride]]] --dimension dim,[min][,[max][,[stride]]] --dmn dim,[min][,[max][,[stride]]] --msa --msa_usr_rdr --msa_user_order Availability: ncbo, nces, ncecat, ncflint, ncks, ncpdq, ncra, ncrcat&linebreak; Short options: -d dim,[min][,[max][,[stride]]]&linebreak; Long options: --dimension dim,[min][,[max][,[stride]]],&linebreak; --dmn dim,[min][,[max][,[stride]]]&linebreak; --msa_usr_rdr, --msa_user_order&linebreak; A multislab is a union of one or more hyperslabs. One defines multislabs by chaining together hyperslab commands, i.e., -d options (Hyperslabs). Support for specifying a multi-hyperslab or multislab for any variable was first added to ncks in late 2002. The other operators received these capabilities in April 2008. Multi-slabbing is often referred to by the acronym MSA, which stands for &textldquo;Multi-Slabbing Algorithm&textrdquo;. As explained below, the user may additionally request that the multislabs be returned in the user-specified order, rather than the on-disk storage order. Although MSA user-ordering has been available in all operators since 2008, most users were unaware of it since the documentation (below, and in the man pages) was not written until July 2013. Multislabs overcome many restraints that limit simple hyperslabs. A single -d option can only specify a contiguous and/or a regularly spaced multi-dimensional data array. Multislabs are constructed from multiple -d options and may therefore have non-regularly spaced arrays. For example, suppose it is desired to operate on all longitudes from 10.0 to 20.0 and from 80.0 to 90.0 degrees. The combined range of longitudes is not selectable in a single hyperslab specfication of the form -d dimension,min,max or -d dimension,min,max,stride because its elements are irregularly spaced in coordinate space (and presumably in index space too). The multislab specification for obtaining these values is simply the union of the hyperslabs specifications that comprise the multislab, i.e.,

ncks -d lon,10.,20. -d lon,80.,90. in.nc out.nc
ncks -d lon,10.,15. -d lon,15.,20. -d lon,80.,90. in.nc out.nc

Any number of hyperslabs specifications may be chained together to specify the multislab. MSA creates an output dimension equal in size to the sum of the sizes of the multislabs. This can be used to extend and or pad coordinate grids. stride Users may specify redundant ranges of indices in a multislab, e.g.,

ncks -d lon,0,4 -d lon,2,9,2 in.nc out.nc

This command retrieves the first five longitudes, and then every other longitude value up to the tenth. Elements 0, 2, and 4 are specified by both hyperslab arguments (hence this is redundant) but will count only once if an arithmetic operation is being performed. This example uses index-based (not coordinate-based) multislabs because the stride option only supports index-based hyper-slabbing. Stride, for more information on the stride option. Multislabs are more efficient than the alternative of sequentially performing hyperslab operations and concatenating the results. I/O This is because NCO employs a novel multislab algorithm to minimize the number of I/O operations when retrieving irregularly spaced data from disk. The NCO multislab algorithm retrieves each element from disk once and only once. Thus users may take some shortcuts in specifying multislabs and the algorithm will obtain the intended values. Specifying redundant ranges is not encouraged, but may be useful on occasion and will not result in unintended consequences. Suppose the Q variable contains three dimensional arrays of distinct chemical constituents in no particular order. We are interested in the NOy species in a certain geographic range. Say that NO, NO2, and N2O5 are elements 0, 1, and 5 of the species dimension of Q. The multislab specification might look something like

ncks -d species,0,1 -d species,5 -d lon,0,4 -d lon,2,9,2 in.nc out.nc

Multislabs are powerful because they may be specified for every dimension at the same time. Thus multislabs obsolete the need to execute multiple ncks commands to gather the desired range of data. <a name="msa_usr_rdr"></a>  <a name="rotate_longitude"></a>  The MSA user-order switch --msa_usr_rdr (or --msa_user_order, both of which shorten to --msa) requests that the multislabs be output in the user-specified order from the command-line, rather than in the input-file on-disk storage order. This allows the user to perform complex data re-ordering in one operation that would otherwise require cumbersome steps of hyperslabbing, concatenating, and permuting. Consider the example of converting datasets stored with the longitude coordinate Lon ranging from [−180,180) to datasets that follow the [0,360) convention.

% ncks -H -v Lon in.nc
Lon[0]=-180
Lon[1]=-90
Lon[2]=0
Lon[3]=90

What is needed is a simple way to rotate longitudes. Although simple in theory, this task requires both mathematics to change the numerical value of the longitude coordinate, data hyperslabbing to split the input on-disk arrays at Greenwich, and data re-ordering within to stitch the western hemisphere onto the eastern hemisphere at the date-line. The --msa user-order switch overrides the default that data are output in the same order in which they are stored on-disk in the input file, and instead stores them in the same order as the multi-slabs are given to the command line. This default is intuitive and is not important in most uses. However, the MSA user-order switch allows users to meet their output order needs by specifying multi-slabs in a certain order. Compare the results of default ordering to user-ordering for longitude:

% ncks -O -H       -v Lon -d Lon,0.,180. -d Lon,-180.,-1.0 in.nc
Lon[0]=-180 
Lon[1]=-90 
Lon[2]=0 
Lon[3]=90 
% ncks -O -H --msa -v Lon -d Lon,0.,180. -d Lon,-180.,-1.0 in.nc
Lon[0]=0 
Lon[1]=90 
Lon[2]=-180 
Lon[3]=-90

The two multi-slabs are the same but they can be presented to screen, or to an output file, in either order. The second example shows how to place the western hemisphere after the eastern hemisphere, although they are stored in the opposite order in the input file. With this background, one sees that the following commands suffice to rotate the input file by 180 degrees longitude:

% ncks -O -v LatLon --msa -d Lon,0.,180. -d Lon,-180.,-1.0 in.nc out.nc
% ncap2 -O -s 'where(Lon < 0) Lon=Lon+360' out.nc out.nc
% ncks --trd -C -H -v LatLon ~/nco/data/in.nc
Lat[0]=-45 Lon[0]=-180 LatLon[0]=0 
Lat[0]=-45 Lon[1]=-90 LatLon[1]=1 
Lat[0]=-45 Lon[2]=0 LatLon[2]=2 
Lat[0]=-45 Lon[3]=90 LatLon[3]=3 
Lat[1]=45 Lon[0]=-180 LatLon[4]=4 
Lat[1]=45 Lon[1]=-90 LatLon[5]=5 
Lat[1]=45 Lon[2]=0 LatLon[6]=6 
Lat[1]=45 Lon[3]=90 LatLon[7]=7 
% ncks --trd -C -H -v LatLon ~/out.nc
Lat[0]=-45 Lon[0]=0 LatLon[0]=2 
Lat[0]=-45 Lon[1]=90 LatLon[1]=3 
Lat[0]=-45 Lon[2]=180 LatLon[2]=0 
Lat[0]=-45 Lon[3]=270 LatLon[3]=1 
Lat[1]=45 Lon[0]=0 LatLon[4]=6 
Lat[1]=45 Lon[1]=90 LatLon[5]=7 
Lat[1]=45 Lon[2]=180 LatLon[6]=4 
Lat[1]=45 Lon[3]=270 LatLon[7]=5

The analogous commands to rotate all fields in a global dataset by 180 degrees in the other direction, i.e., from [0,360) to [−180,180), are:

ncks -O --msa -d lon,181.,360. -d lon,0.,180.0 in.nc out.nc
ncap2 -O -s 'where(lon > 180) lon=lon-360' out.nc out.nc

There are other workable, valid methods to rotate data, yet none are simpler nor more efficient than utilizing MSA user-ordering. Some final comments on applying this algorithm: Be careful to specify hemispheres that do not overlap, e.g., by inadvertently specifying coordinate ranges that both include Greenwich or the date-line. Some users will find using index-based rather than coordinate-based hyperslabs makes this clearer. <a name="wrp"></a>

Wrapped CoordinatesAuxiliary CoordinatesMultislabsShared features

Wrapped Coordinates wrapped coordinates longitude -d dim,[min][,[max][,[stride]]] --dimension dim,[min][,[max][,[stride]]] --dmn dim,[min][,[max][,[stride]]] Availability: ncks&linebreak; Short options: -d dim,[min][,[max][,[stride]]]&linebreak; Long options: --dimension dim,[min][,[max][,[stride]]],&linebreak; --dmn dim,[min][,[max][,[stride]]]&linebreak; A wrapped coordinate is a coordinate whose values increase or decrease monotonically (nothing unusual so far), but which represents a dimension that ends where it begins (i.e., wraps around on itself). Longitude (i.e., degrees on a circle) is a familiar example of a wrapped coordinate. Longitude increases to the East of Greenwich, England, where it is defined to be zero. Halfway around the globe, the longitude is 180 degrees East (or West). Continuing eastward, longitude increases to 360 degrees East at Greenwich. The longitude values of most geophysical data are either in the range [0,360), or [−180,180). In either case, the Westernmost and Easternmost longitudes are numerically separated by 360 degrees, but represent contiguous regions on the globe. For example, the Saharan desert stretches from roughly 340 to 50 degrees East. Extracting the hyperslab of data representing the Sahara from a global dataset presents special problems when the global dataset is stored consecutively in longitude from 0 to 360 degrees. This is because the data for the Sahara will not be contiguous in the input-file but is expected by the user to be contiguous in the output-file. In this case, ncks must invoke special software routines to assemble the desired output hyperslab from multiple reads of the input-file. Assume the domain of the monotonically increasing longitude coordinate lon is

0 <

lon < 360. ncks will extract a hyperslab which crosses the Greenwich meridian simply by specifying the westernmost longitude as min and the easternmost longitude as max. The following commands extract a hyperslab containing the Saharan desert:

ncks -d lon,340.,50. in.nc out.nc
ncks -d lon,340.,50. -d lat,10.,35. in.nc out.nc

The first example selects data in the same longitude range as the Sahara. The second example further constrains the data to having the same latitude as the Sahara. The coordinate lon in the output-file, out.nc, will no longer be monotonic! The values of lon will be, e.g., 340, 350, 0, 10, 20, 30, 40, 50. This can have serious implications should you run out.nc through another operation which expects the lon coordinate to be monotonically increasing. Fortunately, the chances of this happening are slim, since lon has already been hyperslabbed, there should be no reason to hyperslab lon again. Should you need to hyperslab lon again, be sure to give dimensional indices as the hyperslab arguments, rather than coordinate values (Hyperslabs). <a name="aux"></a>  <a name="auxiliary"></a>  <a name="-X"></a>  <a name="std_nm"></a>  <a name="standard_name"></a>

Auxiliary CoordinatesGrid GenerationWrapped CoordinatesShared features

Auxiliary Coordinates -X --auxiliary standard_name coordinates auxiliary coordinates CF conventions -X lon_min,lon_max,lat_min,lat_max --auxiliary lon_min,lon_max,lat_min,lat_max Availability: ncbo, nces, ncecat, ncflint, ncks, ncpdq, ncra, ncrcat&linebreak; Short options: -X lon_min,lon_max,lat_min,lat_max&linebreak; Long options: --auxiliary lon_min,lon_max,lat_min,lat_max&linebreak; Utilize auxiliary coordinates specified in values of the coordinate variable&textrsquo;s standard_name attributes, if any, when interpreting hyperslab and multi-slab options. Also --auxiliary. This switch supports hyperslabbing cell-based grids (aka unstructured grids) over coordinate ranges. When these grids are stored as 1D-arrays of cell data, this feature is helpful at hyperslabbing and/or performing arithmetic on selected geographic regions. This feature cannot be used to select regions of 2D grids (instead use the ncap2 where statement for such grids Where statement). This feature works on datasets that associate coordinate variables to grid-mappings using the CF-convention (CF Conventions) coordinates and standard_name attributes described http://cfconventions.org/cf-conventions/cf-conventions.html#coordinate-systemhere. Currently, NCO understands auxiliary coordinate variables pointed to by the standard_name attributes for latitude and longitude. Cells that contain a value within the user-specified West-East-South-North (aka WESN) bounding box [lon_min,lon_max,lat_min,lat_max] are included in the output hyperslab. The sides of the WESN) bounding box must be specified in degrees (not radians). The specified coordinates must be within the valid data range. This includes boxes that wrap the origin of the longitude coordinate. For example, if the longitude coordinate is stored in [0,360], then a bounding box that straddles the Greenwich meridian in Africa would be specified as, e.g.,

[350,10,-20,20]

, not as

[350,370,-20,20]

. unstructured grid cell-based grid A cell-based or unstructured grid collapses the horizontal spatial information (latitude and longitude) and stores it along a one-dimensional coordinate that has a one-to-one mapping to both latitude and longitude coordinates. Rectangular (in longitude and latitude) horizontal hyperslabs cannot be selected using the typical procedure (Hyperslabs) of separately specifying -d arguments for longitude and latitude. Instead, when the -X is used, NCO learns the names of the latitude and longitude coordinates by searching the standard_name attribute of all variables until it finds the two variables whose standard_name&textrsquo;s are &textldquo;latitude&textrdquo; and &textldquo;longitude&textrdquo;, respectively. This standard_name attribute for latitude and longitude coordinates follows the CF-convention (CF Conventions). Putting it all together, consider a variable gds_3dvar output from simulations on a cell-based geodesic grid. Although the variable contains three dimensions of data (time, latitude, and longitude), it is stored in the netCDF file with only two dimensions, time and gds_crd.

% ncks -m -C -v gds_3dvar ~/nco/data/in.nc
gds_3dvar: type NC_FLOAT, 2 dimensions, 4 attributes, chunked? no, \
 compressed? no, packed? no, ID = 41
gds_3dvar RAM size is 10*8*sizeof(NC_FLOAT) = 80*4 = 320 bytes
gds_3dvar dimension 0: time, size = 10 NC_DOUBLE, dim. ID = 20 \ 
 (CRD)(REC)
gds_3dvar dimension 1: gds_crd, size = 8 NC_FLOAT, dim. ID = 17 (CRD)
gds_3dvar attribute 0: long_name, size = 17 NC_CHAR, value = \ 
 Geodesic variable
gds_3dvar attribute 1: units, size = 5 NC_CHAR, value = meter
gds_3dvar attribute 2: coordinates, size = 15 NC_CHAR, value = \
 lat_gds lon_gds
gds_3dvar attribute 3: purpose, size = 64 NC_CHAR, value = \ 
 Test auxiliary coordinates like those that define geodesic grids

The coordinates attribute lists the names of the latitude and longitude coordinates, lat_gds and lon_gds, respectively. The coordinates attribute is recommended though optional. With it, the user can immediately identify which variables contain the latitude and longitude coordinates. Without a coordinates attribute it would be unclear at first glance whether a variable resides on a cell-based grid. In this example, time is a normal record dimension and gds_crd is the cell-based dimension. The cell-based grid file must contain two variables whose standard_name attributes are &textldquo;latitude&textrdquo;, and &textldquo;longitude&textrdquo;:

% ncks -m -C -v lat_gds,lon_gds ~/nco/data/in.nc
lat_gds: type NC_DOUBLE, 1 dimensions, 4 attributes, \
 chunked? no, compressed? no, packed? no, ID = 37
lat_gds RAM size is 8*sizeof(NC_DOUBLE) = 8*8 = 64 bytes
lat_gds dimension 0: gds_crd, size = 8 NC_FLOAT, dim. ID = 17 (CRD)
lat_gds attribute 0: long_name, size = 8 NC_CHAR, value = Latitude
lat_gds attribute 1: standard_name, size = 8 NC_CHAR, value = latitude
lat_gds attribute 2: units, size = 6 NC_CHAR, value = degree
lat_gds attribute 3: purpose, size = 62 NC_CHAR, value = \ 
 1-D latitude coordinate referred to by geodesic grid variables

lon_gds: type NC_DOUBLE, 1 dimensions, 4 attributes, \
 chunked? no, compressed? no, packed? no, ID = 38
lon_gds RAM size is 8*sizeof(NC_DOUBLE) = 8*8 = 64 bytes
lon_gds dimension 0: gds_crd, size = 8 NC_FLOAT, dim. ID = 17 (CRD)
lon_gds attribute 0: long_name, size = 9 NC_CHAR, value = Longitude
lon_gds attribute 1: standard_name, size = 9 NC_CHAR, value = longitude
lon_gds attribute 2: units, size = 6 NC_CHAR, value = degree
lon_gds attribute 3: purpose, size = 63 NC_CHAR, value = \
 1-D longitude coordinate referred to by geodesic grid variables

In this example lat_gds and lon_gds represent the latitude or longitude, respectively, of cell-based variables. These coordinates (must) have the same single dimension (gds_crd, in this case) as the cell-based variables. And the coordinates must be one-dimensional&textmdash;multidimensional coordinates will not work. This infrastructure allows NCO to identify, interpret, and process (i.e., hyperslab) the variables on cell-based grids as easily as it works with regular grids. To time-average all the values between zero and 180 degrees longitude and between plus and minus 30 degress latitude, we use

ncra -O -X 0.,180.,-30.,30. -v gds_3dvar in.nc out.nc

NCO accepts multiple -X arguments for cell-based grid multi-slabs, just as it accepts multiple -d arguments for multi-slabs of regular coordinates.

ncra -O -X 0.,180.,-30.,30. -X 270.,315.,45.,90. in.nc out.nc

The arguments to -X are always interpreted as floating-point numbers, i.e., as coordinate values rather than dimension indices so that these two commands produce identical results

ncra -X 0.,180.,-30.,30. in.nc out.nc
ncra -X 0,180,-30,30 in.nc out.nc

By contrast, arguments to -d require decimal places to be recognized as coordinates not indices (Hyperslabs). We recommend always using decimal points with -X arguments to avoid confusion. <a name="scrip"></a>  <a name="grid"></a>  <a name="grd"></a>

Grid GenerationRegriddingAuxiliary CoordinatesShared features

Grid Generation Gaussian grid Equi-Angular grid FV grid FV grid CAM-FV grid grid, Fixed grid, Offset grid, FV grid, CAM-FV grid, Equi-Angular grid, Gaussian gridfile --map ESMF SCRIP --rgr key=val Availability: ncks&linebreak; Short options: None&linebreak; Long options: --rgr key=val (multiple invocations allowed)&linebreak; As of NCO version 4.5.2 (August, 2015), ncks generates accurate and complete SCRIP-format gridfiles for select grid types, including uniform, capped and Gaussian rectangular, latitude/longitude grids, global or regional. The grids are stored in an external grid-file. All options pertinent to the grid geometry and metadata are passed to NCO via key-value pairs prefixed by the --rgr option, or its synonym, --regridding. indicator option multi-arguments The option --rgr (and its long option equivalents such as --regridding) indicates the argument syntax will be key=val. As such, --rgr and its synonyms are indicator options that accept arguments supplied one-by-one like --rgr key1=val1 --rgr key2=val2, or aggregated together in multi-argument format like --rgr key1=val1#key2=val2 (Multi-arguments). The text strings that describe the grid and name the file are important aids to convey the grid geometry to other users. These arguments, and their corresponding keys, are the grid title (grd_ttl), and grid filename (grid), respectively. The numbers of latitudes (lat_nbr) and longitudes (lon_nbr) are independent, and together determine the grid storage size. These four options should be considered mandatory, although NCO provides defaults for any arguments omitted. The remaining arguments depend on the whether the grid is global or regional. For global grids, one should specify only two more arguments, the latitude (lat_typ) and longitude (lon_typ) grid-types. These types are chosen as described below from a small selection of options that together define the most common rectangular global grids. For regional grids, one must specify the bounding box, i.e., the edges of the rectangular grid on the North (lat_nrt), South (lat_sth), East (lat_est), and West (lat_nrt) sides. Specifying a bounding box for global grids is redundant and will cause an error to ensure the user intends a global grid. NCO assumes that regional grids are uniform, though it will attempt to produce regional grids of other types if the user specifies other latitude (lat_typ) and longitude (lon_typ) grid-types, e.g., Gaussian or Cap. Edges of a regional bounding box may be specified individually, or in the single-argument forms. The full description of grid-generation arguments, and their corresponding keys, is: grd_ttl --rgr grd_ttl=grd_ttl Grid Title: grd_ttl It is surprisingly difficult to discern the geometric configuration of a grid from the coordinates of a SCRIP-format gridfile. A human-readable grid description should be placed in grd_ttl. Examples include &textldquo;CAM-FV scalar grid 129x256&textrdquo; and &textldquo;T42 Gaussian grid&textrdquo;. scrip_grid --rgr grid=scrip_grid --rgr scrip=scrip_grid Grid File: scrip_grid The grid-generation API was bolted-on to NCO and contains some temporary kludges. For example, the output grid filename is distinct from the output filename of the host ncks command. Specify the output gridfile name scrip_grid with keywords grid or scrip, e.g., --rgr grid=scrip_grid or --rgr scrip=t42_SCRIP.20150901.nc. It is conventional to include a datestamp in the gridfile name. This helps users identify up-to-date and out-of-date grids. Any valid netCDF file may be named as the source (e.g., in.nc). It will not be altered. The destination file (e.g., foo.nc) will be overwritten. Its contents are immaterial. lat_typ lon_typ --rgr lon_typ=lon_typ --rgr lat_typ=lat_typ Grid Types: lat_typ, lon_typ The keys that hold the longitude and latitude gridtypes (which are, by the way, independent of eachother) are lon_typ and lat_typ. The lat_typ options for global grids are uni for Uniform, cap (or fv) for CapThe term FV confusing because it is correct to call any Finite Volume grid (including arbitrary polygons) an FV grid. However, an FV grid has also been used for many years to described the particular type of rectangular grid with caps at the poles used to discretize global model grids for use with the Lin-Rood dynamical core. To reduce confusion, we use &textldquo;Cap grid&textrdquo; to refer to the latter and reserv FV as a straightforward acronym for Finite Volume., and gss for Gaussian. These values are all case-independent, so Gss and gss both work. As of version 4.7.7 (September, 2018), NCO generates perfectly symmetric interface latitudes for Gaussian grids. Previously the interface latitude generation mechanism could accumulate small rounding errors (~1.0e-14). Now symmetry properties are used to ensure perfect symmetry. All other Gaussian grids we have seen compute interfaces as the arithmetic mean of the adjacent Gaussian latitudes, which is patently wrong. To our knowledge NCO is the only map software that generates accurate interface latitudes for a Gaussian grid. We use a Newton-Raphson iteration technique to identify the interface latitudes that enclose the area indicated by the Gaussian weight. As its name suggests, the latitudes in a Uniform-latitude grid are uniformly spaced A Uniform grid in latitude could be called &textldquo;equi-angular&textrdquo; in latitude, but NCO reserves the term Equi-angular or &textldquo;eqa&textrdquo; for grids that have the same uniform spacing in both latitude and longitude, e.g., 1°x1° or 2°x2°. NCO reserves the term Regular to refer to grids that are monotonic and rectangular grids. Confusingly, the angular spacing in a Regular grid need not be uniform, it could be irregular, such as in a Gaussian grid. The term Regular is not too useful in grid-generation, because so many other parameters (spacing, centering) are necessary to disambiguate it.. The Uniform-latitude grid may have any number of latitudes. NCO can only generate longitude grids (below) that are uniformly spaced, so the Uniform-latitude grids we describe are also uniform in the 2D sense. Uniform grids are intuitive, easy to visualize, and simple to program. Hence their popularity in data exchange, visualization, and archives. Moreover, regional grids (unless they include the poles), are free of polar singularities, and thus are well-suited to storage on Uniform grids. Theoretically, a Uniform-latitude grid could have non-uniform longitudes, but NCO currently does not implement non-uniform longitude grids. Their mathematical properties (convergence and excessive resolution at the poles, which can appear as singularities) make Uniform grids fraught for use in global models. One purpose Uniform grids serve in modeling is as &textldquo;offset&textrdquo; or &textldquo;staggered&textrdquo; grids, meaning grids whose centers are the interfaces of another grid. The Finite-Volume (FV) method is often used to represent and solve the equations of motion in climate-related fields. Many FV solutions (including the popular Lin-Rood method as used in the CESM CAM-FV atmospheric model) evaluate scalar (i.e., non-vector) fields (e.g., temperature, water vapor) at gridcell centers of what is therefore called the scalar grid. FV methods (like Lin-Rood) that employ an Arakawa C-grid or D-grid formulation define velocities on the edges of the scalar grid. This CAM-FV velocity grid is therefore &textldquo;staggered&textrdquo; or &textldquo;offset&textrdquo; from the CAM-FV scalar grid by one-half gridcell. The CAM-FV scalar latitude grid has gridpoints (the &textldquo;caps&textrdquo;) centered on each pole to avoid singularities. The offset of a Cap-grid is a Uniform-grid, so the Uniform grid is often called an FV-&textrdquo;offset&textrdquo; or &textldquo;staggered&textrdquo; grid. Hence an NCO Uniform grid is equivalent to an NCL &textldquo;Fixed Offset&textrdquo; grid. For example, a 128x256 Uniform grid is the offset or staggered version of a 129x256 Cap grid (aka FV-grid). Referring the saucer-like cap-points at the poles, NCO uses the term &textldquo;Cap grid&textrdquo; to describe the latitude portion of the FV-scalar grid as used by the CAM-FV Lin-Rood dynamics formulation. NCO accepts the shorthand FV, and the more descriptive &textldquo;Yarmulke&textrdquo;, as synonyms for Cap. A Cap-latitude grid differs from a Uniform-latitude grid in many ways: Most importantly, Cap grids are 2D-representations of numerical grids with cap-midpoints instead of zonal-teeth convergence at the poles. The rectangular 2D-representation of each cap contains gridcells shaped like sharp teeth that converge at the poles similar to the Uniform grid, but the Cap gridcells are meant to be aggregated into a single cell centered at the pole in a dynamical transport algorithm. In other words, the polar teeth are a convenient way to encode a non-rectangular grid in memory into a rectangular array on disk. Hence Cap grids have the unusual property that the poles are labeled as being both the centers and the outer interfaces of all polar gridcells. Second, Cap grids are uniform in angle except at the poles, where the latitudes span half the meridional range of the rest of the gridcells. Even though in the host dynamical model the Cap grid polar points are melded into caps uniform (in angle) with the rest of the grid, the disk representation on disk is not uniform. Nevertheless, some call the Cap grid a uniform-angle grid because the information contained at the poles is aggregated in memory to span twice the range of a single polar gridcell (which has half the normal width). NCL uses the term &textldquo;Fixed grid&textrdquo; for a Cap grid. The &textldquo;Fixed&textrdquo; terminology seems broken. <a name="gss"></a>  Finally, Gaussian grids are the Cartesian representation of global spectral transform models. Gaussian grids typically have an even number of latitudes and so do not have points at the poles. All three latitude grid-type supported by NCO (Uniform, Cap, and Gaussian) are Regular grids in that they are monotonic. The lon_typ options for global grids are grn_ctr and 180_ctr for the first gridcell centered at Greenwich or 180 degrees, respecitvely. And grn_wst and 180_wst for Greenwich or 180 degress lying on the western edge of the first gridcell. Many global models use the grn_ctr longitude grid as their &textldquo;scalar grid&textrdquo; (where, e.g., temperature, humidity, and other scalars are defined). The &textldquo;staggered&textrdquo; or &textldquo;offset&textrdquo; grid (where often the dynamics variables are defined) then must have the grn_wst longitude convention. That way the centers of the scalar grid are the vertices of the offset grid, and visa versa. lat_nbr lon_nbr --rgr latlon=lat_nbr,lon_nbr --rgr lon_nbr=lon_nbr --rgr lat_nbr=lat_nbr Grid Resolution: lat_nbr, lon_nbr The number of gridcells in the horizontal spatial dimensions are lat_nbr and lon_nbr, respectively. There are no restrictions on lon_nbr for any gridtype. Latitude grids do place some restrictions on lat_nbr (see above). As of NCO version 4.5.3, released in October, 2015, the --rgr latlon=lat_nbr,lon_nbr switch may be used to simultaneously specify both latitude and longitude, e.g., --rgr latlon=180,360. <a name="lat_drc"></a>  <a name="lat_drc"></a>  <a name="lat_drc"></a>  n2s s2n lat_drc --rgr lat_drc=lat_drc Latitude Direction: lat_drc The lat_drc option is specifies whether latitudes monotonically increase or decrease in rectangular grids. The two possible values are s2n for grids that begin with the most southerly latitude and end with the most northerly, and n2s for grids that begin with the most northerly latitude and end with the most southerly. By default NCO creates grids whose latitudes run south-to-north. Hence this option is only necessary to create a grid whose latitudes run north-to-south. lat_nrt lat_sth lat_est lat_wst wesn snwe --rgr wesn=lon_wst,lon_est,lat_sth,lon_nrt --rgr snwe=lat_sth,lat_nrt,lon_wst,lon_est --rgr lat_nrt=lat_nrt --rgr lat_nbr=lat_nbr Grid Edges: lon_wst, lon_est, lat_sth, lat_nrt The outer edges of a regional rectangular grid are specified by the North (lat_nrt), South (lat_sth), East (lat_est), and West (lat_nrt) sides. Latitudes and longigudes must be specified in degrees (not radians). Latitude edges must be between -90 and 90. Longitude edges may be positive or negative and separated by no more than 360 degrees. The edges may be specified individually with four arguments, consecutively separated by the multi-argument delimiter (# by default), or together in a short list to the pre-ordered options wesn or snwe. These three specifications are equivalent: ncks ... --rgr lat_sth=30.0 --rgr lat_nrt=70.0 --rgr lon_wst=-120.0 --rgr lon_est=-90.0 ... ncks ... --rgr lat_sth=30.0#lat_nrt=70.0#lon_wst=-120.0#lon_est=-90.0 ... ncks ... --rgr snwe=30.0,70.0,-120.0,-90.0 ...

The first example above supplies the bounding box with four key=val pairs. The second example above supplies the bounding box with a single option in multi-argument format (Multi-arguments). The third example uses a convenience switch introduced to reduce typing. <a name="grd_cmd"></a>  <a name="xmp_grd"></a>  Generating common grids: ECMWF IFS grid ECMWF ERA5 grid NCEP2 grid NASA CMG grid NASA MERRA2 grid CAM-FV grid Equi-angular grid # Access to grid-generation was through ncks, not ncremap, until version 4.7.6 # 180x360 (1x1 degree) Equi-Angular grid, first longitude centered at Greenwich # This is NOT the CMIP6 1x1 grid ncks --rgr ttl='Equi-Angular grid 180x360'#latlon=180,360#lat_typ=uni#lon_typ=grn_ctr \ --rgr scrip=${DATA}/grids/180x360_SCRIP.20150901.nc \ ~zender/nco/data/in.nc ~/foo.nc # Version 4.7.6+ (August, 2018), supports the preferred, more concise, ncremap syntax: # This is NOT the CMIP6 1x1 grid ncremap -G ttl='Equi-Angular grid 180x360'#latlon=180,360#lat_typ=uni#lon_typ=grn_ctr \ -g ${DATA}/grids/180x360_SCRIP.20180901.nc # 180x360 (1x1 degree) Equi-Angular grid, first longitude west edge at Greenwich # This IS the CMIP6 1x1 grid ncremap -G ttl='Equi-Angular grid 180x360'#latlon=180,360#lat_typ=uni#lon_typ=grn_wst \ -g ${DATA}/grids/180x360wst_SCRIP.20180301.nc # 129x256 CAM-FV grid, first longitude centered at Greenwich ncremap -G ttl='CAM-FV scalar grid 129x256'#latlon=129,256#lat_typ=fv#lon_typ=grn_ctr \ -g ${DATA}/grids/129x256_SCRIP.20150901.nc # 192x288 CAM-FV grid, first longitude centered at Greenwich ncremap -G ttl='CAM-FV scalar grid 192x288'#latlon=192,288#lat_typ=fv#lon_typ=grn_ctr \ -g ${DATA}/grids/192x288_SCRIP.20160301.nc # 361x576 NASA MERRA2 FV grid, first longitude centered at DateLine ncremap -G ttl='NASA MERRA2 Cap grid 361x576'#latlon=361,576#lat_typ=cap#lon_typ=180_ctr \ -g ${DATA}/grids/merra2_361x576.20201001.nc # 1441x2880 CAM-FV grid, first longitude centered at Greenwich ncremap -G ttl='CAM-FV scalar grid 1441x2880'#latlon=1441,2880#lat_typ=fv#lon_typ=grn_ctr \ -g ${DATA}/grids/1441x2880_SCRIP.20170901.nc # 1440x2880 ELM/MOSART grid, first longitude west edge at DateLine ncremap -7 -L 1 \ -G ttl='ELM/MOSART 1440x2880 one-eighth degree uniform grid (r0125)'#latlon=1440,2880#lat_typ=uni#lon_typ=180_wst \ -g ${DATA}/grids/r0125_1440x2880.20210401.nc # 91x180 CAM-FV grid, first longitude centered at Greenwich (2 degree grid) ncremap -G ttl='CAM-FV scalar grid 91x180'#latlon=91,180#lat_typ=fv#lon_typ=grn_ctr \ -g ${DATA}/grids/91x180_SCRIP.20170401.nc # 25x48 CAM-FV grid, first longitude centered at Greenwich (7.5 degree grid) ncremap -G ttl='CAM-FV scalar grid 25x48'#latlon=25,48#lat_typ=fv#lon_typ=grn_ctr \ -g ${DATA}/grids/25x48_SCRIP.20170401.nc # 128x256 Equi-Angular grid, Greenwich west edge of first longitude # CAM-FV offset grid for 129x256 CAM-FV scalar grid above ncremap -G ttl='Equi-Angular grid 128x256'#latlon=128,256#lat_typ=uni#lon_typ=grn_wst \ -g ${DATA}/grids/128x256_SCRIP.20150901.nc # T42 Gaussian grid, first longitude centered at Greenwich ncremap -G ttl='T42 Gaussian grid'#latlon=64,128#lat_typ=gss#lon_typ=grn_ctr \ -g ${DATA}/grids/t42_SCRIP.20180901.nc # T62 Gaussian grid, first longitude centered at Greenwich, NCEP2 T62 Gaussian grid ncremap -G ttl='NCEP2 T62 Gaussian grid'#latlon=94,192#lat_typ=gss#lon_typ=grn_ctr#lat_drc=n2s \ -g ${DATA}/grids/ncep2_t62_SCRIP.20191001.nc # F256 Full Gaussian grid, first longitude centered at Greenwich ncremap -7 -L 1 \ -G ttl='ECMWF IFS F256 Full Gaussian grid 512x1024'#latlon=512,1024#lat_typ=gss#lon_typ=grn_ctr#lat_drc=n2s \ -g ${DATA}/grids/f256_scrip.20201001.nc # 513x1024 FV grid, first longitude centered at Greenwich ncremap -7 -L 1 \ -G ttl='FV scalar grid 513x1024'#latlon=513,1024#lat_typ=fv#lon_typ=grn_ctr \ -g ${DATA}/grids/513x1024_SCRIP.20201001.nc # 1025x2048 FV grid, first longitude centered at Greenwich ncremap -7 -L 1 \ -G ttl='FV scalar grid 1025x2048'#latlon=1025,2048#lat_typ=fv#lon_typ=grn_ctr \ -g ${DATA}/grids/1025x2048_SCRIP.20201001.nc # F640 Full Gaussian grid, first longitude centered at Greenwich ncremap -7 -L 1 \ -G ttl='ECMWF IFS F640 Full Gaussian grid 1280x2560'#latlon=1280,2560#lat_typ=gss#lon_typ=grn_ctr#lat_drc=n2s \ -g ${DATA}/grids/f640_scrip.20190601.nc # NASA Climate Modeling Grid (CMG) 3600x7200 (0.05x0.05 degree) Equi-Angular grid # Date-line west edge of first longitude, east edge of last longitude # Write to compressed netCDF4-classic file to reduce filesize ~140x from 2.2 GB to 16 MB ncremap -7 -L 1 \ -G ttl='Equi-Angular grid 3600x7200 (NASA CMG)'#latlon=3600,7200#lat_typ=uni#lon_typ=180_wst \ -g ${DATA}/grids/3600x7200_SCRIP.20160301.nc # DOE E3SM/ACME High Resolution Topography (1 x 1 km grid) for Elevation Classes # Write to compressed netCDF4-classic file to reduce filesize from ~85 GB to 607 MB ncremap -7 -L 1 \ -G ttl='Global latxlon = 18000x36000 ~1 x 1 km'#latlon=18000,36000#lat_typ=uni#lon_typ=grn_ctr \ -g ${DATA}/grids/grd_18000x36000_SCRIP.nc # 1x1 degree Equi-Angular Regional grid over Greenland, centered longitudes ncremap -G ttl='Equi-Angular Greenland 1x1 degree grid'#latlon=30,90#snwe=55.0,85.0,-90.0,0.0#lat_typ=uni#lon_typ=grn_ctr \ -g ${HOME}/greenland_1x1.nc # 721x1440 ECMWF ERA5 resolution in north-to-south order (ERA5/CAMS default order) ncremap -7 --dfl_lvl=1 -G ttl='Cap/FV ECMWF ERA5 grid 0.25x0.25 degree, dimensions 721x1440, cell centers on Poles/Equator (in north-to-south order) and Prime Meridian/Date Line'#latlon=721,1440#lat_drc=n2s#lat_typ=cap#lon_typ=grn_ctr \ -g ${DATA}/grids/era5_n2s_721x1440.nc # 721x1440 ECMWF ERA5 resolution in south-to-north order (E3SM/ELM-offline forcing order) ncremap -7 --dfl_lvl=1 -G ttl='Cap/FV ECMWF ERA5 grid 0.25x0.25 degree, dimensions 721x1440, cell centers on Poles/Equator (in south-to-north order) and Prime Meridian/Date Line'#latlon=721,1440#lat_drc=s2n#lat_typ=cap#lon_typ=grn_ctr \ -g ${DATA}/grids/era5_s2n_721x1440.nc # 360x720 CRUNCEP (E3SM/ELM-offline forcing grid) (NB: CRUNCEP starts at Greenwich, is not r05) ncremap -7 --dfl_lvl=1 -G ttl='CRUNCEP Equi-Angular 0.5x0.5 degree uniform grid, dimensions 360x720, cell edges on Poles/Equator and Prime Meridian/Date Line'#latlon=360,720#lat_typ=uni#lon_typ=Grn_wst \ -g ${DATA}/grids/cruncep_360x720.nc # 360x720 ELM/MOSART grid, first longitude west edge at DateLine (NB: starts at Dateline, "r" stands for "river" grid) ncremap -7 --dfl_lvl=1 -G ttl='Equi-Angular 0.5x0.5 degree uniform grid (r05), dimensions 360x720, cell edges on Poles/Equator and Date Line/Prime Meridian'#latlon=360,720#lat_typ=uni#lon_typ=180_wst \ -g ${DATA}/grids/r05_360x720.nc # 720x1440 ELM/MOSART grid, first longitude west edge at DateLine (NB: starts at Dateline, "r" stands for "river" grid) ncremap -7 --dfl_lvl=1 -G ttl='Equi-Angular 0.25x0.25 degree uniform grid (r025), dimensions 720x1440, cell edges on Poles/Equator and Date Line/Prime Meridian'#latlon=720,1440#lat_typ=uni#lon_typ=180_wst \ -g ${DATA}/grids/r025_720x1440.nc # 105x401 Greenland ERA5 ncremap -G ttl='Equi-Angular Greenland 0.25x0.25 degree ERA5 north-to-south grid'#latlon=105,401#snwe=58.875,85.125,-87.125,13.125#lat_typ=uni#lat_drc=n2s#lon_typ=grn_ctr \ -g ${DATA}/grids/greenland_0.25x0.25_era5.nc # Greenland r025 with SNWE = 59,84,-73,-11 (in round numbers) with RACMO ice mask ncremap -G ttl='Equi-Angular Greenland 0.25x0.25 degree r025 south-to-north grid'#latlon=100,250#snwe=58.875,83.875,-73.25,-10.75#lat_typ=uni#lat_drc=s2n#lon_typ=grn_ctr \ -g ${DATA}/grids/greenland_r025_100x250.nc # NASA Climate Modeling Grid (CMG) 3600x7200 (0.05x0.05 degree, 3'x3') Equi-Angular grid # With land mask derived mainly from GLOBE 30" topography and anywhere Gardner 30" land ice data is valid # Date-line west edge of first longitude, east edge of last longitude # Write to compressed netCDF4-classic file to reduce filesize ~140x from 2.2 GB to 16 MB ncremap -7 -L 1 \ -G ttl='Equi-Angular grid 3-minute=0.05 degree resolution = 3600x7200, NASA CMG boundaries, with land mask derived mainly from GLOBE 30" topography and anywhere Gardner 30" land ice data is valid'#latlon=3600,7200#lat_typ=uni#lon_typ=180_wst \ -g ${DATA}/grids/r005_3600x7200_globe_gardner_landmask.20210501.nc <a name="nfr"></a>  <a name="infer"></a>  <a name="ugrid"></a>  infer --rgr nfr --rgr infer Often researchers face the problem not of generating a known, idealized grid but of understanding an unknown, possibly irregular or curvilinear grid underlying a dataset produced elsewhere. NCO will infer the grid of a datafile by examining its coordinates (and boundaries, if available), reformat that information as necessary to diagnose gridcell areas, and output the results in SCRIP format. As of NCO version 4.5.3, released in October, 2015, the --rgr infer flag activates the machinery to infer the grid rather than construct the grid from other user-specified switches. To infer the grid properties, NCO interrogates input-file for horizontal coordinate information, such as the presence of dimension names rooted in latitude/longitude-naming traditions and conventions. Once NCO identifies the likely horizontal dimensions it looks for horizontal coordinates and bounds. If bounds are not found, NCO assumes the underlying grid comprises quadrilateral cells whose edges are midway between cell centers, for both rectilinear and curvilinear grids. # Infer AIRS swath grid from input, write it to grd_scrip.nc ncks --rgr infer --rgr scrip=${DATA}/sld/rgr/grd_scrip.nc \ ${DATA}/sld/raw/AIRS.2014.10.01.202.L2.TSurfStd.Regrid010.1DLatLon.nc ~/foo.nc When inferring grids, the grid file (grd_scrip.nc) is written in SCRIP format, the input file (AIRS...nc) is read, and the output file (foo.nc) is overwritten (its contents are immaterial). UGRID --rgr ugrid As of NCO version 4.6.6, released in April, 2017, inferred 2D rectangular grids may also be written in UGRID-format (defined http://ugrid-conventions.github.io/ugrid-conventionshere). Request a UGRID mesh with the option --rgr ugrid=fl_ugrid. Currently both UGRID and SCRIP grids must be requested in order to produce the UGRID output, e.g., ncks --rgr infer --rgr ugrid=${HOME}/grd_ugrid.nc \ --rgr scrip=${HOME}/grd_scrip.nc ~/skl_180x360.nc ~/foo.nc The SCRIP gridfile and UGRID meshfile metadata produced for the equiangular 1-by-1 degree global grid are: zender@aerosol:~$ ncks -m ~/grd_scrip.nc netcdf grd_scrip { dimensions: grid_corners = 4 ; grid_rank = 2 ; grid_size = 64800 ; variables: double grid_area(grid_size) ; grid_area:units = "steradian" ; double grid_center_lat(grid_size) ; grid_center_lat:units = "degrees" ; double grid_center_lon(grid_size) ; grid_center_lon:units = "degrees" ; double grid_corner_lat(grid_size,grid_corners) ; grid_corner_lat:units = "degrees" ; double grid_corner_lon(grid_size,grid_corners) ; grid_corner_lon:units = "degrees" ; int grid_dims(grid_rank) ; int grid_imask(grid_size) ; } // group / zender@aerosol:~$ ncks -m ~/grd_ugrid.nc netcdf grd_ugrid { dimensions: maxNodesPerFace = 4 ; nEdges = 129240 ; nFaces = 64800 ; nNodes = 64442 ; two = 2 ; variables: int mesh ; mesh:cf_role = "mesh_topology" ; mesh:standard_name = "mesh_topology" ; mesh:long_name = "Topology data" ; mesh:topology_dimension = 2 ; mesh:node_coordinates = "mesh_node_x mesh_node_y" ; mesh:face_node_connectivity = "mesh_face_nodes" ; mesh:face_coordinates = "mesh_face_x mesh_face_y" ; mesh:face_dimension = "nFaces" ; mesh:edge_node_connectivity = "mesh_edge_nodes" ; mesh:edge_coordinates = "mesh_edge_x mesh_edge_y" ; mesh:edge_dimension = "nEdges" ; int mesh_edge_nodes(nEdges,two) ; mesh_edge_nodes:cf_role = "edge_node_connectivity" ; mesh_edge_nodes:long_name = "Maps every edge to the two nodes that it connects" ; mesh_edge_nodes:start_index = 0 ; double mesh_edge_x(nEdges) ; mesh_edge_x:standard_name = "longitude" ; mesh_edge_x:long_name = "Characteristic longitude of 2D mesh face" ; mesh_edge_x:units = "degrees_east" ; double mesh_edge_y(nEdges) ; mesh_edge_y:standard_name = "latitude" ; mesh_edge_y:long_name = "Characteristic latitude of 2D mesh face" ; mesh_edge_y:units = "degrees_north" ; int mesh_face_nodes(nFaces,maxNodesPerFace) ; mesh_face_nodes:cf_role = "face_node_connectivity" ; mesh_face_nodes:long_name = "Maps every face to its corner nodes" ; mesh_face_nodes:start_index = 0 ; mesh_face_nodes:_FillValue = -2147483648 ; double mesh_face_x(nFaces) ; mesh_face_x:standard_name = "longitude" ; mesh_face_x:long_name = "Characteristic longitude of 2D mesh edge" ; mesh_face_x:units = "degrees_east" ; double mesh_face_y(nFaces) ; mesh_face_y:standard_name = "latitude" ; mesh_face_y:long_name = "Characteristic latitude of 2D mesh edge" ; mesh_face_y:units = "degrees_north" ; double mesh_node_x(nNodes) ; mesh_node_x:standard_name = "longitude" ; mesh_node_x:long_name = "Longitude of mesh nodes" ; mesh_node_x:units = "degrees_east" ; double mesh_node_y(nNodes) ; mesh_node_y:standard_name = "latitude" ; mesh_node_y:long_name = "Latitude of mesh nodes" ; mesh_node_y:units = "degrees_north" ; } // group / <a name="skl"></a>  <a name="skeleton"></a>  skeleton --rgr skl Another task that arises in regridding is characterizing new grids. In such cases it can be helpful to have a &textldquo;skeleton&textrdquo; version of a dataset on the grid, so that grid center and interfaces locations can be assessed, continental outlines can be examined, or the skeleton can be manually populated with data rather than relying on a model. SCRIP files can be difficult to visualize and manipulate, so NCO will provide, if requested, a so-called skeleton file on the user-specified grid. As of NCO version 4.5.3, released in October, 2015, the --rgr skl=fl_skl switch outputs the skeleton file to fl_skl. The skeleton file may then be examined in a dataset viewer, populated with data, and generally serve as a template for what to expect from datasets of the same geometry. # Generate T42 Gaussian grid file t42_SCRIP.nc and skeleton file t42_skl.nc ncks --rgr skl=${DATA}/grids/t42_skl.nc --rgr scrip=${DATA}/grids/t42_SCRIP.nc \ --rgr latlon=64,128#lat_typ=gss#lon_typ=Grn_ctr \ ~zender/nco/data/in.nc ~/foo.nc When generating skeleton files, both the grid file (t42_SCRIP.nc) and the skeleton file (t42_skl.nc) are written, the input file (in.nc) is ignored, and the output file (foo.nc) is overwritten (its contents are immaterial). <a name="map"></a>  <a name="esmf"></a>  <a name="tr"></a>  <a name="tempest"></a>  <a name="scrip"></a>  <a name="rgr"></a>  <a name="rgr_map"></a>  <a name="regrid"></a>

RegriddingClimatology and Bounds SupportGrid GenerationShared features

Regridding map grid-file map-file regridding --map ESMF MOAB TR SCRIP TempestRemap OpenMP --rgr key=val --rgr_map &textndash;map-file Availability: ncclimo, ncks, ncremap&linebreak; Short options: None&linebreak; Long options: --map map-file or --rgr_map map-file&linebreak; --rgr key=val (multiple invocations allowed)&linebreak; --rnr=rnr_thr or --rgr_rnr=rnr_thr or --renormalize=rnr_thr or --renormalization_threshold=rnr_thr&linebreak; NCO includes extensive regridding features in ncclimo (as of version 4.6.0 in May, 2016), ncremap (as of version 4.5.4 in November, 2015) and ncks (since version 4.5.0 in June, 2015). Regridding can involve many choices, options, inputs, and outputs. The appropriate operator for this workflow is the ncremap script which automatically handles many details of regridding and passes the required commands to ncks and external programs. Occasionally users need access to lower-level remapping functionality present in ncks and not exposed to direct manipulation through ncremap or ncclimo. This section describes the lower-level functionality and switches as implemented in ncks. Knowing what these features are will help ncremap and ncclimo users understand the full potential of these operators. ncks supports horizontal regridding of datasets where the grids and weights are all stored in an external map-file. Use the --map or --rgr_map options to specify the map-file, and NCO will regrid the input-file to a new (or possibly the same, aka, an identity mapping) horizontal grid in the output-file, using the input and output grids and mapping weights specified in the ESMF- or SCRIP-format map-file. Currently NCO understands the mapfile formats pioneered by SCRIP (http://oceans11.lanl.gov/svn/SCRIP/trunk/SCRIP) and later extended by ESMF (http://www.earthsystemcog.org/projects/regridweightgen), and adopted (along with Exodus) by TempestRemap (https://github.com/ClimateGlobalChange/tempestremap.git). Those references document quirks in their respectively weight-generation algorithms as to map formats, grid specification, and weight generation. NCO itself produces map-files in the format recommended by CMIP6 and described https://docs.google.com/document/d/1BfVVsKAk9MAsOYstwFSWI2ZBt5mrO_Nmcu7rLGDuL08here. This format differs from ESMF map-file format chiefly in that its metadata are slightly more evolved, self-descriptive, and standardized. Originally NCO supported only weight-application, which is what most people mean by &textldquo;regridding&textrdquo;. As of version 4.9.0, released in December, 2019, NCO also supports weight-generation by its own conservative algorithm. Thus NCO can now apply weights generated by ESMF, NCO, SCRIP, and TempestRemap. NCO reads-in pre-stored weights from the map-file and applies them to (almost) every variable, thereby creating a regridded output-file. Specify regridding with a standard ncks command and options along with the additional specification of a map-file: # Regrid entire file, same output format as input: ncks --map=map.nc in.nc out.nc # Entire file, netCDF4 output: ncks -4 --map=map.nc in.nc out.nc # Deflated netCDF4 output ncks -4 -L 1 --map=map.nc in.nc out.nc # Selected variables ncks -v FS.?,T --map=map.nc in.nc out.nc # Threading ncks -t 8 --map=map.nc in.nc out.nc # Deflated netCDF4 output, threading, selected variables: ncks -4 -L 1 -t 8 -v FS.?,T --map=map.nc in.nc out.nc OpenMP threading works well with regridding large datasets. Threading improves throughput of regridding 1&textndash;10 GB files by factors of 2&textndash;5. Options specific to regridding are described below. equiangular grid Gaussian grid cubed-sphere grid FV grid NCO supports 1D&result;1D, 1D&result;2D, 2D&result;1D, and 2D&result;2D regridding for any unstructured 1D-grid and any rectangular 2D-grid. This has been tested by converting among and between Gaussian, equiangular, FV, unstructured cubed-sphere grids, and regionally refined grids. Support for irregular 2D- and regional grids (e.g., swath-like data) is planned. Renormalization <a name="rnr"></a>  <a name="renormalize"></a>  <a name="renormalization_threshold"></a>  <a name="rgr_rnr"></a>  conservative regridding renormalized regridding missing values data, missing missing_value _FillValue --rnr --renormalize --renormalization_threshold --rgr_rnr Conservative regridding is, for first-order accurate algorithms, a straightforward procedure of identifying gridcell overlap and apportioning values correctly from source to destination. The presence of missing values forces a decision on how to handle destination gridcells where some but not all source cells are valid. NCO allows the user to choose between two distinct weight-application algorithms: &textldquo;conservative&textrdquo; and &textldquo;renormalized&textrdquo;. The &textldquo;conservative&textrdquo; algorithm uses all valid data from the input grid on the output grid once and only once. Destination cells receive the weighted valid values of the source cells. This is conservative because the global integrals of the source and destination fields are equal. Another name for the &textldquo;conservative&textrdquo; weight-application method is therefore &textldquo;integral-preserving&textrdquo;. The &textldquo;renormalized&textrdquo; algorithm divides the destination value by the sum of the valid weights. This produces values equal to the mean of the valid input values, but extended to the entire destination gridcell. Thus renormalization is equivalent to extrapolating valid data to missing regions. Another name for the &textldquo;renormalized&textrdquo; weight-application method is therefore &textldquo;mean-preserving&textrdquo;. Input and output integrals are unequal and renormalized regridding is not conservative. Both algorithms produce identical answers when no missing data maps to the destination gridcell. The renormalized algorithm is useful because it solves some problems, like producing physically unrealistic temperature values, at the expense of incurring others, like non-conservation. Many land and ocean modelers eschew unrealistic gridpoint values, and conservative weight-application often produces &textldquo;weird&textrdquo; values along coastlines or missing data gaps where state variables are regridded to/from small fractions of a gridcell. Renormalization ensures the output values are physically consistent, although the integral of their value times area is not preserved. rnr_thr By default, NCO implements the &textldquo;conservative&textrdquo; algorithm because it has useful properties, is simpler to understand, and requires no additional parameters. To employ the &textldquo;renormalized&textrdquo; algorithm instead, use the --rnr, --rgr_rnr, --rnr_thr, or --renormalize options to supply rnr_thr, the threshold weight for valid destination values. Valid values must cover at least the fraction rnr_thr of the destination gridcell to meet the threshold for a non-missing destination value. When rnr_thr is exceeded, the mean valid value is renormalized by the valid area and placed in the destination gridcell. If the valid area covers less than rnr_thr, then the destination gridcell is assigned the missing value. Valid values of rnr_thr range from zero to one. Keep in mind though, that this threshold is potentially a divisor, and values of zero or very near to zero can lead to floating-point underflow and divide-by-zero errors. For convenience NCO permits users to specify a

rnr_thr = 0.0 threshold weight. This indicates that any valid data should be represented and renormalized on the output grid. Also, renormalization can be explicitly prevented or turned-off by setting rnr_thr to either of the values off or none: ncks --map=map.nc in.nc out.nc # Conservative (global integral-preserving) ncks --rnr=off --map=map.nc in.nc out.nc # Conservative (global integral-preserving) ncks --rnr=0.1 --map=map.nc in.nc out.nc # Renormalized (local mean-preserving with threshold) ncks --rnr=0.0 --map=map.nc in.nc out.nc # Renormalized (local mean-preserving) The first and second examples use the default conservative algorithm. The third example specifies that valid values must cover at least 10% of the destination gridcell to meet the threshold for a non-missing destination value. With valid destination areas of, say 25% or 50%, the renormalized algorithm would produce destination values greater than the conservative algorithm by factors of four or two, respectively. In practice, it may make sense to use the default &textldquo;conservative&textrdquo; algorithm when performing conservative regridding, and the &textldquo;renormalized&textrdquo; algorithm when performing other regridding such as bilinear interpolation or nearest-neighbor. Another consideration is whether the fields being regridded are fluxes or state variables. For example, temperature (unlike heat) and concentrations (amount per unit volume) are not physically conserved quantities under areal-regridding so it often makes sense to interpolate them in a non-conservative fashion, to preserve their fine-scale structure. Few researchers can digest the unphysical values of temperature that the &textldquo;conservative&textrdquo; option will produce in regions rife with missing values. A counter-example is fluxes, which should be physically conserved under areal-regridding. One should consider both the type of field and its conservation properties when choosing a regridding strategy. The regridded value of a variable $\xxx$ at a destination location $\ddd$ can be generally represented as $$ \xxx_{\ddd} = {\sum_{\sss=1}^{\sss=\lmnnbr} \mssflg_{\sss} \sigma_{\sss,\ddd} \xxx_{\sss} \over \sum_{\sss=1}^{\sss=\lmnnbr} \mssflg_{\sss} \sigma_{\sss,\ddd}} $$ where $\xxx_{\ddd}$ is the $\ddd$'th element of the regridded variable, $\xxx_{\sss}$ is the $\sss$'th element of the raw (native grid) variable, $\mssflg_{\sss} = 1$ if $\xxx_{\sss}$ is valid and $\mssflg_{\sss} = 0$ if $\xxx_{\sss}$ is the missing value, and $\sigma_{\sss,\ddd}$ is the overlap weight of $\sss$'th source gridcell with the $\ddd$'th destination gridcell, and $\lmnnbr$ is the total number of source gridcells that overlap (partially or fully) with the destination gridcell. The number of overlap gridcells $\lmnnbr$ is a property of the source and destination grids and the regridding algorithm. The weight-generation software determines $\lmnnbr$ by ``intersecting'' the grids, taking into account higher-order (e.g., local gradient) contributions if the algorithm so-demands, and then generates the overlap weights $\sigma_{\sss,\ddd}$ accordingly. Both source and destination grids may indicate valid gridcells with a mask flag that is binary-valued, zero or one, such that $\mskflg_{\sss} = 1$ (i.e., unmasked) for source gridcells allowed to contribute to the destination grid, and $\mskflg_{\sss} = 0$ (i.e., masked) for gridcells that are forbidden from contributing to the destination grid. There are subtle distinctions between the mask flag $\mskflg_{\sss}$, and the missing value flag $\mssflg_{\sss}$. The mask flag $\mskflg_{\sss}$ does not appear in the formula above because the weight-generator produces no weights for masked source gridcells. Doing otherwise would waste storage space in the map-file, because such weights are, by definition, zero. Furthermore the masks $\mskflg_{\sss}$ and $\mskflg_{\ddd}$ are time-invariant properties of the grids, whereas missing value fields $\mssflg_{\sss}$ (and thus $\mssflg_{\ddd}$) are potentially time-varying characteristics of the fields. Although $\mssflg_{\sss}$ should in theory be treated the same as $\mskflg_{\sss}$ when computing mapping weights $\sigma_{\sss,\ddd}$, in practice this is not done. Different fields may have different patterns of missing values, and managing per-field map-files would be difficult, so traditionally all fields are remapped with the same map-file. That said, it can make sense to treat flux fields and state-variable fields with distinct algorithms, so that a different map-file might be employed for each class of fields. The weight-generation software normalizes $\sigma_{\sss,\ddd}$ such that $\sum_{\sss=1}^{\sss=\lmnnbr} \sigma_{\sss,\ddd} = 1$ when unmasked ($\mskflg_{\sss}=1$) source gridcells completely overlap the destination gridcell. In this case we also have $\sum_{\sss=1}^{\sss=\lmnnbr} \mskflg_{\sss} = \lmnnbr$. Furthermore, if all contributing gridpoints are valid values (i.e., not missing values) then $\mssflg_{\sss} = 1$ so that $\sum_{\sss=1}^{\sss=\lmnnbr} \mssflg_{\sss} = \lmnnbr$. For complete overlap with no masked values and no missing values, then $\mssflg_{\sss} = \mskflg_{\sss} = \sum\sigma_{\sss,\ddd} = 1$ and the generic averaging expression above reduces to a simple weighted mean $\xxx_{\ddd} = \sum_{\sss=1}^{\sss=\lmnnbr} \sigma_{\sss,\ddd} \xxx_{\sss}$. $$ \xxx_{\ddd} = {\sum_{\sss=1}^{\sss=\lmnnbr} \mssflg_{\sss} \frcsgs_{\sss} \sigma_{\sss,\ddd} \xxx_{\sss} \over \sum_{\sss=1}^{\sss=\lmnnbr} \mssflg_{\sss} \frcsgs_{\sss} \sigma_{\sss,\ddd}} $$ Regridder Options Table <a name="rgr_tbl"></a>  NCO automatically annotates the output with relevant metadata such as coordinate bounds, axes, and vertices (a la CF). These annotations include lat_dmn_nm lon_dmn_nm --rgr lon_dmn_nm=lon_dmn_nm --rgr lat_dmn_nm=lat_dmn_nm Horizontal Dimension Names: lat_dmn, lon_dmn The name of the horizontal spatial dimensions assumed to represent latitude and longitude in 2D rectangular input files are lat_dmn_nm and lon_dmn_nm, which default to lat and lon, respectively. Variables that contain a lat_dmn_nm-dimension and a lon_dmn_nm-dimension on a 2D-rectangular input grid will be regridded, and variables regridded to a 2D-rectangular output grid will all contain the lat_dmn_nm- and lon_dmn_nm-dimensions. To treat different dimensions as latitude and longitude, use the options --rgr lat_dmn_nm=lat_dmn_nm and --rgr lon_dmn_nm=lon_dmn_nm. These options applied only to inferring and generating grids until NCO version 4.7.9 (February, 2019). Since then, these options also determine the dimension names in regridded output files. lat_nm lon_nm --rgr lon_nm=lon_nm --rgr lat_nm=lat_nm Horizontal Coordinate Names: lat, lon The name of the horizontal spatial coordinates that represent latitude and longitude in input files are lat_nm and lon_nm, and default to lat and lon, respectively. Variables that contain a lat_dmn_nm-dimension and a lon_dmn_nm-dimension on a 2D input grid will be regridded, and output regridded variables will all contain the lat_nm- and lon_nm-variables. Unless the lat_dmn_nm- and lon_dmn_nm-dimensions are explicitly configured otherwise, they will share the same name as the lat_nm- and lon_nm-variables. Thus variables regridded to a 2D-rectangular output grid usually have lat_nm- and lon_nm as coordinate variables. Variables regridded to a 1D-unstructured output grid will have lat_nm and lon_nm as auxiliary coordinate variables. Variables regridded to a 2D-curvilinear output grid will have lat_nm and lon_nm as multi-dimensional auxiliary coordinate variables. To treat different variables as latitude and longitude, use the options --rgr lat_nm=lat_nm and --rgr lon_nm=lon_nm. Before NCO version 4.7.9 (February, 2019), lat_nm and lon_nm specified both the variable names and, where applicable (i.e., on 2D-grids), the dimensions of the horizontal coordinates in output files. Now the horizontal variable and dimension names in output files may be separately specified. ncol col_nm --rgr col_nm=col_nm Unstructured Dimension Name: col The name of the horizontal spatial dimension assumed to delineate an unstructured grid is col_nm, which defaults to ncol (number of columns), the name CAM employs. Other common names for the columns in an unstructured grid include lndgrid (used by CLM), and nCells (used by MPAS-O). Variables that contain the col_nm-dimension on an unstructured input grid will be regridded, and regridded variables written to an unstructured output grid will all contain the col_nm-dimension. To treat a different dimension as unstructured, use the option --rgr col_nm=col_nm. Note: Often there is no coordinate variable for the col_nm-dimension, i.e., there is no variable named col_nm, although such a coordinate could contain useful information about the unstructured grid. CF latitude longitude axes standard_name degrees_east degrees_north units X axis Y axis auxiliary coordinates Structured Grid Standard Names and Units Longitude and latitude coordinates (both regular and auxiliary, i.e., for unstructured grids) receive CF standard_name values of latitude and longitude, CF axes attributes with values X and Y, and units attributes with values degrees_east and degrees_north, respectively. lat lon Unstructured Grid Auxiliary Coordinates Unstructured grid auxiliary coordinates for longitude and latitude receive CF coordinates attributes with values lon and lat, respectively. lon_bnds lat_bnds nbnd time_bnds lat_bnd_nm lon_bnd_nm --rgr lat_bnd_nm=lat_bnd_nm --rgr lon_bnd_nm=lon_bnd_nm Structured Grid Bounds Variables: bnd, lat_bnd, lon_bnd Structured grids with 1D-coordinates use the dimension bnd_nm (which defaults to nbnd) with the spatial bounds variables in lat_bnd_nm and lon_bnd_nm which default to lon_bnds and lat_bnds, respectively. By default spatial bounds for such structured grids parallel the oft-used temporal bounds dimension (nbnd=2) and variable (time_bnds). Bounds are attached to the horizontal spatial dimensions via their bounds attributes. Change the spatial bounds dimension with the option --rgr bnd_nm=bnd_nm. Rename the spatial bounds variables with the options --rgr lat_bnd_nm=lat_bnd_nm and --rgr lon_bnd_nm=lon_bnd_nm. lat_vertices lon_vertices nv bounds Unstructured Grid Bounds Variables: bnd, lat_bnd, lon_bnd Unstructured grids with 1D-coordinates use the dimension bnd_nm (which defaults to nv, number of vertices) for the spatial bounds variables lat_bnd_nm and lon_bnd_nm which default to lat_vertices and lon_vertices, respectively. It may be impossible to re-use the temporal bounds dimension (often nbnd) for unstructure grids, because the gridcells are not rectangles, and thus require specification of all vertices for each gridpoint, rather than only two parallel interfaces per dimension. These bounds are attached to the horizontal spatial dimensions via their bounds attributes. Change the spatial bounds dimension with the option --rgr bnd_nm=bnd_nm. Rename the spatial bounds variables with the options --rgr lat_bnd_nm=lat_bnd_nm and --rgr lon_bnd_nm=lon_bnd_nm. The temporal bounds dimension in unstructured grid output remains as in the input-file, usually nbnd. lev_dmn_nm ilev_dmn_nm --rgr ilev_dmn_nm=ilev_dmn_nm --rgr lev_dmn_nm=lev_dmn_nm Vertical Dimension Names: lev_dmn, ilev_dmn The name of the dimension(s) associated with the vertical coordinate(s) in multi-level input files are lev_dmn_nm and ilev_dmn_nm, which default to lev and ilev, respectively. Variables that contain a lev_dmn_nm-dimension or an ilev_dmn_nm-dimension will be vertically interpolated to the specified (with vrt_out=vrt_fl) vertical output grid, and will all contain the lev_dmn_nm- and, for hybrid-sigma/pressure interface variables, ilev_dmn_nm-dimensions. To treat different dimensions as the midlayer and interface level dimensions, use the options --rgr lev_dmn_nm=lev_dmn_nm and --rgr ilev_dmn_nm=ilev_dmn_nm options. Pure-pressure grids should use the --rgr lev_dmn_nm=lev_dmn_nm option (to reduce option proliferation, there is no plev_dmn_nm option). These options were introduced in NCO version 4.9.0 (December, 2019). These options also determine the vertical dimension names in vertically interpolated output files. lev_nm ilev_nm plev_nm --rgr lev_nm=lev_nm --rgr ilev_nm=ilev_nm --rgr plev_nm=plev_nm Vertical Coordinate Names: lev, ilev, plev The name of the vertical coordinate variables that represent midpoint levels and interface levels in hybrid-sigma/pressuure input files are lev_nm and ilev_nm, and default to lev and ilev, respectively. While the vertical coordinate in pure-pressure vertical grid files (i.e., the template-file to which data will be interpolated) must be named plev, the vertical coordinate in pure-pressure data files (i.e., the files to be interpolated) may be changed with the --rgr plev_nm=plev_nm option. The name of the vertical coordinate variable that represents pressure levels in pure-pressure grid input data files is plev_nm, and it defaults to plev. To reduce proliferation of command-line options and internal code complexity, the variable and dimension options for pure-pressure vertical coordinate output names re-use the &textldquo;lev&textrdquo; options, i.e., --rgr lev_nm_out=lev_nm_out option. Variables that contain a lev_dmn_nm-dimension or a ilev_dmn_nm-dimension on hybrid-sigma/pressure input grid, or a plev_dmn_nm-dimension on a pure pressure grid, will be regridded, and output in vertically interpolated files on a hybrid-sigma/pressure grid will all contain the lev_nm- and ilev_nm-variables, and output on a pure-pressure grid will contain the lev_nm coordinate. Unless the lev_dmn_nm and ilev_dmn_nm dimensions are explicitly configured otherwise, they will share the same name as the lev_nm/plev_nm and ilev_nm-variables, respectively. Thus variables regridded to a hybrid-sigma/pressure output grid usually have lev_nm- and ilev_nm as coordinate variables. Variables regridded to a pure-pressure output grid will only have a single vertical coordinate variable, lev_nm, which will be an associated coordinate variable if lev_dmn_nm differs from lev_nm. To treat different variables as level and interface-level coordinates, use the options --rgr lev_nm=lev_nm and --rgr ilev_nm=ilev_nm. Before NCO version 4.9.0 (December, 2019), lev_nm and ilev_nm specified both the variable names and, where applicable (i.e., on 2D-grids), the dimensions of the vertical coordinates in output files. Now the vertical variable and dimension names in output files may be separately specified. ps_nm --rgr ps_nm=ps_nm Surface Pressure Names: ps, PS The name of the surface pressure field necessary to reconstruct the layer pressures in the hybrid-sigma/pressure coordinate system is ps_nm which defaults to PS. As of NCO version 5.1.2, released in November, 2022, one may change this with the --rgr ps_nm=ps_nm option. There are, in fact, three similar options, one each for the surface pressure variable in the input data file, the vertical grid file, and in the output (interpolated file). The full option key names are ps_nm (equivalent to ps_nm_in), ps_nm_tpl, and ps_nm_out, respectively. area cell_methods cell_area steradian --no_cll_msr --rgr no_area_out --rgr no_area --rgr area_out=area_nm area_nm SI solid angle extensive variable Gridcell Area: area The variable area_nm (which defaults to area) is, by default, (re-)created in the output_file to hold the gridcell area in steradians. To store the area in a different variable, use the option --rgr area=area_nm. The area_nm variable receives a standard_name attribute of cell_area, a units attribute of steradian (the SI unit of solid angle), and a cell_methods attribute with value lat, lon: sum, which indicates that area_nm is extensive, meaning that its value depends on the gridcell boundaries. Since area_nm is a property of the grid, it is read directly from the map-file rather than regridded itself. To omit the area variable from the output file, set the no_area_out flag. The --no_cll_msr switch to ncremap and ncclimo does this automatically. frc --rgr frc_nm=frc_nm frc_nm extensive variable Gridcell Fraction: frc The variable frc_nm (which defaults to frac_b) is automatically copied to the output_file to hold the valid fraction of each gridcell when certain conditions are met. First, the regridding method must be conservative. Second, at least one value of frc_nm must be non-unity. These conditions ensure that whenever fractional gridcells affect the regridding, they are also placed in the output file. To store the fraction in a different variable, use the option --rgr frc_nm=frc_nm. The frc_nm variable receives a cell_methods attribute with value lat, lon: sum, which indicates that frc_nm is extensive, meaning that its value depends on the gridcell boundaries. Since frc_nm is a property of the grid, it is read directly from the map-file rather than regridded itself. mask --no_msk --no_mask --msk_out --mask_out --rgr no_msk_out --rgr no_mask --rgr msk_nm=msk_nm infer msk_nm Gridcell Mask: mask The variable msk_nm (which defaults to mask_b) can, if present, be copied from the map-file to hold the gridcell mask on the destination grid in output-file. To name the mask differently in the output file, use the option --rgr msk_nm=msk_nm. Since msk_nm is a property of the grid, it is read directly from the map-file rather than regridded itself. To include the mask variable in the output file, set the msk_out flag. To omit the mask variable from the output file, set the no_msk_out flag. In grid inferral and map-generation modes, this option tells the regridder to generate an integer mask map from the variable msk_nm. The mask will be one (i.e., points at that location will contribute to regridding weights) where msk_nm has valid values. The mask will be zero (i.e., points at that location will not contribute to regridding weights) where msk_nm has a missing value. This feature is useful when creating weights between masked grids, e.g., ocean-only points or land-only points. gw --rgr lat_weight=lat_wgt_nm lat_wgt_nm Gaussian weight Latitude weights: lat_wgt Rectangular 2D-grids use the variable lat_wgt_nm, which defaults to gw (originally for &textldquo;Gaussian weight&textrdquo;), to store the 1D-weight appropriate for area-weighting the latitude grid. To store the latitude weight in a different variable, use the option --rgr lat_wgt=lat_wgt_nm. The lat_wgt_nm variable will not appear in 1D-grid output. Weighting statistics by latitude (i.e., by lat_wgt_nm will produce the same answers (up-to round-off error) as weighting by area (i.e., by area_nm) in grids that have both variables. The former requires less memory because lat_wgt_nm is 1D), whereas the latter is more general because area_nm works on any grid. mapping_file source_file Provenance Attributes The map-file and input-file names are stored in the output-file global attributes mapping_file and source_file, respectively. <a name="slat"></a>  <a name="slon"></a>  <a name="stg"></a>  <a name="stagger"></a>  <a name="no_stagger"></a>  --no_stg --no_stagger --no_stg_grd --stg --stagger --stg_grd slat slon w_stag CAM FV AMWG staggered-grid no stagger stagger Staggered Grid Coordinates and Weights Owing to its heritage as an early CCM analysis tool, NCO tries to create output interoperable with other CESM analysis tools. Like many models, CAM computes and archives thermodynamic state variables on gridcell centers, and computes dynamics variables (zonal and meridional winds U and V, respectively) on gridcell edges (interfaces). The dual-grid, sometimes called the &textldquo;staggered grid&textrdquo;, formed by connecting edge centers is thus the natural location for storing output dynamics variables. Most dynamical cores of CAM archives horizontal winds at gridcell centers under the names U, and V. For CAM-FV, these are interpolated from the computed interface winds archived as US, and VS (which are on the staggered grid coordinate system). Some analysis packages, such as the AMWG diagnostics, require access to these dual-grid coordinates with the names slat and slon (for &textldquo;staggered&textrdquo; latitude and longitude). Until NCO version 4.9.8 (released March, 2021), the NCO regridder output these coordinates, along with the latitude weights (called w_stag), by default when the input was on a cap (aka FV) grid so that the result could be processed by AMWG diagnostics. Setting the no_stagger flag turns-off archiving the staggered grid (i.e., slat, slon, and w_stag). Do this with the --no_stg_grd flag in ncremap. ncclimo always sets this --no_stagger flag. As of NCO version 4.9.8 (released March, 2021), the default ncremap and ncclimo behavior is to omit the staggered grid. The new flag --stg_grd turns-on outputting the staggered grid, and thus recovers the previous default behavior.

One may supply muliple --rgr key=value options to simultaneously customize multiple grid-field names. The following examples may all be assumed to end with the standard options --map=map.nc in.nc out.nc. ncks --rgr lat_nm=latitude --rgr lon_nm=longitude ncks --rgr col_nm=column --rgr lat_wgt=lat_wgt ncks --rgr bnd_nm=bounds --rgr lat_bnd_nm=lat_bounds --rgr lon_bnd_nm=lon_bounds ncks --rgr bnd_nm=vertices --rgr lat_bnd_nm=lat_vrt --rgr lon_bnd_nm=lon_vrt The first command causes the regridder to associate the latitude and longitude dimensions with the dimension names latitude and longitude (instead of the defaults, lat and lon). The second command causes the regridder to associate the independent columns in an unstructured grid with the dimension name column (instead of the default, ncol) and the variable containing latitude weights to be named lat_wgt (instead of the default, gw). The third command associates the latitude and longitude bounds with the dimension bounds (instead of the default, nbnd) and the variables lat_bounds and lon_bounds (instead of the defaults, lat_bnds and lon_bnds, respectively). The fourth command associates the latitude and longitude bounds with the dimension vertices (instead of the default, nv) and the variables lat_vrt and lon_vrt (instead of the defaults, lat_vertices and lon_vertices, respectively). When used with an identity remapping files, regridding can signficantly enhance the metadata and therefore the dataset usability. Consider these selected metadata (those unchanged are not shown for brevity) associated with the variable FSNT from typical unstructured grid (CAM-SE cubed-sphere) output before and after an identity regridding: # Raw model output before regridding netcdf ne30_FSNT { dimensions: nbnd = 2 ; ncol = 48602 ; time = UNLIMITED ; // (1 currently) variables: float FSNT(time,ncol) ; FSNT:long_name = "Net solar flux at top of model" ; double time(time) ; time:long_name = "time" ; time:bounds = "time_bnds" ; double time_bnds(time,nbnd) ; time_bnds:long_name = "time interval endpoints" ; } // group / # Same model output after identity regridding netcdf dogfood { dimensions: nbnd = 2 ; ncol = 48602 ; nv = 5 ; time = 1 ; variables: float FSNT(time,ncol) ; FSNT:long_name = "Net solar flux at top of model" ; FSNT:coordinates = "lat lon" ; double lat(ncol) ; lat:long_name = "latitude" ; lat:standard_name = "latitude" ; lat:units = "degrees_north" ; lat:axis = "Y" ; lat:bounds = "lat_vertices" ; lat:coordinates = "lat lon" ; double lat_vertices(ncol,nv) ; lat_vertices:long_name = "gridcell latitude vertices" ; double lon(ncol) ; lon:long_name = "longitude" ; lon:standard_name = "longitude" ; lon:units = "degrees_east" ; lon:axis = "X" ; lon:bounds = "lon_vertices" ; lon:coordinates = "lat lon" ; double lon_vertices(ncol,nv) ; lon_vertices:long_name = "gridcell longitude vertices" ; double time(time) ; time:long_name = "time" ; time:bounds = "time_bnds" ; double time_bnds(time,nbnd) ; time_bnds:long_name = "time interval endpoints" ; } // group / The raw model output lacks the CF coordinates and bounds attributes that the regridder adds. The metadata turns lat and lon into auxiliary coordinate variables (Auxiliary Coordinates) which can then be hyperslabbed (with -X) using latitude/longitude coordinates bounding the region of interest: % ncks -u -H -X 314.6,315.3,-35.6,-35.1 -v FSNT dogfood.nc time[0]=31 ncol[0] FSNT[0]=344.575 W/m2 ncol[0] lat[0]=-35.2643896828 degrees_north ncol[0] nv[0] lat_vertices[0]=-35.5977213708 ncol[0] nv[1] lat_vertices[1]=-35.5977213708 ncol[0] nv[2] lat_vertices[2]=-35.0972113817 ncol[0] nv[3] lat_vertices[3]=-35.0972113817 ncol[0] nv[4] lat_vertices[4]=-35.0972113817 ncol[0] lon[0]=315 degrees_east ncol[0] nv[0] lon_vertices[0]=315 ncol[0] nv[1] lon_vertices[1]=315 ncol[0] nv[2] lon_vertices[2]=315.352825437 ncol[0] nv[3] lon_vertices[3]=314.647174563 ncol[0] nv[4] lon_vertices[4]=314.647174563 time[0]=31 days since 1979-01-01 00:00:00 time[0]=31 nbnd[0] time_bnds[0]=0 time[0]=31 nbnd[1] time_bnds[1]=31 Thus auxiliary coordinate variables help to structure unstructured grids. The expanded metadata annotations from an identity regridding may obviate the need to place unstructured data on a rectangular grid. For example, statistics for regions that can be expressed as unions of rectangular regions can now be performed on the native (unstructured) grid. Here are some quick examples of regridding from common models. All examples require in.nc out.nc at the end. # Identity re-map E3SM/ACME CAM-SE Cubed-Sphere output (to improve metadata) ncks --map=${DATA}/maps/map_ne30np4_to_ne30np4_aave.20150603.nc # Convert E3SM/ACME CAM-SE Cubed Sphere output to rectangular lat/lon ncks --map=${DATA}/maps/map_ne30np4_to_fv129x256_aave.150418.nc # Convert CAM3 T42 output to Cubed-Sphere grid ncks --map=${DATA}/maps/map_ne30np4_to_t42_aave.20150601.nc <a name="clm_nfo"></a>  <a name="cb"></a>  <a name="clm_bnd"></a>  <a name="climatology_information"></a>

Climatology and Bounds SupportUDUnits SupportRegriddingShared features

Climatology and Bounds Support --clm_nfo --cb --clm_bnd --climatology_information Availability: nces, ncra, ncrcat&linebreak; Short options: None&linebreak; Long options: --cb=yr_srt,yr_end,mth_srt,mth_end,tpd&linebreak; --clm_bnd=yr_srt,yr_end,mth_srt,mth_end,tpd&linebreak; --clm_nfo=yr_srt,yr_end,mth_srt,mth_end,tpd&linebreak; --climatology_information=yr_srt,yr_end,mth_srt,mth_end,tpd&linebreak; (NB: This section describes support for generating CF-compliant bounds variables and attributes, i.e., metadata. For instructions on constructing climatologies themselves, see the ncclimo documentation). As of NCO version 4.9.4 (September, 2020) ncra introduces the --clm_bnd option, a powerful method to fully implement the CF bounds, climatology, and cell_methods attributes defined by CF Conventions. The new method updates the previous --cb and --c2b methods introduced in version 4.6.0 which only worked for monthly mean data. The newer --cb method also works for climatological diurnally resolved input, and for datasets that contain more than more than one record. This option takes as argument a comma-separated list of five relevant input parameters: --cb=yr_srt,yr_end,mth_srt,mth_end,tpd, where yr_srt is the climatology start-year, yr_end is the climatology end-year, mth_srt is the climatology start-month (in [1..12] format), mth_end is the climatology end-month (in [1..12] format), and tpd is the number of timestpes per day (with the special exception that

tpd=0 indicates monthly data, not diurnally-resolved data). For example, a seasonal summer climatology created from monthly mean input data spanning June, 2000 to August, 2020 should call ncra with --clm_bnd=2000,2020,6,8,0, whereas a diurnally resolved climatology of the same period with 6-hourly input data resolution would use --clm_bnd=2000,2020,6,8,4. The ncclimo command internally uses --clm_bnd extensively.

# Average monthly means into a climatological month
ncra --cb=2014,2016,1,1,0 2014_01.nc 2015_01.nc 2016_01.nc clm_JAN.nc
# Average seasonally contiguous climatological monthly means into NH winter
ncra --cb=2013,2016,12,2,0 -w 31,31,28 DEC.nc JAN.nc FEB.nc DJF.nc
# Average seasonally discontiguous climatological means into NH winter
ncra --cb=2014,2016,1,12,0 -w 31,28,31 JAN.nc FEB.nc DEC.nc JFD.nc
# Reduce four climatological seasons to make an annual climatology
ncra --cb=2014,2016,1,12,0 -w 92,92,91,90 MAM.nc JJA.nc SON.nc DJF.nc ANN.nc
# Reduce twelve monthly climatologies to make into an annual climatology
ncra --cb=2014,2016,1,12,0 -w 31,28,31,30,31,30,31,31,30,31,30,31 clm_??.nc ANN.nc

In the fourth and fifth examples, NCO uses the number of input files (3 and 4, respectively) to discriminate between seasonal and annual climatologies since the other arguments to --cb are identical. When using this option, NCO expects each output file to contain max(1,tpd) records. nces and ncra both accept the --cb option. While ncra almost always reduces the input dataset over the record dimension, nces never does. This makes it easy to use nces to combine and create climatologies of diurnally resolved input files.

# Average diurnally resolved monthly means into a climatology
nces --cb=2014,2016,1,1,8 2014_01.nc 2015_01.nc 2016_01.nc clm_JAN.nc
# Average seasonally contiguous diurnally resolved means into a season
nces --cb=2013,2016,12,2,8 -w 31,31,28 DEC.nc JAN.nc FEB.nc DJF.nc
# Average seasonally discontiguous diurnally resolved means into a season
nces --cb=2014,2016,1,12,8 -w 31,28,31 JAN.nc FEB.nc DEC.nc JFD.nc
# Reduce four diurnally resolved seasons to make an annual climatology
nces --cb=2014,2016,1,12,8 -w 92,92,91,90 MAM.nc JJA.nc SON.nc DJF.nc ANN.nc
# Reduce twelve diurnally resolved months to make into an annual climatology
nces --cb=2014,2016,1,12,8 -w 31,28,31,30,31,30,31,31,30,31,30,31 clm_??.nc ANN.nc

Every input in the above set of examples must have eight records, and that number will appear in the output as well. <a name="udunits"></a>  <a name="UDUnits"></a>  <a name="UDUnits2"></a>

UDUnits SupportRebasing Time CoordinateClimatology and Bounds SupportShared features

UDUnits Support UDUnits Unidata units attribute, units -d dim,[min][,[max][,[stride]]] --dimension dim,[min][,[max][,[stride]]] --dmn dim,[min][,[max][,[stride]]] Availability: ncbo, nces, ncecat, ncflint, ncks, ncpdq, ncra, ncrcat, ncwa&linebreak; Short options: -d dim,[min][,[max][,[stride]]]&linebreak; Long options: --dimension dim,[min][,[max][,[stride]]],&linebreak; --dmn dim,[min][,[max][,[stride]]]&linebreak; There is more than one way to hyperskin a cat. The http://www.unidata.ucar.edu/software/udunitsUDUnits package provides a library which, if present, NCO uses to translate user-specified physical dimensions into the physical dimensions of data stored in netCDF files. Unidata provides UDUnits under the same terms as netCDF, so sites should install both. Compiling NCO with UDUnits support is currently optional but may become required in a future version of NCO. Two examples suffice to demonstrate the power and convenience of UDUnits support. MKS units First, consider extraction of a variable containing non-record coordinates with physical dimensions stored in MKS units. In the following example, the user extracts all wavelengths in the visible portion of the spectrum in terms of the units very frequently used in visible spectroscopy, microns:

% ncks --trd -C -H -v wvl -d wvl,"0.4 micron","0.7 micron" in.nc
wvl[0]=5e-07 meter

units The hyperslab returns the correct values because the wvl variable is stored on disk with a length dimension that UDUnits recognizes in the units attribute. The automagical algorithm that implements this functionality is worth describing since understanding it helps one avoid some potential pitfalls. First, the user includes the physical units of the hyperslab dimensions she supplies, separated by a simple space from the numerical values of the hyperslab limits. She encloses each coordinate specifications in quotes so that the shell does not break the value-space-unit string into separate arguments before passing them to NCO. Double quotes ("foo") or single quotes ('foo') are equally valid for this purpose. Second, NCO recognizes that units translation is requested because each hyperslab argument contains text characters and non-initial spaces. Third, NCO determines whether the wvl is dimensioned with a coordinate variable that has a units attribute. coordinate variable In this case, wvl itself is a coordinate variable. The value of its units attribute is meter. Thus wvl passes this test so UDUnits conversion is attempted. If the coordinate associated with the variable does not contain a units attribute, then NCO aborts. Fourth, NCO passes the specified and desired dimension strings (microns are specified by the user, meters are required by NCO) to the UDUnits library. Fifth, the UDUnits library that these dimension are commensurate and it returns the appropriate linear scaling factors to convert from microns to meters to NCO. If the units are incommensurate (i.e., not expressible in the same fundamental MKS units), or are not listed in the UDUnits database, then NCO aborts since it cannot determine the user&textrsquo;s intent. Finally, NCO uses the scaling information to convert the user-specified hyperslab limits into the same physical dimensions as those of the corresponding cooridinate variable on disk. At this point, NCO can perform a coordinate hyperslab using the same algorithm as if the user had specified the hyperslab without requesting units conversion. units time The translation and dimensional interpretation of time coordinates shows a more powerful, and probably more common, UDUnits application. In this example, the user prints all data between 4 PM and 7 PM on December 8, 1999, from a variable whose time dimension is hours since the year 1900:

% ncks -u -H -C -v time_udunits -d time_udunits,"1999-12-08 \
  16:00:0.0","1999-12-08 19:00:0.0" in.nc
time_udunits[1]=876018 hours since 1900-01-01 00:00:0.0

stride whitespace Here, the user invokes the stride (Stride) capability to obtain every other timeslice. This is possible because the UDUnits feature is additive, not exclusive&textmdash;it works in conjunction with all other hyperslabbing (Hyperslabs) options and in all operators which support hyperslabbing. The following example shows how one might average data in a time period spread across multiple input files

ncra -d time,"1939-09-09 12:00:0.0","1945-05-08 00:00:0.0" \
  in1.nc in2.nc in3.nc out.nc

Note that there is no excess whitespace before or after the individual elements of the -d argument. shell This is important since, as far as the shell knows, -d takes only one command-line argument. Parsing this argument into its component dim,[min][,[max][,[stride]]] elements (Hyperslabs) is the job of NCO. When unquoted whitespace is present between these elements, the shell passes NCO arugment fragments which will not parse as intended. NCO implemented support for the UDUnits2 library with version 3.9.2 (August, 2007). The http://www.unidata.ucar.edu/software/udunits/udunits-2/udunits2.htmlUDUnits2 package supports non-ASCII characters and logarithmic units. We are interested in user-feedback on these features. <a name="UDUNITS2_XML_PATH"></a>  UDUNITS2_XML_PATH One aspect that deserves mention is that UDUnits, and thus NCO, supports run-time definition of the location of the relevant UDUnits databases. UDUnits2 (specifically, the function ut_read_xml()) uses the environment variable UDUNITS2_XML_PATH, if any, to find its all-important XML database, named udunits2.xml by default. If UDUNITS2_XML_PATH is undefined, then UDUnits2 looks in the fall-back default initial location that was hardcoded when the UDUnits2 library was built. This location varies depending upon your operating system and UDUnits2 ncompilation settings. If UDUnits2 is correctly linked yet cannot find the XML database in either of these locations, then NCO will report that the UDUnits2 library has failed to initialize. To fix this, export the full location (path+name) of the UDUnits2 XML database file udunits2.xml to the shell: # Example UDUnits2 XML database locations: export UDUNITS2_XML_PATH='/opt/homebrew/share/udunits/udunits2.xml' # Homebrew export UDUNITS2_XML_PATH='/opt/local/share/udunits/udunits2.xml' # MacPorts export UDUNITS2_XML_PATH="${HOME}/anaconda/share/udunits/udunits2.xml" # Anaconda One can then invoke (without recompilation) NCO again, and UDUnits2 should work. This run-time flexibility can enable the full functionality of pre-built binaries on machines with libraries in different locations. @cindex @code{UDUNITS_PATH} @acronym{NCO} supported for UDUnits @w{version 1} for many years, and deprecated this feature in about 2015. Some UDUnits @w{version 1} feature may still work (we do not know). If you are stuck with a UDUnits @w{version 1} installation you might try to specify the directory which contains the UDUnits database, @file{udunits.dat}, via the @code{UDUNITS_PATH} environment variable: @example # UDUnits1 export UDUNITS_PATH='/unusual/location/share/udunits' @end example Climate and Forecast Metadata Convention CF conventions The http://www.unidata.ucar.edu/software/udunitsUDUnits package documentation describes the supported formats of time dimensions. Among the metadata conventions that adhere to these formats are the http://cf-pcmdi.llnl.govClimate and Forecast (CF) Conventions and the http://ferret.wrc.noaa.gov/noaa_coop/coop_cdf_profile.htmlCooperative Ocean/Atmosphere Research Data Service (COARDS) Conventions. The following -d arguments extract the same data using commonly encountered time dimension formats:

-d time,'1918-11-11 00:00:0.0','1939-09-09 00:00:0.0'
-d time,'1918-11-11 00:00:0.0','1939-09-09 00:00:0.0'
-d time,'1918-11-11T00:00:0.0Z','1939-09-09T00:00:0.0Z'
-d time,'1918-11-11','1939-09-09'
-d time,'1918-11-11','1939-9-9'

All of these formats include at least one dash - in a non-leading character position (a dash in a leading character position is a negative sign). NCO assumes that a space, colon, or non-leading dash in a limit string indicates that a UDUnits units conversion is requested. Some date formats like YYYYMMDD that are valid in UDUnits are ambiguous to NCO because it cannot distinguish a purely numerical date (i.e., no dashes or text characters in it) from a coordinate or index value:

-d time,1918-11-11 # Interpreted as the date November 11, 1918
-d time,19181111   # Interpreted as time-dimension index 19181111
-d time,19181111.  # Interpreted as time-coordinate value 19181111.0

Hence, use the YYYY-MM-DD format rather than YYYYMMDD for dates. As of version 4.0.0 (January, 2010), NCO supports some calendar attributes specified by the CF conventions. Supported types: "365_day"/"noleap", "360_day", "gregorian", "standard" Unsupported types: "366_day"/"all_leap","proleptic_gregorian","julian","none"

Unsupported types default to mixed Gregorian/Julian as defined by UDUnits. An Example: Consider the following netCDF variable

variables:
  double lon_cal(lon_cal) ;
    lon_cal:long_name = "lon_cal" ;
    lon_cal:units = "days since 1964-2-28 0:0:0" ;
    lon_cal:calendar = "365_day" ;
data:
  lon_cal = 1,2,3,4,5,6,7,8,9,10;

ncks -v lon_cal -d lon_cal,'1964-3-1 0:00:0.0','1964-3-4 00:00:0.0' results in lon_cal=1,2,3,4. MKS units God netCDF variables should always be stored with MKS (i.e., God&textrsquo;s) units, so that application programs may assume MKS dimensions apply to all input variables. The UDUnits feature is intended to alleviate NCO users&textrsquo; pain when handling MKS units. It connects users who think in human-friendly units (e.g., miles, millibars, days) to extract data which are always stored in God&textrsquo;s units, MKS (e.g., meters, Pascals, seconds). The feature is not intended to encourage writers to store data in esoteric units (e.g., furlongs, pounds per square inch, fortnights). <a name="time_rebase"></a>  <a name="rbs"></a>

Rebasing Time CoordinateMultiple Record DimensionsUDUnits SupportShared features

Rebasing Time Coordinate Availability: ncra, ncrcat Short options: None&linebreak; Time rebasing is invoked when numerous files share a common record coordinate, and the record coordinate basetime (not the time increment, e.g., days or hours) changes among input files. The rebasing is performed automatically if and only if UDUnits is installed. Rebasing occurs when the record coordinate is a time-based variable, and times are recorded in units of a time-since-basetime, and the basetime changes from file to file. Since the output file can have only one unit (i.e., one basetime) for the record coordinate, NCO, in such cases, chooses the units of the first input file to be the units of the output file. It is necessary to &textldquo;rebase&textrdquo; all the input record variables to this output time unit in order for the output file to have the correct values. For example suppose the time coordinate is in hours and each day in January is stored in its own daily file. Each daily file records the temperature variable tpt(time) with an (unadjusted) time coordinate value between 0&textndash;23 hours, and uses the units attribute to advance the base time:

file01.nc time:units="hours since 1990-1-1"   
file02.nc time:units="hours since 1990-1-2"   
...
file31.nc time:units="hours since 1990-1-31"

// Mean noontime temperature in January
ncra -v tpt -d time,"1990-1-1 12:00:00","1990-1-31 23:59:59",24 \
      file??.nc noon.nc    

// Concatenate day2 noon through day3 noon records
ncrcat -v tpt -d time,"1990-1-2 12:00:00","1990-1-3 11:59:59" \ 
      file01.nc file02.nc file03.nc noon.nc    

// Results: time is "re-based" to the time units in "file01.nc"
time=36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, \
     51, 52, 53, 54, 55, 56, 57, 58, 59 ;
  
// If we repeat the above command but with only two input files...
ncrcat -v tpt -d time,"1990-1-2 12:00:00","1990-1-3 11:59:59" \
      file02.nc file03 noon.nc    

// ...then output time coordinate is based on time units in "file02.nc"
time = 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, \ 
     26, 27, 28, 29, 30, 31, 32, 33, 34, 35 ;

As of NCO version 4.2.1 (August, 2012), NCO automatically rebases not only the record coordinate (time, here) but also any cell boundaries associated with the record coordinate (e.g., time_bnds) (CF Conventions). As of NCO version 4.4.9 (May, 2015), NCO also rebases any climatology boundaries associated with the record coordinate (e.g., climatology_bounds) (CF Conventions). As of NCO version 4.6.3 (December, 2016), NCO also rebases the time coordinate when the units differ between files. For example the first file may have units="days since 2014-03-01" and the second file units="hours since 2014-03-10 00:00". <a name="mrd"></a>

Multiple Record DimensionsMissing ValuesRebasing Time CoordinateShared features

Multiple Record Dimensions netCDF4 --mrd --multiple_record_dimensions Availability: ncecat, ncpdq Short options: None&linebreak; Long options: --mrd&linebreak; The netCDF3 file format allows only one record dimension, and that dimension must be the first dimension (i.e., the least rapidly varying dimension) of any variable in which it appears. This imposes certain rules on how operators must perform operations that alter the ordering of dimensions or the number of record variables. The netCDF4 file format has no such restrictions. Files and variables may have any number of record dimensions in any order. This additional flexibility of netCDF4 can only be realized by selectively abandoning the constraints that would make operations behave completely consistently between netCDF3 and netCDF4 files. NCO chooses, by default, to impose netCDF3-based constraints on netCDF4 files. This reduces the number of unanticipated consequences and keeps the operators functioning in a familiar way. Put another way, NCO limits production of additional record dimensions so processing netCDF4 files leads to the same results as processing netCDF3 files. Users can override this default with the --mrd (or --multiple_record_dimension) switch, which enables netCDF4 variables to accumulate additional record dimensions. How can additional record dimensions be produced? Most commonly ncecat (in record-aggregate mode) defines a new leading record dimension. In netCDF4 files this becomes an additional record dimension unless the original record dimension is changed to a fixed dimension (as must be done in netCDF3 files). Also when ncpdq reorders dimensions it can preserve the &textldquo;record&textrdquo; property of record variables. ncpdq tries to define as a record dimension whichever dimension ends up first in a record variable, and, in netCDF4 files, this becomes an additional record dimension unless the original record dimension is changed to a fixed dimension (as must be done in netCDF3 files). It it easier if ncpdq and ncecat do not increase the number of record dimensions in a variable so that is the default. Use --mrd to override this. <a name="missing_value"></a>  <a name="_FillValue"></a>  <a name="fll_val"></a>  <a name="mss_val"></a>  <a name="mss"></a>

Missing ValuesChunkingMultiple Record DimensionsShared features

Missing values missing values data, missing averaging data missing_value _FillValue Availability: ncap2, ncbo, ncclimo, nces, ncflint, ncpdq, ncra, ncremap, ncwa&linebreak; Short options: None&linebreak; The phrase missing data refers to data points that are missing, invalid, or for any reason not intended to be arithmetically processed in the same fashion as valid data. arithmetic operators All NCO arithmetic operators attempt to handle missing data in an intelligent fashion. There are four steps in the NCO treatment of missing data: Identifying variables that may contain missing data. NCO follows the convention that missing data should be stored with the _FillValue specified in the variable&textrsquo;s _FillValue attributes. The only way NCO recognizes that a variable may contain missing data is if the variable has a _FillValue attribute. In this case, any elements of the variable which are numerically equal to the _FillValue are treated as missing data. NCO adopted the behavior that the default attribute name, if any, assumed to specify the value of data to ignore is _FillValue with version 3.9.2 (August, 2007). Prior to that, the missing_value attribute, if any, was assumed to specify the value of data to ignore. Supporting both of these attributes simultaneously is not practical. Hence the behavior NCO once applied to missing_value it now applies to any _FillValue. NCO now treats any missing_value as normal data The old functionality, i.e., where the ignored values are indicated by missing_value not _FillValue, may still be selected at NCO build time by compiling NCO with the token definition CPPFLAGS='-UNCO_USE_FILL_VALUE'. . ncrename ncatted It has been and remains most advisable to create both _FillValue and missing_value attributes with identical values in datasets. Many legacy datasets contain only missing_value attributes. NCO can help migrating datasets between these conventions. One may use ncrename (ncrename netCDF Renamer) to rename all missing_value attributes to _FillValue:

ncrename -a .missing_value,_FillValue inout.nc

Alternatively, one may use ncatted (ncatted netCDF Attribute Editor) to add a _FillValue attribute to all variables

ncatted -O -a _FillValue,,o,f,1.0e36 inout.nc

Converting the _FillValue to the type of the variable, if neccessary. Consider a variable var of type var_type with a _FillValue attribute of type att_type containing the value _FillValue. As a guideline, the type of the _FillValue attribute should be the same as the type of the variable it is attached to. If var_type equals att_type then NCO straightforwardly compares each value of var to _FillValue to determine which elements of var are to be treated as missing data. C language If not, then NCO converts _FillValue from att_type to var_type by using the implicit conversion rules of C, or, if att_type is NC_CHAR For example, the DOE ARM program often uses att_type = NC_CHAR and _FillValue = -99999.. , by typecasting the results of the C function strtod(_FillValue). ncatted You may use the NCO operator ncatted to change the _FillValue attribute and all data whose data is _FillValue to a new value (ncatted netCDF Attribute Editor). Identifying missing data during arithmetic operations. performance operator speed speed execution time arithmetic operators When an NCO arithmetic operator processes a variable var with a _FillValue attribute, it compares each value of var to _FillValue before performing an operation. Note the _FillValue comparison imposes a performance penalty on the operator. Arithmetic processing of variables which contain the _FillValue attribute always incurs this penalty, even when none of the data are missing. Conversely, arithmetic processing of variables which do not contain the _FillValue attribute never incurs this penalty. In other words, do not attach a _FillValue attribute to a variable which does not contain missing data. This exhortation can usually be obeyed for model generated data, but it may be harder to know in advance whether all observational data will be valid or not. Treatment of any data identified as missing in arithmetic operators. nces ncra ncwa ncbo ncflint NCO averagers (ncra, nces, ncwa) do not count any element with the value _FillValue towards the average. ncbo and ncflint define a _FillValue result when either of the input values is a _FillValue. Sometimes the _FillValue may change from file to file in a multi-file operator, e.g., ncra. NCO is written to account for this (it always compares a variable to the _FillValue assigned to that variable in the current file). Suffice it to say that, in all known cases, NCO does &textldquo;the right thing&textrdquo;. It is impossible to determine and store the correct result of a binary operation in a single variable. One such corner case occurs when both operands have differing _FillValue attributes, i.e., attributes with different numerical values. Since the output (result) of the operation can only have one _FillValue, some information may be lost. In this case, NCO always defines the output variable to have the same _FillValue as the first input variable. Prior to performing the arithmetic operation, all values of the second operand equal to the second _FillValue are replaced with the first _FillValue. Then the arithmetic operation proceeds as normal, comparing each element of each operand to a single _FillValue. Comparing each element to two distinct _FillValue&textrsquo;s would be much slower and would be no likelier to yield a more satisfactory answer. In practice, judicious choice of _FillValue values prevents any important information from being lost. <a name="chunking"></a>  <a name="cnk"></a>  <a name="cnk_sz"></a>  <a name="chunk_size"></a>

ChunkingQuantization AlgorithmsMissing ValuesShared features

Chunking --cnk_byt --cnk_csh --cnk_dmn --cnk_map --cnk_min --cnk_plc --cnk_scl --chunk_byte --chunk_cache --chunk_dimension --chunk_map --chunk_min --chunk_policy --chunk_scalar chunking Availability: ncap2, ncbo, nces, ncecat, ncflint, ncks, ncpdq, ncra, ncrcat, ncwa&linebreak; Short options: none&linebreak; Long options: --cnk_byt sz_byt, --chunk_byte sz_byt&linebreak; --cnk_csh sz_byt, --chunk_cache sz_byt&linebreak; --cnk_dmn dmn_nm,sz_lmn, --chunk_dimension dmn_nm,sz_lmn&linebreak;, --cnk_map cnk_map, --chunk_map cnk_map,&linebreak; --cnk_min sz_byt, --chunk_min sz_byt,&linebreak; --cnk_plc cnk_plc, --chunk_policy cnk_plc,&linebreak; --cnk_scl sz_lmn, --chunk_scalar sz_lmn&linebreak; All netCDF4-enabled NCO operators that define variables support a plethora of chunksize options. Chunking can significantly accelerate or degrade read/write access to large datasets. Dataset chunking issues are described by THG and Unidata http://www.hdfgroup.org/HDF5/doc/H5.user/Chunking.htmlhere, http://www.unidata.ucar.edu/blogs/developer/en/entry/chunking_data_why_it_mattershere, and http://www.unidata.ucar.edu/blogs/developer/en/entry/chunking_data_choosing_shapeshere. NCO authors are working on generalized algorithms and applications of chunking strategies (stay tuned for more in 2018). <a name="chunk_cache"></a>  <a name="cnk_csh"></a>  <a name="cache"></a>  <a name="csh"></a>  chunk cache size cache size --cnk_csh sz --chunk_cache sz As of NCO version 4.6.5 (March, 2017), NCO supports run-time alteration of the chunk cache size. By default, the cache size is set (by the --with-chunk-cache-size option to configure) at netCDF compile time. The --cnk_csh sz option sets the cache size to sz bytes for all variables. When the debugging level is set (with -D dbg_lvl) to three or higher, NCO prints the current value of the cache settings for informational purposes. Also --chunk_cache. Increasing cache size from the default can dramatically accelerate time to aggregate and rechunk multiple large input datasets, e.g.,

ncrcat -4 -L 1 --cnk_csh=1000000000 --cnk_plc=g3d --cnk_dmn=time,365 \
       --cnk_dmn=lat,1800 --cnk_dmn=lon,3600 in*.nc4 out.nc

In this example all 3D variables the input datasets (which may or may not be chunked already) are re-chunked to a size of 365 along the time dimension. Because the default chunk cache size of about 4 MB is too small to manipulate the large chunks, we reset the cache to 1 GB. The operation completes much faster, and subsequent reads along the time dimension will be much more rapid. <a name="blocksize"></a>  <a name="blk"></a>  chunking policy chunking map chunksize blocksize The NCO chunking implementation is designed to be flexible. Users control four aspects of the chunking implementation. These are the chunking policy, chunking map, chunksize, and minimum chunksize. The chunking policy determines which variables to chunk, and the chunking map determines how (with what exact sizes) to chunk those variables. These are high-level mechanisms that apply to an entire file and all variables and dimensions. The chunksize option allows per-dimension specification of sizes that will override the selected (or default) chunking map. The distinction between elements and bytes is subtle yet crucial to understand. Elements refers to values of an array, whereas bytes refers to the memory size required to hold the elements. These measures differ by a factor of four or eight for NC_FLOAT or NC_DOUBLE, respectively. The option --cnk_scl takes an argument sz_lmn measured in elements. The options --cnk_byt, --cnk_csh, and --cnk_min take arguments sz_byt measured in bytes. Use the --cnk_min=sz_byt option to set the minimum size in bytes (not elements) of variables to chunk. This threshold is intended to restrict use of chunking to variables for which it is efficient. By default this minimum variable size for chunking is twice the system blocksize (when available) and is 8192 bytes otherwise. Users may set this to any value with the --cnk_min=sz_byt switch. To guarantee that chunking is performed on all arrays, regardless of size, set the minimum size to one byte (not to zero bytes). hyperslab ncpdq packing The chunking implementation is similar to a hybrid of the ncpdq packing policies (ncpdq netCDF Permute Dimensions Quickly) and hyperslab specifications (Hyperslabs). Each aspect is intended to have a sensible default, so that many users only need to set one switch to obtain sensible chunking. Power users can tune chunking with the three switches in tandem to obtain optimal performance. By default, NCO preserves the chunking characteristics of the input file in the output file This behavior became the default in November 2014 with NCO version 4.4.7. Prior versions would always use netCDF default chunking in the output file when no NCO chunking switches were activated, regardless of the chunking in the input file.. In other words, preserving chunking requires no switches or user intervention. Users specify the desired chunking policy with the -P switch (or its long option equivalents, --cnk_plc and --chunk_policy) and its cnk_plc argument. As of August, 2014, six chunking policies are implemented:&linebreak; all g2d g3d r1d xpl xst cnk_all cnk_g2d cnk_g3d cnk_r1d cnk_xpl cnk_xst plc_all plc_g2d plc_g3d plc_r1d plc_xpl plc_xst Chunk All Variables Definition: Chunk all variables possible. For obvious reasons, scalar variables cannot be chunked.&linebreak; Alternate invocation: ncchunk&linebreak; cnk_plc key values: all, cnk_all, plc_all&linebreak; Mnemonic: All&linebreak; Chunk Variables with at least Two Dimensions [default] Definition: Chunk all variables possible with at least two dimensions&linebreak; Alternate invocation: none&linebreak; cnk_plc key values: g2d, cnk_g2d, plc_g2d&linebreak; Mnemonic: Greater than or equal to 2 Dimensions&linebreak; Chunk Variables with at least Three Dimensions Definition: Chunk all variables possible with at least three dimensions&linebreak; Alternate invocation: none&linebreak; cnk_plc key values: g3d, cnk_g3d, plc_g3d&linebreak; Mnemonic: Greater than or equal to 3 Dimensions&linebreak; Chunk One-Dimensional Record Variables Definition: Chunk all 1-D record variables&linebreak; Alternate invocation: none&linebreak; Any specified (with --cnk_dmn) record dimension chunksizes will be applied only to 1-D record variables (and to no other variables). Other dimensions may be chunked with their own --cnk_dmn options that will apply to all variables. cnk_plc key values: r1d, cnk_r1d, plc_r1d&linebreak; Mnemonic: Record 1-D variables&linebreak; Chunk Variables Containing Explicitly Chunked Dimensions Definition: Chunk all variables possible that contain at least one dimension whose chunksize was explicitly set with the --cnk_dmn option. Alternate invocation: none&linebreak; cnk_plc key values: xpl, cnk_xpl, plc_xpl&linebreak; Mnemonic: EXPLicitly specified dimensions&linebreak; Chunk Variables that are already Chunked Definition: Chunk only variables that are already chunked in the input file. When used in conjunction with cnk_map=xst this option preserves and copies the chunking parameters from the input to the output file. Alternate invocation: none&linebreak; cnk_plc key values: xst, cnk_xst, plc_xst&linebreak; Mnemonic: EXiSTing chunked variables&linebreak; Chunk Variables with NCO recommendations Definition: Chunk all variables according to NCO best practices. This is a virtual option that ensures the chunking policy is (in the subjective opinion of the authors) the best policy for typical usage. As of NCO version 4.4.8 (February, 2015), this virtual policy implements map_rew for 3-D variables and map_lfp for all other variables.&linebreak; Alternate invocation: none&linebreak; cnk_plc key values: nco, cnk_nco, plc_nco&linebreak; Mnemonic: NetCDFOperator&linebreak; Unchunking Definition: Unchunk all variables possible. The HDF5 storge layer requires that record variables (i.e., variables that contain at least one record dimension) must be chunked. Also variables that are compressed or use checksums must be chunked. Such variables cannot be unchunked.&linebreak; Alternate invocation: ncunchunk&linebreak; cnk_plc key values: uck, cnk_uck, plc_uck, none, unchunk&linebreak; Mnemonic: UnChunK&linebreak;

Equivalent key values are fully interchangeable. Multiple equivalent options are provided to satisfy disparate needs and tastes of NCO users working with scripts and from the command line. chunking map degenerate dimension cnk_map -M cnk_map --cnk_map cnk_map --map cnk_map The chunking algorithms must know the chunksizes of each dimension of each variable to be chunked. The correspondence between the input variable shape and the chunksizes is called the chunking map. The user specifies the desired chunking map with the -M switch (or its long option equivalents, --cnk_map and --chunk_map) and its cnk_map argument. Nine chunking maps are currently implemented:&linebreak; dmn scl prd lfp rd1 xst rew nc4 nco cnk_dmn cnk_scl cnk_prd cnk_lfp cnk_rd1 cnk_xst cnk_rew cnk_nc4 cnk_nco map_dmn map_scl map_prd map_lfp map_rd1 map_xst map_rew map_nc4 map_nco Chris Barker Chunksize Equals Dimension Size Definition: Chunksize defaults to dimension size. Explicitly specify chunksizes for particular dimensions with --cnk_dmn option. In most cases this chunksize will be applied in all variables that contain the specified dimension. Some chunking policies noted above allow (fxm), and others (fxm) prevent this chunksize from applying to all variables.&linebreak; cnk_map key values: dmn, cnk_dmn, map_dmn&linebreak; Mnemonic: DiMeNsion&linebreak; Chunksize Equals Dimension Size except Record Dimension Definition: Chunksize equals dimension size except record dimension has size one. Explicitly specify chunksizes for particular dimensions with --cnk_dmn option.&linebreak; cnk_map key values: rd1, cnk_rd1, map_rd1&linebreak; Mnemonic: Record Dimension size 1&linebreak; Chunksize Equals Scalar Size Specified Definition: Chunksize for all dimensions is set with the --cnk_scl=sz_lmn option. For this map sz_lmn itself becomes the chunksize of each dimension. This is in contrast to the cnk_prd map, where the rth root of sz_lmn) becomes the chunksize of each dimension.&linebreak; cnk_map key values: scl, cnk_scl, map_scl&linebreak; Mnemonic: SCaLar&linebreak; cnk_map key values: xpl, cnk_xpl, map_xpl&linebreak; Mnemonic: EXPLicitly specified dimensions&linebreak; Chunksize Product Matches Scalar Size Specified Definition: The product of the chunksizes for each variable matches (approximately equals) the size specified with the --cnk_scl=sz_lmn option. A dimension of size one is said to be degenerate. For a variable of rank R (i.e., with R non-degenerate dimensions), the chunksize in each non-degenerate dimension is (approximately) the Rth root of sz_lmn. This is in contrast to the cnk_scl map, where sz_lmn itself becomes the chunksize of each dimension.&linebreak; cnk_map key values: prd, cnk_prd, map_prd&linebreak; Mnemonic: PRoDuct&linebreak; Chunksize Lefter Product Matches Scalar Size Specified Definition: The product of the chunksizes for each variable (approximately) equals the size specified with the --cnk_byt=sz_byt (not --cnk_dfl) option. This is accomplished by using dimension sizes as chunksizes for the rightmost (most rapidly varying) dimensions, and then &textldquo;flexing&textrdquo; the chunksize of the leftmost (least rapidly varying) dimensions such that the product of all chunksizes matches the specified size. All L-dimensions to the left of and including the first record dimension define the left-hand side. To be precise, if the total size (in bytes) of the variable is var_sz, and if the specified (with --cnk_byt) product of the R &textldquo;righter&textrdquo; dimensions (those that vary more rapidly than the first record dimension) is sz_byt, then chunksize (in bytes) of each of the L lefter dimensions is (approximately) the Lth root of

var_sz/sz_byt. This map was first proposed by Chris Barker.&linebreak; cnk_map key values: lfp, cnk_lfp, map_lfp&linebreak; Mnemonic: LeFter Product&linebreak; Chunksize Equals Existing Chunksize Definition: Chunksizes are copied from the input to the output file for every variable that is chunked in the input file. Variables not chunked in the input file will be chunked with default mappings.&linebreak; cnk_map key values: xst, cnk_xst, map_xst&linebreak; Mnemonic: EXiST&linebreak; Chunksize Balances 1D and (N-1)-D Access to N-D Variable [default for netCDF4 input] Definition: Chunksizes are chosen so that 1-D and (N-1)-D hyperslabs of 3-D variables (e.g., point-timeseries or latitude/longitude surfaces of 3-D fields) both require approximately the same number of chunks. Hence their access time should be balanced. Russ Rew explains the motivation and derivation for this strategy http://www.unidata.ucar.edu/blogs/developer/en/entry/chunking_data_choosing_shapeshere.&linebreak; cnk_map key values: rew, cnk_rew, map_rew&linebreak; Mnemonic: Russ REW&linebreak; Chunksizes use netCDF4 defaults Definition: Chunksizes are determined by the underlying netCDF library. All variables selected by the current chunking policy have their chunksizes determined by netCDF library defaults. The default algorithm netCDF uses to determine chunksizes has changed through the years, and thus depends on the netCDF library version. This map can be used to reset (portions of) previously chunked files to default chunking values.&linebreak; cnk_map key values: nc4, cnk_nc4, map_nc4&linebreak; Mnemonic: NetCDF4&linebreak; Chunksizes use NCO recommendations [default for netCDF3 input] Definition: Chunksizes are determined by the currently recommended NCO map. This is a virtual option that ensures the chunking map is (in the subjective opinion of the authors) the best map for typical usage. As of NCO version 4.4.9 (May, 2015), this virtual map calls map_lfp.&linebreak; cnk_map key values: nco, cnk_nco, map_nco&linebreak; Mnemonic: NetCDFOperator&linebreak;

It is possible to combine the above chunking map algorithms with user-specified per-dimension (though not per-variable) chunksizes that override specific chunksizes determined by the maps above. The user specifies the per-dimension chunksizes with the (equivalent) long options --cnk_dmn or --chunk_dimension). The option takes two comma-separated arguments, dmn_nm,sz_lmn, which are the dimension name and its chunksize (in elements, not bytes), respectively. The --cnk_dmn option may be used as many times as necessary. The default behavior of chunking depends on several factors. As mentioned above, when no chunking options are explicitly specified by the user, then NCO preserves the chunking characteristics of the input file in the output file. This is equivalent to specifying both cnk_plc and cnk_map as &textldquo;existing&textrdquo;, i.e., --cnk_plc=xst --cnk_map=xst. If output netCDF4 files are chunked with the default behavior of the netCDF4 library. When any chunking parameter except cnk_plc or cnk_map is specified (such as cnk_dmn or cnk_scl), then the &textldquo;existing&textrdquo; policy and map are retained and the output chunksizes are modified where necessary in accord with the user-specified parameter. When cnk_map is specified and cnk_plc is not, then NCO picks (what it thinks is) the optimal chunking policy. This has always been policy map_g2d. When cnk_plc is specified and cnk_map is not, then NCO picks (what it thinks is) the optimal chunking map. This has always been map map_rd1. To start afresh and return to netCDF4 chunking defaults, select cnk_map=nc4. <a name="xmp_cnk"></a>  <a name="xmp_chunk"></a>

# Simple chunking and unchunking
ncks -O -4 --cnk_plc=all     in.nc out.nc # Chunk in.nc
ncks -O -4 --cnk_plc=unchunk in.nc out.nc # Unchunk in.nc

# Chunk data then unchunk it, printing informative metadata
ncks -O -4 -D 4 --cnk_plc=all ~/nco/data/in.nc ~/foo.nc
ncks -O -4 -D 4 --cnk_plc=uck ~/foo.nc ~/foo.nc

# Set total chunksize to 8192 B
ncks -O -4 -D 4 --cnk_plc=all --cnk_byt=8192 ~/nco/data/in.nc ~/foo.nc

# More complex chunking procedures, with informative metadata
ncks -O -4 -D 4 --cnk_scl=8 ~/nco/data/in.nc ~/foo.nc
ncks -O -4 -D 4 --cnk_scl=8 dstmch90_clm.nc ~/foo.nc
ncks -O -4 -D 4 --cnk_dmn lat,64 --cnk_dmn lon,128 dstmch90_clm.nc \ 
 ~/foo.nc 
ncks -O -4 -D 4 --cnk_plc=uck ~/foo.nc ~/foo.nc
ncks -O -4 -D 4 --cnk_plc=g2d --cnk_map=rd1 --cnk_dmn lat,32 \
 --cnk_dmn lon,128 dstmch90_clm_0112.nc ~/foo.nc

# Chunking works with all operators...
ncap2 -O -4 -D 4 --cnk_scl=8 -S ~/nco/data/ncap2_tst.nco \ 
 ~/nco/data/in.nc ~/foo.nc
ncbo -O -4 -D 4 --cnk_scl=8 -p ~/nco/data in.nc in.nc ~/foo.nc
ncecat -O -4 -D 4 -n 12,2,1 --cnk_dmn lat,32 \ 
 -p /data/zender/dstmch90 dstmch90_clm01.nc ~/foo.nc
ncflint -O -4 -D 4 --cnk_scl=8 ~/nco/data/in.nc ~/foo.nc
ncpdq -O -4 -D 4 -P all_new --cnk_scl=8 -L 5 ~/nco/data/in.nc ~/foo.nc
ncrcat -O -4 -D 4 -n 12,2,1 --cnk_dmn lat,32 \ 
 -p /data/zender/dstmch90 dstmch90_clm01.nc ~/foo.nc
ncwa -O -4 -D 4 -a time --cnk_plc=g2d --cnk_map=rd1 --cnk_dmn lat,32 \ 
 --cnk_dmn lon,128 dstmch90_clm_0112.nc ~/foo.nc

<a name="r1d"></a>  Chunking policy r1d changes the chunksize of 1-D record variables (and no other variables) to that specified (with --cnk_dmn) chunksize. Any specified record dimension chunksizes will be applied to 1-D record variables only. Other dimensions may be chunked with their own --cnk_dmn options that will apply to all variables. For example,

ncks --cnk_plc=r1d --cnk_dmn=time,1000. in.nc out.nc

This sets time chunks to 1000 only in 1-D record variables. Without the r1d policy, time chunks would change in all variables. record dimension It is appropriate to conclude by informing users about an aspect of chunking that may not be expected. Three types of variables are always chunked: Record variables, Deflated (compressed) variables, and Checksummed variables. Hence all variables that contain a record dimension are also chunked (since data must be chunked in all dimensions, not just one). Unless otherwise specified by the user, the other (fixed, non-record) dimensions of record variables are assigned default chunk sizes. The HDF5 layer does all this automatically to optimize the on-disk variable/file storage geometry of record variables. Do not be surprised to learn that files created without any explicit instructions to activate chunking nevertheless contain chunked variables. <a name="qnt"></a>  <a name="qnt_alg"></a>  <a name="quantize_algorithm"></a>  <a name="ppq"></a>  <a name="PPQ"></a>  <a name="gbr"></a>  <a name="GBR"></a>  <a name="BitGroom"></a>  <a name="GranularBitRound"></a>  <a name="BitShave"></a>  <a name="BitSet"></a>  <a name="DigitRound"></a>  <a name="BitGroomRound"></a>  <a name="HalfShave"></a>  <a name="BruteForce"></a>  <a name="BitRound"></a>

Quantization AlgorithmsCompressionChunkingShared features

Quantization Algorithms PPQ GBR BAA BitGroom Granular BitRound BitShave BitSet DigitRound BitGroomRound HalfShave BruteForce BitRound --qnt_alg --quantize_algorithm Quantization algorithm Availability: ncbo, ncecat, nces, ncflint, ncks, ncpdq, ncra, ncrcat, ncwa&linebreak; Short options: None&linebreak; Long options: --qnt_alg alg_nm&linebreak; --quantize_algorithm alg_nm&linebreak; As of version 5.2.5 (May 2024), NCO supports a simple API to specify quantization algorithms. This method uses the --qnt_alg=alg_nm option, where alg_nm is a happy, friendly, abbreviation or full English string for the quantization algorithm name. ncks -7 -L 1 --qnt default=3 in.nc out.nc # Granular BitRound (NSD) ncks -7 -L 1 --qnt_alg=btg --qnt default=3 in.nc out.nc # BitGroom (NSD) ncks -7 -L 1 --qnt_alg=shv --qnt default=3 in.nc out.nc # BitShave (NSD) ncks -7 -L 1 --qnt_alg=set --qnt default=3 in.nc out.nc # BitSet (NSD) ncks -7 -L 1 --qnt_alg=dgr --qnt default=3 in.nc out.nc # DigitRound (NSD) ncks -7 -L 1 --qnt_alg=gbr --qnt default=3 in.nc out.nc # Granular BitRound (NSD) ncks -7 -L 1 --qnt_alg=bgr --qnt default=3 in.nc out.nc # BitGroomRound (NSD) ncks -7 -L 1 --qnt_alg=sh2 --qnt default=9 in.nc out.nc # HalfShave (NSB) ncks -7 -L 1 --qnt_alg=brt --qnt default=3 in.nc out.nc # BruteForce (NSD) ncks -7 -L 1 --qnt_alg=btr --qnt default=9 in.nc out.nc # BitRound (NSB) The algorithm strings shown above give only a hint as to the flexibility of synonyms recognized as algorithm names. For example, synonyms for btr include (case-insensitive versions of) bitround, bit round, and bit-round. Try it and see! Behind the scenes, NCO translates the algorithm name to an enumerated bit-adjustment-algorithm BAA value. The BAA interface is undocumented and unsupported, however. This is to give the maintainers to change the unlying algorithm organization. ncks -7 -L 1 --ppc default=3 in.nc out.nc # Granular BitRound (NSD) ncks -7 -L 1 --baa=0 --ppc default=3 in.nc out.nc # BitGroom (NSD) ncks -7 -L 1 --baa=1 --ppc default=3 in.nc out.nc # BitShave (NSD) ncks -7 -L 1 --baa=2 --ppc default=3 in.nc out.nc # BitSet (NSD) ncks -7 -L 1 --baa=3 --ppc default=3 in.nc out.nc # DigitRound (NSD) ncks -7 -L 1 --baa=4 --ppc default=3 in.nc out.nc # Granular BitRound (NSD) ncks -7 -L 1 --baa=5 --ppc default=3 in.nc out.nc # BitGroomRound (NSD) ncks -7 -L 1 --baa=6 --ppc default=9 in.nc out.nc # HalfShave (NSB) ncks -7 -L 1 --baa=7 --ppc default=3 in.nc out.nc # BruteForce (NSD) ncks -7 -L 1 --baa=8 --ppc default=9 in.nc out.nc # BitRound (NSB) Although the qnt_alg and BAA APIs are equivalent, the BAA values may change in the future so using the qnt_alg interface is recommended. <a name="bg"></a>  <a name="bitgrooming"></a>  <a name="Bit-Grooming"></a>  <a name="BG"></a>  <a name="ppc"></a>  <a name="PPC"></a>  <a name="compression"></a>  <a name="cmp"></a>  <a name="nsd"></a>  <a name="NSD"></a>  <a name="dsd"></a>  <a name="DSD"></a>  <a name="qnt"></a>

CompressionDeflationQuantization AlgorithmsShared features

Compression --cmp_sng --compression codecs Zstandard Bzip2 Granular BitRound --ppc --precision_preserving_compression --quantize --qnt lossy compression quantization rounding Huffman coding gzip zlib Availability: ncbo, ncecat, nces, ncflint, ncks, ncpdq, ncra, ncrcat, ncwa&linebreak; Short options: None&linebreak; Long options: --ppc var1[,var2[,...]]=prc,&linebreak; --precision_preserving_compression var1[,var2[,...]]=prc,&linebreak; --qnt var1[,var2[,...]]=prc&linebreak; --quantize var1[,var2[,...]]=prc&linebreak; --qnt_alg alg_nm&linebreak; --quantize_algorithm alg_nm&linebreak; --cmp cmp_sng&linebreak; --cmp codec1[,params1[|codec2[,params2[|...]]]]&linebreak; --codec codec1[,params1[|codec2[,params2[|...]]]]&linebreak; Compression is a rapidly developing area in geoscientific software, and NCO is no exception. Documentation of these features can quickly become out-of-date. A brief review of compression support from the early days until now: NCO first supported Linear Packing with ncpdq in 2004. The advent of netCDF4 allowed NCO to support lossless compression with the DEFLATE algorithm beginning in 2007. Nearly a decade elapsed before the next features came in 2015 when, thanks to support from the DOE, we developed the lossy BitGroom quantization algorithm. NCO soon introduced a flexible per-variable API (--ppc and --qnt) to support it, and its relatives BitShave, and BitSet, in all arithmetic operators. This work helped spur interest and research on other Bit Adjustment Algorithms (BAAs) that perform quantization. In 2020 we reduced the quantization error of BitGroom by implementing IEEE-rounding (aka BitRound), and newer quantization algorithms including BitGroomRound, HalfShave, and DigitRound R. Kouznetsov contributed the masking techinques used in BitRound, BitGroomRound, and HalfShave. Thanks Rostislav!. These algorithms are all accessible via the --baa option. In 2020 NSF awarded support for us to develop the Community Codec Respository to put a friendlier API on the HDF5 shared-library filter mechanism so that all netCDF users could shift to more modern and efficient codecs than DEFLATE. This strategy aligned with needs of operational forecasters supported by software engineers at the NOAA Environmental Modeling Center (EMC), who contributed to developing and testing the CCR E. Hartnett of NOAA EMC is co-founder and co-maintainer of the CCR. Thanks Ed!. By the end of 2021 the CCR supported codecs for Bzip2, Zstandard, BitGroom, and Granular BitRound. The CCR performance helped persuade the netCDF team at Unidata of the importance and practicality of expanding compression options in the base netCDF C-library beyond the venerable DEFLATE algorithm. Together, we merged the quantization, Bzip2, and Zstandard codecs and API from the CCR into what became (in June, 2022) netCDF version 4.9.0 D. Heimbigner (Unidata) helped implement all these features into netCDF. Thanks Dennis!. NCO version 5.1.0 (released in July, 2022) unified these advances by fully supporting its own quantization methods, CCR codecs, generic (i.e., non-CCR) HDF5 codecs, and the quantization algorithms and modern lossless codecs in netCDF 4.9.0. Access to the quantization and compression options is available through three complementary, backwards-compatible, and overlapping APIs designed to accomodate the successive generations of compression features. The original generation of compression options remain accessible through the standard ncpdq (for Linear Packing) and NCO-wide -L option (or its synonyms --deflate and --dfl_lvl) for DEFLATE. The second generation of compression options refers to the --qnt, --ppc, and --baa (and related synonyms) options that control the type and level of quantization algorithm, and the variables to operate on. These options call quantization and rounding routines implemented within NCO itself, rather than in external libraries. The new --cmp_sng (and synonyms) option provides an API for NCO to invoke all lossy and lossless codecs in external libraries, including the netCDF C-library, the CCR, and generic HDF5 codecs. The --cmp_sng (and synonyms --cmp and --compression) options take as argument a string cmp_sng which contains a list of quantization and compression algorithms and their respective parameters. The cmp_sng must adhere to a superset of the filter-list API introduced by the nccopy command and reported in the netCDF _Filter attribute. This API uses the UNIX pipe symbole | to separate the codecs applied as HDF5 filters to a variable: % ncks --hdn -m in.nc | grep _Filter u:_Filter = "307,2|32015,3" ; U:_Filter = "32001,2,2,4,4185932,5,1,1" ; % ncdump -h -s in.nc | grep _Filter u:_Filter = "307,2|32015,3" ; U:_Filter = "32001,2,2,4,4185932,5,1,1" ; The above example shows variables compressed with two successive codecs. The variable u was compressed with codecs with HDF5 filter IDs 307 and 32015, respectively. NCO translates these mysterious HDF5 numeric filter IDs into filter names in the CDL comments when invoked with a higher debugging level: % ncks -D 2 --hdn -m in.nc | grep _Filter u:_Filter = "307,2|32015,3" ; // char codec(s): Bzip2, Zstandard U:_Filter = "32001,2,2,4,4185932,5,1,1" ; // char codec(s): \ Blosc Shuffle, Blosc LZ4 <a name="qnt"></a>  <a name="dfl"></a>  <a name="zst"></a>  <a name="bz2"></a>  <a name="shf"></a>  <a name="gbr"></a>  <a name="btg"></a>  <a name="bgr"></a>  <a name="btr"></a>  <a name="f32"></a>  <a name="dns"></a>  dfl zst bz2 shf gbr btg bgr f32 dns Shuffle Fletcher32 You may provide any operator (besides ncrename and ncatted) with a cmp_sng comprised of an arbitrary number of HDF5 filters specified either by numeric ID or by name, including NCO-supported synonyms: % ncks --cmp="307,2|32015,3" in.nc out.nc # Filter numeric IDs % ncks --cmp="Bzip2,2|Zstandard,3" in.nc out.nc # Filter names % ncks --cmp="bzp,2|zst,3" in.nc out.nc # Filter abbreviations NCO also uses this API to invoke the netCDF quantization algorithms such as Granular BitGroom and BitRound. netCDF records the operation of quantization algorithms in a _Quantize attribute. % ncks --cmp="gbr,2|zst,3" in.nc out.nc % ncks -D 2 --hdn -m out.nc | grep 'Filt|Quant' u:_QuantizeGranularBitRoundNumberOfSignificantDigits = 2 ; u:_Filter = "32015,3" ; // char Codec(s): Zstandard NCO calls the filters in the order specified. Thus, it is important to follow the above example and to specify compression pre-filters like quantization and Shuffle prior to any lossless codecs. However, the netCDF library imposes rules that can override the user-specified order for the Shuffle and Fletcher32 filters as described below (these are always helpful in real-world situations). The --cmp option specifies a global compression configuration. This is fine for lossless codecs, since there is little evidence to motivate per-variable lossless compression levels for real-world data. By contrast, it is often desirable to configure quantization levels on a per-variable basis. This is because the real information content of geophysical variables can differ strongly due to a multitude of factors including the field meaning, spatial location, and dimensional units. NCO applies any quantization filter specified in cmp_sng uniformly to all variables encountered (the --qnt quantization option/API is still available for per-variable quantization parameters, as discussed below). The one exception is that NCO prohibits quantization of &textldquo;coordinate-like variables&textrdquo;. Variables that are tradiational (1-dimensional) coordinates, or that are mentioned in the values of CF bounds, climatology, coordinates, or grid_mapping attributes, all count as coordinate-like variables. Such variables include quantities like gridcell areas and boundaries. NCO eschews quantizing such variables to avoid unforeseen or unanticipated degradation of numerical accuracy due to propagation of quantization errors in post-processing. Specifying the compression string with codec names should make lossy and lossless compression easier for users to understand and employ. There are, however, a few wrinkles and legacy conventions that might surprise users when first encountered. For instance, the codecs for DEFLATE, Shuffle, and Fletcher32 have always been built-in to netCDF. Users can instruct NCO to apply these filters with the new API, yet their application to a dataset will still be reported using the &textldquo;old&textrdquo; per-filter attributes: % ncks --cmp="fletcher32|shuffle|deflate" in.nc out.nc % ncks --hdn -m out.nc ... u:_DeflateLevel = 1 ; u:_Shuffle = "true" ; u:_Fletcher32 = "true" ; ... As this example shows, it is not required to give filter parameter arguments to all filters. When the user omits filter parameters (e.g., compression level, NSD, NSB, or other filter configurator) for select filters that require such a parameter, NCO automatically inserts an appropriate default filter parameter. NCO assumes default parameters 1, 4, 1, and 3, for the lossless filters DEFLATE, Shuffle, Bzip2, and Zstandard, respectively. NCO assumes default parameters 3, 3, and 9, for the lossy quantization algorithms BitGroom, Granular BitGroom, and BitRound, respectively. Note that the netCDF filter for the Fletcher32 checksum algorithm does not accept a parameter argument, and NCO ignores any parameters provided to this filter. % ncks --cmp="fletcher32|shuffle|granularbr|deflate|zstandard" ... % ncks --cmp="f32|shf|gbr|dfl|zst" ... # Shorthand, default parameters % ncks --cmp="f32|shf,4|gbr,3|dfl,1|zst,3" ... # Explicit parameters The cmd_sng option supports an arbitrary number of filters. An example that compressess then reads a file with most netCDF-supported algorithms shows the mixture of filter-specific attributes (_Shuffle, _DeflateLevel, _Fletcher32) and generic filter attributes (_Filter) that result: % ncks --cmp='f32|shf|gbr|dfl|bz2|zst' in.nc out.nc % ncks --hdn -m out.nc float u(time) ; u:long_name = "Zonal wind speed" ; u:units = "meter second-1" ; u:_QuantizeGranularBitRoundNumberOfSignificantDigits = 3 ; u:_Storage = "chunked" ; u:_ChunkSizes = 1024 ; u:_Filter = "307,1|32015,3" ; u:_DeflateLevel = 1 ; u:_Shuffle = "true" ; u:_Fletcher32 = "true" ; u:_Endianness = "little" ; Note that the _Filter value is a valid cmp_sng for use as an argument to the --cmp_sng option. This enables users to easily duplicate the compression settings of one dataset in another dataset. One can also find the global cmp_sng that NCO used to compress a dataset in the global history attribute. In the absence of instructions to the contrary, NCO preserves the compression settings of datasets that it copies or subsets. It simply defaults to copying the per-variable compression settings from the input file. If the copy or subset command includes global compression instructions (i.e., the --cmp or -L options), those instructions will override the per-variable settings in the input file. The user can eliminate all compression filters by setting cmp_sng to the special value none (or to its synonyms uncompress, decompress, defilter, or unset). % ncks --cmp='f32|shf|gbr|dfl|bz2|zst' in.nc out.nc % ncks --cmp='none' out.nc none.nc % ncks --hdn -m none.nc float u(time) ; u:long_name = "Zonal wind speed" ; u:units = "meter second-1" ; u:_QuantizeGranularBitRoundNumberOfSignificantDigits = 3 ; u:_Storage = "chunked" ; u:_ChunkSizes = 1024 ; u:_Endianness = "little" ; The uncompressed copy has no filter attributes remaining because all filters have been removed. The _Quantize attribute remains because the quantization was applied as an internal algorithm not as an HDF5 filter. In contrast to lossless compression filters, lossy quantization algorithms can never be &textldquo;removed&textrdquo; much less undone because, by definition, they are lossy. Removing compression is &textldquo;all or nothing&textrdquo; in that there is currently no way to remove only one or a few codecs and leave the rest in place. To do that, the user must instead recompress the dataset using a cmp_sng that includes only the desired codecs. % ncks --cmp='shf|zst' ... # Compress % ncks --cmp='none' ... # Decompress (remove Shuffle and Zstd) % ncks --cmp=zst,4 ... # Recompress to new Zstd level, no Shuffle Shuffle is an important filter than can boost lossless compression ratios of geoscientific data by 10&textndash;20% (fgr:qnt_cr_dfl). Figurefgr:qnt_cr_dfl fgr/qnt_cr_dfl5.5in Quantization and then compression by DEFLATE, including the contribution of Shuffle. Both the netCDF library and NCO continue to treat the Shuffle filter specially. If the Shuffle and DEFLATE algorithms are both invoked through the standard netCDF API (i.e., nc_def_var_deflate()), then the netCDF library ensures that the Shuffle filter is called before DEFLATE, indeed before any filter except Fletcher32 (which performs a checksum, not compression). This behavior is welcome as it avoids inadvertent mis-use of Shuffle. Prior to version 5.1.0, NCO always invoked Shuffle with DEFLATE, and did not expose the Shuffle filter to user control. NCO now exposes Shuffle to user control for all filters. To preserve backward compatibility, invoking the DEFLATE algorithm with the dfl or deflate names (or with the numeric HDF5 filter ID of 1) still sets the Shuffle filter to true. To invoke DEFLATE without Shuffle, use the special filter names dns or DEFLATE No Shuffe. Specifying the Shuffle filter along with any Blosc compressor causes NCO to invoke the Blosc version of Shuffle instead of the HDF5 version of Shuffle. The Blosc Shuffle should execute more rapidly because it takes advantage of AVX2 instructions, etc. In summary, users must explicitly request Shuffle for all non-DEFLATE codecs, otherwise Shuffle will not be employed prior to those codecs. % ncks --cmp='dfl' ... # Invoke Shuffle then DEFLATE % ncks --cmp='shf|dfl' ... # Same (default Shuffle stride 4-bytes) % ncks --cmp='dns' ... # Invoke DEFLATE (no Shuffle) % ncks --cmp='shf|dns' ... # Invoke Shuffle then DEFLATE % ncks --cmp='zst' ... # Invoke Zstandard (no Shuffle) % ncks --cmp='shf|zst' ... # Invoke Shuffle then Zstandard % ncks --cmp='zst|shf' ... # Same (netCDF enforces Shuffle-first rule) % ncks --cmp='shf' ... # Invoke only Shuffle % ncks --cmp='shf,8|zst' ... # Shuffle stride 8-bytes then Zstandard % ncks --cmp='shf,8|dfl' ... # Shuffle stride remains default 4-bytes % ncks --cmp='bls' ... # Invoke Blosc (LZ by default) without Shuffle % ncks --cmp='shf|bls' ... # Invoke Blosc (not HDF5) Shuffle, then Blosc LZ The last example above shows how to invoke Shuffle with non-default byte stride Full disclosure: Documentation of the meaning of the Shuffle parameter is scarce. I think though am not certain that the Shuffle parameter refers to the number of contiguous byte-groups that the algorithm rearranges a chunk of data into. I call this the stride. Thus the default stride of 4 means that Shuffle rearranges a chunk of 4-byte integers into four consecutive sequences, the first comprises all the leading bytes, the second comprises all the second bytes, etc. A well-behaved stride should evenly divide the number of bytes in a data chunk.. The netCDF library, and thus NCO, always uses the default Shuffle stride (4 bytes) with the DEFLATE filter. Specifying a different stride only has an effect with non-DEFLATE filters. This ensures NCO&textrsquo;s default behavior with DEFLATE does not change. As explained above, invoke DEFLATE with --cmp=dns instead of --cmp=dfl if you wish to suppress the otherwise automatic invocation of Shuffle. Note that NCO and netCDF implement their quantization algorithms internally, whereas the CCR implements them as external shared-library codecs (valid only with netCDF4 files). Since these quantization algorithms leave data in IEEE format no codec/filter is required to read (or write) them. Quantization therefore works with netCDF3 datasets, not just netCDF4. netCDF applications that attempt to read data compressed with shared-library filters must be linked to the same shared filters or the &textldquo;decompression&textrdquo; step will fail. Datasets produced with netCDF or CCR-supported codecs (Bzip2, DEFLATE, Zstandard) will be readable by all users who upgrade to netCDF 4.9.0 or later or who install the CCR. There is no difference between a losslessly compressed dataset produced with a CCR-supplied vs.&noeos; a netCDF-supplied filter. However, reading a dataset quantized by a CCR filter (e.g., BitGroom or GranularBG) requires access to the CCR filter, which forces users to take an extra step to install the CCR. This is an unfortunate artifact of implementing quantization as a codec (which the CCR must do) vs.&noeos; an internal numerical function (which netCDF and NCO do). Where possible, people should encode datasets with netCDF-supported algorithms and codecs in preference to CCR or raw HDF5 codecs. Doing so will increase dataset portability. Limitations of Current Compression API The NCO filter API will evolve in a (hopefully) backward-compatible manner as experience and feedback are gained. All filter parameters are eventually passed to HDF5 as unsigned integers. By contrast, the current API treats all input arguments in cmp_sng signed integers for ease-of-use. For example, it is easier to specify a Zstandard filter level of negative one as zstd,-1 than as zstd,4294967295. NCO currently has no easy way to specify the floating-point configuration parameters required by some CCR filters and many external filters. That said, most of the code to support the filter parameter syntax documented at https://docs.unidata.ucar.edu/netcdf-c/current/filters.html and implemented in ncgen and nccopy is ready and we expect to support that syntax in NCO 5.0.9. That syntax is backward compatible with the integer-only input assumptions already embedded in NCO 5.1.0. In the meantime, we request your feedback, use-cases, and suggestions before adopting a final approach. Another limitation of NCO 5.1.0 was that the Blosc filters would fail without explanation. This is why the manual does not yet document much about Blosc filters. The Blosc issues were fixed upstream in netCDF version 4.9.1. netCDF 4.9.0 contained some other inadvertent mistakes that were fixed in 4.9.1. First, the quantization algorithms internal to netCDF work only on datasets of type NETCDF4 in netCDF 4.9.0. A recently discovered bug prevents them from working on NETCDF4_CLASSIC-format datasets Quantization may never be implemented in netCDF for any CLASSIC or other netCDF3 formats since there is no compression advantage to doing so. Use the NCO implementation to quantize to netCDF3 output formats.. The fix to the NETCDF4_CLASSIC bug was officially released in netCDF 4.9.1. Note that this bug did not affect the same quantization algorithms as implemented in NCO (or in the CCR, for that matter). In other words, quantization to netCDF3 and NETCDF4_CLASSIC-format output files always works when invoked through the --qnt (not --cmp) option. This restriction will only affects netCDF prior to 4.9.1. Per-variable quantization settings must also be invoked through --qnt (not --cmp) for all output formats, until and unless this feature is migrated to --cmp (there are no plans to do so). Another sticky wicket expected that was fixed in netCDF 4.9.1 was the use of the Blosc codec. NCZarr uses Blosc internally, however the HDF5 Blosc codec in netCDF 4.9.0 was not robust. We plan to advertise the advantages of Blosc more fully in future versions of NCO. Feedback on any or all of these constraints is welcome. Best Practices for Real World Lossy Compression The workflow to compress large-scale, user-facing datasets requires consideration of multiple factors including storage space, speed, accuracy, information content, portability, and user-friendliness. We have found that this blend is best obtained by using per-variable quantization together with global lossless compression. NCO can configure per-variable quantization levels together with global lossless filters when the quantization algorithm is specified with the --qnt option/API and the lossless filters are specified with the --cmp_sng option/API. Granular BitRound (GranularBR) and BitRound are state-of-the-art quantization algorithms that are configured with the number of significant decimal digits (NSD) or number of significant bits (NSB), respectively. One can devise an optimal approach in about four steps: First, select a global lossless codec that produces a reasonable tradeoff between compression ratio (CR) and speed. The speed of the final workflow will depend mostly on the lossless codec employed and its compression settings. Try a few standard codecs with Shuffle to explore this tradeoff: ncks -7 --cmp='shf|zst' ... ncks -7 --cmp='shf|dfl' ... ncks -7 --cmp='shf|bz2' ... The -7 switch creates output in NETCDF4_CLASSIC format. This highly portable format supports codecs and is also mandated by many archives such as CMIP6. The only other viable format choice is -4 for NETCDF4. That format must be used if any variables make use of the extended netCDF4 atomic types. Our experience with ESM data shows that Bzip2 often yields the best CR (fgr:qnt_cr_bz2). However Bzip2 is much slower than Zstandard which yields a comparable CR. Figurefgr:qnt_cr_bz2 fgr/qnt_cr_bz25.5in Quantization and then compression by Bzip2, including the contribution of Shuffle. The second step is to choose the quantization algorithm and its default level. Quantization can significantly improve the CR without sacrificing any scientifically meaningful data. Lossless algorithms are unlikely to significantly alter the workflow throughput unless applied so agressively that they considerably reduce the entropy seen by the lossless codec. The goal in this step is to choose the strongest quantization that preserves all the meaningful precision of most fields, and that dials-in the CR to the desired value. ncks -7 --qnt default=3 ... # GranularBR, NSD=3 ncks -7 --qnt default=3 ... # Same ncks -7 --qnt_alg=gbr --ppc default=3 ... # Same ncks -7 --qnt_alg=gbr --ppc dfl=3 ... # Same ncks -7 --qnt_alg=btr --ppc dfl=9 ... # BitRound, NSB=9 ncks -7 --baa=8 --ppc default=9 ... # Same As an argument to --qnt, the keyword dfl is just a synonym for default and has nothing to do with DEFLATE. The third step is to develop per-variable exceptions to the default quantization of the previous step. This can be a process of trial-and-error, or semi-automated through techniques such as determining an acceptable information content threshold for each variable See, e.g., the procedure described in &textldquo;Compressing atmospheric data into its real information content&textrdquo; by M.~Klower et al., available at https://doi.org/10.1038/s43588-021-00156-2.. The per-variable arguments to --qnt can take many forms: ncks --qnt p,w,z=5 --qnt q,RH=4 --qnt T,u,v=3 # Multiple options ncks --qnt p,w,z=5#q,RH=4#T,u,v=3 ... # Combined option ncks --qnt Q.?=5#FS.?,FL.?=4#RH=.3 ... # Regular expressions ncks --qnt_alg=btr --qnt p,w,z=15#q,RH=12#T,u,v=9 ... # BitRound (NSB) ncks --qnt_alg=btr --qnt CO2=15#AER.?=12#U,V=6 ... # Klower et al. No compression is necessary in this step, which presumably involves evaluating the adequacy of the quantized values at matching observations or at meeting other error metrics. The fourth and final step combines the lossless and lossy algorithms to produce the final workflow: ncks -7 --qnt dfl=3 --cmp='shf|zst' ... # A useful starting point? ncks -7 --qnt default=3#Q.?=5#FS.?,FL.?=4 --cmp='shf|zst' ... ncks -7 --qnt_alg=gbr --qnt default=3#Q.?=5#FS.?,FL.?=4 --cmp='shf|zst' ... ncks -7 --qnt_alg=btr --qnt default=9#Q.?=15#FS.?,FL.?=12 --cmp='shf|zst' ... The example above uses Zstandard (fgr:qnt_cr_zst) because it is significant faster than other codecs with comparable CRs, e.g., Bzip2. Figurefgr:qnt_cr_zst fgr/qnt_cr_zst5.5in Quantization and then compression by Zstandard, including the contribution of Shuffle. Older Compression API NCO implements or accesses four different compression algorithms, the standard lossless DEFLATE algorithm and three lossy compression algorithms. All four algorithms reduce the on-disk size of a dataset while sacrificing no (lossless) or a tolerable amount (lossy) of precision. First, NCO can access the lossless DEFLATE algorithm, a combination of Lempel-Ziv encoding and Huffman coding, algorithm on any netCDF4 dataset (Deflation). Because it is lossless, this algorithm re-inflates deflated data to their full original precision. This algorithm is accessed via the HDF5 library layer (which itself calls the zlib library also used by gzip), and is unavailable with netCDF3. * Linear Packing::

* Precision-Preserving Compression::

Linear PackingPrecision-Preserving CompressionCompressionCompression Linear Packing The three lossy compression algorithms are Linear Packing (Packed data), and two precision-preserving algorithms. Linear packing quantizes data of a higher precision type into a lower precision type (often NC_SHORT) that thus stores a fewer (though constant) number of bytes per value. Linearly packed data unpacks into a (much) smaller dynamic range than the floating-point data can represent. The type-conversion and reduced dynamic range of the data allows packing to eliminate bits typically used to store an exponent, thus improving its packing efficiency. Packed data also can also be deflated for additional space savings. A limitation of linear packing is that unpacking data stored as integers into the linear range defined by scale_factor and add_offset rapidly loses precision outside of a narrow range of floating-point values. Variables packed as NC_SHORT, for example, can represent only about 64000 discrete values in the range

-32768*scale_factor+add_offset

32767*scale_factor+add_offset

. The precision of packed data equals the value of scale_factor, and scale_factor is usually chosen to span the range of valid data, not to represent the intrinsic precision of the variable. In other words, the precision of packed data cannot be specified in advance because it depends on the range of values to quantize. Precision-Preserving CompressionLinear PackingCompression Precision-Preserving Compression PPC LSD Least Significant Digit quantization NCO implemented the final two lossy compression algorithms in version 4.4.8 (February, 2015). These are both Precision-Preserving Compression (PPC) algorithms and since standard terminology for precision is remarkably imprecise, so is our nomenclature. The operational definition of &textldquo;significant digit&textrdquo; in our precision preserving algorithms is that the exact value, before rounding or quantization, is within one-half the value of the decimal place occupied by the Least Significant Digit (LSD) of the rounded value. For example, the value

pi = 3.14

correctly represents the exact mathematical constant pi to three significant digits because the LSD of the rounded value (i.e., 4) is in the one-hundredths digit place, and the difference between the exact value and the rounded value is less than one-half of one one-hundredth, i.e., (

3.14159265358979323844 - 3.14 = 0.00159 < 0.005

). Number of Significant Digits NSD Bit-Grooming One PPC algorithm preserves the specified total Number of Signifcant Digits (NSD) of the value. For example there is only one significant digit in the weight of most &textldquo;eight-hundred pound gorillas&textrdquo; that you will encounter, i.e., so

nsd=1. This is the most straightforward measure of precision, and thus NSD is the default PPC algorithm. Decimal Significant Digits DSD The other PPC algorithm preserves the number of Decimal Significant Digits (DSD), i.e., the number of significant digits following (positive, by convention) or preceding (negative) the decimal point. For example, 0.008 and 800 have, respectively, three and negative two digits digits following the decimal point, corresponding to

dsd=3 and

dsd=-2. The only justifiable NSD for a given value depends on intrinsic accuracy and error characteristics of the model or measurements, and not on the units with which the value is stored. The appropriate DSD for a given value depends on these intrinsic characteristics and, in addition, the units of storage. This is the fundamental difference between the NSD and DSD approaches. The eight-hundred pound gorilla always has

nsd=1 regardless of whether the value is stored in pounds or in some other unit. DSD corresponding to this weight is

dsd=-2 if the value is stored in pounds,

dsd=4 if stored in megapounds. Users may wish to express the precision to be preserved as either NSD or DSD. Invoke PPC with the long option --ppc var=prc, or give the same arguments to the synonyms --precision_preserving_compression, --qnt, or --quantize. Here var is the variable to quantize, and prc is its precision. indicator option multi-arguments The option --qnt (and its long option equivalents such as --ppc and --quantize) indicates the argument syntax will be key=val. As such, --qnt and its synonyms are indicator options that accept arguments supplied one-by-one like --qnt key1=val1 --qnt key2=val2, or aggregated together in multi-argument format like --qnt key1=val1#key2=val2 (Multi-arguments). The default algorithm assumes prc specifies NSD precision, e.g., T=2 means

nsd=2. Prepend prc with a decimal point to specify DSD precision, e.g., T=.2 means

dsd=2. NSD precision must be specified as a positive integer. DSD precision may be a positive or negative integer; and is specified as the negative base 10 logarithm of the desired precision, in accord with common usage. For example, specifying T=.3 or T=.-2 tells the DSD algorithm to store only enough bits to preserve the value of T rounded to the nearest thousandth or hundred, respectively. CF conventions coordinates attribute climatology attribute bounds attribute Setting var to default has the special meaning of applying the associated NSD or DSD algorithm to all floating point variables except coordinate variables. Variables not affected by default include integer and non-numeric atomic types, coordinates, and variables mentioned in the bounds, climatology, or coordinates attribute of any variable. NCO applies PPC to coordinate variables only if those variables are explicitly specified (i.e., not with the default=prc mechanism. NCO applies PPC to integer-type variables only if those variables are explicitly specified (i.e., not with the default=prc, and only if the DSD algorithm is invoked with a negative prc. To prevent PPC from applying to certain non-coordinate variables (e.g., gridcell_area or gaussian_weight), explicitly specify a precision exceeding 7 (for NC_FLOAT) or 15 (for NC_DOUBLE) for those variables. Since these are the maximum representable precisions in decimal digits, NCO turns-off PPC (i.e., does nothing) when more precision is requested. bitmask significand IEEE 754 The time-penalty for compressing and uncompressing data varies according to the algorithm. The Number of Significant Digit (NSD) algorithm quantizes by bitmasking, and employs no floating-point math. The Decimal Significant Digit (DSD) algorithm quantizes by rounding, which does require floating-point math. Hence NSD is likely faster than DSD, though the difference has not been measured. NSD creates a bitmask to alter the significand of IEEE 754 floating-point data. The bitmask is one for all bits to be retained and zero or one for all bits to be ignored. The algorithm assumes that the number of binary digits (i.e., bits) necessary to represent a single base-10 digit is

ln(10)/ln(2) = 3.32

. The exact numbers of bits Nbit retained for single and double precision values are

ceil(3.32*

nsd)+1 and

ceil(3.32*

nsd)+2, respectively. Once these reach 23 and 53, respectively, bitmasking is completely ineffective. This occurs at

nsd=6.3 and 15.4, respectively. The DSD algorithm, by contrast, uses rounding to remove undesired precision. rint() C99 IEEE significand The rounding Rounding is performed by the internal math library rint() family of functions that were standardized in C99. The exact alorithm employed is

val := rint(scale*val)/scale where scale is the nearest power of 2 that exceeds

10**

prc, and the inverse of scale is used when

prc < 0. For

qnt = 3 or

qnt = -2, for example, we have

scale = 1024 and

scale = 1/128. zeroes the greatest number of significand bits consistent with the desired precision. To demonstrate the change in IEEE representation caused by PPC rounding algorithms, consider again the case of pi, represented as an NC_FLOAT. The IEEE 754 single precision representations of the exact value (3.141592...), the value with only three significant digits treated as exact (3.140000...), and the value as stored (3.140625) after PPC-rounding with either the NSD (

prc=3) or DSD (

prc=2) algorithm are, respectively, ncks -O -v pi --qnt pi=1 ~/nco/data/in.nc ~/foo_nsd1.nc ncks -O -v pi --qnt pi=2 ~/nco/data/in.nc ~/foo_nsd2.nc ncks -O -v pi --qnt pi=3 ~/nco/data/in.nc ~/foo_nsd3.nc ncks -O -v pi --qnt pi=4 ~/nco/data/in.nc ~/foo_nsd4.nc ncks -O -v pi --qnt pi=5 ~/nco/data/in.nc ~/foo_nsd5.nc ncks -O -v pi --qnt pi=6 ~/nco/data/in.nc ~/foo_nsd6.nc ncks -O -v pi --qnt pi=7 ~/nco/data/in.nc ~/foo_nsd7.nc ncks -O -v pi --qnt pi=8 ~/nco/data/in.nc ~/foo_nsd8.nc ncks -O -v pi --qnt pi=9 ~/nco/data/in.nc ~/foo_nsd9.nc ncks -O -v pi --qnt pi=.2 ~/nco/data/in.nc ~/foo_dsd2.nc ncks -s %20.16e -C -H ~/foo_nsd.nc ncks -s %20.16e -C -H ~/foo_dsd.nc ccc --tst=bnr --flt_foo=3.14159265358979323844 2> /dev/null | grep "Binary of float" ccc --tst=bnr --flt_foo=3.14000000000000000000 2> /dev/null | grep "Binary of float" ccc --tst=bnr --flt_foo=3.00000000000000000000 2> /dev/null | grep "Binary of float" ccc --tst=bnr --flt_foo=3.12500000000000000000 2> /dev/null | grep "Binary of float" ccc --tst=bnr --flt_foo=3.14062500000000000000 2> /dev/null | grep "Binary of float" ccc --tst=bnr --flt_foo=3.14062500000000000000 2> /dev/null | grep "Binary of float" ccc --tst=bnr --flt_foo=3.14147949218750000000 2> /dev/null | grep "Binary of float"

S Exponent  Fraction (Significand)   Decimal    Notes
0 100000001 0010010000111111011011 # 3.14159265 Exact
0 100000001 0010001111010111000011 # 3.14000000
0 100000001 0010010000000000000000 # 3.14062500 NSD = 3
0 100000001 0010010000000000000000 # 3.14062500 DSD = 2

The string of trailing zero-bits in the rounded values facilitates byte-stream compression. Note that the NSD and DSD algorithms do not always produce results that are bit-for-bit identical, although they do in this particular case. <a name="ppc_tbl_bit"></a>  Reducing the preserved precision of NSD-rounding produces increasingly long strings of identical-bits amenable to compression:

S Exponent  Fraction (Significand)   Decimal    Notes
0 100000001 0010010000111111011011 # 3.14159265 Exact
0 100000001 0010010000111111011011 # 3.14159265 NSD = 8
0 100000001 0010010000111111011010 # 3.14159262 NSD = 7
0 100000001 0010010000111111011000 # 3.14159203 NSD = 6
0 100000001 0010010000111111000000 # 3.14158630 NSD = 5
0 100000001 0010010000111100000000 # 3.14154053 NSD = 4
0 100000001 0010010000000000000000 # 3.14062500 NSD = 3
0 100000001 0010010000000000000000 # 3.14062500 NSD = 2
0 100000001 0010000000000000000000 # 3.12500000 NSD = 1

The consumption of about 3 bits per digit of base-10 precision is evident, as is the coincidence of a quantized value that greatly exceeds the mandated precision for

NSD = 2

. Although the NSD algorithm generally masks some bits for all

nsd <= 7 (for NC_FLOAT), compression algorithms like DEFLATE may need byte-size-or-greater (i.e., at least eight-bit) bit patterns before their algorithms can take advantage of of encoding such patterns for compression. Do not expect significantly enhanced compression from

nsd > 5 (for NC_FLOAT) or

nsd > 14 (for NC_DOUBLE). Clearly values stored as NC_DOUBLE (i.e., eight-bytes) are susceptible to much greater compression than NC_FLOAT for a given precision because their significands explicitly contain 53 bits rather than 23 bits. Maintaining non-biased statistical properties during lossy compression requires special attention. The DSD algorithm uses rint(), which rounds toward the nearest even integer. Thus DSD has no systematic bias. However, the NSD algorithm uses a bitmask technique susceptible to statistical bias. Zeroing all non-significant bits is guaranteed to produce numbers quantized to the specified tolerance, i.e., half of the decimal value of the position occupied by the LSD. However, always zeroing the non-significant bits results in quantized numbers that never exceed the exact number. This would produce a negative bias in statistical quantities (e.g., the average) subsequently derived from the quantized numbers. To avoid this bias, our NSD implementation rounds non-significant bits down (to zero) or up (to one) in an alternating fashion when processing array data. In general, the first element is rounded down, the second up, and so on. This results in a mean bias quite close to zero. The only exception is that the floating-point value of zero is never quantized upwards. For simplicity, NSD always rounds scalars downwards. DEFLATE HDF5 gzip gunzip Although NSD or DSD are different algorithms under the hood, they both replace the (unwanted) least siginificant bits of the IEEE significand with a string of consecutive zeroes. Byte-stream compression techniques, such as the gzip DEFLATE algorithm compression available in HDF5, always compress zero-strings more efficiently than random digits. The net result is netCDF files that utilize compression can be significantly reduced in size. This feature only works when the data are compressed, either internally (by netCDF) or externally (by another user-supplied mechanism). It is most straightfoward to compress data internally using the built-in compression and decompression supported by netCDF4. For convenience, NCO automatically activates file-wide Lempel-Ziv deflation (Deflation) level one (i.e., -L 1) when PPC is invoked on any variable in a netCDF4 output file. This makes PPC easier to use effectively, since the user need not explicitly specify deflation. Any explicitly specified deflation (including no deflation, -L 0) will override the PPC deflation default. If the output file is a netCDF3 format, NCO will emit a message suggesting internal netCDF4 or external netCDF3 compression. netCDF3 files compressed by an external utility such as gzip accrue approximately the same benefits (shrinkage) as netCDF4, although with netCDF3 the user or provider must uncompress (e.g., gunzip) the file before accessing the data. There is no benefit to rounding numbers and storing them in netCDF3 files unless such custom compression/decompression is employed. Without that, one may as well maintain the undesired precision. The user accesses PPC through a single switch, --ppc, repeated as many times as necessary. To apply the NSD algorithm to variable u use, e.g.,

ncks -7 --qnt u=2 in.nc out.nc

The output file will preserve only two significant digits of u. The options -4 or -7 ensure a netCDF4-format output (regardless of the input file format) to support internal compression. It is recommended though not required to write netCDF4 files after PPC. For clarity the -4/-7 switches are omitted in subsequent examples. <a name="QuantizeBitGroomNumberOfSignificantDigits"></a>  <a name="QuantizeGranularBitRoundNumberOfSignificantDigits"></a>  <a name="QuantizeBitShaveNumberOfSignificantDigits"></a>  <a name="QuantizeBitSetNumberOfSignificantDigits"></a>  <a name="QuantizeDigitRoundNumberOfSignificantDigits"></a>  <a name="QuantizeBitGroomRoundNumberOfSignificantDigits"></a>  <a name="QuantizeHalfShaveNumberOfSignificantBits"></a>  <a name="QuantizeBruteForceNumberOfSignificantDigits"></a>  <a name="QuantizeBitRoundNumberOfSignificantBits"></a>  <a name="number_of_significant_digits"></a>  <a name="least_significant_digit"></a>  QuantizeBitGroomNumberOfSignificantDigits QuantizeBitSetNumberOfSignificantDigits QuantizeBitShaveNumberOfSignificantDigits QuantizeDigitRoundNumberOfSignificantDigits QuantizeGranularBitRoundNumberOfSignificantDigits QuantizeBitGroomRoundNumberOfSignificantDigits QuantizeHalfShaveNumberOfSignificantBits QuantizeBruteForceNumberOfSignificantDigits QuantizeBitRoundNumberOfSignificantBits number_of_significant_digits number_of_significant_bits least_significant_digit Jeff Whitaker Rich Signell John Caron NCO attaches attributes that indicate the algorithm used and degree of precision retained for each variable affected by PPC. The NSD and DSD algorithms store the attributes QuantizeBitGroomNumberOfSignificantDigits Prior to NCO version 5.0.3 (October, 2021), NCO stored the NSD attribute number_of_significant_digits. However, this was deemed too ambiguous, given the increasing number of supported quantization methods. The new attribute names better disambiguate which algorithm was used to quantize the data. They also harmonize better with the metadata produced by the upcoming netCDF library quantization features. and least_significant_digit A suggestion by Rich Signell and the nc3tonc4 tool by Jeff Whitaker inspired NCO to implement PPC. Note that NCO implements a different DSD algorithm than nc3tonc4, and produces slightly different (not bit-for-bit) though self-consistent and equivalent results. nc3tonc4 records the precision of its DSD algorithm in the attribute least_significant_digit and NCO does the same for consistency. The Unidata blog http://www.unidata.ucar.edu/blogs/developer/en/entry/compression_by_bit_shavinghere also shows how to compress IEEE floating-point data by zeroing insignificant bits. The author, John Caron, writes that the technique has been called &textldquo;bit-shaving&textrdquo;. We call the algorithm of always rounding-up &textldquo;bit-setting&textrdquo;. And we named the algorithm produced by alternately rounding up and down (with a few other bells and whistles) &textldquo;bit-grooming&textrdquo;. Imagine orthogonally raking an already-groomed Japanese rock garden. The criss-crossing tracks increase the pattern&textrsquo;s entropy, and this entropy produces self-compensating instead of accumulating errors during statistical operations., respectively. As of version 5.0.3 (October 2021), NCO supports a more complete set of Precision-Preserving Quantization (PPQ) filters than was previously documented here. The default algorithm has been changed from BitGroom with BitRound masks from R. Kouznetsov (2021), to what we call Granular BitRound (GBR). GBR combines features of BitGroom, BitRound, and DigitRound by Delaunay et al. (2019). GBR improves compression ratios by ~20% relative to BitGroom for NSD=3 on our benchmark 1 GB climate model output dataset. Since it quantizes a few more bits than BitGroom (and BitGroomRound) for a given NSD, GBR produces significantly larger quantization errors than those algorithms as well. These NSD algorithms write an algorithm-specific attribute, e.g., QuantizeGranularBitRoundNumberOfSignificantDigits or QuantizeDigitRoundNumberOfSignificantDigits. Documentation on these algorithms is best found in the literature. While Bit-Groom/Shave/Set are described above, documentation on Bit-Adjustment-Algorithms (BAA) 3&textndash;8 will be improved in the future. As of version 5.0.5 (January 2022), NCO supports two quantization codecs (BitRound and HalfShave) that expect a user-specified number of explicit significant bits (NSB, or &textldquo;keepbits&textrdquo;) to retain The terminology of significant bits (not to mention digits) can be confusing. The IEEE standard devotes 24 and 53 bits, respectively, to the mantissas that determine the precision of single and double precision floating-point numbers. However, the first (i.e., the most significant) of those bits is implicit, and is not explicitly stored. Its value is one unless all of the exponent bits are zero. The implicit bit is significant thought it is not explicitly stored and so cannot be quantized. Therefore single and double precision floats have only 23 and 52 explicitly stored bits, respectively, that can be &textldquo;kept&textrdquo; and therefore quantized. Each explicit bit kept is as significant as the implicit bit. Thus the number of &textldquo;keepbits&textrdquo; is one less than the number of significant bits, i.e., the bits that contribute to the precision of an IEEE value. The BitRound quantization algorithm in NCO and in netCDF accept as an input parameter the number of keepbits, i.e., the number of explicit significant bits NESB to retain (i.e., not mask to zero). Unfortunately the acronym NSB has been used instead of the more accurate acronym NESB, and at this point it is difficult to change. Therefore the NSB acronym and parameter as used by NCO and netCDF should be interpreted as &textldquo;number of stored bits&textrdquo; (i.e., keepbits) not the &textldquo;number of significant bits&textrdquo;. . The NSB argument contrasts with the number of significant digits (NSD) parameter expected by the other codecs (like BitGroom, DigitRound, and GranularBR). The valid ranges of NSD are 1&textndash;7 for NC_FLOAT and 1&textndash;15 for NC_DOUBLE. The valid ranges of NSB are 1&textndash;23 for NC_FLOAT and 1&textndash;52 for NC_DOUBLE. The upper limits of NSB are the number of explicitly represented bits in the IEEE single- and double-precision formats (the implicit bit does not count). It is safe to attempt PPC on input that has already been rounded. Variables can be made rounder, not sharper, i.e., variables cannot be &textldquo;un-rounded&textrdquo;. Thus PPC attempted on an input variable with an existing PPC attribute proceeds only if the new rounding level exceeds the old, otherwise no new rounding occurs (i.e., a &textldquo;no-op&textrdquo;), and the original PPC attribute is retained rather than replaced with the newer value of prc. To request, say, five significant digits (

nsd=5) for all fields, except, say, wind speeds which are only known to integer values (

dsd=0) in the supplied units, requires --ppc twice:

ncks -4 --qnt default=5 --qnt u,v=.0 in.nc out.nc

To preserve five digits in all variables except coordinate variables and u and v, first specify a default value with the default keyword (or synonym keywords dfl, global, or glb), and separately specify the exceptions with per-variable key-value pairs:

ncks --qnt default=5 --qnt u,v=20 in.nc out.nc

The --qnt option may be specified any number of times to support varying precision types and levels, and each option may aggregate all the variables with the same precision

ncks --qnt p,w,z=5 --qnt q,RH=4 --qnt T,u,v=3 in.nc out.nc
ncks --qnt p,w,z=5#q,RH=4#T,u,v=3 in.nc out.nc # Multi-argument format

Any var argument may be a regular expression. This simplifies generating lists of related variables:

ncks --qnt Q.?=5 --qnt FS.?,FL.?=4 --qnt RH=.3 in.nc out.nc
ncks --qnt Q.?=5#FS.?,FL.?=4#RH=.3 in.nc out.nc # Multi-argument format

Although PPC-rounding instantly reduces data precision, on-disk storage reduction only occurs once the data are compressed. <a name="ppc_tbl_ffc"></a>  How can one be sure the lossy data are sufficiently precise? PPC preserves all significant digits of every value. The DSD algorithm uses floating-point math to round each value optimally so that it has the maximum number of zeroed bits that preserve the specified precision. The NSD algorithm uses a theoretical approach (3.2 bits per base-10 digit), tuned and tested to ensure the worst case quantization error is less than half the value of the minimum increment in the least significant digit. Note for Info users: The definition of error metrics relies heavily on mathematical expressions which cannot be easily represented in Info. See the ./nco.pdfprinted manual for much more detailed and complete documentation of this subject. We define several metrics to quantify the quantization error. The mean error~$\pslavg$ and mean absolute error~$\pslmebs$ incurred in quantizing a variable from its true values~$\xxx_{\idx}$ to quantized values~$\qqq_{\idx}$ are, respectively, $$ \pslavg = {\sum_{\idx=1}^{\idx=\lmnnbr} \mssflg_{\idx} \mskflg_{\idx} \wgt_{\idx} ( \xxx_{\idx} - \qqq_{\idx} ) \over \sum_{\idx=1}^{\idx=\lmnnbr} \mssflg_{\idx} \mskflg_{\idx} \wgt_{\idx}}\qquad\hbox{and}\qquad \pslmebs = {\sum_{\idx=1}^{\idx=\lmnnbr} \mssflg_{\idx} \mskflg_{\idx} \wgt_{\idx} | \xxx_{\idx} - \qqq_{\idx} | \over \sum_{\idx=1}^{\idx=\lmnnbr} \mssflg_{\idx} \mskflg_{\idx} \wgt_{\idx}} $$ where $\mssflg_{\idx}$ is~1 unless $\xxx_{\idx}$~equals the missing value, $\mskflg_{\idx}$ is~1 unless $\xxx_{\idx}$~is masked, and $\wgt_{\idx}$~is the weight. The maximum and minimum errors $\pslmax$ and~$\pslmin$ are both signed $$ \pslmax = {\rm max}(\xxx_{\idx} - \qqq_{\idx})\qquad\hbox{and}\qquad \pslmin = {\rm min}(\xxx_{\idx} - \qqq_{\idx}) $$ while the maximum and minimum absolute errors $\pslmabs$ and $\pslmibs$ are positive-definite. $$ \pslmabs = {\rm max}|\xxx_{\idx} - \qqq_{\idx}| = {\rm max}(|\pslmax|,|\pslmin|) $$ $$ \pslmibs = {\rm min}|\xxx_{\idx} - \qqq_{\idx}| = {\rm min}(|\pslmax|,|\pslmin|) $$ Typically $\pslmibs = 0$ for quantization, since many exact values need no quantization. Bit-shifting zeros into the least significant bits (@acronym{LSB}s) always underestimates true values so that $\pslmax = 0$. Conversely, bit-shifting ones into the @acronym{LSB}s always overestimates true values so that $\pslmin = 0$. Our @acronym{NSD} algorithm is balanced because it alternates bit-shifting zeroes and ones. Balanced algorithms should yield $\pslmax \approx -\pslmin$, $\pslmabs \approx \pslmibs$, and $\pslavg \approx 0$. The three most important error metrics for quantization are $\pslmabs$, $\pslmebs$, and~$\pslavg$. The upper bound (worst case) quantization performance is~$\pslmabs$. $\pslmebs$~measures the absolute mean accuracy of quantization, and does not allow positive and negative offsets to compensate eachother and conceal poor performance. The difference bewtween~$\pslmabs$ and~$\pslmebs$ indicates how much of an outlier the worst case is. The mean accuracy~$\pslavg$ indicates whether statistical properties of quantized numbers will accurately reflect the true values. All three metrics are expressed in terms of the fraction of the ten&textrsquo;s place occupied by the LSD. If the LSD is the hundreds digit or the thousandths digit, then the metrics are fractions of 100, or of 1/100, respectively. PPC algorithms should produce maximum absolute errors no greater than 0.5 in these units. If the LSD is the hundreds digit, then quantized versions of true values will be within fifty of the true value. It is much easier to satisfy this tolerance for a true value of 100 (only 50% accuracy required) than for 999 (5% accuracy required). Thus the minimum accuracy guaranteed for

nsd=1 ranges from 5&textndash;50%. For this reason, the best and worst cast performance usually occurs for true values whose LSD value is close to one and nine, respectively. Of course most users prefer

prc > 1 because accuracies increase exponentially with prc. Continuing the previous example to

prc=2, quantized versions of true values from 1000&textndash;9999 will also be within 50 of the true value, i.e., have accuracies from 0.5&textndash;5%. In other words, only two significant digits are necessary to guarantee better than 5% accuracy in quantization. We recommend that dataset producers and users consider quantizing datasets with

nsd=3. This guarantees accuracy of 0.05&textndash;0.5% for individual values. Statistics computed from ensembles of quantized values will, assuming the mean error $\pslavg$ @clear flg

Emean is small, have much better accuracy than 0.5%. This accuracy is the most that can be justified for many applications. To demonstrate these principles we conduct error analyses on an artificial, reproducible dataset, and on an actual dataset of observational analysis values. The artificial dataset employed is one million evenly spaced values from 1.0&textndash;2.0. The analysis data are

N=13934592

values of the temperature field from the NASA MERRA analysis of 20130601. The table summarizes quantization accuracy based on the three metrics. NSD Number of Significant Digits. Emabs Maximum absolute error. Emebs Mean absolute error. Emean Mean error.

9.9692099683868690e+36f = 0 1111100 111100000000000000000000 NC_FILL_FLOAT 2.3509885615147286E-38f = 0 0000000 111111111111111111111111 Recommended 9.9692099683868690e+36 = 0 1000111100 11110000000000000000000000000000000000000000000000000 NC_FILL_DOUBLE 2.84809453888921745415348453979E-306 = 0 0000000111 11111111111111111111111111111111111111111111111111111 Recommended 2.22507385850720088902458687609E-308 = 0 0000000000 11111111111111111111111111111111111111111111111111111 1.17549435082228737746264717725E-38 = 0 0111000000 11111111111111111111111111111111111111111111111111111 1.17549435082228737746264717725E-38 = 0 0000000 100000000000000000000000 # Accuracy tests # Satellite: ncks -3 -O -v T ${DATA}/hdf/MERRA300.prod.assim.inst3_3d_asm_Cp.20130601.nc ~/foo_T.nc ncatted -a scale_factor,,d,, -a add_offset,,d,, ~/foo_T.nc ncap2 -O -v -s 'ppc=T;nsd1=ppc;nsd2=ppc;nsd3=ppc;nsd4=ppc;nsd5=ppc;nsd6=ppc;nsd7=ppc' ~/foo_T.nc ~/foo_ppc_in.nc # Artificial double ncap2 -O -v -s 'defdim("dmn",1000000);ppc=array(1.0,1.e-6,$dmn);nsd1=ppc;nsd2=ppc;nsd3=ppc;nsd4=ppc;nsd5=ppc;nsd6=ppc;nsd7=ppc' ~/nco/data/in.nc ~/foo_ppc_in.nc # Artificial float ncap2 -O -v -s 'defdim("dmn",1000000);ppc=float(array(1.0,1.e-6,$dmn));nsd1=ppc;nsd2=ppc;nsd3=ppc;nsd4=ppc;nsd5=ppc;nsd6=ppc;nsd7=ppc' ~/nco/data/in.nc ~/foo_ppc_in.nc # nc3tonc4 nc3tonc4 -o --quantize=nsd1=1,nsd2=2,nsd3=3,nsd4=4,nsd5=5,nsd6=6,nsd7=7 --quiet=1 ~/foo_ppc_in.nc ~/foo_ppc_out.nc # DSD ncks -O -C --ppc nsd1=.1 --ppc nsd2=.2 --ppc nsd3=.3 --ppc nsd4=.4 --ppc nsd5=.5 --ppc nsd6=.6 --ppc nsd7=.7 ~/foo_ppc_in.nc ~/foo_ppc_out.nc # NSD ncks -O -C --ppc nsd1=1 --ppc nsd2=2 --ppc nsd3=3 --ppc nsd4=4 --ppc nsd5=5 --ppc nsd6=6 --ppc nsd7=7 ~/foo_ppc_in.nc ~/foo_ppc_out.nc # Generic rounding test: ncbo -O -C ~/foo_ppc_out.nc ~/foo_ppc_in.nc ~/foo_ppc_dff.nc ncbo -O -C -y dvd ~/foo_ppc_dff.nc ~/foo_ppc_in.nc ~/foo_ppc_rat.nc ncap2 -O -v -s 'nsd1*=10;nsd2*=100;nsd3*=1000;nsd4*=10000;nsd5*=100000;nsd6*=1000000;nsd7*=10000000' ~/foo_ppc_rat.nc ~/foo_ppc_rat_scl.nc ncwa -O -C -y avg ~/foo_ppc_rat_scl.nc ~/foo_ppc_avg.nc ncwa -O -C -y max ~/foo_ppc_rat_scl.nc ~/foo_ppc_max.nc ncwa -O -C -y min ~/foo_ppc_rat_scl.nc ~/foo_ppc_min.nc ncwa -O -C -y mabs ~/foo_ppc_rat_scl.nc ~/foo_ppc_mabs.nc ncwa -O -C -y mebs ~/foo_ppc_rat_scl.nc ~/foo_ppc_mebs.nc ncwa -O -C -y mibs ~/foo_ppc_rat_scl.nc ~/foo_ppc_mibs.nc

Artificial Data: N=1000000 values in [1.0,2.0) in steps of 1.0e-6
Single-Precision        Double-Precision   Single-Precision
NSD Emabs Emebs Emean   Emabs Emebs Emean  DSD Emabs Emebs Emean
 1  0.31  0.11  4.1e-4  0.31  0.11  4.0e-4  1  0.30  0.11 -8.1e-4  
 2  0.39  0.14  6.8e-5  0.39  0.14  5.5e-5  2  0.39  0.14 -1.3e-4
 3  0.49  0.17  1.0e-6  0.49  0.17 -5.5e-7  3  0.49  0.17 -2.0e-5
 4  0.30  0.11  3.2e-7  0.30  0.11 -6.1e-6  4  0.30  0.11  5.1e-8
 5  0.37  0.13  3.1e-7  0.38  0.13 -5.6e-6  5  0.38  0.13  2.6e-6
 6  0.36  0.12 -4.4e-7  0.48  0.17 -4.1e-7  6  0.48  0.17  7.2e-6
 7  0.00  0.00  0.0     0.30  0.10  1.5e-7  7  0.00  0.00  0.0     

Observational Analysis: N=13934592 values MERRA Temperature 20130601
Single-Precision        
NSD Emabs Emebs Emean   
 1  0.31  0.11  2.4e-3
 2  0.39  0.14  3.8e-4
 3  0.49  0.17 -9.6e-5 
 4  0.30  0.11  2.3e-3
 5  0.37  0.13  2.2e-3
 6  0.36  0.13  1.7e-2
 7  0.00  0.00  0.0

All results show that PPC quantization performs as expected. Absolute maximum errors

Emabs < 0.5 for all prc. For

1 <=

prc <= 6, quantization results in comparable maximum absolute and mean absolute errors Emabs and Emebs, respectively. Mean errors Emean are orders of magnitude smaller because quantization produces over- and under-estimated values in balance. When

prc=7, quantization of single-precision values is ineffective, because all available bits are used to represent the maximum precision of seven digits. The maximum and mean absolute errors Emabs and Emebs are nearly identical across algorithms, precisions, and dataset types. This is consistent with both the artificial data and empirical data being random, and thus exercising equally strengths and weaknesses of the algorithms over the course of millions of input values. We generated artificial arrays with many different starting values and interval spacing and all gave qualitatively similar results. The results presented are the worst obtained. The artificial data has much smaller mean error Emean than the observational analysis. The reason why is unclear. It may be because the temperature field is concentrated in particular ranges of values (and associated quantization errors) prevalent on Earth, e.g.,

200 <

T < 320. It is worth noting that the mean error

Emean < 0.01 for

1 <=

prc < 6, and that Emean is typically at least two or more orders of magnitude less than Emabs. Thus quantized values with precisions as low as

prc=1 still yield highly significant statistics by contemporary scientific standards. Testing shows that PPC quantization enhances compression of typical climate datasets. The degree of enhancement depends, of course, on the required precision. Model results are often computed as NC_DOUBLE then archived as NC_FLOAT to save space. # Unidata Compression Blog: # http://www.unidata.ucar.edu/blogs/developer/en/entry/netcdf_compression # Table entries fl=${DATA}/dstmch90/dstmch90_clm.nc fl=${DATA}/hdf/b1850c5cn_doe_polar_merged_0_cesm1_2_0_HD+MAM4+tun2b.hp.e003.cam.h0.0001-01.nc fl=${DATA}/hdf/MERRA300.prod.assim.inst3_3d_asm_Cp.20130601.nc fl=${DATA}/hdf/OMI-Aura_L2-OMIAuraSO2_2012m1222-o44888_v01-00-2014m0107t114720.h5 # ncks PPC only sz_rgn=`ls -l ${fl} | cut -d ' ' -f 5` fmt_fnc(){ gawk '{print "old=" $1/1000000 " MB, new=" $2/1000000 " MB, cmp=" 100*$2/$1 "%"}';} cmd="ls -l ${fl}";sz_new=`${cmd} | cut -d ' ' -f 5`;fmt_fnc ${sz_rgn} ${sz_new} sz_new=`bzip2 -1 -f ${fl};ls -l ${fl}.bz2 | cut -d ' ' -f 5`;bunzip2 ${fl}.bz2;echo ${sz_rgn} ${sz_new} | gawk '{print "old=" $1/1000000 " MB, new=" $2/1000000 " MB, cmp=" 100*$2/$1 "%"}' sz_new=`bzip2 -9 -f ${fl};ls -l ${fl}.bz2 | cut -d ' ' -f 5`;bunzip2 ${fl}.bz2;echo ${sz_rgn} ${sz_new} | gawk '{print "old=" $1/1000000 " MB, new=" $2/1000000 " MB, cmp=" 100*$2/$1 "%"}' sz_new=`ncks -O -7 -L 0 ${fl} ~/foo.nc;ls -l ~/foo.nc | cut -d ' ' -f 5`;echo ${sz_rgn} ${sz_new} | gawk '{print "old=" $1/1000000 " MB, new=" $2/1000000 " MB, cmp=" 100*$2/$1 "%"}' sz_new=`ncks -O -7 -L 1 ${fl} ~/foo.nc;ls -l ~/foo.nc | cut -d ' ' -f 5`;echo ${sz_rgn} ${sz_new} | gawk '{print "old=" $1/1000000 " MB, new=" $2/1000000 " MB, cmp=" 100*$2/$1 "%"}' sz_new=`ncks -O -7 -L 9 ${fl} ~/foo.nc;ls -l ~/foo.nc | cut -d ' ' -f 5`;echo ${sz_rgn} ${sz_new} | gawk '{print "old=" $1/1000000 " MB, new=" $2/1000000 " MB, cmp=" 100*$2/$1 "%"}' sz_new=`ncpdq -O -7 -L 0 ${fl} ~/foo.nc;ls -l ~/foo.nc | cut -d ' ' -f 5`;echo ${sz_rgn} ${sz_new} | gawk '{print "old=" $1/1000000 " MB, new=" $2/1000000 " MB, cmp=" 100*$2/$1 "%"}' sz_new=`ncpdq -O -7 -L 1 ${fl} ~/foo.nc;ls -l ~/foo.nc | cut -d ' ' -f 5`;echo ${sz_rgn} ${sz_new} | gawk '{print "old=" $1/1000000 " MB, new=" $2/1000000 " MB, cmp=" 100*$2/$1 "%"}' sz_new=`ncks -O -7 -L 1 --ppc default=7 ${fl} ~/foo.nc;ls -l ~/foo.nc | cut -d ' ' -f 5`;echo ${sz_rgn} ${sz_new} | gawk '{print "old=" $1/1000000 " MB, new=" $2/1000000 " MB, cmp=" 100*$2/$1 "%"}' sz_new=`ncks -O -7 -L 1 --ppc default=6 ${fl} ~/foo.nc;ls -l ~/foo.nc | cut -d ' ' -f 5`;echo ${sz_rgn} ${sz_new} | gawk '{print "old=" $1/1000000 " MB, new=" $2/1000000 " MB, cmp=" 100*$2/$1 "%"}' sz_new=`ncks -O -7 -L 1 --ppc default=5 ${fl} ~/foo.nc;ls -l ~/foo.nc | cut -d ' ' -f 5`;echo ${sz_rgn} ${sz_new} | gawk '{print "old=" $1/1000000 " MB, new=" $2/1000000 " MB, cmp=" 100*$2/$1 "%"}' sz_new=`ncks -O -7 -L 1 --ppc default=4 ${fl} ~/foo.nc;ls -l ~/foo.nc | cut -d ' ' -f 5`;echo ${sz_rgn} ${sz_new} | gawk '{print "old=" $1/1000000 " MB, new=" $2/1000000 " MB, cmp=" 100*$2/$1 "%"}' sz_new=`ncks -O -7 -L 1 --ppc default=3 ${fl} ~/foo.nc;ls -l ~/foo.nc | cut -d ' ' -f 5`;echo ${sz_rgn} ${sz_new} | gawk '{print "old=" $1/1000000 " MB, new=" $2/1000000 " MB, cmp=" 100*$2/$1 "%"}' sz_new=`ncks -O -7 -L 1 --ppc default=2 ${fl} ~/foo.nc;ls -l ~/foo.nc | cut -d ' ' -f 5`;echo ${sz_rgn} ${sz_new} | gawk '{print "old=" $1/1000000 " MB, new=" $2/1000000 " MB, cmp=" 100*$2/$1 "%"}' sz_new=`ncks -O -7 -L 1 --ppc default=1 ${fl} ~/foo.nc;ls -l ~/foo.nc | cut -d ' ' -f 5`;echo ${sz_rgn} ${sz_new} | gawk '{print "old=" $1/1000000 " MB, new=" $2/1000000 " MB, cmp=" 100*$2/$1 "%"}' sz_new=`ncpdq -O -7 -L 1 --ppc default=4 ${fl} ~/foo.nc;ls -l ~/foo.nc | cut -d ' ' -f 5`;echo ${sz_rgn} ${sz_new} | gawk '{print "old=" $1/1000000 " MB, new=" $2/1000000 " MB, cmp=" 100*$2/$1 "%"}' sz_new=`ncpdq -O -7 -L 9 --ppc default=4 ${fl} ~/foo.nc;ls -l ~/foo.nc | cut -d ' ' -f 5`;echo ${sz_rgn} ${sz_new} | gawk '{print "old=" $1/1000000 " MB, new=" $2/1000000 " MB, cmp=" 100*$2/$1 "%"}' sz_new=`ncpdq -O -3 --ppc default=4 ${fl} ~/foo.nc;bzip2 -1 -f ~/foo.nc;ls -l ~/foo.nc.bz2 | cut -d ' ' -f 5`;echo ${sz_rgn} ${sz_new} | gawk '{print "old=" $1/1000000 " MB, new=" $2/1000000 " MB, cmp=" 100*$2/$1 "%"}' <a name="ppc_tbl_nc"></a>  Burrows-Wheeler algorithm bzip2 This table summarizes the performance of lossless and lossy compression on two typical, or at least random, netCDF data files. The files were taken from representative model-simulated and satellite-retrieved datasets. Only floating-point data were compressed. No attempt was made to compress integer-type variables as they occupy an insignificant fraction of every dataset. The columns are Type File-type: N3 for netCDF CLASSIC, N4 for NETCDF4, N7 for NETCDF4_CLASSIC (which comprises netCDF3 data types and structures with netCDF4 storage features like compression), H4 for HDF4, and H5 for HDF5. N4/7 means results apply to both N4 and N7 filetypes. LLC Type of lossless compression employed, if any. Bare numbers refer to the strength of the DEFLATE algorithm employed internally by netCDF4/HDF5, while numbers prefixed with B refer to the block size employed by the Burrows-Wheeler algorithm in bzip2. PPC Number of significant digits retained by the precision-preserving compression NSD algorithm. Pck Y if the default ncpdq packing algorithm (convert floating-point types to NC_SHORT) was employed. Size Resulting filesize in MB. % Compression ratio, i.e., resulting filesize relative to original size, in percent. In some cases the original files is already losslessly compressed. The compression ratios reported are relative to the size of the original file as distributed, not as optimally losslessly compressed.

A dash (-) indicates the associated compression feature was not employed. # Lines trimmed from table as redundant/misleading N7 1 4 Y 7.9 22.9 ncpdq --ppc default=4 N7 9 4 Y 7.7 22.1 ncpdq -L 9 --ppc default=4 N3 B1 4 Y 6.3 18.1 ncpdq --ppc default=4 bzip2 -1 N7 1 4 Y 26.3 21.9 ncpdq --ppc default=4 N7 9 4 Y 25.6 21.4 ncpdq -L 9 --ppc default=4 N3 B1 4 Y 20.9 17.4 ncpdq --ppc default=4 bzip2 -1 N4/7 1 4 Y 133.6 54.7 ncpdq -L 1 --ppc default=4 N4/7 9 4 Y 127.0 52.0 ncpdq -L 9 --ppc default=4 N3 B1 4 Y 114.0 46.7 ncpdq --ppc default=4 bzip2 -1 N4 1 4 Y 13.0 44.0 ncpdq -L 1 --ppc default=4 N4 9 4 Y 12.5 42.5 ncpdq -L 9 --ppc default=4

# dstmch90_clm.nc
Type LLC PPC Pck  Size   %    Flags and Notes
  N3   -   -  -   34.7 100.0  Original is not compressed
  N3  B1   -  -   28.9  83.2  bzip2 -1
  N3  B9   -  -   29.3  84.4  bzip2 -9
  N7   -   -  -   35.0 101.0     
  N7   1   -  -   28.2  81.3  -L 1
  N7   9   -  -   28.0  80.8  -L 9
  N7   -   -  Y   17.6  50.9  ncpdq -L 0
  N7   1   -  Y    7.9  22.8  ncpdq -L 1
  N7   1   7  -   28.2  81.3  --ppc default=7
  N7   1   6  -   27.9  80.6  --ppc default=6
  N7   1   5  -   25.9  74.6  --ppc default=5
  N7   1   4  -   22.3  64.3  --ppc default=4
  N7   1   3  -   18.9  54.6  --ppc default=3
  N7   1   2  -   14.5  43.2  --ppc default=2
  N7   1   1  -   10.0  29.0  --ppc default=1

# b1850c5cn_doe_polar_merged_0_cesm1_2_0_HD+MAM4+tun2b.hp.e003.cam.h0.0001-01.nc
Type LLC PPC Pck  Size   %    Flags and Notes
  N3   -   -  -  119.8 100.0  Original is not compressed
  N3  B1   -  -   84.2  70.3  bzip2 -1
  N3  B9   -  -   84.8  70.9  bzip2 -9
  N7   -   -  -  120.5 100.7     
  N7   1   -  -   82.6  69.0  -L 1
  N7   9   -  -   82.1  68.6  -L 9
  N7   -   -  Y   60.7  50.7  ncpdq -L 0
  N7   1   -  Y   26.0  21.8  ncpdq -L 1
  N7   1   7  -   82.6  69.0  --ppc default=7
  N7   1   6  -   81.9  68.4  --ppc default=6
  N7   1   5  -   77.2  64.5  --ppc default=5
  N7   1   4  -   69.0  57.6  --ppc default=4
  N7   1   3  -   59.3  49.5  --ppc default=3
  N7   1   2  -   49.5  41.3  --ppc default=2
  N7   1   1  -   38.2  31.9  --ppc default=1

# MERRA300.prod.assim.inst3_3d_asm_Cp.20130601.hdf
Type LLC PPC Pck  Size   %    Flags and Notes
  H4   5   -  -  244.3 100.0  Original is compressed
  H4  B1   -  -  244.7 100.1  bzip2 -1
  N4   5   -  -  214.5  87.8
  N7   5   -  -  210.6  86.2  
  N4  B1   -  -  215.4  88.2  bzip2 -1
  N4  B9   -  -  214.8  87.9  bzip2 -9
  N3   -   -  -  617.1 252.6
N4/7   -   -  -  694.0 284.0  -L 0
N4/7   1   -  -  223.2  91.3  -L 1
N4/7   9   -  -  207.3  84.9  -L 9
N4/7   -   -  Y  347.1 142.1  ncpdq -L 0
N4/7   1   -  Y  133.6  54.7  ncpdq -L 1
N4/7   1   7  -  223.1  91.3  --ppc default=7
N4/7   1   6  -  225.1  92.1  --ppc default=6
N4/7   1   5  -  221.4  90.6  --ppc default=5
N4/7   1   4  -  201.4  82.4  --ppc default=4
N4/7   1   3  -  185.3  75.9  --ppc default=3
N4/7   1   2  -  150.0  61.4  --ppc default=2
N4/7   1   1  -  100.8  41.3  --ppc default=1

# OMI-Aura_L2-OMIAuraSO2_2012m1222-o44888_v01-00-2014m0107t114720.h5
Type LLC PPC Pck  Size   %    Flags and Notes
  H5   5   -  -   29.5 100.0  Original is compressed
  H5  B1   -  -   29.3  99.6  bzip2 -1
  N4   5   -  -   29.5 100.0
  N4  B1   -  -   29.3  99.6  bzip2 -1
  N4  B9   -  -   29.3  99.4  bzip2 -9
  N4   -   -  -   50.7 172.3  -L 0
  N4   1   -  -   29.8 101.3  -L 1
  N4   9   -  -   29.4  99.8  -L 9
  N4   -   -  Y   27.7  94.0  ncpdq -L 0
  N4   1   -  Y   12.9  43.9  ncpdq -L 1
  N4   1   7  -   29.7 100.7  --ppc default=7
  N4   1   6  -   29.7 100.8  --ppc default=6
  N4   1   5  -   27.3  92.8  --ppc default=5
  N4   1   4  -   23.8  80.7  --ppc default=4
  N4   1   3  -   20.3  69.0  --ppc default=3
  N4   1   2  -   15.1  51.2  --ppc default=2
  N4   1   1  -    9.9  33.6  --ppc default=1

NCAR CAM GCM A selective, per-variable approach to PPC yields the best balance of precision and compression yet requires the dataset producer to understand the intrinsic precision of each variable. Such a specification for a GCM dataset might look like this (using names for the NCAR CAM model):

# Be conservative on non-explicit quantities, so default=5
# Some quantities deserve four significant digits
# Many quantities, such as aerosol optical depths and burdens, are 
# highly uncertain and only useful to three significant digits.
ncks -7 -O \
--qnt default=5 \
--qnt AN.?,AQ.?=4 \
--qnt AER.?,AOD.?,ARE.?,AW.?,BURDEN.?=3 \
ncar_cam.nc ~/foo.nc

DeflationMD5 digestsCompressionShared features

Deflation -L --deflate --dfl_lvl Lempel-Ziv deflation compression deflation Availability: ncap2, ncbo, ncclimo, nces, ncecat, ncflint, ncks, ncpdq, ncra, ncrcat, ncremap, ncwa&linebreak; Short options: -L&linebreak; Long options: --dfl_lvl, --deflate&linebreak; All NCO operators that define variables support the netCDF4 feature of storing variables compressed with the lossless DEFLATE compression algorithm. DEFLATE combines the Lempel-Ziv encoding with Huffman coding. The specific version used by netCDF4/HDF5 is that implemented in the zlib library used by gzip. Activate deflation with the -L dfl_lvl short option (or with the same argument to the --dfl_lvl or --deflate long options). Specify the deflation level dfl_lvl on a scale from no deflation (dfl_lvl = 0) to maximum deflation (dfl_lvl = 9). Under the hood, this selects the compression blocksize. Minimal deflation (dfl_lvl = 1) achieves considerable storage compression with little time penalty. Higher deflation levels require more time for compression. File sizes resulting from minimal (dfl_lvl = 1) and maximal (dfl_lvl = 9) deflation levels typically differ by less than 10% in size. To compress an entire file using deflation, use

ncks -4 -L 0 in.nc out.nc # No deflation (fast, no time penalty)
ncks -4 -L 1 in.nc out.nc # Minimal deflation (little time penalty)
ncks -4 -L 9 in.nc out.nc # Maximal deflation (much slower)

Unscientific testing shows that deflation compresses typical climate datasets by 30-60%. Packing, a lossy compression technique available for all netCDF files (see Packed data), can easily compress files by 50%. Packed data may be deflated to squeeze datasets by about 80%:

ncks  -4 -L 1 in.nc out.nc # Minimal deflation (~30-60% compression)
ncks  -4 -L 9 in.nc out.nc # Maximal deflation (~31-63% compression)
ncpdq         in.nc out.nc # Standard packing  (~50% compression)
ncpdq -4 -L 9 in.nc out.nc # Deflated packing  (~80% compression)

ncks ncks prints deflation parameters, if any, to screen (ncks netCDF Kitchen Sink). <a name="md5"></a>  <a name="digest"></a>  <a name="hash"></a>  <a name="integrity"></a>  <a name="security"></a>

MD5 digestsBuffer sizesDeflationShared features

MD5 digests --md5_digest --md5_dgs --md5_wrt_att --md5_write_attribute integrity security digest hash MD5 digest Availability: ncecat, ncks, ncrcat&linebreak; Short options: &linebreak; Long options: --md5_dgs, --md5_digest, --md5_wrt_att, --md5_write_attribute&linebreak; As of NCO version 4.1.0 (April, 2012), NCO supports data integrity verification using the MD5 digest algorithm. This support is currently implemented in ncks and in the multi-file concatenators ncecat and ncrcat. Activate it with the --md5_dgs or --md5_digest long options. As of NCO version 4.3.3 (July, 2013), NCO will write the MD5 digest of each variable as an NC_CHAR attribute named MD5. This support is currently implemented in ncks and in the multi-file concatenators ncecat and ncrcat. Activate it with the --md5_wrt_att or --md5_write_attribute long options. The behavior and verbosity of the MD5 digest is operator-dependent. MD5 digests may be activated in both ncks invocation types, the one-filename argument mode for printing sub-setted and hyperslabbed data, and the two-filename argument mode for copying that data to a new file. Both modes will incur minor overhead from performing the hash algorithm for each variable read, and each variable written will have an additional attribute named MD5. When activating MD5 digests with ncks it is assumed that the user wishes to print the digest of every variable when the debugging level exceeds one. ncks displays an MD5 digest as a 32-character hexadecimal string in which each two characters represent one byte of the 16-byte digest:

> ncks --trd -D 2 -C --md5 -v md5_a,md5_abc ~/nco/data/in.nc
...
ncks: INFO MD5(md5_a) = 0cc175b9c0f1b6a831c399e269772661
md5_a = 'a' 
ncks: INFO MD5(md5_abc) = 900150983cd24fb0d6963f7d28e17f72
lev[0]=100 md5_abc[0--2]='abc' 
> ncks --trd -D 2 -C -d lev,0 --md5 -v md5_a,md5_abc ~/nco/data/in.nc
...
ncks: INFO MD5(md5_a) = 0cc175b9c0f1b6a831c399e269772661
md5_a = 'a' 
ncks: INFO MD5(md5_abc) = 0cc175b9c0f1b6a831c399e269772661
lev[0]=100 md5_abc[0--0]='a'

In fact these examples demonstrate the validity of the hash algorithm since the MD5 hashes of the strings &textldquo;a&textrdquo; and &textldquo;abc&textrdquo; are widely known. The second example shows that the hyperslab of variable md5_abc (= &textldquo;abc&textrdquo;) consisting of only its first letter (= &textldquo;a&textrdquo;) has the same hash as the variable md5_a (&textldquo;a&textrdquo;). This illustrates that MD5 digests act only on variable data, not on metadata. When activating MD5 digests with ncecat or ncrcat it is assumed that the user wishes to verify that every variable written to disk has the same MD5 digest as when it is subsequently read from disk. This incurs the major additional overhead of reading in each variable after it is written and performing the hash algorithm again on that to compare to the original hash. Moreover, it is assumed that such operations are generally done in &textldquo;production mode&textrdquo; where the user is not interested in actually examining the digests herself. The digests proceed silently unless the debugging level exceeds three:

> ncecat -O -D 4 --md5 -p ~/nco/data in.nc in.nc ~/foo.nc | grep MD5
...
ncecat: INFO MD5(wnd_spd) = bec190dd944f2ce2794a7a4abf224b28
ncecat: INFO MD5 digests of RAM and disk contents for wnd_spd agree
> ncrcat -O -D 4 --md5 -p ~/nco/data in.nc in.nc ~/foo.nc | grep MD5
...
ncrcat: INFO MD5(wnd_spd) = 74699bb0a72b7f16456badb2c995f1a1
ncrcat: INFO MD5 digests of RAM and disk contents for wnd_spd agree

Regardless of the debugging level, an error is returned when the digests of the variable read from the source file and from the output file disagree. These rules may further evolve as NCO pays more attention to data integrity. We welcome feedback and suggestions from users. <a name="bfr_sz_hnt"></a>  <a name="bfr"></a>  <a name="buffer"></a>

Buffer sizesRAM disksMD5 digestsShared features

Buffer sizes --bfr_sz_hnt Buffer sizes File buffers stat() system call I/O block size System calls Availability: All operators&linebreak; Short options: &linebreak; Long options: --bfr_sz_hnt, --buffer_size_hint&linebreak; As of NCO version 4.2.0 (May, 2012), NCO allows the user to request specific buffer sizes to allocate for reading and writing files. This buffer size determines how many system calls the netCDF layer must invoke to read and write files. By default, netCDF uses the preferred I/O block size returned as the st_blksize member of the stat structure returned by the stat() system call On modern Linux systems the block size defaults to 8192 B. The GLADE filesystem at NCAR has a block size of 512 kB.. Otherwise, netCDF uses twice the system pagesize. Larger sizes can increase access speed by reducing the number of system calls netCDF makes to read/write data from/to disk. Because netCDF cannot guarantee the buffer size request will be met, the actual buffer size granted by the system is printed as an INFO statement.

# Request 2 MB file buffer instead of default 8 kB buffer
> ncks -O -D 3 --bfr_sz=2097152 ~/nco/data/in.nc ~/foo.nc
...
ncks: INFO nc__open() will request file buffer size = 2097152 bytes
ncks: INFO nc__open() opened file with buffer size = 2097152 bytes
...

RAM disksUnbuffered I/OBuffer sizesShared features

RAM disks --ram_all --create_ram --open_ram --diskless_all RAM disks RAM files NC_DISKLESS diskless files memory requirements memory available RAM swap space peak memory usage Availability: All operators (works with netCDF3 files only)&linebreak; Short options: &linebreak; Long options: --ram_all, --create_ram, --open_ram, --diskless_all&linebreak; As of NCO version 4.2.1 (August, 2012), NCO supports the use of diskless files, aka RAM disks, for access and creation of netCDF3 files (these options have no effect on netCDF4 files). Two independent switches, --open_ram and --create_ram, control this feature. Before describing the specifics of these switches, we describe why many NCO operations will not benefit from them. Essentially, reading/writing from/to RAM rather than disk only hastens the task when reads/writes to disk are avoided. Most NCO operations are simple enough that they require a single read-from/write-to disk for every block of input/output. Diskless access does not change this, but it does add an extra read-from/write-to RAM. However this extra RAM write/read does avoid contention for limited system resources like disk-head access. Operators which may benefit from RAM disks include ncwa, which may need to read weighting variables multiple times, the multi-file operators ncra, ncrcat, and ncecat, which may try to write output at least once per input file, and ncap2 scripts which may be arbitrarily long and convoluted. The --open_ram switch causes input files to copied to RAM when opened. All further metadata and data access occurs in RAM and thus avoids access time delays caused by disk-head movement. Usually input data is read at most once so it is unlikely that requesting input files be stored in RAM will save much time. The likeliest exceptions are files that are accessed numerous times, such as those repeatedly analyzed by ncap2. Invoking --open_ram, --ram_all, or --diskless_all uses much more system memory. To copy the input file to RAM increases the sustained memory use by exactly the on-disk filesize of the input file, i.e.,

MS += FT

. For large input files this can be a huge memory burden that starves the rest of the NCO analysis of sufficient RAM. To be safe, use --open_ram, --ram_all, or --diskless_all only on files that are much (say at least a factor of four) smaller than your available system RAM. See Memory Requirements for further details. RAM variables The --create_ram switch causes output files to be created in RAM, rather than on disk. These files are copied to disk only when closed, i.e., when the operator completes. Creating files in RAM may save time, especially with ncap2 computations that are iterative, e.g., loops, and for multi-file operators that write output every record (timestep) or file. RAM files provide many of the same benefits as RAM variables in such cases (RAM variables). Two switches, --ram_all and --diskless_all, are convenient shortcuts for specifying both --create_ram and --open_ram. Thus

ncks in.nc out.nc # Default: Open in.nc on disk, write out.nc to disk
ncks --open_ram in.nc out.nc # Open in.nc in RAM, write out.nc to disk
ncks --create_ram in.nc out.nc # Create out.nc in RAM, write to disk
# Open in.nc in RAM, create out.nc in RAM, then write out.nc to disk
ncks --open_ram --create_ram in.nc out.nc
ncks --ram_all in.nc out.nc # Same as above
ncks --diskless_all in.nc out.nc # Same as above

It is straightforward to demonstrate the efficacy of RAM disks. For NASA we constructed a test that employs ncecat an arbitrary number (set to one hundred thousand) of files that are all symbolically linked to the same file. Everything is on the local filesystem (not DAP). # Create symbolic links for benchmark cd ${DATA}/nco # Do all work here for idx in {1..99999}; do idx_fmt=`printf "%05d" ${idx}` /bin/ln -s ${DATA}/nco/LPRM-AMSR_E_L3_D_SOILM3_V002-20120512T111931Z_20020619.nc \ ${DATA}/nco/${idx_fmt}.nc done # Benchmark time to ncecat one hundred thousand files time ncecat --create_ram -O -u time -v ts -d Latitude,40.0 \ -d Longitude,-105.0 -p ${DATA}/nco -n 99999,5,1 00001.nc ~/foo.nc Run normally on a laptop in 201303, this completes in 21 seconds. The --create_ram reduces the elapsed time to 9 seconds. Some of this speed may be due to using symlinks and caching. However, the efficacy of --create_ram is clear. Placing the output file in RAM avoids thousands of disk writes. It is not unreasonable to for NCO to process a million files like this in a few minutes. However, there is no substitute for benchmarking with real files. temporary output files temporary files --no_tmp_fl A completely independent way to reduce time spent writing files is to refrain from writing temporary output files. This is accomplished with the --no_tmp_fl switch (Temporary Output Files). <a name="share_all"></a>  <a name="share"></a>  <a name="create_share"></a>  <a name="open_share"></a>  <a name="unbuffered_io"></a>  <a name="unbuffered"></a>  <a name="uio"></a>

Unbuffered I/OPacked dataRAM disksShared features

Unbuffered I/O --share_all --create_share --open_share --unbuffered_io --uio unbuffered I/O NC_SHARE concurrent access shared access caching Availability: All operators (works on netCDF3 files only)&linebreak; Short options: &linebreak; Long options: --share_all, --create_share, --open_share, --unbuffered_io, --uio&linebreak; As of NCO version 4.9.4 (July, 2020), NCO supports unbuffered I/O with netCDF3 files when requested with the --unbuffered_io flag, or its synonyms --uio or --share_all. (Note that these options work only with netCDF3 files and have no affect on netCDF4 files). These flags turn-off the default I/O buffering mode for both newly created and existing datasets. For finer-grained control, use the --create_share switch to request unbuffered I/O only for newly created datasets, and the --open_share switch to request unbuffered I/O only for existing datasets. Typically these options only significantly reduce throughput time when large record variables are written or read. Normal I/O buffering copies the data to be read/written into an intermediate buffer in order to avoid numerous small reads/writes. Unbuffered I/O avoids this intermediate step and can therefore execute (sometimes much) faster when read/write lengths are large. <a name="pck"></a>  <a name="pack"></a>

Packed dataOperation TypesUnbuffered I/OShared features

Packed data packing unpacking add_offset scale_factor missing_value _FillValue pack(x) unpack(x) --hdf_upk --hdf_unpack Availability: ncap2, ncbo, nces, ncflint, ncpdq, ncra, ncwa&linebreak; Short options: None&linebreak; Long options: --hdf_upk, --hdf_unpack&linebreak; The phrase packed data refers to data which are stored in the standard netCDF3 lossy linear packing format. See ncks netCDF Kitchen Sink for a description of deflation, a lossless compression technique available with netCDF4 only. Packed data may be deflated to save additional space. Standard Packing Algorithm Packing The standard netCDF linear packing algorithm (described http://www.unidata.ucar.edu/software/netcdf/docs/netcdf/Attribute-Conventions.htmlhere) produces packed data with the same dynamic range as the original but which requires no more than half the space to store. NCO will always use this algorithm for packing. Like all packing algorithms, linear packing is lossy. Just how lossy depends on the values themselves, especially their range. The packed variable is stored (usually) as type NC_SHORT with the two attributes required to unpack the variable, scale_factor and add_offset, stored at the original (unpacked) precision of the variable Although not a part of the standard, NCO enforces the policy that the _FillValue attribute, if any, of a packed variable is also stored at the original precision.. Let min and max be the minimum and maximum values of x. $$ \rm \eqalign{\hbox{scale\_factor} &= (\hbox{max}-\hbox{min})/\hbox{ndrv}\cr \hbox{add\_offset} &= (\hbox{min}+\hbox{max})/2\cr \hbox{pck} &= (\hbox{upk}-\hbox{add\_offset})/\hbox{scale\_factor}\cr &= {\hbox{ndrv}\times[\hbox{upk}-(\hbox{min}+\hbox{max})/2]\over\hbox{max}-\hbox{min}}\cr} $$ scale_factor = (max-min)/ndrv&linebreak; add_offset = 0.5*(min+max)&linebreak; pck = (upk-add_offset)/scale_factor = (upk-0.5*(min+max))*ndrv/(max-min)&linebreak; where ndrv is the number of discrete representable values for given type of packed variable. The theoretical maximum value for ndrv is two raised to the number of bits used to store the packed variable. Thus if the variable is packed into type NC_SHORT, a two-byte datatype, then there are at most

2^{16} = 65536

distinct values representable. In practice, the number of discretely representible values is taken to be two less than the theoretical maximum. This leaves space for a missing value and solves potential problems with rounding that may occur during the unpacking of the variable. Thus for NC_SHORT,

ndrv = 65536 - 2 = 65534

. Less often, the variable may be packed into type NC_CHAR, where

ndrv = 2^{8} - 2 = 256 - 2 = 254

, or type NC_INT where where

ndrv = 2^{32} - 2 = 4294967295 - 2 = 4294967293

. One useful feature of the (lossy) netCDF packing algorithm is that lossless packing algorithms perform well on top of it. <a name="upk"></a>  <a name="unpack"></a>  Standard (Default) Unpacking Algorithm Unpacking The unpacking algorithm depends on the presence of two attributes, scale_factor and add_offset. If scale_factor is present for a variable, the data are multiplied by the value scale_factor after the data are read. If add_offset is present for a variable, then the add_offset value is added to the data after the data are read. If both scale_factor and add_offset attributes are present, the data are first scaled by scale_factor before the offset add_offset is added. $$ \rm \eqalign{\hbox{upk} &= \hbox{scale\_factor}\times\hbox{pck} + \hbox{add\_offset}\cr &= {\hbox{pck}\times(\hbox{max}-\hbox{min})\over\hbox{ndrv}} + {\hbox{min}+\hbox{max}\over2}\cr} $$ upk = scale_factor*pck + add_offset = (max-min)*pck/ndrv + 0.5*(min+max)&linebreak; NCO will use this algorithm for unpacking unless told otherwise as described below. When scale_factor and add_offset are used for packing, the associated variable (containing the packed data) is typically of type byte or short, whereas the unpacked values are intended to be of type int, float, or double. An attribute&textrsquo;s scale_factor and add_offset and _FillValue, if any, should all be of the type intended for the unpacked data, i.e., int, float or double. Non-Standard Packing and Unpacking Algorithms <a name="hdf_upk"></a>  <a name="hdf_unpack"></a>  interoperability HDF unpacking Many (most?) files originally written in HDF4 format use poorly documented packing/unpacking algorithms that are incompatible and easily confused with the netCDF packing algorithm described above. The unpacking component of the &textldquo;conventional&textrdquo; HDF algorithm (described http://www.hdfgroup.org/HDF5/doc/UG/UG_frame10Datasets.htmlhere and in Section 3.10.6 of the HDF4 Users Guide http://www.hdfgroup.org/release4/doc/UsrGuide_html/UG_PDF.pdfhere, and in the FAQ for MODIS MOD08 data http://modis-atmos.gsfc.nasa.gov/MOD08_D3/faq.htmlhere) is $$ \rm \hbox{upk} = \hbox{scale\_factor}\times(\hbox{pck} - \hbox{add\_offset}) $$ upk = scale_factor*(pck - add_offset)&linebreak; The unpacking component of the HDF algorithm employed for MODIS MOD13 data is $$ \rm \hbox{upk} = (\hbox{pck} - \hbox{add\_offset})/\hbox{scale\_factor} $$ upk = (pck - add_offset)/scale_factor&linebreak; The unpacking component of the HDF algorithm employed for MODIS MOD04 data is the same as the netCDF algorithm. @acronym{HDF5} only implements D-scaling, aka, decimal-scaling bit-packing. D-Scaling uses @code{add_offset} as a minimum data value, and @code{scale_factor} as the integer power @w{of 10} by which the @code{add_offset} corrected data are divided before storage. Confusingly, the (incompatible) netCDF and HDF algorithms both store their parameters in attributes with the same names (scale_factor and add_offset). Data packed with one algorithm should never be unpacked with the other; doing so will result in incorrect answers. Unfortunately, few users are aware that their datasets may be packed, and fewer know the details of the packing algorithm employed. This is what we in the &textldquo;bizness&textrdquo; call an interoperability issue because it hampers data analysis performed on heterogeneous systems. As described below, NCO automatically unpacks data before performing arithmetic. This automatic unpacking occurs silently since there is usually no reason to bother users with these details. There is as yet no generic way for NCO to know which packing convention was used, so NCO assumes the netCDF convention was used. NCO uses the same convention for unpacking unless explicitly told otherwise with the --hdf_upk (also --hdf_unpack) switch. Until and unless a method of automatically detecting the packing method is devised, it must remain the user&textrsquo;s responsibility to tell NCO when to use the HDF convention instead of the netCDF convention to unpack. If your data originally came from an HDF file (e.g., NASA EOS) then it was likely packed with the HDF convention and must be unpacked with the same convention. Our recommendation is to only request HDF unpacking when you are certain. Most packed datasets encountered by NCO will have used the netCDF convention. Those that were not will hopefully produce noticeably weird values when unpacked by the wrong algorithm. Before or after panicking, treat this as a clue to re-try your commands with the --hdf_upk switch. See ncpdq netCDF Permute Dimensions Quickly for an easy technique to unpack data packed with the HDF convention, and then re-pack it with the netCDF convention. Handling of Packed Data by Other Operators All NCO arithmetic operators understand packed data. The operators automatically unpack any packed variable in the input file which will be arithmetically processed. For example, ncra unpacks all record variables, and ncwa unpacks all variable which contain a dimension to be averaged. These variables are stored unpacked in the output file. On the other hand, arithmetic operators do not unpack non-processed variables. For example, ncra leaves all non-record variables packed, and ncwa leaves packed all variables lacking an averaged dimension. These variables (called fixed variables) are passed unaltered from the input to the output file. Hence fixed variables which are packed in input files remain packed in output files. Completely packing and unpacking files is easily accomplished with ncpdq (ncpdq netCDF Permute Dimensions Quickly). Pack and unpack individual variables with ncpdq and the ncap2 pack() and unpack() functions (Methods and functions). <a name="op_typ"></a>

Operation TypesType ConversionPacked dataShared features

Operation Types operation types avg sqravg avgsqr min max mabs mebs mibs rmssdn rms tabs ttl sqrt average mean total minimum maximum root-mean-square standard deviation variance -y op_typ --operation op_typ --op_typ op_typ Availability: ncap2, ncra, nces, ncwa&linebreak; Short options: -y&linebreak; Long options: --operation, --op_typ&linebreak; The -y op_typ switch allows specification of many different types of operations Set op_typ to the abbreviated key for the corresponding operation: avg Mean value sqravg Square of the mean avgsqr Mean of sum of squares max Maximum value min Minimum value mabs Maximum absolute value mebs Mean absolute value mibs Minimum absolute value rms Root-mean-square (normalized by N) rmssdn Root-mean square (normalized by N-1) sqrt Square root of the mean tabs Sum of absolute values ttl Sum of values

coordinate variable NCO assumes coordinate variables represent grid axes, e.g., longitude. The only rank-reduction which makes sense for coordinate variables is averaging. Hence NCO implements the operation type requested with -y on all non-coordinate variables, not on coordinate variables. When an operation requires a coordinate variable to be reduced in rank, i.e., from one dimension to a scalar or from one dimension to a degenerate (single value) array, then NCO always averages the coordinate variable regardless of the arithmetic operation type performed on the non-coordinate variables. The mathematical definition of each arithmetic operation is given below. ncwa netCDF Weighted Averager, for additional information on masks and normalization. If an operation type is not specified with -y then the operator performs an arithmetic average by default. Averaging is described first so the terminology for the other operations is familiar. Note for Info users: The definition of mathematical operations involving rank reduction (e.g., averaging) relies heavily on mathematical expressions which cannot be easily represented in Info. See the ./nco.pdfprinted manual for much more detailed and complete documentation of this subject. The masked, weighted average of a variable $\xxx$ can be generally represented as $$ \bar \xxx_{\jjj} = {\sum_{\idx=1}^{\idx=\lmnnbr} \mssflg_{\idx} \mskflg_{\idx} \wgt_{\idx} \xxx_{\idx} \over \sum_{\idx=1}^{\idx=\lmnnbr} \mssflg_{\idx} \mskflg_{\idx} \wgt_{\idx}} $$ where $\bar \xxx_{\jjj}$ is the $\jjj$'th element of the output hyperslab, $\xxx_{\idx}$ is the $\idx$'th element of the input hyperslab, $\mssflg_{\idx}$ is~1 unless $\xxx_{\idx}$ equals the missing value, $\mskflg_{\idx}$ is~1 unless $\xxx_{\idx}$ is masked, and $\wgt_{\idx}$ is the weight. This formiddable formula represents a simple weighted average whose bells and whistles are all explained below. It is not too early to note, however, that when $\mssflg_{\idx} = \mskflg_{\idx} = \wgt_{\idx} = 1$, the generic averaging expression above reduces to a simple arithmetic average. Furthermore, $\mskflg_{\idx} = \wgt_{\idx} = 1$ for all operators except @command{ncwa}. These variables are included in the discussion below for completeness, and for possible future use in other operators. The size $\outnbr$ of the output hyperslab for a given variable is the product of all the dimensions of the input variable which are not averaged over. The size $\lmnnbr$ of the input hyperslab contributing to each $\bar \xxx_{\jjj}$ is simply the product of the sizes of all dimensions which are averaged over (i.e., dimensions specified with @samp{-a}). Thus $\lmnnbr$ is the number of input elements which @emph{potentially} contribute to each output element. An input element $\xxx_{\idx}$ contributes to the output element $\xxx_{\jjj}$ except in two conditions: @cindex missing values @enumerate @item $\xxx_{\idx}$ equals the @var{missing value} (@pxref{Missing Values}) for the variable. @item $\xxx_{\idx}$ is located at a point where the mask condition (@pxref{Mask condition}) is false. @end enumerate Points $\xxx_{\idx}$ in either of these two categories do not contribute to $\xxx_{\jjj}$---they are ignored. We now define these criteria more rigorously. Each~$\xxx_{\idx}$ has an associated Boolean weight~$\mssflg_{\idx}$ whose value is~0 or~1 (false or true). The value of~$\mssflg_{\idx}$ is~1 (true) unless $\xxx_{\idx}$ equals the @var{missing value} (@pxref{Missing Values}) for the variable. Thus, for a variable with no @code{_FillValue} attribute, $\mssflg_{\idx}$~is always~1. All @acronym{NCO} arithmetic operators (@command{ncbo}, @command{ncra}, @command{nces}, @command{ncflint}, @command{ncwa}) treat missing values analogously. Besides (weighted) averaging, @command{ncwa}, @command{ncra}, and @command{nces} also compute some common non-linear operations which may be specified with the @samp{-y} switch (@pxref{Operation Types}). The other rank-reducing operations are simple variations of the generic weighted mean described above. The total value of~$\xxx$ (@code{-y ttl}) is $$ \bar \xxx_{\jjj} = \sum_{\idx=1}^{\idx=\lmnnbr} \mssflg_{\idx} \mskflg_{\idx} \wgt_{\idx} \xxx_{\idx} $$ @cindex @code{-N} @cindex @code{numerator} Note that the total is the same as the numerator of the mean of~$\xxx$, and may also be obtained in @command{ncwa} by using the @samp{-N} switch (@pxref{ncwa netCDF Weighted Averager}). The minimum value of~$\xxx$ (@code{-y min}) is $$ \bar \xxx_{\jjj} = \min [ \mssflg_{1} \mskflg_{1} \wgt_{1} \xxx_{1}, \mssflg_{2} \mskflg_{2} \wgt_{2} \xxx_{2}, \ldots, \mssflg_{\lmnnbr} \mskflg_{\lmnnbr} \wgt_{\lmnnbr} \xxx_{\lmnnbr} ] $$ Analogously, the maximum value of~$\xxx$ (@code{-y max}) is $$ \bar \xxx_{\jjj} = \max [ \mssflg_{1} \mskflg_{1} \wgt_{1} \xxx_{1}, \mssflg_{2} \mskflg_{2} \wgt_{2} \xxx_{2}, \ldots, \mssflg_{\lmnnbr} \mskflg_{\lmnnbr} \wgt_{\lmnnbr} \xxx_{\lmnnbr} ] $$ Thus the minima and maxima are determined after any weights are applied. The total absolute value of~$\xxx$ (@code{-y tabs}) is $$ \bar \xxx_{\jjj} = \sum_{\idx=1}^{\idx=\lmnnbr} \mssflg_{\idx} \mskflg_{\idx} \wgt_{\idx} |\xxx_{\idx}| $$ The minimum absolute value of~$\xxx$ (@code{-y mibs}) is $$ \bar \xxx_{\jjj} = \min [ \mssflg_{1} \mskflg_{1} \wgt_{1} |\xxx_{1}|, \mssflg_{2} \mskflg_{2} \wgt_{2} |\xxx_{2}|, \ldots, \mssflg_{\lmnnbr} \mskflg_{\lmnnbr} \wgt_{\lmnnbr} |\xxx_{\lmnnbr}| ] $$ Analogously, the maximum absolute value of~$\xxx$ (@code{-y mabs}) is $$ \bar \xxx_{\jjj} = \max [ \mssflg_{1} \mskflg_{1} \wgt_{1} |\xxx_{1}|, \mssflg_{2} \mskflg_{2} \wgt_{2} |\xxx_{2}|, \ldots, \mssflg_{\lmnnbr} \mskflg_{\lmnnbr} \wgt_{\lmnnbr} |\xxx_{\lmnnbr}| ] $$ Thus the minimum and maximum absolute values are determined after any weights are applied. The mean absolute value of~$\xxx$ (@code{-y mebs}) is $$ \bar \xxx_{\jjj} = {\sum_{\idx=1}^{\idx=\lmnnbr} \mssflg_{\idx} \mskflg_{\idx} \wgt_{\idx} |\xxx_{\idx}| \over \sum_{\idx=1}^{\idx=\lmnnbr} \mssflg_{\idx} \mskflg_{\idx} \wgt_{\idx}} $$ The square of the mean value of~$\xxx$ (@code{-y sqravg}) is $$ \bar \xxx_{\jjj} = \left( {\sum_{\idx=1}^{\idx=\lmnnbr} \mssflg_{\idx} \mskflg_{\idx} \wgt_{\idx} \xxx_{\idx} \over \sum_{\idx=1}^{\idx=\lmnnbr} \mssflg_{\idx} \mskflg_{\idx} \wgt_{\idx}} \right)^{2} $$ The mean of the sum of squares of~$\xxx$ (@code{-y avgsqr}) is $$ \bar \xxx_{\jjj} = {\sum_{\idx=1}^{\idx=\lmnnbr} \mssflg_{\idx} \mskflg_{\idx} \wgt_{\idx} \xxx^{2}_{\idx} \over \sum_{\idx=1}^{\idx=\lmnnbr} \mssflg_{\idx} \mskflg_{\idx} \wgt_{\idx}} $$ If $\xxx$ represents a deviation from the mean of another variable, $\xxx_{\idx} = \yyy_{\idx} - \bar{\yyy}$ (possibly created by @command{ncbo} in a previous step), then applying @code{avgsqr} to $\xxx$ computes the approximate variance of $\yyy$. Computing the true variance of~$\yyy$ requires subtracting~1 from the denominator, discussed below. For a large sample size however, the two results will be nearly indistinguishable. The root mean square of~$\xxx$ (@code{-y rms}) is $$ \bar \xxx_{\jjj} = \sqrt{ {\sum_{\idx=1}^{\idx=\lmnnbr} \mssflg_{\idx} \mskflg_{\idx} \wgt_{\idx} x^{2}_{\idx} \over \sum_{\idx=1}^{\idx=\lmnnbr} \mssflg_{\idx} \mskflg_{\idx} \wgt_{\idx}}} $$ Thus @code{rms} simply computes the squareroot of the quantity computed by @code{avgsqr}. The root mean square of~$\xxx$ with standard-deviation-like normalization (@code{-y rmssdn}) is implemented as follows. When weights are not specified, this function is the same as the root mean square of~$\xxx$ except one is subtracted from the sum in the denominator $$ \bar \xxx_{\jjj} = \sqrt{ {\sum_{\idx=1}^{\idx=\lmnnbr} \mssflg_{\idx} \mskflg_{\idx} x^{2}_{\idx} \over -1 + \sum_{\idx=1}^{\idx=\lmnnbr} \mssflg_{\idx} \mskflg_{\idx}} } $$ If $\xxx$ represents the deviation from the mean of another variable, $\xxx_{\idx} = \yyy_{\idx} - \bar{\yyy}$, then applying @code{rmssdn} to $\xxx$ computes the standard deviation of~$\yyy$. In this case the $-1$ in the denominator compensates for the degree of freedom already used in computing $\bar{\yyy}$ in the numerator. Consult a statistics book for more details. When weights are specified it is unclear how to compensate for this extra degree of freedom. Weighting the numerator and denominator of the above by $\wgt_{\idx}$ and subtracting one from the denominator is only appropriate when all the weights are~1.0. When the weights are arbitrary (e.g., Gaussian weights), subtracting one from the sum in the denominator does not necessarily remove one degree of freedom. Therefore when @code{-y rmssdn} is requested and weights are specified, @command{ncwa} actually implements the @code{rms} procedure. @command{nces} and @command{ncra}, which do not allow weights to be specified, always implement the @code{rmssdn} procedure when asked. @ignore 20130827: Fedora Core 19 (FC19) broke here with "./nco.texi:6394: Missing dollarsign inserted." Ubuntu always built nco.texi fine Adding a dollarsign character right here breaks Ubuntu builds too Hence I must carefully spell-out the word dollarsign instead 20130829: Making many smaller TeX environments does not solve problem 20130910: Using latest texinfo.tex from GNU does not solve problem 20130910: Karl Berry solved problem by fixing bug in texinfo.tex Bug was triggered in Fedora by apostrophe in "User's Guide" (manual title) Bug not present in texinfo.tex version 2008-04-18.10 (used by Ubuntu 13.04) Bug present in texinfo.tex version 2013-02-01.11 (used by FC19) Bug just fixed in texinfo.tex version 2013-09-11 (committed by Karl) nco/autobld/texinfo.tex now contains fixed version Breakage always occurs near here @end ignore The square root of the mean of~$\xxx$ (@code{-y sqrt}) is $$ \bar \xxx_{\jjj} = \sqrt{ {\sum_{\idx=1}^{\idx=\lmnnbr} \mssflg_{\idx} \mskflg_{\idx} \wgt_{\idx} \xxx_{\idx} \over \sum_{\idx=1}^{\idx=\lmnnbr} \mssflg_{\idx} \mskflg_{\idx} \wgt_{\idx}}} $$ The definitions of some of these operations are not universally useful. Mostly they were chosen to facilitate standard statistical computations within the NCO framework. We are open to redefining and or adding to the above. If you are interested in having other statistical quantities defined in NCO please contact the NCO project (Help Requests and Bug Reports). EXAMPLES <a name="mabs"></a>  <a name="mebs"></a>  <a name="mibs"></a>  <a name="min"></a>  <a name="max"></a>  <a name="rms"></a>  Suppose you wish to examine the variable prs_sfc(time,lat,lon) which contains a time series of the surface pressure as a function of latitude and longitude. Find the minimum value of prs_sfc over all dimensions:

ncwa -y min -v prs_sfc in.nc foo.nc

Find the maximum value of prs_sfc at each time interval for each latitude:

ncwa -y max -v prs_sfc -a lon in.nc foo.nc

Find the root-mean-square value of the time-series of prs_sfc at every gridpoint:

ncra -y rms -v prs_sfc in.nc foo.nc
ncwa -y rms -v prs_sfc -a time in.nc foo.nc

The previous two commands give the same answer but ncra is preferred because it has a smaller memory footprint. degenerate dimension A dimension of size one is said to be degenerate. By default, ncra leaves the (degenerate) time dimension in the output file (which is usually useful) whereas ncwa removes the time dimension (unless -b is given). These operations work as expected in multi-file operators. Suppose that prs_sfc is stored in multiple timesteps per file across multiple files, say jan.nc, feb.nc, march.nc. We can now find the three month maximum surface pressure at every point.

nces -y max -v prs_sfc jan.nc feb.nc march.nc out.nc

<a name="standard_deviation"></a>  <a name="stddvn"></a>  <a name="sdn"></a>  <a name="sdv"></a>  <a name="xmp_sdn"></a>  standard deviation It is possible to use a combination of these operations to compute the variance and standard deviation of a field stored in a single file or across multiple files. The procedure to compute the temporal standard deviation of the surface pressure at all points in a single file in.nc involves three steps.

ncwa -O -v prs_sfc -a time in.nc out.nc
ncbo -O -v prs_sfc in.nc out.nc out.nc 
ncra -O -y rmssdn out.nc out.nc

First construct the temporal mean of prs_sfc in the file out.nc. Next overwrite out.nc with the anomaly (deviation from the mean). Finally overwrite out.nc with the root-mean-square of itself. Note the use of -y rmssdn (rather than -y rms) in the final step. This ensures the standard deviation is correctly normalized by one fewer than the number of time samples. The procedure to compute the variance is identical except for the use of -y avgsqr instead of -y rmssdn in the final step. ncap2 can also compute statistics like standard deviations. Brute-force implementation of formulae is one option, e.g.,

ncap2 -s 'prs_sfc_sdn=sqrt((prs_sfc-prs_sfc.avg($time)^2). \
      total($time)/($time.size-1))' in.nc out.nc

The operation may, of course, be broken into multiple steps in order to archive intermediate quantities, such as the time-anomalies

ncap2 -s 'prs_sfc_anm=prs_sfc-prs_sfc.avg($time)' \
      -s 'prs_sfc_sdn=sqrt((prs_sfc_anm^2).total($time)/($time.size-1))' \
      in.nc out.nc

ncap2 supports intrinsic standard deviation functions (Operation Types) which simplify the above expression to

ncap2 -s 'prs_sfc_sdn=(prs_sfc-prs_sfc.avg($time)).rmssdn($time)' in.nc out.nc

These instrinsic functions compute the answer quickly and concisely. The procedure to compute the spatial standard deviation of a field in a single file in.nc involves three steps.

ncwa -O -v prs_sfc,gw -a lat,lon -w gw in.nc out.nc
ncbo -O -v prs_sfc,gw in.nc out.nc out.nc
ncwa -O -y rmssdn -v prs_sfc -a lat,lon -w gw out.nc out.nc

First the spatially weighted (by -w gw) mean values are written to the output file, as are the mean weights. The initial output file is then overwritten with the gridpoint deviations from the spatial mean. It is important that the output file after the second line contain the original, non-averaged weights. This will be the case if the weights are named so that NCO treats them like a coordinate (CF Conventions). One such name is gw, and any variable whose name begins with msk_ (for &textldquo;mask&textrdquo;) or wgt_ (for &textldquo;weight&textrdquo;) will likewise be treated as a coordinate, and will be copied (not differenced) straight from in.nc to out.nc in the second step. When using weights to compute standard deviations one must remember to include the weights in the initial output files so that they may be used again in the final step. Finally the root-mean-square of the appropriately weighted spatial deviations is taken. No elegant ncap2 solution exists to compute weighted standard deviations. Those brave of heart may try to formulate one. A general formula should allow weights to have fewer than and variables to have more than the minimal spatial dimensions (latitude and longitude). @c Until 20151229 ncap2 example was FUBAR, and did not handle weights in denominator @example ncap2 -s 'prs_sfc_sdn=((gw*(prs_sfc-((gw*prs_sfc).ttl($lat,$lon)/gw.ttl($lat,$lon)))^2).ttl($lat,$lon)/ \ gw.ttl()).sqrt()' in.nc out.nc @end example Note how the weight multiplies the variable prior to computing the the anomalies and the standard deviation. The procedure to compute the standard deviation of a time-series across multiple files involves one extra step since all the input must first be collected into one file.

ncrcat -O -v tpt in.nc in.nc foo1.nc
ncwa -O -a time foo1.nc foo2.nc
ncbo -O -v tpt foo1.nc foo2.nc foo3.nc
ncra -O -y rmssdn foo3.nc out.nc

The first step assembles all the data into a single file. Though this may consume a lot of temporary disk space, it is more or less required by the ncbo operation in the third step. <a name="typ_cnv"></a>

Type ConversionBatch ModeOperation TypesShared features

Type Conversion type conversion Availability (automatic type conversion): ncap2, ncbo, nces, ncflint, ncra, ncwa&linebreak; Short options: None (it&textrsquo;s automatic)&linebreak; Availability (manual type conversion): nces, ncra, ncwa&linebreak; Short options: None&linebreak; Long options: --dbl, --flt, --rth_dbl, --rth_flt&linebreak; promotion demotion automatic type conversion manual type conversion Type conversion refers to the casting or coercion of one fundamental or atomic data type to another, e.g., converting NC_SHORT (two bytes) to NC_DOUBLE (eight bytes). Type conversion always promotes or demotes the range and/or precision of the values a variable can hold. Type conversion is automatic when the language carries out this promotion according to an internal set of rules without explicit user intervention. In contrast, manual type conversion refers to explicit user commands to change the type of a variable or attribute. Most type conversion happens automatically, yet there are situations in which manual type conversion is advantageous. * Automatic type conversion::

* Promoting Single-precision to Double::

* Manual type conversion::

Automatic type conversionPromoting Single-precision to DoubleType ConversionType Conversion Automatic type conversion There are at least two reasons to avoid type conversions. First, type conversions are expensive since they require creating (temporary) buffers and casting each element of a variable from its storage type to some other type and then, often, converting it back. Second, a dataset&textrsquo;s creator perhaps had a good reason for storing data as, say, NC_FLOAT rather than NC_DOUBLE. In a scientific framework there is no reason to store data with more precision than the observations merit. Normally this is single-precision, which guarantees 6&textndash;9 digits of precision. Reasons to engage in type conversion include avoiding rounding errors and out-of-range limitations of less-precise types. This is the case with most integers. Thus NCO defaults to automatically promote integer types to floating-point when performing lengthy arithmetic, yet NCO defaults to not promoting single to double-precision floats. Before discussing the more subtle floating-point issues, we first examine integer promotion. We will show how following parsimonious conversion rules dogmatically can cause problems, and what NCO does about that. That said, there are situations in which implicit conversion of single- to double-precision is also warranted. Understanding the narrowness of these situations takes time, and we hope the reader appreciates the following detailed discussion. Consider the average of the two NC_SHORTs 17000s and 17000s. A straightforward average without promotion results in garbage since the intermediate value which holds their sum is also of type NC_SHORT and thus overflows on (i.e., cannot represent) values greater than 32,767 $32767 = 2^{15}-1$ @clear flg

32767 = 2^15-1

. There are valid reasons for expecting this operation to succeed and the NCO philosophy is to make operators do what you want, not what is purest. Thus, unlike C and Fortran, but like many other higher level interpreted languages, NCO arithmetic operators will perform automatic type conversion on integers when all the following conditions are met Operators began performing automatic type conversions before arithmetic in NCO version 1.2, August, 2000. Previous versions never performed unnecessary type conversion for arithmetic.: The requested operation is arithmetic. This is why type conversion is limited to the operators ncap2, ncbo, nces, ncflint, ncra, and ncwa. The arithmetic operation could benefit from type conversion. Operations that could benefit include averaging, summation, or any &textldquo;hard&textrdquo; arithmetic that could overflow or underflow. Larger representable sums help avoid overflow, and more precision helps to avoid underflow. Type conversion does not benefit searching for minima and maxima (-y min, or -y max). The variable on disk is of type NC_BYTE, NC_CHAR, NC_SHORT, or NC_INT. Type NC_DOUBLE is not promoted because there is no type of higher precision. Conversion of type NC_FLOAT is discussed in detail below. When it occurs, it follows the same procedure (promotion then arithmetic then demotion) as conversion of integer types. When these criteria are all met, the operator promotes the variable in question to type NC_DOUBLE, performs all the arithmetic operations, casts the NC_DOUBLE type back to the original type, and finally writes the result to disk. The result written to disk may not be what you expect, because of incommensurate ranges represented by different types, and because of (lack of) rounding. First, continuing the above example, the average (e.g., -y avg) of 17000s and 17000s is written to disk as 17000s. The type conversion feature of NCO makes this possible since the arithmetic and intermediate values are stored as NC_DOUBLEs, i.e., 34000.0d and only the final result must be represented as an NC_SHORT. Without the type conversion feature of NCO, the average would have been garbage (albeit predictable garbage near -15768s). Similarly, the total (e.g., -y ttl) of 17000s and 17000s written to disk is garbage (actually -31536s) since the final result (the true total) of

34000

is outside the range of type NC_SHORT. trunc() After arithmetic is computed in double-precision for promoted variables, the intermediate double-precision values must be demoted to the variables&textrsquo; original storage type (e.g., from NC_DOUBLE to NC_SHORT). NCO has handled this demotion in three ways in its history. Prior to October, 2011 (version 4.0.8), NCO employed the C library truncate function, trunc() C language The actual type conversions with trunction were handled by intrinsic type conversion, so the trunc() function was never explicitly called, although the results would be the same if it were.. Truncation rounds x to the nearest integer not larger in absolute value. For example, truncation rounds 1.0d, 1.5d, and 1.8d to the same value, 1s. Clearly, truncation does not round floating-point numbers to the nearest integer! Yet truncation is how the C language performs implicit conversion of real numbers to integers. Neil Davis NCO stopped using truncation for demotion when an alert user (Neil Davis) informed us that this caused a small bias in the packing algorithm employed by ncpdq. This led to NCO adopting rounding functions for demotion. Rounding functions eliminated the small bias in the packing algorithm. lround(). From February, 2012 through March, 2013 (versions 4.0.9&textndash;4.2.6), NCO employed the C library family of rounding functions, lround(). These functions round x to the nearest integer, halfway cases away from zero. The problem with lround() is that it always rounds real values ending in .5 away from zero. This rounds, for example, 1.5d and 2.5d to 2s and 3s, respectively. lrint(). IEEE Since April, 2013 (version 4.3.0), NCO has employed the other C library family of rounding functions, lrint(). This algorithm rounds x to the nearest integer, using the current rounding direction. Halfway cases are rounded to the nearest even integer. This rounds, for example, both 1.5d and 2.5d to the same value, 2s, as recommended by the IEEE. This rounding is symmetric: up half the time, down half the time. This is the current and hopefully final demotion algorithm employed by NCO. Hence because of automatic conversion, NCO will compute the average of 2s and 3s in double-precision arithmetic as

(

2.0d + 3.0d)/2.0d) = 2.5d. It then demotes this intermediate result back to NC_SHORT and stores it on disk as

trunc(2.5d) = 2s (versions up to 4.0.8),

lround(2.5d) = 3s (versions 4.0.9&textndash;4.2.6), and

lrint(2.5d) = 2s (versions 4.3.0 and later). <a name="sp_dp"></a>  <a name="dbl"></a>  Promoting Single-precision to DoubleManual type conversionAutomatic type conversionType Conversion Promoting Single-precision to Double promotion implicit conversion --dbl --rth_dbl --flt --rth_flt Promotion of real numbers from single- to double-precision is fundamental to scientific computing. When it should occur depends on the precision of the inputs and the number of operations. Single-precision (four-byte) numbers contain about seven significant figures, while double-precision contain about sixteen. More, err, precisely, the IEEE single-precision representation gives from 6 to 9 significant decimal digits precision According to Wikipedia&textrsquo;s summary of IEEE standard 754, &textldquo;If a decimal string with at most 6 significant digits is converted to IEEE 754 single-precision and then converted back to the same number of significant decimal, then the final string should match the original; and if an IEEE 754 single-precision is converted to a decimal string with at leastn 9 significant decimal and then converted back to single, then the final number must match the original&textrdquo;.. And the IEEE double-precision representation gives from 15 to 17 significant decimal digits precision According to Wikipedia&textrsquo;s summary of IEEE standard 754, &textldquo;If a decimal string with at most 15 significant digits is converted to IEEE 754 double-precision representation and then converted back to a string with the same number of significant digits, then the final string should match the original; and if an IEEE 754 double precision is converted to a decimal string with at least 17 significant digits and then converted back to double, then the final number must match the original&textrdquo;.. Hence double-precision numbers represent about nine digits more precision than single-precision numbers. Given these properties, there are at least two possible arithmetic conventions for the treatment of real numbers: C language Fortran Conservative, aka Fortran Convention Automatic type conversion during arithmetic in the Fortran language is, by default, performed only when necessary. All operands in an operation are converted to the most precise type involved the operation before the arithmetic operation. Expressions which involve only single-precision numbers are computed entirely in single-precision. Expressions involving mixed precision types are computed in the type of higher precision. NCO by default employs the Fortan Convention for promotion. Aggressive, aka C Convention The C language is by default much more aggressive (and thus wasteful) than Fortran, and will always implicitly convert single- to double-precision numbers, even when there is no good reason. All real-number standard C library functions are double-precision, and C programmers must take extra steps to only utilize single precision arithmetic. The high-level interpreted data analysis languages IDL, Matlab, and NCL all adopt the C Convention. NCO does not automatically promote NC_FLOAT because, in our judgement, the performance penalty of always doing so would outweigh the potential benefits. The now-classic text &textldquo;Numerical Recipes in C&textrdquo; discusses this point under the section &textldquo;Implicit Conversion of Float to Double&textrdquo; See page 21 in Section 1.2 of the First edition for this gem: One does not need much experience in scientific computing to recognize that the implicit conversion rules are, in fact, sheer madness! In effect, they make it impossible to write efficient numerical programs. . That said, such promotion is warranted in some circumstances. For example, rounding errors can accumulate to worrisome levels during arithmetic performed on large arrays of single-precision floats. This use-case occurs often in geoscientific studies of climate where thousands-to-millions of gridpoints may contribute to a single average. If the inputs are all single-precision, then so should be the output. However the intermediate results where running sums are accumulated may suffer from too much rounding or from underflow unless computed in double-precision. The order of operations matters to floating-point math even when the analytic expressions are equal. Cautious users feel disquieted when results from equally valid analyses differ in the final bits instead of agreeing bit-for-bit. For example, averaging arrays in multiple stages produces different answers than averaging them in one step. This is easily seen in the computation of ensemble averages by two different methods. The NCO test file in.nc contains single- and double-precision representations of the same temperature timeseries as tpt_flt and tpt_dbl. Pretend each datapoint in this timeseries represents a monthly-mean temperature. We will mimic the derivation of a fifteen-year ensemble-mean January temperature by concatenating the input file five times, and then averaging the datapoints representing January two different ways. In Method 1 we derive the 15-year ensemble January average in two steps, as the average of three five-year averages. This method is naturally used when each input file contains multiple years and multiple input files are needed For example, the CMIP5 archive tends to distribute monthly average timeseries in 50-year chunks.. In Method 2 we obtain 15-year ensemble January average in a single step, by averaging all 15 Januaries at one time:

# tpt_flt and tpt_dbl are identical except for precision
ncks -C -v tpt_flt,tpt_dbl ~/nco/data/in.nc
# tpt_dbl = 273.1, 273.2, 273.3, 273.4, 273.5, 273.6, 273.7, 273.8, 273.9, 274
# tpt_flt = 273.1, 273.2, 273.3, 273.4, 273.5, 273.6, 273.7, 273.8, 273.9, 274
# Create file with five "ten-month years" (i.e., 50 timesteps) of temperature data
ncrcat -O -v tpt_flt,tpt_dbl -p ~/nco/data in.nc in.nc in.nc in.nc in.nc ~/foo.nc
# Average 1st five "Januaries" (elements 1, 11, 21, 31, 41)
ncra --flt -O -F -d time,1,,10 ~/foo.nc ~/foo_avg1.nc
# Average 2nd five "Januaries" (elements 2, 12, 22, 32, 42)
ncra --flt -O -F -d time,2,,10 ~/foo.nc ~/foo_avg2.nc
# Average 3rd five "Januaries" (elements 3, 13, 23, 33, 43)
ncra --flt -O -F -d time,3,,10 ~/foo.nc ~/foo_avg3.nc
# Method 1: Obtain ensemble January average by averaging the averages
ncra --flt -O ~/foo_avg1.nc ~/foo_avg2.nc ~/foo_avg3.nc ~/foo_avg_mth1.nc
# Method 2: Obtain ensemble January average by averaging the raw data
# Employ ncra's "subcycle" feature (http://nco.sf.net/nco.html#ssc)
ncra --flt -O -F -d time,1,,10,3 ~/foo.nc ~/foo_avg_mth2.nc
# Difference the two methods
ncbo -O ~/foo_avg_mth1.nc ~/foo_avg_mth2.nc ~/foo_avg_dff.nc
ncks ~/foo_avg_dff.nc
# tpt_dbl = 5.6843418860808e-14 ;
# tpt_flt = -3.051758e-05 ;

Although the two methods are arithmetically equivalent, they produce slightly different answers due to the different order of operations. Moreover, it appears at first glance that the single-precision answers suffer from greater error than the double-precision answers. In fact both precisions suffer from non-zero rounding errors. The answers differ negligibly to machine precision, which is about seven significant figures for single precision floats (tpt_flt), and sixteen significant figures for double precision (tpt_dbl). The input precision determines the answer precision. IEEE arithmetic guarantees that two methods will produce bit-for-bit identical answers only if they compute the same operations in the same order. Bit-for-bit identical answers may also occur by happenstance when rounding errors exactly compensate one another. This is demonstrated by repeating the example above with the --dbl (or --rth_dbl for clarity) option which forces conversion of single-precision numbers to double-precision prior to arithmetic. Now ncra will treat the first value of tpt_flt, 273.1000f, as 273.1000000000000d. Arithmetic on tpt_flt then proceeds in double-precision until the final answer, which is converted back to single-precision for final storage.

# Average 1st five "Januaries" (elements 1, 11, 21, 31, 41)
ncra --dbl -O -F -d time,1,,10 ~/foo.nc ~/foo_avg1.nc
# Average 2nd five "Januaries" (elements 2, 12, 22, 32, 42)
ncra --dbl -O -F -d time,2,,10 ~/foo.nc ~/foo_avg2.nc
# Average 3rd five "Januaries" (elements 3, 13, 23, 33, 43)
ncra --dbl -O -F -d time,3,,10 ~/foo.nc ~/foo_avg3.nc
# Method 1: Obtain ensemble January average by averaging the averages
ncra --dbl -O ~/foo_avg1.nc ~/foo_avg2.nc ~/foo_avg3.nc ~/foo_avg_mth1.nc
# Method 2: Obtain ensemble January average by averaging the raw data
# Employ ncra's "subcycle" feature (http://nco.sf.net/nco.html#ssc)
ncra --dbl -O -F -d time,1,,10,3 ~/foo.nc ~/foo_avg_mth2.nc
# Difference the two methods
ncbo -O ~/foo_avg_mth1.nc ~/foo_avg_mth2.nc ~/foo_avg_dff.nc
# Show differences
ncks ~/foo_avg_dff.nc
# tpt_dbl = 5.6843418860808e-14 ;
# tpt_flt = 0 ;

The --dbl switch has no effect on the results computed from double-precision inputs. But now the two methods produce bit-for-bit identical results from the single-precision inputs! This is due to the happenstance of rounding along with the effects of the --dbl switch. The --flt and --rth_flt switches are provided for symmetry. They enforce the traditional NCO and Fortran convention of keeping single-precision arithmetic in single-precision unless a double-precision number is explicitly involved. We have shown that forced promotion of single- to double-precision prior to arithmetic has advantages and disadvantages. The primary disadvantages are speed and size. Double-precision arithmetic is 10&textndash;60% slower than, and requires twice the memory of single-precision arithmetic. The primary advantage is that rounding errors in double-precision are much less likely to accumulate to values near the precision of the underlying geophysical variable. For example, if we know temperature to five significant digits, then a rounding error of 1-bit could affect the least precise digit of temperature after 1,000&textndash;10,000 consecutive one-sided rounding errors under the worst possible scenario. Many geophysical grids have tens-of-thousands to millions of points that must be summed prior to normalization to compute an average. It is possible for single-precision rouding errors to accumulate and degrade the precision in such situtations. Double-precision arithmetic mititgates this problem, so --dbl would be warranted. TREFHT CAM3 GCM This can be seen with another example, averaging a global surface temperature field with ncwa. The input contains a single-precision global temperature field (stored in TREFHT) produced by the CAM3 general circulation model (GCM) run and stored at 1.9 by 2.5 degrees resolution. This requires 94 latitudes and 144 longitudes, or

13,824

total surface gridpoints, a typical GCM resolution in 2008&textndash;2013. These input characteristics are provided only to show the context to the interested reader, equivalent results would be found in statistics of any dataset of comparable size. Models often represent Earth on a spherical grid where global averages must be created by weighting each gridcell by its latitude-dependent weight (e.g., a Gaussian weight stored in gw), or by the surface area of each contributing gridpoint (stored in area). Like many geophysical models and most GCMs, CAM3 runs completely in double-precision yet stores its archival output in single-precision to save space. In practice such models usually save multi-dimensional prognostic and diagnostic fields (like TREFHT(lat,lon)) as single-precision, while saving all one-dimensional coordinates and weights (here lat, lon, and gw(lon)) as double-precision. The gridcell area area(lat,lon) is an extensive grid property that should be, but often is not, stored as double-precision. To obtain pure double-precision arithmetic and storage of the globla mean temperature, we first create and store double-precision versions of the single-precision fields:

ncap2 -O -s 'TREFHT_dbl=double(TREFHT);area_dbl=double(area)' in.nc in.nc

The single- and double-precision temperatures may each be averaged globally using four permutations for the precision of the weight and of the intermediate arithmetic representation: Single-precision weight (area), single-precision arithmetic Double-precision weight (gw), single-precision arithmetic Single-precision weight (area), double-precision arithmetic Double-precision weight (gw), double-precision arithmetic

# NB: Values below are printed with C-format %5.6f using
# ncks -H -C -s '%5.6f' -v TREFHT,TREFHT_dbl out.nc
# Single-precision weight (area), single-precision arithmetic
ncwa --flt -O -a lat,lon -w area in.nc out.nc
# TREFHT     = 289.246735 
# TREFHT_dbl = 289.239964
# Double-precision weight (gw),   single-precision arithmetic
ncwa --flt -O -a lat,lon -w gw   in.nc out.nc
# TREFHT     = 289.226135
# TREFHT_dbl = 289.239964
# Single-precision weight (area), double-precision arithmetic
ncwa --dbl -O -a lat,lon -w area in.nc out.nc
# TREFHT     = 289.239960
# TREFHT_dbl = 289.239964
# Double-precision weight (gw),   double-precision arithmetic
ncwa --dbl -O -a lat,lon -w gw   in.nc out.nc
# TREFHT     = 289.239960
# TREFHT_dbl = 289.239964

First note that the TREFHT_dbl average never changes because TREFHT_dbl(lat,lon) is double-precision in the input file. As described above, NCO automatically converts all operands involving to the highest precision involved in the operation. So specifying --dbl is redundant for double-precision inputs. Second, the single-precision arithmetic averages of the single-precision input TREFHT differ by

289.246735 - 289.226135 = 0.0206

from eachother, and, more importantly, by as much as

289.239964 - 289.226135 = 0.013829

from the correct (double-precision) answer. These averages differ in the fifth digit, i.e., they agree only to four significant figures! Given that climate scientists are concerned about global temperature variations of a tenth of a degree or less, this difference is large. Global mean temperature changes significant to climate scientists are comparable in size to the numerical artifacts produced by the averaging procedure. rounding random walk Why are the single-precision numerical artifacts so large? Each global average is the result of multiplying almost 15,000 elements each by its weight, summing those, and then dividing by the summed weights. Thus about 50,000 single-precision floating-point operations caused the loss of two to three significant digits of precision. The net error of a series of independent rounding errors is a random walk phenomena Michael Prather Thanks to Michael J. Prather for explaining this to me.. Successive rounding errors displace the answer further from the truth. An ensemble of such averages will, on average, have no net bias. In other words, the expectation value of a series of IEEE rounding errors is zero. And the error of any given sequence of rounding errors obeys, for large series, a Gaussian distribution centered on zero. mantissa exponent Single-precision numbers use three of their four eight-bit bytes to represent the mantissa so the smallest representable single-precision mantissa is

\epsilon \equiv 2^{-23} = 1.19209 \times 10^{-7}

. This

\epsilon

is the smallest x such that

1.0 + x \ne 1.0

. This is the rounding error for non-exact precision-numbers. Applying random walk theory to rounding, it can be shown that the expected rounding error after n inexact operations is

\sqrt{2n/\pi}

for large n. The expected (i.e., mean absolute) rounding error in our example with

13,824

additions is about

\sqrt{2 \times 13824 / \pi} = 91.96

. Hence, addition alone of about fifteen thousand single-precision floats is expected to consume about two significant digits of precision. This neglects the error due to the inner product (weights times values) and normalization (division by tally) aspects of a weighted average. The ratio of two numbers each containing a numerical bias can magnify the size of the bias. In summary, a global mean number computed from about 15,000 gridpoints each with weights can be expected to lose up to three significant digits. Since single-precision starts with about seven significant digits, we should not expect to retain more than four significant digits after computing weighted averages in single-precision. The above example with TREFHT shows the expected four digits of agreement. beer The NCO results have been independently validated to the extent possible in three other languages: C, Matlab, and NCL. C and NCO are the only languages that permit single-precision numbers to be treated with single precision arithmetic:

# Double-precision weight (gw),   single-precision arithmetic (C)
ncwa_3528514.exe
# TREFHT     = 289.240112
# Double-precision weight (gw),   double-precision arithmetic (C)
# TREFHT     = 289.239964
# Single-precision weight (area), double-precision arithmetic (Matlab)
# TREFHT     = 289.239964
# Double-precision weight (gw),   double-precision arithmetic (Matlab)
# TREFHT     = 289.239964
# Single-precision weight (area), double-precision arithmetic (NCL)
ncl < ncwa_3528514.ncl
# TREFHT     = 289.239960
# TREFHT_dbl = 289.239964
# Double-precision weight (gw),   double-precision arithmetic (NCL)
# TREFHT     = 289.239960
# TREFHT_dbl = 289.239964

All languages tested (C, Matlab, NCL, and NCO) agree to machine precision with double-precision arithmetic. Users are fortunate to have a variety of high quality software that liberates them from the drudgery of coding their own. Many packages are free (as in beer)! As shown above NCO permits one to shift to their float-promotion preferences as desired. No other language allows this with a simple switch. To summarize, until version 4.3.6 (September, 2013), the default arithmetic convention of NCO adhered to Fortran behavior, and automatically promoted single-precision to double-precision in all mixed-precision expressions, and left-alone pure single-precision expressions. This is faster and more memory efficient than other conventions. However, pure single-precision arithmetic can lose too much precision when used to condense (e.g., average) large arrays. Statistics involving about

n = 10,000

single-precision inputs will lose about 2&textndash;3 digits if not promoted to double-precision prior to arithmetic. The loss scales with the squareroot of n. For larger n, users should promote floats with the --dbl option if they want to preserve more than four significant digits in their results. The --dbl and --flt switches are only available with the NCO arithmetic operators that could potentially perform more than a few single-precision floating-point operations per result. These are nces, ncra, and ncwa. Each is capable of thousands to millions or more operations per result. By contrast, the arithmetic operators ncbo and ncflint perform at most one floating-point operation per result. Providing the --dbl option for such trivial operations makes little sense, so the option is not currently made available. We are interested in users&textrsquo; opinions on these matters. The default behavior was changed from --flt to --dbl with the release of NCO version 4.3.6 (October 2013). We will change the default back to --flt if users prefer. Or we could set a threshold (e.g.,

n \ge 10000

) after which single- to double-precision promotion is automatically invoked. Or we could make the default promotion convention settable via an environment variable (GSL does this a lot). Please let us know what you think of the selected defaults and options. Manual type conversionPromoting Single-precision to DoubleType Conversion Manual type conversion ncap2 ncap2 provides intrinsic functions for performing manual type conversions. This, for example, converts variable tpt to external type NC_SHORT (a C-type short), and variable prs to external type NC_DOUBLE (a C-type double).

ncap2 -s 'tpt=short(tpt);prs=double(prs)' in.nc out.nc

With ncap2 there also is the convert() method that takes an integer argument. For example the above statements become:

ncap2 -s 'tpt=tpt.convert(NC_SHORT);prs=prs.convert(NC_DOUBLE)' in.nc out.nc

Can also use convert() in combination with type() so to make variable ilev_new the same type as ilev just do:

ncap2 -s 'ilev_new=ilev_new.convert(ilev.type())' in.nc out.nc

ncap2 netCDF Arithmetic Processor, for more details. <a name="ovr"></a>  <a name="-O"></a>

Batch ModeGlobal Attribute AdditionType ConversionShared features

Batch Mode batch mode overwriting files appending to files force overwrite force append -O -A --overwrite --ovr --apn --append Availability: All operators&linebreak; Short options: -O, -A&linebreak; Long options: --ovr, --overwrite, --apn, --append&linebreak; If the output-file specified for a command is a pre-existing file, then the operator will prompt the user whether to overwrite (erase) the existing output-file, attempt to append to it, or abort the operation. However, interactive questions reduce productivity when processing large amounts of data. Therefore NCO also implements two ways to override its own safety features, the -O and -A switches. Specifying -O tells the operator to overwrite any existing output-file without prompting the user interactively. Specifying -A tells the operator to attempt to append to any existing output-file without prompting the user interactively. These switches are useful in batch environments because they suppress interactive keyboard input. NB: As of 20120515, ncap2 is unable to append to files that already contain the appended dimensions. <a name="gaa"></a>  <a name="glb"></a>  <a name="glb_att_add"></a>

Global Attribute AdditionHistory AttributeBatch ModeShared features

Global Attribute Addition global attributes attributes, global --gaa --glb att_nm=att_val --glb --glb_att_add ncatted Availability: All operators&linebreak; Short options: None&linebreak; Long options: --glb, --gaa, --glb_att_add&linebreak; --glb att_nm=att_val (multiple invocations allowed)&linebreak; All operators can add user-specified global attributes to output files. As of NCO version 4.5.2 (July, 2015), NCO supports multiple uses of the --glb (or equivalent --gaa or --glb_att_add) switch. indicator option multi-arguments The option --gaa (and its long option equivalents such as --glb_att_add) indicates the argument syntax will be key=val. As such, --gaa and its synonyms are indicator options that accept arguments supplied one-by-one like --gaa key1=val1 --gaa key2=val2, or aggregated together in multi-argument format like --gaa key1=val1#key2=val2 (Multi-arguments). The switch takes mandatory arguments --glb att_nm=att_val where att_nm is the desired name of the global attribute to add, and att_val is its value. Currently only text attributes are supported (recorded as type NC_CHAR), and regular expressions are not allowed (unlike ncatted netCDF Attribute Editor). Attributes are added in &textldquo;Append&textrdquo; mode, meaning that values are appended to pre-existing values, if any. Multiple invocations can simplify the annotation of output file at creation (or modification) time: ncra --glb machine=${HOSTNAME} --glb created_by=${USER} in*.nc out.nc As of NCO version 4.6.2 (October, 2016), one may instead combine the separate invocations into a single list of invocations separated by colons: ncra --glb machine=${HOSTNAME}:created_by=${USER} in*.nc out.nc The list may contain any number of key-value pairs. Special care must be taken should a key or value contain a delimiter (i.e., a colon) otherwise NCO will interpret the colon as a delimiter and will attempt to create a new attribute. To protect a colon from being interpreted as an argument delimiter, precede it with a backslash. The global attribution addition feature helps to avoid the performance penalty incurred by using ncatted separately to annotate large files. Should users emit a loud hue and cry, we will consider ading the functionality of ncatted to the front-end of all operators, i.e., accepting valid ncatted arguments to modify attributes of any type and to apply regular expressions. <a name="hst"></a>  <a name="history"></a>  <a name="-h"></a>

History AttributeFile List AttributesGlobal Attribute AdditionShared features

History Attribute history timestamp global attributes attributes, global -h --hst --history Availability: All operators&linebreak; Short options: -h&linebreak; Long options: --hst, --history&linebreak; All operators automatically append a history global attribute to any file they create or modify. The history attribute consists of a timestamp and the full string of the invocation command to the operator, e.g., Mon May 26 20:10:24 1997: ncks in.nc out.nc. The full contents of an existing history attribute are copied from the first input-file to the output-file. The timestamps appear in reverse chronological order, with the most recent timestamp appearing first in the history attribute. Since NCO adheres to the history convention, the entire data processing path of a given netCDF file may often be deduced from examination of its history attribute. As of May, 2002, NCO is case-insensitive to the spelling of the history attribute name. Thus attributes named History or HISTORY (which are non-standard and not recommended) will be treated as valid history attributes. When more than one global attribute fits the case-insensitive search for &textldquo;history&textrdquo;, the first one found is used. ncatted To avoid information overkill, all operators have an optional switch (-h, --hst, or --history) to override automatically appending the history attribute (ncatted netCDF Attribute Editor). Note that the -h switch also turns off writing the nco_input_file_list-attribute for multi-file operators (File List Attributes). history_of_appended_files provenance As of NCO version 4.5.0 (June, 2015), NCO supports its own convention to retain the history-attribute contents of all files that were appended to a file Note that before version 4.5.0, NCO could, in append (-A) mode only, inadvertently overwrite the global metadata (including history) of the output file with that of the input file. This is opposite the behavior most would want.. This convention stores those contents in the history_of_appended_files attribute, which complements the history-attribute to provide a more complete provenance. These attributes may appear something like this in output: // global attributes: :history = "Thu Jun 4 14:19:04 2015: ncks -A /home/zender/foo3.nc /home/zender/tmp.nc\n", "Thu Jun 4 14:19:04 2015: ncks -A /home/zender/foo2.nc /home/zender/tmp.nc\n", "Thu Jun 4 14:19:04 2015: ncatted -O -a att1,global,o,c,global metadata only in foo1 /home/zender/foo1.nc\n", "original history from the ur-file serving as the basis for subsequent appends." ; :history_of_appended_files = "Thu Jun 4 14:19:04 2015: Appended file \ /home/zender/foo3.nc had following \"history\" attribute:\n", "Thu Jun 4 14:19:04 2015: ncatted -O -a att2,global,o,c,global metadata only in foo3 /home/zender/foo3.nc\n", "history from foo3 from which data was appended to foo1 after data from foo2 was appended\n", "Thu Jun 4 14:19:04 2015: Appended file /home/zender/foo2.nc had following \"history\" attribute:\n", "Thu Jun 4 14:19:04 2015: ncatted -O -a att2,global,o,c,global metadata only in foo2 /home/zender/foo2.nc\n", "history of some totally different file foo2 from which data was appended to foo1 before foo3 was appended\n", :att1 = "global metadata only in foo1" ; Note that the history_of_appended_files-attribute is only created, and will only exist, in a file that is, or descends from a file that was, appended to. The optional switch -h (or --hst or --history) also overrides automatically appending the history_of_appended_files attribute. <a name="fl_lst_in_att"></a>

File List AttributesCF ConventionsHistory AttributeShared features

File List Attributes nco_input_file_list nco_input_file_number stdin global attributes attributes, global -H --fl_lst_in --file_list Availability: All binary executables&linebreak; Short options: -H&linebreak; Long options: --fl_lst_in, --file_list&linebreak; Many methods of specifying large numbers of input file names pass these names via pipes, encodings, or argument transfer programs (Large Numbers of Files). When these methods are used, the input file list is not explicitly passed on the command line. This results in a loss of information since the history attribute no longer contains the complete input information from which the file was created. NCO solves this dilemma by archiving an attribute that contains the input file list. When the input file list to an operator is specified via stdin, the operator, by default, attaches two global attributes to any file(s) they create or modify. The nco_input_file_number global attribute contains the number of input files, and nco_input_file_list contains the file names, specified as standard input to the multi-file operator. This information helps to verify that all input files the user thinks were piped through stdin actually arrived. Without the nco_input_file_list attribute, the information is lost forever and the &textldquo;chain of evidence&textrdquo; would be broken. The -H switch overrides (turns off) the default behavior of writing the input file list global attributes when input is from stdin. The -h switch does this too, and turns off the history attribute as well (History Attribute). Hence both switches allows space-conscious users to avoid storing what may amount to many thousands of filenames in a metadata attribute. <a name="CF"></a>  <a name="cnv"></a>  <a name="cnv_ACME"></a>  <a name="ACME"></a>  <a name="acme"></a>  <a name="cnv_E3SM"></a>  <a name="E3SM"></a>  <a name="e3sm"></a>  <a name="cnv_CF"></a>  <a name="cnv_CCSM"></a>

CF ConventionsARM ConventionsFile List AttributesShared features

CF Conventions ACME conventions E3SM conventions CF conventions CCSM conventions MPAS conventions UDUnits ORO area datesec date gw hyai hyam hybi hybm lat_bnds lon_bnds msk_* wgt_* angleEdge areaCell areaTriangle cellMask cellsOnCell cellsOnEdge cellsOnVertex dcEdge dvEdge edgesOnCell edgesOnEdge edgesOnVertex indexToCellID indexToEdgeID indexToVertexID kiteAreasOnVertex latCell latEdge latVertex lonCell lonEdge lonVertex maxLevelCell meshDensity nEdgesOnCell nEdgesOnEdge vertexMask verticesOnCell verticesOnEdge weightsOnEdge xCell xEdge xVertex yCell yEdge yVertex zCell zEdge zVertex Availability: ncbo, nces, ncecat, ncflint, ncpdq, ncra, ncwa&linebreak; Short options: None&linebreak; NCO recognizes some Climate and Forecast (CF) metadata conventions, and applies special rules to such data. NCO was contemporaneous with COARDS and still contains some rules to handle older model datasets that pre-date CF, such as NCAR CCM and early CCSM datasets. Such datasets may not contain an explicit Conventions attribute (e.g., CF-1.0). Nevertheless, we refer to all such metadata collectively as CF metadata. Skip this section if you never work with CF metadata. The latest CF netCDF conventions are described http://cfconventions.org/1.10.htmlhere. Most CF netCDF conventions are transparent to NCO. There are no known pitfalls associated with using any NCO operator on files adhering to these conventions. NCO applies some rules that are not in CF, or anywhere else, because experience shows that they simplify data analysis, and stay true to the NCO mantra to do what users want. Here is a general sense of NCO&textrsquo;s CF-support: • Understand and implement NUG recommendations such as the history attribute, packing conventions, and attention to units. • Special handling of variables designated as coordinates, bounds, or ancillary variables, so that users subsetting a certain variable automatically obtain all related variables. • Special handling and prevention of meaningless operations (e.g., the root-mean-square of latitude) so that coordinates and bounds preserve meaningful information even as normal (non-coordinate) fields are statistically transformed. • Understand units and certain calendars so that hyperslabs may be specified in physical units, and so that user needs not manually decode per-file time specifications. • Understand auxiliary coordinates so that irregular hyperslabs may be specified on complex geometric grids. • Check for CF-compliance on netCDF3 and netCDF4 and HDF files. • Convert netCDF4 and HDF files to netCDF3 for strict CF-compliance. Finally, a main use of NCO is to &textldquo;produce CF&textrdquo;, i.e., to improve CF-compliance by annotating metadata, renaming objects (attributes, variables, and dimensions), permuting and inverting dimensions, recomputing values, and data compression. <a name="prc_xcp"></a>  Currently, NCO determines whether a datafile is a CF output datafile simply by checking (case-insensitively) whether the value of the global attribute Conventions (if any) equals CF-1.0 or NCAR-CSM Should Conventions equal either of these in the (first) input-file, NCO will apply special rules to certain variables because of their usual meaning in CF files. NCO will not average the following variables often found in CF files: ntrm, ntrn, ntrk, ndbase, nsbase, nbdate, nbsec, mdt, mhisf. These variables contain scalar metadata such as the resolution of the host geophysical model and it makes no sense to change their values. non-coordinate grid properties Furthermore, the size and rank-preserving arithmetic operators try not to operate on certain grid properties. These operators are ncap2, ncbo, nces, ncflint, and ncpdq (when used for packing, not for permutation). These operators do not operate, by default, on (i.e., add, subtract, pack, etc.) the following variables: ORO, area, datesec, date, gw, hyai, hyam, hybi. hybm, lat_bnds, lon_bnds, msk_*, and wgt_*. These variables represent Gaussian weights, land/sea masks, time fields, hybrid pressure coefficients, and latititude/longitude boundaries. We call these fields non-coordinate grid properties. Coordinate grid properties are easy to identify because they are coordinate variables such as latitude and longitude. Users usually want all grid properties to remain unaltered in the output file. To be treated as a grid property, the variable name must exactly match a name in the above list, or be a coordinate variable. Handling of msk_* and wgt_* is exceptional in that any variable whose name starts with msk_ or wgt_ is considered to be a &textldquo;mask&textrdquo; or a &textldquo;weight&textrdquo; and is thus preserved (not operated on when arithmetic can be avoided). As of NCO version 4.7.7 (September, 2018), NCO began to explicitly identify files adhering to the MPAS convention. These files have a global attribute Conventions attribute that contains the string or MPAS. Size and rank-preserving arithmetic operators will not operate on these MPAS non-coordinate grid properties: angleEdge, areaCell, areaTriangle, cellMask, cellsOnCell, cellsOnEdge, cellsOnVertex, dcEdge, dvEdge, edgesOnCell, edgesOnEdge, edgesOnVertex, indexToCellID, indexToEdgeID, indexToVertexID, kiteAreasOnVertex, latCell, latEdge, latVertex, lonCell, lonEdge, lonVertex, maxLevelCell, meshDensity, nEdgesOnCell, nEdgesOnEdge, vertexMask, verticesOnCell, verticesOnEdge, weightsOnEdge, xCell, xEdge, xVertex, yCell, yEdge, yVertex, zCell, zEdge, and zVertex. As of NCO version 4.5.0 (June, 2015), NCO began to support behavior required for the DOE E3SM/ACME program, and we refer to these rules collectively as the E3SM/ACME convention. GMT date_written time_written gmtime() The first E3SM/ACME rule implemented is that the contents of input-file variables named date_written and time_written, if any, will be updated to the current system-supplied (with gmtime()) GMT-time as the variables are copied to the output-file. You must spoof NCO if you would like any grid properties or other special CF fields processed normally. For example rename the variables first with ncrename, or alter the Conventions attribute. <a name="cnv_CF_bnd"></a>  <a name="bnd"></a>  <a name="bounds"></a>  bounds attribute bounds convention As of NCO version 4.0.8 (April, 2011), NCO supports the CF bounds convention for cell boundaries described http://cfconventions.org/cf-conventions/cf-conventions.html#cell-boundarieshere. This convention allows coordinate variables (including multidimensional coordinates) to describe the boundaries of their cells. This is done by naming the variable which contains the bounds in in the bounds attribute. Note that coordinates of rank

N

have bounds of rank

N+1

. NCO-generated subsets of CF-compliant files with bounds attributes will include the coordinates specified by the bounds attribute, if any. Hence the subsets will themselves be CF-compliant. Bounds are subject to the user-specified override switches (including -c and -C) described in Subsetting Coordinate Variables. lev ilev hyai hybi The CAM/EAM family of atmospheric models does not output a bounds variable or attribute corresponding to the lev coordinate. This prevents NCO from activating its CF bounds machinery when lev is extracted. As of version 4.7.7 (September, 2018), NCO works around this by outputting the ilev coordinate (and hyai, hybi) whenever the lev coordinate is also output. <a name="cnv_CF_clm"></a>  <a name="clm"></a>  <a name="climatology"></a>  climatology attribute climatology convention As of NCO version 4.4.9 (May, 2015), NCO supports the CF climatology convention for climatological statistics described http://cfconventions.org/Data/cf-conventions/cf-conventions-1.7/build/cf-conventions.html#climatological-statisticshere. This convention allows coordinate variables (including multidimensional coordinates) to describe the (possibly nested) periods and statistical methods of their associated statistics. This is done by naming the variable which contains the periods and methods in the climatology attribute. Note that coordinates of rank

N

have climatology bounds of rank

N+1

. NCO-generated subsets of CF-compliant files with climatology attributes will include the variables specified by the climatology attribute, if any. Hence the subsets will themselves be CF-compliant. Climatology variables are subject to the user-specified override switches (including -c and -C) described in Subsetting Coordinate Variables. <a name="cnv_CF_ncl"></a>  <a name="ncl"></a>  <a name="ancillary"></a>  ancillary_variables attribute ancillary variables convention As of NCO version 4.4.5 (July, 2014), NCO supports the CF ancillary_variables convention for described http://cfconventions.org/cf-conventions/cf-conventions.html#ancillary-datahere. This convention allows ancillary variables to be associated with one or more primary variables. NCO attaches any such variables to the extraction list along with the primary variable and its usual (one-dimensional) coordinates, if any. Ancillary variables are subject to the user-specified override switches (including -c and -C) described in Subsetting Coordinate Variables. <a name="cnv_CF_cll_msr"></a>  <a name="cll_msr"></a>  <a name="cell_measures"></a>  <a name="no_cll_msr"></a>  <a name="no_cell_measures"></a>  cell_measures attribute cell measures convention As of NCO version 4.6.4 (January, 2017), NCO supports the CF cell_measures convention described http://cfconventions.org/cf-conventions/cf-conventions.html#cell-measureshere. This convention allows variables to indicate which other variable or variables contains area or volume information about a gridcell. These measures variables are pointed to by the cell_measures attribute. The CDL specification of a measures variable for area looks like

orog:cell_measures = "area: areacella"

where areacella is the name of the measures variable. Unless the default behavior is overridden, NCO attaches any measures variables to the extraction list along with the primary variable and other associated variables. By definition, measures variables are a subset of the rank of the variable they measure. The most common case is that the measures variable for area is the same size as 2D fields (like surface air temperature) and much smaller than 3D fields (like full air temperature). In such cases the measures variable might occupy 50% of the space of a dataset consisting of only one 2D field. Extraction of measures variables is subject to the user-specified override switches (including -c and -C) described in Subsetting Coordinate Variables. To conserve space without sacrificing too much metadata, NCO makes it possible to override the extraction of measures variables independent of extracting other associated variables. Override the default with --no_cell_measures or --no_cll_msr. These options are available in all operators that perform subsetting (i.e., all operators except ncatted and ncrename). <a name="cnv_CF_frm_trm"></a>  <a name="frm_trm"></a>  <a name="formula_terms"></a>  <a name="no_frm_trm"></a>  <a name="no_formula_terms"></a>  formula_terms attribute cell measures convention As of NCO version 4.6.4 (January, 2017), NCO supports the CF formula_terms convention described http://cfconventions.org/cf-conventions/cf-conventions.html#formula-termshere. This convention encodes formulas used to construct (usually vertical) coordinate grids. The CDL specification of a vertical coordinate formula for looks like

lev:standard_name = "atmosphere_hybrid_sigma_pressure_coordinate"
lev:formula_terms = "a: hyam b: hybm p0: P0 ps: PS"

where standard_name contains the standardized name of the formula variable and formula_terms contains a list of the variables used, called formula variables. Above the formula variables are hyam, hybm, P0, and PS. Unless the default behavior is overridden, NCO attaches any formula variables to the extraction list along with the primary variable and other associated variables. By definition, formula variables are a subset of the rank of the variable they define. One common case is that the formula variables for constructing a 3D height grid involves a 2D variable (like surface pressure, or elevation). In such cases the formula variables typically constitute only a small fraction of a dataset consisting of one 3D field. Extraction of formula variables is subject to the user-specified override switches (including -c and -C) described in Subsetting Coordinate Variables. To conserve space without sacrificing too much metadata, NCO makes it possible to override the extraction of formula variables independent of extracting other associated variables. Override the default with --no_formula_terms or --no_frm_trm. These options are available in all operators that perform subsetting (i.e., all operators except ncatted and ncrename). <a name="cnv_CF_grd"></a>  <a name="grd_map"></a>  <a name="grid_mapping"></a>  grid_mapping attribute ancillary variables convention As of NCO version 4.6.0 (May, 2016), NCO supports the CF grid_mapping convention for described http://cfconventions.org/cf-conventions/cf-conventions.html#grid-mappings-and-projectionshere. This convention allows descriptions of map-projections to be associated with variables. NCO attaches any such map-projection variables to the extraction list along with the primary variable and its usual (one-dimensional) coordinates, if any. Map-projection variables are subject to the user-specified override switches (including -c and -C) described in Subsetting Coordinate Variables. <a name="cnv_CF_crd"></a>  <a name="coordinates"></a>  coordinates coordinates convention coordinate variable auxiliary coordinates subsetting -C -c --no_coords --no_crd --coords --crd As of NCO version 3.9.6 (January, 2009), NCO supports the CF coordinates convention described http://cfconventions.org/cf-conventions/cf-conventions.html#coordinate-systemhere. This convention allows variables to specify additional coordinates (including mult-idimensional coordinates) in a space-separated string attribute named coordinates. NCO attaches any such coordinates to the extraction list along with the variable and its usual (one-dimensional) coordinates, if any. These auxiliary coordinates are subject to the user-specified override switches (including -c and -C) described in Subsetting Coordinate Variables. Elimination of reduced dimensions from the coordinates attribute helps ensure that rank-reduced variables become completely independent from their former dimensions. As of NCO version 4.4.9 (May, 2015), NCO may modify the coordinates attribute to assist this. In particular, ncwa eliminates from the coordinates attribute any dimension that it collapses, e.g., by averaging. The former presence of this dimension will usually be indicated by the CF cell_methods convention described http://cfconventions.org/cf-conventions/cf-conventions.html#cell-methodshere. Hence the CF cell_methods and coordinates conventions can be said to work in tandem to characterize the state and history of a variable&textrsquo;s analysis. <a name="cnv_CF_cll_mth"></a>  <a name="cll_mth"></a>  <a name="cell_methods"></a>  <a name="no_cll_mth"></a>  <a name="no_cell_methods"></a>  cell_methods --cll_mth --no_cll_mth --cell_methods --no_cell_methods cell methods convention As of NCO version 4.4.2 (February, 2014), NCO supports some of the CF cell_methods http://cfconventions.org/cf-conventions/cf-conventions.html#cell-methodsconvention to describe the analysis procedures that have been applied to data. The convention creates (or appends to an existing) cell_methods attribute a space-separated list of couplets of the form dmn: op where dmn is a comma-separated list of dimensions previously contained in the variable that have been reduced by the arithmetic operation op. For example, the cell_methods value time: mean says that the variable in question was averaged over the time dimension. In such cases time will either be a scalar variable or a degenerate dimension or coordinate. This simply means that it has been averaged-over. The value time, lon: mean lat: max says that the variable in question is the maximum zonal mean of the time averaged original variable. Which is to say that the variable was first averaged over time and longitude, and then the residual latitudinal array was reduced by choosing the maximum value. Since the cell methods convention may alter metadata in an undesirable (or possibly incorrect) fashion, we provide switches to ensure it is always or never used. Use long-options --cll_mth or --cell_methods to invoke the algorithm (true by default), and options --no_cll_mth or --no_cell_methods to turn it off. These options are only available in the operators ncwa and ncra. <a name="cnv_ARM"></a>  <a name="ARM"></a>  <a name="arm"></a>

ARM ConventionsOperator VersionCF ConventionsShared features

ARM Conventions ARM conventions time_offset base_time time Availability: ncrcat&linebreak; Short options: None&linebreak; ncrcat has been programmed to correctly handle data files which utilize the Atmospheric Radiation Measurement (ARM) Program http://www.arm.gov/data/time.stmconvention for time and time offsets. If you do not work with ARM data then you may skip this section. ARM data files store time information in two variables, a scalar, base_time, and a record variable, time_offset. Subtle but serious problems can arise when these type of files are blindly concatenated without CF or ARM support. NCO implements rebasing (Rebasing Time Coordinate) as necessary on both CF and ARM files. Rebasing chains together consecutive input-files and produces an output-file which contains the correct time information. For ARM files this is expecially complex because the time coordinates are often stored as type NC_CHAR. Currently, ncrcat determines whether a datafile is an ARM datafile simply by testing for the existence of the variables base_time, time_offset, and the dimension time. If these are found in the input-file then ncrcat will automatically perform two non-standard, but hopefully useful, procedures. First, ncrcat will ensure that values of time_offset appearing in the output-file are relative to the base_time appearing in the first input-file (and presumably, though not necessarily, also appearing in the output-file). Second, if a coordinate variable named time is not found in the input-files, then ncrcat automatically creates the time coordinate in the output-file. The values of time are defined by the ARM conventions

time = base_time + time_offset. Thus, if output-file contains the time_offset variable, it will also contain the time coordinate. history global attributes attributes, global A short message is added to the history global attribute whenever these ARM-specific procedures are executed. <a name="vrs"></a>  <a name="version"></a>

Operator VersionARM ConventionsShared features

Operator Version version RCS -r --revision --version --vrs Availability: All operators&linebreak; Short options: -r&linebreak; Long options: --revision, --version, or --vrs&linebreak; All operators can be told to print their version information, library version, copyright notice, and compile-time configuration with the -r switch, or its long-option equivalent revision. The --version or --vrs switches print the operator version information only. The internal version number varies between operators, and indicates the most recent change to a particular operator&textrsquo;s source code. This is useful in making sure you are working with the most recent operators. The version of NCO you are using might be, e.g., 3.9.5. Using -r on, say, ncks, produces something like NCO netCDF Operators version "3.9.5" last modified 2008/05/11 built May 12 2008 on neige by zender Copyright (C) 1995--2008 Charlie Zender ncks version 20090918. This tells you that ncks contains all patches up to version 3.9.5, which dates from May 11, 2008. <a name="rfr"></a>

Reference ManualContributingShared featuresTop Reference Manual This chapter presents reference pages for each of the operators individually. The operators are presented in alphabetical order. command line switches All valid command line switches are included in the syntax statement. Recall that descriptions of many of these command line switches are provided only in Shared features, to avoid redundancy. Only options specific to, or most useful with, a particular operator are described in any detail in the sections below. * ncap2 netCDF Arithmetic Processor::

* ncatted netCDF Attribute Editor::

* ncbo netCDF Binary Operator::

* ncchecker netCDF Compliance Checker::

* ncclimo netCDF Climatology Generator::

* ncecat netCDF Ensemble Concatenator::

* nces netCDF Ensemble Statistics::

* ncflint netCDF File Interpolator::

* ncks netCDF Kitchen Sink::

* ncpdq netCDF Permute Dimensions Quickly::

* ncra netCDF Record Averager::

* ncrcat netCDF Record Concatenator::

* ncremap netCDF Remapper::

* ncrename netCDF Renamer::

* ncwa netCDF Weighted Averager::

<a name="ncap"></a>  <a name="ncap2"></a>  ncap2 netCDF Arithmetic Processorncatted netCDF Attribute EditorReference ManualReference Manual

ncap2 netCDF Arithmetic Processor parser lexer arithmetic processor ncap ncap2 ncap2 understands a relatively full-featured language of operations, including loops, conditionals, arrays, and math functions. ncap2 is the most rapidly changing NCO operator and its documentation is incomplete. The distribution file data/ncap2_tst.nco contains an up-to-date overview of its syntax and capabilities. The data/*.nco distribution files (especially bin_cnt.nco, psd_wrf.nco, and rgr.nco) contain in-depth examples of ncap2 solutions to complex problems. SYNTAX

ncap2 [-3] [-4] [-5] [-6] [-7] [http://nco.sf.net/nco.html#-A-A] [-C] [-c] 
[-D dbg] [-F] [-f] [--glb ...] [-H] [-h] [--hdf] [--hdr_pad nbr] [--hpss]
[-L dfl_lvl] [-l path] [--no_tmp_fl] [-O] [-o output-file]
[-p path] [-R] [-r] [--ram_all]
[-s algebra] [-S fl.nco] [-t thr_nbr] [-v]
[input-file] [output-file]

DESCRIPTION ncap2 arithmetically processes netCDF files. ncap2 is the successor to ncap which was put into maintenance mode in November, 2006, and completely removed from NCO in March, 2018. This documentation refers to ncap2 implements its own domain-specific language to produc a powerful superset ncap-functionality. ncap2 may be renamed ncap one day! script file --script-file --fl_spt --script --spt The processing instructions are contained either in the NCO script file fl.nco or in a sequence of command line arguments. The options -s (or long options --spt or --script) are used for in-line scripts and -S (or long options --fl_spt, --nco_script, or --script-file) are used to provide the filename where (usually multiple) scripting commands are pre-stored. ncap2 was written to perform arbitrary algebraic transformations of data and archive the results as easily as possible. derived fields Missing Values, for treatment of missing values. The results of the algebraic manipulations are called derived fields. --usr_dfn_var --output_user_defined_variables Unlike the other operators, ncap2 does not accept a list of variables to be operated on as an argument to the -v option (Subsetting Files). In ncap2, -v is a switch that takes no arguments and indicates that ncap2 should output only user-defined variables (and coordinates associated with variables used in deriving them). ncap2 neither accepts nor understands the -x switch. We recommend making this distinction clear by using --usr_dfn_var (or its synonym, --output_user_defined_variables, both introduced in NCO version 5.1.9 in October, 2023) instead of -v, which may be deprecated. appending variables NB: As of 20120515, ncap2 is unable to append to files that already contain the appended dimensions. Providing a name for output-file is optional if input-file is a netCDF3 format, in which case ncap2 attempts to write modifications directly to input-file (similar to the behavior of ncrename and ncatted). Format-constraints prevent this type of appending from working on a netCDF4 format input-file. In any case, reading and writing the same file can be risky and lead to unexpected consequences (since the file is being both read and written), so in normal usage we recommend providing output-file (which can be the same as input-file since the changes are first written to an intermediate file). As of NCO version 4.8.0 (released May, 2019), ncap2 does not require that input-file be specified when output-file has no dependency on it. Prior to this, ncap2 required users to specify a dummy input-file even if it was not used to construct output-file. Input files are always read by ncap2, and dummy input files are read though not used for anything nor modified. Now ncap2 -s 'quark=1' ~/foo.nc # Create new foo.nc ncap2 -s 'print(quark)' ~/foo.nc # Print existing foo.nc ncap2 -O -s 'quark=1' ~/foo.nc # Overwrite old with new foo.nc ncap2 -s 'quark=1' ~/foo.nc ~/foo.nc # Add to old foo.nc * Syntax of ncap2 statements::

* Expressions::

* Dimensions::

* Left hand casting::

* Arrays and hyperslabs::

* Attributes::

* Value List::

* Number literals::

* if statement::

* Print & String methods::

* Missing values ncap2::

* Methods and functions::

* RAM variables::

* Where statement::

* Loops::

* Include files::

* Sort methods::

* UDUnits script::

* Vpointer::

* Irregular grids::

* Bilinear interpolation::

* GSL special functions::

* GSL interpolation::

* GSL least-squares fitting::

* GSL statistics::

* GSL random number generation::

* Examples ncap2::

* Intrinsic mathematical methods::

* Operator precedence and associativity ::

* ID Quoting::

* make_bounds() function::

* solar_zenith_angle function::

<a name="att_prp"></a>  Defining new variables in terms of existing variables is a powerful feature of ncap2. derived fields Derived fields inherit the metadata (i.e., attributes) of their ancestors, if any, in the script or input file. When the derived field is completely new (no identically-named ancestors exist), then it inherits the metadata (if any) of the left-most variable on the right hand side of the defining expression. This metadata inheritance is called attribute propagation. Attribute propagation is intended to facilitate well-documented data analysis, and we welcome suggestions to improve this feature. The only exception to this rule of attribute propagation is in cases of left hand casting (Left hand casting). The user must manually define the proper metadata for variables defined using left hand casting. <a name="syn"></a>  Syntax of ncap2 statementsExpressionsncap2 netCDF Arithmetic Processorncap2 netCDF Arithmetic Processor Syntax of ncap2 statements statement syntax Mastering ncap2 is relatively simple. Each valid statement statement consists of standard forward algebraic expression. The fl.nco, if present, is simply a list of such statements, whitespace, and comments. C language The syntax of statements is most like the computer language C. The following characteristics of C are preserved: Array syntax array syntax [] (array delimiters) Arrays elements are placed within [] characters; Array indexing array indexing Arrays are 0-based; Array storage array storage Last dimension is most rapidly varying; Assignment statements assignment statement semi-colon ; (end of statement) A semi-colon ; indicates the end of an assignment statement. Comments comments /*...*/ (comment) // (comment) Multi-line comments are enclosed within /* */ characters. Single line comments are preceded by // characters. Nesting including files nesting #include Files may be nested in scripts using #include script. The #include command is not followed by a semi-colon because it is a pre-processor directive, not an assignment statement. The filename script is interpreted relative to the run directory. Attribute syntax attribute syntax &arobase; (attribute) The at-sign &arobase; is used to delineate an attribute name from a variable name.

<a name="ncap_xpr"></a>  ExpressionsDimensionsSyntax of ncap2 statementsncap2 netCDF Arithmetic Processor expressions Expressions Expressions are the fundamental building block of ncap2. Expressions are composed of variables, numbers, literals, and attributes. C language The following C operators are &textldquo;overloaded&textrdquo; and work with scalars and multi-dimensional arrays:

Arithmetic Operators: * / % + - ^
Binary Operators:     > >= < <= == != == || && >> <<
Unary Operators:      + - ++ -- !
Conditional Operator: exp1 ? exp2 : exp3
Assign Operators:     = += -= /= *=

In the following section a variable also refers to a number literal which is read in as a scalar variable: Arithmetic and Binary Operators Consider var1 &textrsquo;op&textrsquo; var2 Precision • When both operands are variables, the result has the precision of the higher precision operand. • When one operand is a variable and the other an attribute, the result has the precision of the variable. • When both operands are attributes, the result has the precision of the more precise attribute. • The exponentiation operator &textldquo;^&textrdquo; is an exception to the above rules. When both operands have type less than NC_FLOAT, the result is NC_FLOAT. When either type is NC_DOUBLE, the result is also NC_DOUBLE. broadcasting variables rank Rank • The Rank of the result is generally equal to Rank of the operand that has the greatest number of dimensions. • If the dimensions in var2 are a subset of the dimensions in var1 then its possible to make var2 conform to var1 through broadcasting and or dimension reordering. • Broadcasting a variable means creating data in non-existing dimensions by copying data in existing dimensions. • More specifically: If the numbers of dimensions in var1 is greater than or equal to the number of dimensions in var2 then an attempt is made to make var2 conform to var1 ,else var1 is made to conform to var2. If conformance is not possible then an error message will be emitted and script execution will cease.&linebreak; Even though the logical operators return True(1) or False(0) they are treated in the same way as the arithmetic operators with regard to precision and rank.&linebreak; Examples: dimensions: time=10, lat=2, lon=4 Suppose we have the two variables: double P(time,lat,lon); float PZ0(lon,lat); // PZ0=1,2,3,4,5,6,7,8; Consider now the expression: PZ=P-PZ0 PZ0 is made to conform to P and the result is PZ0 = 1,3,5,7,2,4,6,8, 1,3,5,7,2,4,6,8, 1,3,5,7,2,4,6,8, 1,3,5,7,2,4,6,8, 1,3,5,7,2,4,6,8, 1,3,5,7,2,4,6,8, 1,3,5,7,2,4,6,8, 1,3,5,7,2,4,6,8, 1,3,5,7,2,4,6,8, 1,3,5,7,2,4,6,8, Once the expression is evaluated then PZ will be of type double; Consider now start=four-att_var@double_att; // start =-69 and is of type intger; four_pow=four^3.0f // four_pow=64 and is of type float three_nw=three_dmn_var_sht*1.0f; // type is now float start@n1=att_var@short_att*att_var@int_att; // start@n1=5329 and is type int Binary Operators &linebreak; binary Operators Unlike C the binary operators return an array of values. There is no such thing as short circuiting with the AND/OR operators. Missing values are carried into the result in the same way they are with the arithmetic operators. When an expression is evaluated in an if() the missing values are treated as true.&linebreak; The binary operators are, in order of precedence:

	
!   Logical Not
----------------------------
<<  Less Than Selection
>>  Greater Than Selection
----------------------------
>   Greater than
>=  Greater than or equal to
<   Less than
<=  Less than or equal to
----------------------------
==  Equal to
!=  Not equal to
----------------------------
&&  Logical AND
----------------------------
||  Logical OR
----------------------------

To see all operators: Operator precedence and associativity Examples:

tm1=time>2 && time <7;  // tm1=0, 0, 1, 1, 1, 1, 0, 0, 0, 0 double
tm2=time==3 || time>=6; // tm2=0, 0, 1, 0, 0, 1, 1, 1, 1, 1 double
tm3=int(!tm1);          // tm3=1, 1, 0, 0, 0, 0, 1, 1, 1, 1 int
tm4=tm1 && tm2;         // tm4=0, 0, 1, 0, 0, 1, 0, 0, 0, 0 double
tm5=!tm4;               // tm5=1, 1, 0, 1, 1, 0, 1, 1, 1, 1 double

Regular Assign Operator&linebreak; var1 &textrsquo;=&textrsquo; exp1 &linebreak; If var1 does not already exist in Input or Output then var1 is written to Output with the values, type and dimensions from expr1. If var1 is in Input only it is copied to Output first. Once the var is in Ouptut then the only reqirement on expr1 is that the number of elements must match the number already on disk. The type of expr1 is converted as necessary to the disk type. If you wish to change the type or shape of a variable in Input then you must cast the variable. See Left hand casting

time[time]=time.int();
three_dmn_var_dbl[time,lon,lat]=666L;

Other Assign Operators +=,-=,*=./= &linebreak; var1 &textrsquo;ass_op&textrsquo; exp1 &linebreak; if exp1 is a variable and it doesn&textrsquo;t conform to var1 then an attempt is made to make it conform to var1. If exp1 is an attribute it must have unity size or else have the same number of elements as var1. If expr1 has a different type to var1 the it is converted to the var1 type.

z1=four+=one*=10 // z1=14 four=14 one=10;	
time-=2          // time= -1,0,1,2,3,4,5,6,7,8

Increment/Decrement Operators &linebreak; These work in a similar fashion to their regular C counterparts. If say the variable four is input only then the statement ++four effectively means read four from input increment each element by one, then write the new values to Output; Example:

n2=++four;   n2=5, four=5 
n3=one--+20; n3=21  one=0;	 
n4=--time;   n4=time=0.,1.,2.,3.,4.,5.,6.,7.,8.,9.;

Conditional Operator ?:&linebreak; conditional Operator exp1 ? exp2 : exp3 &linebreak; The conditional operator (or ternary Operator) is a succinct way of writing an if/then/else. If exp1 evaluates to true then exp2 is returned else exp3 is returned. Example: weight_avg=weight.avg(); weight_avg@units= (weight_avg == 1 ? "kilo" : "kilos"); PS_nw=PS-(PS.min() > 100000 ? 100000 : 0); <a name="clp"></a>  <a name="clipping"></a>  Clipping Operators clipping operators << Less-than Clipping&linebreak; For arrays, the less-than selection operator selects all values in the left operand that are less than the corresponding value in the right operand. If the value of the left side is greater than or equal to the corresponding value of the right side, then the right side value is placed in the result >> Greater-than Clipping&linebreak; For arrays, the greater-than selection operator selects all values in the left operand that are greater than the corresponding value in the right operand. If the value of the left side is less than or equal to the corresponding value of the right side, then the right side value is placed in the result.

Example:

RDM2=RDM >> 100.0 // 100,100,100,100,126,126,100,100,100,100 double
RDM2=RDM <<  90s  // 1, 9, 36, 84, 90, 90, 84, 36, 9, 1 int

<a name="ncap_dims"></a>  <a name="defdim"></a>  DimensionsLeft hand castingExpressionsncap2 netCDF Arithmetic Processor Dimensions defining dimensions in ncap2 defdim() Dimensions are defined in Output using the defdim() function.

defdim("cnt",10); # Dimension size is fixed by default
defdim("cnt",10,NC_UNLIMITED); # Dimension is unlimited (record dimension)
defdim("cnt",10,0); # Dimension is unlimited (record dimension)
defdim("cnt",10,1); # Dimension size is fixed
defdim("cnt",10,737); # All non-zero values indicate dimension size is fixed

This dimension name must then be prefixed with a dollar-sign $ when referred to in method arguments or left-hand-casting, e.g.,

new_var[$cnt]=time;
temperature[$time,$lat,$lon]=35.5;
temp_avg=temperature.avg($time);

The size method allows dimension sizes to be used in arithmetic expressions:

time_avg=time.total()/$time.size;

Increase the size of a new variable by one and set new member to zero:

defdim("cnt_new",$cnt.size+1);
new_var[$cnt_new]=0.0;
new_var(0:($cnt_new.size-2))=old_var;

To define an unlimited dimension, simply set the size to zero

defdim("time2",0)

Dimension Abbreviations &linebreak; It is possible to use dimension abbreviations as method arguments:&linebreak; $0 is the first dimension of a variable&linebreak; $1 is the second dimension of a variable&linebreak; $n is the n+1 dimension of a variable&linebreak;

float four_dmn_rec_var(time,lat,lev,lon);
double three_dmn_var_dbl(time,lat,lon);

four_nw=four_dmn_rev_var.reverse($time,$lon)
four_nw=four_dmn_rec_var.reverse($0,$3);

four_avg=four_dmn_rec_var.avg($lat,$lev);  
four_avg=four_dmn_rec_var.avg($1,$2);  

three_mw=three_dmn_var_dbl.permute($time,$lon,$lat);
three_mw=three_dmn_var_dbl.permute($0,$2,$1);

ID Quoting &linebreak; If the dimension name contains non-regular characters use ID quoting: See ID Quoting

defdim("a--list.A",10);
A1['$a--list.A']=30.0;

GOTCHA &linebreak; It is not possible to manually define in Output any dimensions that exist in Input. When a variable from Input appears in an expression or statement its dimensions in Input are automagically copied to Output (if they are not already present) <a name="lhc"></a>  <a name="lhs"></a>  Left hand castingArrays and hyperslabsDimensionsncap2 netCDF Arithmetic Processor Left hand casting hybrid coordinate system left hand casting LHS The following examples demonstrate the utility of the left hand casting ability of ncap2. Consider first this simple, artificial, example. If lat and lon are one dimensional coordinates of dimensions lat and lon, respectively, then addition of these two one-dimensional arrays is intrinsically ill-defined because whether lat_lon should be dimensioned lat by lon or lon by lat is ambiguous (assuming that addition is to remain a commutative procedure, i.e., one that does not depend on the order of its arguments). Differing dimensions are said to be orthogonal to one another, and sets of dimensions which are mutually exclusive are orthogonal as a set and any arithmetic operation between variables in orthogonal dimensional spaces is ambiguous without further information. The ambiguity may be resolved by enumerating the desired dimension ordering of the output expression inside square brackets on the left hand side (LHS) of the equals sign. This is called left hand casting because the user resolves the dimensional ordering of the RHS of the expression by specifying the desired ordering on the LHS.

ncap2 -s 'lat_lon[lat,lon]=lat+lon' in.nc out.nc
ncap2 -s 'lon_lat[lon,lat]=lat+lon' in.nc out.nc

The explicit list of dimensions on the LHS, [lat,lon] resolves the otherwise ambiguous ordering of dimensions in lat_lon. In effect, the LHS casts its rank properties onto the RHS. Without LHS casting, the dimensional ordering of lat_lon would be undefined and, hopefully, ncap2 would print an error message. <a name="prs_mdp"></a>  Consider now a slightly more complex example. In geophysical models, a coordinate system based on a blend of terrain-following and density-following surfaces is called a hybrid coordinate system. In this coordinate system, four variables must be manipulated to obtain the pressure of the vertical coordinate: PO is the domain-mean surface pressure offset (a scalar), PS is the local (time-varying) surface pressure (usually two horizontal spatial dimensions, i.e. latitude by longitude), hyam is the weight given to surfaces of constant density (one spatial dimension, pressure, which is orthogonal to the horizontal dimensions), and hybm is the weight given to surfaces of constant elevation (also one spatial dimension). This command constructs a four-dimensional pressure prs_mdp from the four input variables of mixed rank and orthogonality:

ncap2 -s 'prs_mdp[time,lat,lon,lev]=P0*hyam+PS*hybm' in.nc out.nc

Manipulating the four fields which define the pressure in a hybrid coordinate system is easy with left hand casting. <a name="pdel"></a>  <a name="prs_ntf"></a>  Finally, we show how to use interface quantities to define midpoint quantities. In particular, we will define interface pressures using the standard CESM output hybrid coordinate parameters, and then difference those interface pressures to obtain the pressure difference between the interfaces. The pressure difference is necessary obtain gridcell mass path and density (which are midpoint quantities). Definitions are as in the above example, with new variables hyai and hybi defined at grid cell vertical interfaces (rather than midpoints like hyam and hybm). The approach naturally fits into two lines: cat > ~/pdel.nco << 'EOF' *prs_ntf[time,lat,lon,ilev]=P0*hyai+PS*hybi; // Requires NCO 4.5.4 and later: prs_dlt[time,lat,lon,lev]=prs_ntf(:,:,:,1:$ilev.size-1)-prs_ntf(:,:,:,0:$ilev.size-2); // Derived variable that require pressure thickness: // Divide by gravity to obtain total mass path in layer aka mpl [kg m-2] mpl=prs_dlt/grv_sfc; // Multiply by mass mixing ratio to obtain mass path of constituent mpl_CO2=mpl*mmr_CO2; EOF ncap2 -O -v -S ~/pdel.nco ~/nco/data/in.nc ~/foo.nc ncks -O -C -v prs_dlt ~/foo.nc The first line defines the four-dimensional interface pressures prs_ntf as a RAM variable because those are not desired in the output file. The second differences each pressure level from the pressure above it to obtain the pressure difference. This line employs both left-hand casting and array hyperslabbing. However, this syntax only works with NCO version 4.5.4 (November, 2015) and later because earlier versions require that LHS and RHS dimension names (not just sizes) match. From the pressure differences, one can obtain the mass path in each layer as shown. Another reason to cast a variable is to modify the shape or type of a variable already in Input

gds_var[gds_crd]=gds_var.double();
three_dmn_var_crd[lat,lon,lev]=10.0d;
four[]=four.int();

<a name="ncap_arr"></a>  <a name="array"></a>  Arrays and hyperslabsAttributesLeft hand castingncap2 netCDF Arithmetic Processor Arrays and hyperslabs array array function arrays findgen-equivalent indgen-equivalent Generating a regularly spaced n-dimensional array with ncap2 is simple with the array() function. The function comes in three (overloaded) forms

(A) var_out=array(val_srt,val_inc,$dmn_nm); // One-dimensional output
(B) var_out=array(val_srt,val_inc,var_tpl); // Multi-dimensional output
(C) var_out=array(val_srt,val_inc,/$dmn1,$dmn2...,$dmnN/); // Multi-dimensional output

val_srt Starting value of the array. The type of the array will be the type of this starting value. val_inc Spacing (or increment) between elements. var_tpl Variable from which the array can derive its shape 1D or nD

One-Dimensional Arrays&linebreak; Use form (A) or (B) above for 1D arrays:

# var_out will be NC_DOUBLE:
var_out=array(10.0,2,$time) // 10.5,12.5,14.5,16.5,18.5,20.5,22.5,24.5,26.5,28.5

// var_out will be NC_UINT, and "shape" will duplicate "ilev"
var_out=array(0ul,2,ilev) // 0,2,4,6

// var_out will be NC_FLOAT
var_out=array(99.0f,2.5,$lon) // 99,101.5,104,106.5

// Create an array of zeros 
var_out=array(0,0,$time) // 0,0,0,0,0,0,0,0,0,0 

// Create array of ones
var_out=array(1.0,0.0,$lon) // 1.0,1.0,1.0,1.0

n-Dimensional Arrays&linebreak; Use form (B) or (C) for creating n-D arrays.&linebreak; NB: In (C) the final argument is a list of dimensions

// These are equivalent
var_out=array(1.0,2.0,three_dmn_var);
var_out=array(1.0,2.0,/$lat,$lev,$lon/);

// var_out is NC_BYTE
var_out=array(20b, -4, /$lat,$lon/); // 20,16,12,8,4,0,-4,-8  

srt=3.14159f;
inc=srt/2.0f;
var_out(srt,inc,var_2D_rrg);
// 3.14159, 4.712385, 6.28318, 7.853975, 9.42477, 10.99557, 12.56636, 14.13716 ;

hyperslabs Hyperslabs in ncap2 are more limited than hyperslabs with the other NCO operators. ncap2 does not understand the shell command-line syntax used to specify multi-slabs, wrapped co-ordinates, negative stride or coordinate value limits. However with a bit of syntactic magic they are all are possible. ncap2 accepts (in fact, it requires) N-hyperslab arguments for a variable of rank N:

var1(arg1,arg2 ... argN);

where each hyperslab argument is of the form

start:end:stride

and the arguments for different dimensions are separated by commas. If start is omitted, it defaults to zero. If end is omitted, it defaults to dimension size minus one. If stride is omitted, it defaults to one. If a single value is present then it is assumed that that dimension collapses to a single value (i.e., a cross-section). The number of hyperslab arguments MUST equal the variable&textrsquo;s rank. Hyperslabs on the Right Hand Side of an assign&linebreak; A simple 1D example: ($time.size=10) od[$time]={20,22,24,26,28,30,32,34,36,38}; od(7); // 34 od(7:); // 34,36,38 od(:7); // 20,22,24,26,28,30,32,34 od(::4); // 20,28,36 od(1:6:2) // 22,26,30 od(:) // 20,22,24,26,28,30,32,34,36,38 A more complex three dimensional example: ($lat.size=2,$lon.size=4) th[$time,$lat,$lon]= {1, 2, 3, 4, 5, 6, 7, 8, 9,10,11,12,13,14,15,16, 17,18,19,20,21,22,23,24, -99,-99,-99,-99,-99,-99,-99,-99, 33,34,35,36,37,38,39,40, 41,42,43,44,45,46,47,48, 49,50,51,52,53,54,55,56, -99,58,59,60,61,62,63,64, 65,66,67,68,69,70,71,72, -99,74,75,76,77,78,79,-99 }; th(1,1,3); // 16 th(2,0,:); // 17, 18, 19, 20 th(:,1,3); // 8, 16, 24, -99, 40, 48, 56, 64, 72, -99 th(::5,:,0:3:2); // 1, 3, 5, 7, 41, 43, 45, 47 If hyperslab arguments collapse to a single value (a cross-section has been specified), then that dimension is removed from the returned variable. If all the values collapse then a scalar variable is returned. So, for example, the following is valid:

th_nw=th(0,:,:)+th(9,:,:); 
// th_nw has dimensions $lon,$lat 
// NB: the time dimension has become degenerate

The following is invalid:

th_nw=th(0,:,0:1)+th(9,:,0:1);

because the $lon dimension now only has two elements. The above can be calculated by using a LHS cast with $lon_nw as replacement dim for $lon:

defdim("lon_nw",2);
th_nw[$lat,$lon_nw]=th(0,:,0:1)+th(9,:,0:1);

Hyperslabs on the Left Hand Side of an assign&linebreak; When hyperslabing on the LHS, the expression on the RHS must evaluate to a scalar or a variable/attribute with the same number of elements as the LHS hyperslab. Set all elements of the last record to zero:

th(9,:,:)=0.0;

Set first element of each lon element to 1.0:

th(:,:,0)=1.0;

One may hyperslab on both sides of an assign. For example, this sets the last record to the first record:

th(9,:,:)=th(0,:,:);

Say th0 represents pressure at height=0 and th1 represents pressure at height=1. Then it is possible to insert these hyperslabs into the records

prs[$time,$height,$lat,$lon]=0.0;
prs(:,0,:,:)=th0;
prs(:,1,:,:)=th1;

Reverse method&linebreak; reverse() Use the reverse() method to reverse a dimension&textrsquo;s elements in a variable with at least one dimension. This is equivalent to a negative stride, e.g., th_rv=th(1,:,:).reverse($lon); // {12,11,10,9 }, {16,15,14,13} od_rv=od.reverse($time); // {38,36,34,32,30,28,26,24,22,20} Permute methodp&linebreak; permute() Use the permute() method to swap the dimensions of a variable. The number and names of dimension arguments must match the dimensions in the variable. If the first dimension in the variable is of record type then this must remain the first dimension. If you want to change the record dimension then consider using ncpdq. Consider the variable:

float three_dmn_var(lat,lev,lon);
three_dmn_var_prm=three_dmn_var.permute($lon,$lat,$lev);
// The permuted values are
three_dmn_var_prm= 
  0,4,8,
  12,16,20,
  1,5,9,
  13,17,21,
  2,6,10,
  14,18,22,
  3,7,11,
  15,19,23;

<a name="ncap_att"></a>  AttributesValue ListArrays and hyperslabsncap2 netCDF Arithmetic Processor Attributes attributesncap2 Refer to attributes with var_nm&arobase;att_nm. The following are all valid statements: global@text="Test Attributes"; /* Assign a global variable attribute */ a1[$time]=time*20; a1@long_name="Kelvin"; a1@min=a1.min(); a1@max=a1.max(); a1@min++; --a1@max; a1(0)=a1@min; a1($time.size-1)=a1@max; NetCDF allows all attribute types to have a size between one NC_MAX_ATTRS. Here is the metadata for variable a1:

double a1(time) ;
  a1:long_name = "Kelvin" ;
  a1:max = 199. ;
  a1:min = 21. ;
  a1:trip1 = 1, 2, 3 ;
  a1:triplet = 21., 110., 199. ;

These basic methods can be used with attributes: size(), type(), and exists(). For example, to save an attribute text string in a variable: defdim("sng_len",a1@long_name.size()); sng_arr[$sng_len]=a1@long_name; // sng_arr now contains "Kelvin" Attributes defined in a script are stored in memory and are written to the output file after script completion. To stop the attribute being written use the ram_delete() method or use a bogus variable name. Attribute Propagation and Inheritance attribute propagation attribute inheritance • Attribute propagation occurs in a regular assign statement. The variable being defined on the LHS gets copies of the attributes from the leftermost variable on the RHS. • Attribute Inheritance: The LHS variable &textldquo;inherits&textrdquo; attributes from an Input variable with the same name • It is possible to have a regular assign statement for which both propagation and inheritance occur.

// prs_mdp inherits attributes from P0:
prs_mdp[time,lat,lon,lev]=P0*hyam+hybm*PS;
// th_min inherits attributes from three_dmn_var_dbl:
th_min=1.0 + 2*three_dmn_var_dbl.min($time);

Attribute Concatenation&linebreak; attribute concatenation push The push() function concatenates attributes, or appends an &textldquo;expression&textrdquo; to a pre-existing attribute. It comes in two forms

(A) att_new=push(att_exp, expr)
(B) att_size=push(&att_nm,expr)

In form (A) The first argument should be an attribute identifier or an expression that evaluates to an attribute. The second argument can evalute to an attribute or a variable. The second argument is then converted to the type of att_exp; and appended to att_exp ; and the resulting attribute is returned.&linebreak; In form (B) the first argument is a call-by-reference attribute identifier (which may not yet exist). The second argument is then evaluated (and type-converted as needed) and appended to the call-by-reference atttribute. The final size of the attribute is then returned. temp@range=-10.0; push(&temp@range,12.0); // temp@range=-10.0,12.0 numbers@squares=push(1,4); numbers@squares=push(numbers@squares,9); push(&number@squares,16.0); push(&number@squares,25ull); // numbers@squares=1,4,9,16,25 Now some text examples.&linebreak; Remember, an atttribute identifier that begins with &arobase; implies a global attribute. For example, &textrsquo;&arobase;institution&textrsquo; is short for &textrsquo;global&arobase;institution&textrsquo;. global@greetings=push("hello"," world !!"); global@greek={"alpha"s,"beta"s,"gamma"s}; // Append an NC_STRING push(&@greek,"delta"s); // Pushing an NC_CHAR to a NC_STRING attribute is allowed, it is converted to an an NC_CHAR @e="epsilon"; push(&@greek,@e); push(&@greek,"zeta"); // Pushing a single NC_STRING to an NC_CHAR is not allowed @h="hello"; push(&@h," again"s); // BAD PUSH If the attribute name contains non-regular characters use ID quoting: 'b..m1@c--lost'=23; See ID Quoting. <a name="value_list"></a>  <a name="initialize"></a>  Value ListNumber literalsAttributesncap2 netCDF Arithmetic Processor Value List value listncap2 A value list is a special type of attribute. It can only be used on the RHS of the assign family of statements.&linebreak; That is =, +=, -=, *=, /=&linebreak; A value list CANNOT be involved in any logical, binary, or arithmetical operations (except those above).&linebreak; A value list CANNOT be used as a function argument.&linebreak; A value list CANNOT have nested value lists.&linebreak; The type of a value list is the type of the member with the highest type.&linebreak; value list a1@trip={1,2,3}; a1@trip+={3,2,1}; // 4,4,4 a1@triplet={a1@min,(a1@min+a1@max)/2,a1@max}; lon[lon]={0.0,90.0,180.0,270.0}; lon*={1.0,1.1,1.2,1.3} dlon[lon]={1b,2s,3ull,4.0f}; // final type NC_FLOAT a1@ind={1,2,3}+{4,4,4}; // BAD a1@s=sin({1.0,16.0}); // BAD One can also use a value_list to create an attribute of type NC_STRING. Remember, a literal string of type NC_STRING has a postfix &textrsquo;s&textrsquo;. A value list of NC_CHAR has no semantic meaning and is plain wrong. array[lon]={1.0,2.,4.0,7.0}; array@numbers={"one"s, "two"s, "four"s, "seven"s}; // GOOD ar[lat]={0,20} ar@numbers={"zero","twenty"}; // BAD <a name="ncap_num"></a>  <a name="ncap_string"></a>  Number literalsif statementValue Listncap2 netCDF Arithmetic Processor Number literals number literals ncap2 The table below lists the postfix character(s) to add to a number literal (aka, a naked constant) for explicit type specification. The same type-specification rules are used for variables and attributes. A floating-point number without a postfix defaults to NC_DOUBLE, while an integer without a postfix defaults to type NC_INT: var[$rlev]=0.1; // Variable will be type NC_DOUBLE var[$lon_grd]=2.0; // Variable will be type NC_DOUBLE var[$gds_crd]=2e3; // Variable will be type NC_DOUBLE var[$gds_crd]=2.0f; // Variable will be type NC_FLOAT (note "f") var[$gds_crd]=2e3f; // Variable will be type NC_FLOAT (note "f") var[$gds_crd]=2; // Variable will be type NC_INT var[$gds_crd]=-3; // Variable will be type NC_INT var[$gds_crd]=2s; // Variable will be type NC_SHORT var[$gds_crd]=-3s; // Variable will be type NC_SHORT var@att=41.; // Attribute will be type NC_DOUBLE var@att=41.f; // Attribute will be type NC_FLOAT var@att=41; // Attribute will be type NC_INT var@att=-21s; // Attribute will be type NC_SHORT var@units="kelvin"; // Attribute will be type NC_CHAR There is no postfix for characters, use a quoted string instead for NC_CHAR. ncap2 interprets a standard double-quoted string as a value of type NC_CHAR. In this case, any receiving variable must be dimensioned as an array of NC_CHAR long enough to hold the value. To use the newer netCDF4 types NCO must be compiled/linked to the netCDF4 library and the output file must be of type NETCDF4: var[$time]=1UL; // Variable will be type @code{NC_UINT} var[$lon]=4b; // Variable will be type @code{NC_BYTE} var[$lat]=5ull; // Variable will be type @code{NC_UINT64} var[$lat]=5ll; // Variable will be type @code{NC_INT64} var@att=6.0d; // Attribute will be type @code{NC_DOUBLE} var@att=-666L; // Attribute will be type @code{NC_INT} var@att="kelvin"s; // Attribute will be type @code{NC_STRING} (note the "s") NC_CHAR NC_STRING Use a post-quote s for NC_STRING. Place the letter s immediately following the double-quoted string to indicate that the value is of type NC_STRING. In this case, the receiving variable need not have any memory allocated to hold the string because netCDF4 handles that memory allocation. Suppose one creates a file containing an ensemble of model results, and wishes to label the record coordinate with the name of each model. The NC_STRING type is well-suited to this because it facilitates storing arrays of strings of arbitrary length. This is sophisticated, though easy with ncap2: % ncecat -O -u model cesm.nc ecmwf.nc giss.nc out.nc % ncap2 -4 -O -s 'model[$model]={"cesm"s,"ecmwf"s,"giss"s}' out.nc out.nc The key here to place an s character after each double-quoted string value to indicate an NC_STRING type. The -4 ensures the output filetype is netCDF4 in case the input filetype is not. netCDF3/4 Types b|B NC_BYTE, a signed 1-byte integer none NC_CHAR, an ISO/ASCII character s|S NC_SHORT, a signed 2-byte integer l|L NC_INT, a signed 4-byte integer f|F NC_FLOAT, a single-precision (4-byte) floating-point number d|D NC_DOUBLE, a double-precision (8-byte) floating-point number netCDF4 Types ub|UB NC_UBYTE, an unsigned 1-byte integer us|US NC_USHORT, an unsigned 2-byte integer u|U|ul|UL NC_UINT, an unsigned 4-byte integer ll|LL NC_INT64, a signed 8-byte integer ull|ULL NC_UINT64, an unsigned 8-byte integer s NC_STRING, a string of arbitrary length

<a name="ncap_if"></a>  if statementPrint & String methodsNumber literalsncap2 netCDF Arithmetic Processor if statement if() The syntax of the if statement is similar to its C counterpart. The Conditional Operator (ternary operator) has also been implemented. if(exp1) stmt1; else if(exp2) stmt2; else stmt3; # Can use code blocks as well: if(exp1){ stmt1; stmt1a; stmt1b; }else if(exp2) stmt2; else{ stmt3; stmt3a; stmt3b; } For a variable or attribute expression to be logically true all its non-missing value elements must be logically true, i.e., non-zero. The expression can be of any type. Unlike C there is no short-circuiting of an expression with the OR (||) and AND (&&) operators. The whole expression is evaluated regardless if one of the AND/OR operands are True/False. # Simple example if(time > 0) print("All values of time are greater than zero\n"); else if(time < 0) print("All values of time are less than zero\n"); else { time_max=time.max(); time_min=time.min(); print("min value of time=");print(time_min,"%f"); print("max value of time=");print(time_max,"%f"); } # Example from ddra.nco if(fl_typ == fl_typ_gcm){ var_nbr_apx=32; lmn_nbr=1.0*var_nbr_apx*varsz_gcm_4D; /* [nbr] Variable size */ if(nco_op_typ==nco_op_typ_avg){ lmn_nbr_avg=1.0*var_nbr_apx*varsz_gcm_4D; // Block size lmn_nbr_wgt=dmnsz_gcm_lat; /* [nbr] Weight size */ } // !nco_op_typ_avg }else if(fl_typ == fl_typ_stl){ var_nbr_apx=8; lmn_nbr=1.0*var_nbr_apx*varsz_stl_2D; /* [nbr] Variable size */ if(nco_op_typ==nco_op_typ_avg){ lmn_nbr_avg=1.0*var_nbr_apx*varsz_stl_2D; // Block size lmn_nbr_wgt=dmnsz_stl_lat; /* [nbr] Weight size */ } // !nco_op_typ_avg } // !fl_typ Conditional Operator&linebreak;

// netCDF4 needed for this example
th_nw=(three_dmn_var_sht >= 0 ? three_dmn_var_sht.uint() : \
       three_dmn_var_sht.int());

<a name="ncap_prn"></a>  Print & String methodsMissing values ncap2if statementncap2 netCDF Arithmetic Processor Print & String methods print() ncap2 The print statement comes in a variety of forms:

(A)   print(variable_name, format string?);
(A1)  print(expression/string, format string?);

(B)   sprint(expression/string, format string?);
(B1)  sprint4(expression/string, format string?);

print() &linebreak;&linebreak; If the variable exists in I/O then it is printed in a similar fashion to ncks -H. print(lon); lon[0]=0 lon[1]=90 lon[2]=180 lon[3]=270 print(byt_2D) lat[0]=-90 lon[0]=0 byt_2D[0]=0 lat[0]=-90 lon[1]=90 byt_2D[1]=1 lat[0]=-90 lon[2]=180 byt_2D[2]=2 lat[0]=-90 lon[3]=270 byt_2D[3]=3 lat[1]=90 lon[0]=0 byt_2D[4]=4 lat[1]=90 lon[1]=90 byt_2D[5]=5 lat[1]=90 lon[2]=180 byt_2D[6]=6 lat[1]=90 lon[3]=270 byt_2D[7]=7 If the first argument is NOT a variable the form (A1) is invoked. print(mss_val_fst@_FillValue); mss_val_fst@_FillValue, size = 1 NC_FLOAT, value = -999 print("This function \t is monotonic\n"); This function is monotonic print(att_var@float_att) att_var@float_att, size = 7 NC_FLOAT, value = 73, 72, 71, 70.01, 69.001, 68.01, 67.01 print(lon*10.0) lon, size = 4 NC_DOUBLE, value = 0, 900, 1800, 2700 If the format string is specified then the results from (A) and (A1) forms are the same print(lon_2D_rrg,"%3.2f,"); 0.00,0.00,180.00,0.00,180.00,0.00,180.00,0.00, print(lon*10.0,"%g,") 0,900,1800,2700, print(att_var@float_att,"%g,") 73,72,71,70.01,69.001,68.01,67.01, sprint() & sprint4() &linebreak;&linebreak; These functions work in an identical fashion to (A1) except that sprint() outputs a regular netCDF3 NC_CHAR attribute and sprint4() outputs a netCDF4 NC_STRING attribute time@units=sprint(yyyy,"days since %d-1-1") bnd@num=sprint4(bnd_idx,"Band number=%d") time@arr=sprint4(time,"%.2f,") // "1.00,2.00,3.00,4.00,5.00,6.00,7.00,8.00,9.00,10.00," You can also use sprint4() to convert a NC_CHAR string to a NC_STRING string and sprint() to convert a NC_STRING to a NC_CHAR lat_1D_rct@long_name = "Latitude for 2D rectangular grid stored as 1D arrays"; // // convert to NC_STRING lat_1D_rct@long_name = sprint4(lat_1D_rct@long_name) hyperslab a netCDF string &linebreak;&linebreak; It is possible to index-into an NC_CHAR string, similar to a C-String. Unlike a C-String, however, an NC_CHAR string has no null-character to mark its termination. One CANNOT index into an NC_STRING string. One must must convert to an NC_CHAR first. global@greeting="hello world!!!" @h=@greeting(0:4); // "hello" @w=@greeting(6:11); // "world" // can use negative inidices @x=@greeting(-3:-1); // "!!!" // can use stride @n=@greeting(::2); // "hlowrd!" // concatenation global@new_greeting=push(@h, " users !!!"); // "hello users!!!" @institution="hotel california"s; @h=@institution(0:4); // BAD // convert NC_STRING to NC_CHAR @is=sprint(@institution); @h=@is(0:4); // "hotel" // convert NC_CHAR to NC_STRING @h=sprint4(@h); get_vars_in() & get_vars_out()

att_lst=get_vars_in(att_regexp?) 
att_lst=get_vars_out(att_regexp?)

These functions are used to create a list of vars in Input or Output. The optional arg &textrsquo;att_regexp&textrsquo;. Can be an NC_CHAR att or a NC_STRING att. If NC_CHAR then only a single reg-exp can be specified. If NC_STRING then multiple reg-exp can be specified. The output is allways an NC_STRING att. The matching works in an identical fashion to the -v switch in ncks. if there is no arg then all vars are returned. @slist=get_vars_in("^time"); // "time", "time_bnds", "time_lon", "time_udunits" // Use NC_STRINGS @regExp={".*_bnd"s,".*_grd"s} @slist=get_vars_in(@regExp); // "lat_bnd", "lat_grd", "lev_bnd", "lon_grd", "time_bnds", "cnv_CF_grd" <a name="ncap_miss"></a>  Missing values ncap2Methods and functionsPrint & String methodsncap2 netCDF Arithmetic Processor Missing values ncap2 missing values ncap2 Missing values operate slightly differently in ncap2 Consider the expression where op is any of the following operators (excluding &textrsquo;=&textrsquo;)

Arithmetic operators ( * / % + - ^ )
Binary Operators     ( > >= < <= == != == || && >> << ) 
Assign Operators     ( += -= /= *= ) 

var1 'op' var2

If var1 has a missing value then this is the value used in the operation, otherwise the missing value for var2 is used. If during the element-by-element operation an element from either operand is equal to the missing value then the missing value is carried through. In this way missing values &textrsquo;percolate&textrsquo; or propagate through an expression.&linebreak; Missing values associated with Output variables are stored in memory and are written to disk after the script finishes. During script execution its possible (and legal) for the missing value of a variable to take on several different values.

# Consider the variable:
int rec_var_int_mss_val_int(time); =-999,2,3,4,5,6,7,8,-999,-999;
rec_var_int_mss_val_int:_FillValue = -999;

n2=rec_var_int_mss_val_int + rec_var_int_mss_val_int.reverse($time); 

n2=-999,-999,11,11,11,11,11,11,999,-999;

<a name="missing"></a>  <a name="mask_miss"></a>  <a name="has_miss"></a>  <a name="delete_miss"></a>  <a name="set_miss"></a>  <a name="get_miss"></a>  <a name="change_miss"></a>  <a name="number_miss"></a>  The following methods query or manipulate missing value (aka _FillValue information associated with a variable. The methods that &textldquo;manipulate&textrdquo; only succeed on variables in Output. set_miss(expr) set_miss() The numeric argument expr becomes the new missing value, overwriting the old missing value, if any. The argument given is converted if necessary to the variable&textrsquo;s type. NB: This only changes the missing value attribute. Missing values in the original variable remain unchanged, and thus are no long considered missing values. They are effectively &textldquo;orphaned&textrdquo;. Thus set_miss() is normally used only when creating new variables. The intrinsic function change_miss() (see below) is typically used to edit values of existing variables. change_miss(expr) change_miss() Sets or changes (any pre-existing) missing value attribute and missing data values to expr. NB: This is an expensive function since all values must be examined. Use this function when changing missing values for pre-existing variables. get_miss() get_miss() Returns the missing value of a variable. If the variable exists in Input and Output then the missing value of the variable in Output is returned. If the variable has no missing value then an error is returned. delete_miss() delete_miss() Delete the missing value associated with a variable. number_miss() number_miss() Count the number of missing values a variable contains. has_miss() has_miss() Returns 1 (True) if the variable has a missing value associated with it. else returns 0 (False) missing() missing(), mask_miss() This function creates a True/False mask array of where the missing value is set. It is syntatically equivalent to (var_in == var_in.get_miss()), except that requires deleting the missing value before-hand.

th=three_dmn_var_dbl; th.change_miss(-1e10d); /* Set values less than 0 or greater than 50 to missing value */ where(th < 0.0 || th > 50.0) th=th.get_miss(); # Another example: new[$time,$lat,$lon]=1.0; new.set_miss(-997.0); // Extract all elements evenly divisible by 3 where (three_dmn_var_dbl%3 == 0) new=three_dmn_var_dbl; elsewhere new=new.get_miss(); // Print missing value and variable summary mss_val_nbr=three_dmn_var_dbl.number_miss(); print(three_dmn_var_dbl@_FillValue); print("Number of missing values in three_dmn_var_dbl: "); print(mss_val_nbr,"%d"); print(three_dmn_var_dbl); // Find total number of missing values along dims $lat and $lon mss_ttl=three_dmn_var_dbl.missing().ttl($lat,$lon); print(mss_ttl); // 0, 0, 0, 8, 0, 0, 0, 1, 0, 2 ; simple_fill_miss(var) simple_fill_miss() This function takes a variable and attempts to fill missing values using an average of up to the 4 nearest neighbour grid points. The method used is iterative (up to 1000 cycles). For very large areas of missing values results can be unpredictable. The given variable must be at least 2D; and the algorithm assumes that the last two dims are lat/lon or y/x weighted_fill_miss(var) weighted_fill_miss() Weighted_fill_miss is more sophisticated. Up to 8 nearest neighbours are used to calculate a weighted average. The weighting used is the inverse square of distance. Again the method is iterative (up to 1000 cycles). The area filled is defined by the final two dims of the variable. In addition this function assumes the existance of coordinate vars the same name as the last two dims. if it doesn&textrsquo;t find these dims it will gently exit with warning.

<a name="ncap_mth"></a>  <a name="ncap2_mth"></a>  Methods and functionsRAM variablesMissing values ncap2ncap2 netCDF Arithmetic Processor Methods and functions The convention within this document is that methods can be used as functions. However, functions are not and cannot be used as methods. Methods can be daisy-chained d and their syntax is cleaner than functions. Method names are reserved words and CANNOT be used as variable names. The command ncap2 -f shows the complete list of methods available on your build.

n2=sin(theta) 
n2=theta.sin() 
n2=sin(theta)^2 + cos(theta)^2 
n2=theta.sin().pow(2) + theta.cos()^2

This statement chains together methods to convert three_dmn_var_sht to type double, average it, then convert this back to type short:

three_avg=three_dmn_var_sht.double().avg().short();

Aggregate Methods &linebreak; These methods mirror the averaging types available in ncwa. The arguments to the methods are the dimensions to average over. Specifying no dimensions is equivalent to specifying all dimensions i.e., averaging over all dimensions. A masking variable and a weighting variable can be manually created and applied as needed. avg() avg() Mean value sqravg() sqravg() Square of the mean avgsqr() Mean of sum of squares max() max() Maximum value min() min() Minimum value mabs() mabs() Maximum absolute value mebs() mebs() Mean absolute value mibs() mibs() Minimum absolute value rms() Root-mean-square (normalize by N) rmssdn() rmssdn() Root-mean square (normalize by N-1) tabs() or ttlabs() tabs() Sum of absolute values ttl() or total() or sum() ttl() Sum of values

// Average a variable over time
four_time_avg=four_dmn_rec_var($time);

Packing Methods &linebreak; For more information see Packed data and ncpdq netCDF Permute Dimensions Quickly&linebreak; pack() & pack_short() pack() The default packing algorithm is applied and variable is packed to NC_SHORT pack_byte() pack_byte() Variable is packed to NC_BYTE pack_short() pack_short() Variable is packed to NC_SHORT pack_int() pack_int() Variable is packed to NC_INT unpack() unpack() The standard unpacking algorithm is applied.

NCO automatically unpacks packed data before arithmetically modifying it. After modification NCO stores the unpacked data. To store it as packed data again, repack it with, e.g., the pack() function. To ensure that temperature is packed in the output file, regardless of whether it is packed in the input file, one uses, e.g.,

ncap2 -s 'temperature=pack(temperature-273.15)' in.nc out.nc

All the above pack functions also take the additional two arguments scale_factor, add_offset. Both arguments must be included:

ncap2 -v -O -s 'rec_pck=pack(three_dmn_rec_var,-0.001,40.0);' in.nc foo.nc

Basic Methods &linebreak; These methods work with variables and attributes. They have no arguments. size() size() Total number of elements ndims() ndims() Number of dimensions in variable type() type() Returns the netcdf type (see previous section) exists() exists() Return 1 (true) if var or att is present in I/O else return 0 (false) getdims() getdims() Returns an NC_STRING attribute of all the dim names of a variable

Utility Methods &linebreak; These functions are used to manipulate missing values and RAM variables. Missing values ncap2 set_miss(expr) Takes one argument, the missing value. Sets or overwrites the existing missing value. The argument given is converted if necessary to the variable type. (NB: pre-existing missing values, if any, are not converted). change_miss(expr) Changes the missing value elements of the variable to the new missing value (NB: an expensive function). get_miss() Returns the missing value of a variable in Input or Output delete_miss() Deletes the missing value associated with a variable. has_miss() Returns 1 (True) if the variable has a missing else returns 0 (False) number_miss Returns the number of missing values a variable contains ram_write() Writes a RAM variable to disk i.e., converts it to a regular disk type variable ram_delete() Deletes a RAM variable or an attribute

PDQ Methods&linebreak; See ncpdq netCDF Permute Dimensions Quickly reverse(dim args) Reverse the dimension ordering of elements in a variable. permute(dim args) Re-shape variables by re-ordering the dimensions. All the dimensions of the variable must be specified in the arguments. A limitation of this permute (unlike ncpdq) is that the record dimension cannot be re-assigned.

// Swap dimensions about and reorder along lon

lat_2D_rrg_new=lat_2D_rrg.permute($lon,$lat).reverse($lon);
lat_2D_rrg_new=0,90,-30,30,-30,30,-90,0

Type Conversion Methods and Functions&linebreak; These methods allow ncap2 to convert variables and attributes to the different netCDF types. For more details on automatic and manual type conversion see (Type Conversion). netCDF4 types are only available if you have compiled/links NCO with the netCDF4 library and the Output file is HDF5. netCDF3/4 Types byte() byte() convert to NC_BYTE, a signed 1-byte integer char() char() convert to NC_CHAR, an ISO/ASCII character short() sshort() convert to NC_SHORT, a signed 2-byte integer int() int() convert to NC_INT, a signed 4-byte integer float() float() convert to NC_FLOAT, a single-precision (4-byte) floating-point number double() double() convert to NC_DOUBLE, a double-precision (8-byte) floating-point number netCDF4 Types ubyte() ubyte() convert to NC_UBYTE, an unsigned 1-byte integer ushort() ushort() convert to NC_USHORT, an unsigned 2-byte integer uint() uint() convert to NC_UINT, an unsigned 4-byte integer int64() int64() convert to NC_INT64, a signed 8-byte integer uint64() unit64() convert to NC_UINT64, an unsigned 8-byte integer

You can also use the convert() method to do type conversion. This takes an integer agument. For convenience, ncap2 defines the netCDF pre-processor tokens as RAM variables. For example you may wish to convert a non-floating point variable to the same type as another variable.

lon_type=lon.type();
if(time.type() != NC_DOUBLE && time.type() != NC_FLOAT) 
   time=time.convert(lon_type);

Intrinsic Mathematical Methods &linebreak; The list of mathematical methods is system dependant. For the full list Intrinsic mathematical methods All the mathematical methods take a single argument except atan2() and pow() which take two. If the operand type is less than float then the result will be of type float. Arguments of type double yield results of type double. Like the other methods, you are free to use the mathematical methods as functions.

n1=pow(2,3.0f)    // n1 type float
n2=atan2(2,3.0)   // n2 type double
n3=1/(three_dmn_var_dbl.cos().pow(2))-tan(three_dmn_var_dbl)^2; // n3 type double

<a name="ncap_ram"></a>  RAM variables RAM variablesWhere statementMethods and functionsncap2 netCDF Arithmetic Processor RAM variables Unlike regular variables, RAM variables are never written to disk. Hence using RAM variables in place of regular variables (especially within loops) significantly increases execution speed. Variables that are frequently accessed within for or where clauses provide the greatest opportunities for optimization. To declare and define a RAM variable simply prefix the variable name with an asterisk (*) when the variable is declared/initialized. To delete RAM variables (and recover their memory) use the ram_delete() method. To write a RAM variable to disk (like a regular variable) use ram_write(). ram_write() ram_delete()

*temp[$time,$lat,$lon]=10.0;     // Cast
*temp_avg=temp.avg($time);      // Regular assign
temp.ram_delete();              // Delete RAM variable
temp_avg.ram_write();           // Write Variable to output

// Create and increment a RAM variable from "one" in Input
*one++;   
// Create RAM variables from the variables three and four in Input.
// Multiply three by 10 and add it to four. 
*four+=*three*=10; // three=30, four=34

<a name="where"></a>  <a name="ncap_whr"></a>  <a name="ncap_where"></a>  Where statementLoopsRAM variablesncap2 netCDF Arithmetic Processor Where statement where() The where() statement combines the definition and application of a mask and can lead to succinct code. The syntax of a where() statement is: // Single assign ('elsewhere' is optional) where(mask) var1=expr1; elsewhere var1=expr2; // Multiple assigns where(mask){ var1=expr1; var2=expr2; ... }elsewhere{ var1=expr3 var2=expr4 var3=expr5; ... } • The only expression allowed in the predicate of a where is assign, i.e., &textrsquo;var=expr&textrsquo;. This assign differs from a regular ncap2 assign. The LHS var must already exist in Input or Output. The RHS expression must evaluate to a scalar or a variable/attribute of the same size as the LHS variable. • Consider when both the LHS and RHS are variables: For every element where mask condition is True, the corresponding LHS variable element is re-assigned to its partner element on the RHS. In the elsewhere part the mask is logically inverted and the assign process proceeds as before. • If the mask dimensions are a subset of the LHS variable&textrsquo;s dimensions, then it is made to conform; if it cannot be made to conform then script execution halts. • Missing values in the mask evaluate to False in the where code/block statement and to True in the elsewhere block/statement. • LHS variable elements set to missing value are treated just like any other elements and can be re-assigned as the mask dictates • LHS variable cannot include subscripts. If they do script execution will terminate. See below example for work-araound. Consider the variables float lon_2D_rct(lat,lon); and float var_msk(lat,lon);. Suppose we wish to multiply by two the elements for which var_msk equals 1:

where(var_msk == 1) lon_2D_rct=2*lon_2D_rct;

Suppose that we have the variable int RDM(time) and that we want to set its values less than 8 or greater than 80 to 0:

where(RDM < 8 || RDM > 80) RDM=0;

To use where on a variable hyperslab, define and use a temporary variable, e.g., *var_tmp=var2(:,0,:,:); where (var1 < 0.5) var_tmp=1234; var2(;,0,:,;)=var_tmp; ram_delete(var_tmp); <a name="WRF"></a>  <a name="SLD"></a>  <a name="wrf"></a>  <a name="sld"></a>  Weather and Research Forecast (WRF) Model Swath-like Data (SLD) WRF (Weather and Research Forecast Model) SLD (Swath-like Data) Consider irregularly gridded data, described using rank 2 coordinates: double lat(south_north,east_west), double lon(south_north,east_west), double temperature(south_north,east_west). This type of structure is often found in regional weather/climate model (such as WRF) output, and in satellite swath data. For this reason we call it &textldquo;Swath-like Data&textrdquo;, or SLD. To find the average temperature in a region bounded by [lat_min,lat_max] and [lon_min,lon_max]: temperature_msk[$south_north,$east_west]=0.0; where((lat >= lat_min && lat <= lat_max) && (lon >= lon_min && lon <= lon_max)) temperature_msk=temperature; elsewhere temperature_msk=temperature@_FillValue; temp_avg=temperature_msk.avg(); temp_max=temperature.max(); <a name="NARR"></a>  <a name="narr"></a>  NARR (North American Regional Reanalysis)a North American Regional Reanalysis (NARR) For North American Regional Reanalysis (NARR) data (example http://dust.ess.uci.edu/diwg/narr_uwnd.199605.ncdataset) the procedure looks like this ncap2 -O -v -S ~/narr.nco ${DATA}/hdf/narr_uwnd.199605.nc ~/foo.nc where narr.nco is an ncap2 script like this: /* North American Regional Reanalysis (NARR) Statistics NARR stores grids with 2-D latitude and longitude, aka Swath-like Data (SLD) Here we work with three variables: lat(y,x), lon(y,x), and uwnd(time,level,y,x); To study sub-regions of SLD, we use masking techniques: 1. Define mask as zero times variable to be masked Then mask automatically inherits variable attributes And average below will inherit mask attributes 2. Optionally, create mask as RAM variable (as below with asterisk *) NCO does not write RAM variable to output Masks are often unwanted, and can be big, so this speeds execution 3. Example could be extended to preserve mean lat and lon of sub-region Follow uwnd example to do this: lat_sk=0.0*lat ... lat_avg=lat.avg($y,$x) */ *uwnd_msk=0.0*uwnd; where((lat >= 35.6 && lat <= 37.0) && (lon >= -100.5 && lon <= -99.0)) uwnd_msk=uwnd; elsewhere uwnd_msk=uwnd@_FillValue; // Average only over horizontal dimensions x and y (preserve level and time) uwnd_avg=uwnd_msk.avg($y,$x); Stripped of comments and formatting, this example is a three-statement script executed by a one-line command. NCO needs only this meagre input to unpack and copy the input data and attributes, compute the statistics, and then define and write the output file. Unless the comments pointed out that wind variable (uwnd) was four-dimensional and the latitude/longitude grid variables were both two-dimensional, there would be no way to tell. This shows how NCO hides from the user the complexity of analyzing multi-dimensional SLD. We plan to extend such SLD features to more operators soon. <a name="ncap_lop"></a>  LoopsInclude filesWhere statementncap2 netCDF Arithmetic Processor Loops while() for() ncap2 supplies for() loops and while() loops. They are completely unoptimized so use them only with RAM variables unless you want thrash your disk to death. To break out of a loop use the break command. To iterate to the next cycle use the continue command. // Set elements in variable double temp(time,lat) // If element < 0 set to 0, if element > 100 set to 100 *sz_idx=$time.size; *sz_jdx=$lat.size; for(*idx=0;idx<sz_idx;idx++) for(*jdx=0;jdx<sz_jdx;jdx++) if(temp(idx,jdx) > 100) temp(idx,jdx)=100.0; else if(temp(idx,jdx) < 0) temp(idx,jdx)=0.0; // Are values of co-ordinate variable double lat(lat) monotonic? *sz=$lat.size; for(*idx=1;idx<sz;idx++) if(lat(idx)-lat(idx-1) < 0.0) break; if(idx == sz) print("lat co-ordinate is monotonic\n"); else print("lat co-ordinate is NOT monotonic\n"); // Sum odd elements *idx=0; *sz=$lat_nw.size; *sum=0.0; while(idx<sz){ if(lat(idx)%2) sum+=lat(idx); idx++; } ram_write(sum); print("Total of odd elements ");print(sum);print("\n"); <a name="ncap_inc"></a>  Include filesSort methodsLoopsncap2 netCDF Arithmetic Processor Include files include The syntax of an include-file is:

#include "script.nco"
#include "/opt/SOURCES/nco/data/tst.nco"

If the filename is relative and not absolute then the directory searched is relative to the run-time directory. It is possible to nest include files to an arbitrary depth. A handy use of inlcude files is to store often used constants. Use RAM variables if you do not want these constants written to nc-file. output-file.

// script.nco
// Sample file to #include in ncap2 script
*pi=3.1415926535; // RAM variable, not written to output
*h=6.62607095e-34; // RAM variable, not written to output
e=2.71828; // Regular (disk) variable, written to output

As of NCO version 4.6.3 (December, 2016), The user can specify the directory(s) to be searched by specifing them in the UNIX environment var NCO_PATH. The format used is identical to the UNIX PATH. The directory(s) are only searched if the include filename is relative.

export NCO_PATH=":/home/henryb/bin/:/usr/local/scripts:/opt/SOURCES/nco/data:"

<a name="srt"></a>  <a name="sort"></a>  <a name="remap"></a>  Sort methodsUDUnits scriptInclude filesncap2 netCDF Arithmetic Processor sort methods sort asort dsort remap unmap invert_map In ncap2 there are multiple ways to sort data. Beginning with NCO 4.1.0 (March, 2012), ncap2 support six sorting functions:

var_out=sort(var_in,&srt_map); // Ascending sort
var_out=asort(var_in,&srt_map); // Accending sort 
var_out=dsort(var_in,&srt_map); // Desending sort     
var_out=remap(var_in,srt_map); // Apply srt_map to var_in
var_out=unmap(var_in,srt_map); // Reverse what srt_map did to var_in
dsr_map=invert_map(srt_map); // Produce "de-sort" map that inverts srt_map

The first two functions, sort() and asort() sort, in ascending order, all the elements of var_in (which can be a variable or attribute) without regard to any dimensions. The third function, dsort() does the same but sorts in descending order. Remember that ascending and descending sorts are specified by asort() and dsort(), respectively. These three functions are overloaded to take a second, optional argument called the sort map srt_map, which should be supplied as a call-by-reference variable, i.e., preceded with an ampersand. If the sort map does not yet exist, then it will be created and returned as an integer type the same shape as the input variable. The output var_out of each sort function is a sorted version of the input, var_in. The output var_out of the two mapping functions the result of applying (with remap() or un-applying (with unmap()) the sort map srt_map to the input var_in. To apply the sort map with remap() the size of the variable must be exactly divisible by the size of the sort map. The final function invert_map() returns the so-called de-sorting map dsr_map which is the inverse of the input map srt_map. This gives the user access to both the forward and inverse sorting maps: a1[$time]={10,2,3,4,6,5,7,3,4,1}; a1_sort=sort(a1); print(a1_sort); // 1, 2, 3, 3, 4, 4, 5, 6, 7, 10; a2[$lon]={2,1,4,3}; a2_sort=sort(a2,&a2_map); print(a2); // 1, 2, 3, 4 print(a2_map); // 1, 0, 3, 2; If the map variable does not exist prior to the sort() call, then it will be created with the same shape as the input variable and be of type NC_INT. If the map variable already exists, then the only restriction is that it be of at least the same size as the input variable. To apply a map use remap(var_in,srt_map). defdim("nlat",5); a3[$lon]={2,5,3,7}; a4[$nlat,$lon]={ 1, 2, 3, 4, 5, 6, 7, 8, 9,10,11,12, 13,14,15,16, 17,18,19,20}; a3_sort=sort(a3,&a3_map); print(a3_map); // 0, 2, 1, 3; a4_sort=remap(a4,a3_map); print(a4_sort); // 1, 3, 2, 4, // 5, 7, 6, 8, // 9,11,10,12, // 13,15,14,16, // 17,19,18,20; a3_map2[$nlat]={4,3,0,2,1}; a4_sort2=remap(a4,a3_map2); print(a4_sort2); // 3, 5, 4, 2, 1 // 8, 10, 9,7, 6, // 13,15,14,12,11, // 18,20,19,17,16 As in the above example you may create your own sort map. To sort in descending order, apply the reverse() method after the sort(). Here is an extended example of how to use ncap2 features to hyperslab an irregular region based on the values of a variable not a coordinate. The distinction is crucial: hyperslabbing based on dimensional indices or coordinate values is straightforward. Using the values of single or multi-dimensional variable to define a hyperslab is quite different.

cat > ~/ncap2_foo.nco << 'EOF'
// Purpose: Save irregular 1-D regions based on variable values

// Included in NCO User Guide at http://nco.sf.net/nco.html#sort

/* NB: Single quotes around EOF above turn off shell parameter 
    expansion in "here documents". This in turn prevents the
    need for protecting dollarsign characters in NCO scripts with
    backslashes when the script is cut-and-pasted (aka "moused") 
    from an editor or e-mail into a shell console window */

/* Copy coordinates and variable(s) of interest into RAM variable(s)
   Benefits:
   1. ncap2 defines writes all variables on LHS of expression to disk
      Only exception is RAM variables, which are stored in RAM only
      Repeated operations on regular variables takes more time, 
      because changes are written to disk copy after every change.
      RAM variables are only changed in RAM so script works faster
      RAM variables can be written to disk at end with ram_write()
   2. Script permutes variables of interest during processing
      Safer to work with copies that have different names
      This discourages accidental, mistaken use of permuted versions
   3. Makes this script a more generic template:
      var_in instead of specific variable names everywhere */
*var_in=one_dmn_rec_var;
*crd_in=time;
*dmn_in_sz=$time.size; // [nbr] Size of input arrays

/* Create all other "intermediate" variables as RAM variables 
   to prevent them from cluttering the output file.
   Mask flag and sort map are same size as variable of interest */
*msk_flg=var_in;
*srt_map=var_in;

/* In this example we mask for all values evenly divisible by 3
   This is the key, problem-specific portion of the template
   Replace this where() condition by that for your problem
   Mask variable is Boolean: 1=Meets condition, 0=Fails condition */
where(var_in % 3 == 0) msk_flg=1; elsewhere msk_flg=0;

// print("msk_flg = ");print(msk_flg); // For debugging...

/* The sort() routine is overloaded, and takes one or two arguments
   The second argument (optional) is the "sort map" (srt_map below)
   Pass the sort map by reference, i.e., prefix with an ampersand
   If the sort map does not yet exist, then it will be created and 
   returned as an integer type the same shape as the input variable.
   The output of sort(), on the LHS, is a sorted version of the input
   msk_flg is not needed in its original order after sort()
   Hence we use msk_flg as both input to and output from sort()
   Doing this prevents the need to define a new, unneeded variable */
msk_flg=sort(msk_flg,&srt_map);

// Count number of valid points in mask by summing the one's
*msk_nbr=msk_flg.total();

// Define output dimension equal in size to number of valid points
defdim("crd_out",msk_nbr);

/* Now sort the variable of interest using the sort map and remap()
   The output, on the LHS, is the input re-arranged so that all points
   meeting the mask condition are contiguous at the end of the array
   Use same srt_map to hyperslab multiple variables of the same shape
   Remember to apply srt_map to the coordinate variables */
crd_in=remap(crd_in,srt_map);
var_in=remap(var_in,srt_map);

/* Hyperslab last msk_nbr values of variable(s) of interest */
crd_out[crd_out]=crd_in((dmn_in_sz-msk_nbr):(dmn_in_sz-1));
var_out[crd_out]=var_in((dmn_in_sz-msk_nbr):(dmn_in_sz-1));

/* NB: Even though we created all variables possible as RAM variables,
   the original coordinate of interest, time, is written to the ouput.
   I'm not exactly sure why. For now, delete it from the output with: 
   ncks -O -x -v time ~/foo.nc ~/foo.nc
   */ 
EOF
ncap2 -O -v -S ~/ncap2_foo.nco ~/nco/data/in.nc ~/foo.nc
ncks -O -x -v time ~/foo.nc ~/foo.nc
ncks ~/foo.nc

Here is an extended example of how to use ncap2 features to sort multi-dimensional arrays based on the coordinate values along a single dimension.

cat > ~/ncap2_foo.nco << 'EOF'
/* Purpose: Sort multi-dimensional array based on coordinate values
   This example sorts the variable three_dmn_rec_var(time,lat,lon)
   based on the values of the time coordinate. */

// Included in NCO User Guide at http://nco.sf.net/nco.html#sort

// Randomize the time coordinate
time=10.0*gsl_rng_uniform(time);
//print("original randomized time = \n");print(time);

/* The sort() routine is overloaded, and takes one or two arguments
   The first argument is a one dimensional array
   The second argument (optional) is the "sort map" (srt_map below)
   Pass the sort map by reference, i.e., prefix with an ampersand
   If the sort map does not yet exist, then it will be created and 
   returned as an integer type the same shape as the input variable.
   The output of sort(), on the LHS, is a sorted version of the input */

time=sort(time,&srt_map);
//print("sorted time (ascending order) and associated sort map =\n");print(time);print(srt_map);

/* sort() always sorts in ascending order
   The associated sort map therefore re-arranges the original,
   randomized time array into ascending order.
   There are two methods to obtain the descending order the user wants
   1) We could solve the problem in ascending order (the default)
   and then apply the reverse() method to re-arrange the results.
   2) We could change the sort map to return things in descending
   order of time and solve the problem directly in descending order. */

// Following shows how to do method one:

/* Expand the sort map to srt_map_3d, the size of the data array
   1. Use data array to provide right shape for the expanded sort map
   2. Coerce data array into an integer so srt_map_3d is an integer
   3. Multiply data array by zero so 3-d map elements are all zero
   4. Add the 1-d sort map to the 3-d sort map (NCO automatically resizes)
   5. Add the spatial (lat,lon) offsets to each time index 
   6. de-sort using the srt_map_3d
   7. Use reverse to obtain descending in time order
   Loops could accomplish the same thing (exercise left for reader)
   However, loops are slow for large datasets */

/* Following index manipulation requires understanding correspondence
   between 1-d (unrolled, memory order of storage) and access into that
   memory as a multidimensional (3-d, in this case) rectangular array.
   Key idea to understand is how dimensionality affects offsets */ 
// Copy 1-d sort map into 3-d sort map
srt_map_3d=(0*int(three_dmn_rec_var))+srt_map;
// Multiply base offset by factorial of lesser dimensions
srt_map_3d*=$lat.size*$lon.size;
lon_idx=array(0,1,$lon);
lat_idx=array(0,1,$lat)*$lon.size;
lat_lon_idx[$lat,$lon]=lat_idx+lon_idx;
srt_map_3d+=lat_lon_idx;

print("sort map 3d =\n");print(srt_map_3d);

// Use remap() to re-map the data
three_dmn_rec_var=remap(three_dmn_rec_var,srt_map_3d);

// Finally, reverse data so time coordinate is descending
time=time.reverse($time);
//print("sorted time (descending order) =\n");print(time);
three_dmn_rec_var=three_dmn_rec_var.reverse($time);

// Method two: Key difference is srt_map=$time.size-srt_map-1;
EOF
ncap2 -O -v -S ~/ncap2_foo.nco ~/nco/data/in.nc ~/foo.nc

<a name="udunits_fnc"></a>  <a name="units_cnv"></a>  UDUnits scriptVpointerSort methodsncap2 netCDF Arithmetic Processor UDUnits script UDUnits As of NCO version 4.6.3 (December, 2016), ncap2 includes support for UDUnits conversions. The function is called udunits. Its syntax is

varOut=udunits(varIn,"UnitsOutString")

The udunits() function looks for the attribute of varIn&arobase;units and fails if it is not found. A quirk of this function that due to attribute propagation varOut&arobase;units will be overwritten by varIn&arobase;units. It is best to re-initialize this attribute AFTER the call. In addition if varIn&arobase;units is of the form "time_interval since basetime" then the calendar attribute varIn&arobase;calendar will read it. If it does not exist then the calendar used defaults to mixed Gregorian/Julian as defined by UDUnits. If varIn is not a floating-point type then it is promoted to NC_DOUBLE for the system call in the UDUnits library, and then demoted back to its original type after. T[lon]={0.0,100.0,150.0,200.0}; T@units="Celsius"; // Overwrite variable T=udunits(T,"kelvin"); print(T); // 273.15, 373.15, 423.15, 473.15 ; T@units="kelvin"; // Rebase coordinate days to hours timeOld=time; print(timeOld); // 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 ; timeOld@units="days since 2012-01-30"; @units="hours since 2012-02-01 01:00"; timeNew=udunits(timeOld, @units); timeNew@units=@units; print(timeNew); // -25, -1, 23, 47, 71, 95, 119, 143, 167, 191 ; tOld=time; // NB: Calendar=365_day has NO Leap year tOld@calendar="365_day"; tOld@units="minutes since 2012-02-28 23:58:00.00"; @units="seconds since 2012-03-01 00:00"; tNew=udunits(tOld, @units); tNew@units=@units; print(tNew); // -60, 0, 60, 120, 180, 240, 300, 360, 420, 480 strftime() The var_str=strtime(var_time,fmt_sng) method takes a time-based variable and a format string and returns an NC_STRING variable (of the same shape as var_time) of time-stamps in the form specified by &textrsquo;fmt_sng&textrsquo;. In order to run this command output type must be netCDF4. ncap2 -4 -v -O -s 'time_str=strftime(time,"%Y-%m-%d");' in.nc foo.nc time_str="1964-03-13", "1964-03-14", "1964-03-15", "1964-03-16", "1964-03-17", "1964-03-18", "1964-03-19", "1964-03-20", "1964-03-21", "1964-03-22" ; Under the hood there are a few steps invoved: First the method reads var_time&arobase;units and var_time&arobase;calendar (if present) then converts var_time to seconds since 1970-01-01. It then converts these possibly UTC seconds to the standard struture struct *tm. Finally strftime() is called with fmt_sng and the *tm struct. The C-standard strftime() is used as defined in time.h. If the method is called without fmt_sng then the following default is used: "%Y-%m-%d %H:%M:%S". The method regular takes a single var argument and uses the above default string. ncap2 -4 -v -O -s 'time_str=regular(time);' in.nc foo.nc time_str = "1964-03-13 21:09:00", "1964-03-14 21:09:00", "1964-03-15 21:09:00", "1964-03-16 21:09:00", "1964-03-17 21:09:00", "1964-03-18 21:09:00", "1964-03-19 21:09:00", "1964-03-20 21:09:00", "1964-03-21 21:09:00", "1964-03-22 21:09:00" ; Another working example ncap2 -v -O -s 'ts=strftime(frametime(0),"%Y-%m-%d/envlog_netcdf_L1_ua-mac_%Y-%m-%d.nc");' in.nc out.nc ts="2017-08-11/envlog_netcdf_L1_ua-mac_2017-08-11.nc" <a name="vpointer"></a>  <a name="vp"></a>  VpointerIrregular gridsUDUnits scriptncap2 netCDF Arithmetic Processor Vpointer vpointer A variable-pointer or vpointer is a pointer to a variable or attribute. It is most useful when one needs to apply a set of operations on a list of variables. For example, after regular processing one may wish to set the _FillValue of all NC_FLOAT variables to a particular value, or to create min/max attributes for all 3D variables of type NC_DOUBLE. A vpointer is not a &textrsquo;pointer&textrsquo; to a memory location in the C/C++ sense. Rather the vpointer is a text attribute that contains the name of a variable. To use the pointer simply prefix the pointer with *. Then, most places where you use VAR_ID you can use *vpointer_nm. There are a variety of ways to maintain a list of strings in ncap2. The easiest method is to use an NC_STRING attribute. Below is a simple illustration that uses a vpointer of type NC_CHAR. Remember an attribute starting with &arobase; implies &textrsquo;global&textrsquo;, e.g., &arobase;vpx is short for global&arobase;vpx. idx=9; idy=20; t2=time; global@vpx="idx"; // Increment idx by one *global@vpx++; print(idx); // Multiply by 5 *@vpx*=5; // idx now 50 print(idx); // Add 200 (long method) *@vpx=*@vpx+200; //idx now 250 print(idx); @vpy="idy"; // Add idx idy to get idz idz=*@vpx+*@vpy; // idz == 270 print(idz); // We can also reference variables in the input file // Can use an existing attribute pointer since attributes are not written // to the netCDF file until after the script has finished. @vpx="three_dmn_var"; // We can convert this variable to type NC_DOUBLE and // write it to ouptut all at once *@vpx=*@vpx.double(); The following script writes to the output files all variables that are of type NC_DOUBLE and that have at least two dimensions. It then changes their _FillValue to 1.0E-9. The function get_vars_in() creates an NC_STRING attribute that contains all of the variable names in the input file. Note that a vpointer must be a plain attribute, NOT an a attribute expression. Thus in the below script using *all(idx) would be a fundamental mistake. In the below example the vpointer var_nm is of type NC_STRING. @all=get_vars_in(); *sz=@all.size(); *idx=0; for(idx=0;idx<sz;idx++){ // @var_nm is of type NC_STRING @var_nm=@all(idx); if(*@var_nm.type() == NC_DOUBLE && *@var_nm.ndims() >= 2){ *@var_nm=*@var_nm; *@var_nm.change_miss(1e-9d); } } The following script writes to the output file all 3D/4D variables of type NC_FLOAT. Then for each variable it calculates a range attribute that contains the maximum and minimum values, and a total attribute that is the sum of all the elements. In this example vpointers are used to &textrsquo;point&textrsquo; to attributes. @all=get_vars_in(); *sz=@all.size(); for(*idx=0;idx<sz;idx++){ @var_nm=@all(idx); if(*@var_nm.ndims() >= 3){ *@var_nm=*@var_nm.float(); // The push function also takes a call-by-ref attribute: if it does not already exist then it will be created // The call below pushes a NC_STRING to an att so the end result is a list of NC_STRINGS push(&@prc,@var_nm); } } *sz=@prc.size(); for(*idx=0;idx<sz;idx++){ @var_nm=@prc(idx); // We can work with attribute pointers as well // sprint() ouptut is of type NC_CHAR @att_total=sprint(@var_nm,"%s@total"); @att_range=sprint(@var_nm,"%s@range"); // If you are still confused then print out the attributes print(@att_total); print(@att_range); *@att_total=*@var_nm.total(); *@att_range={min(*@var_nm),max(*@var_nm)}; } This is the CDL dump of a variable processed by the above script: float three_dmn_var_int(time, lat, lon) ; three_dmn_var_int:_FillValue = -99.f ; three_dmn_var_int:long_name = "three dimensional record variable of type int" ; three_dmn_var_int:range = 1.f, 80.f ; three_dmn_var_int:total = 2701.f ; three_dmn_var_int:units = "watt meter-2" ; <a name="rct"></a>  Irregular gridsBilinear interpolationVpointerncap2 netCDF Arithmetic Processor Irregular Grids irregular grids rectangular grids non-rectangular grids non-standard grids mask NCO is capable of analyzing datasets for many different underlying coordinate grid types. netCDF was developed for and initially used with grids comprised of orthogonal dimensions forming a rectangular coordinate system. We call such grids standard grids. It is increasingly common for datasets to use metadata to describe much more complex grids. Let us first define three important coordinate grid properties: regularity, rectangularity, and structure. Grids are regular if the spacing between adjacent is constant. For example, a 4-by-5 degree latitude-longitude grid is regular because the spacings between adjacent latitudes (4 degrees) are constant as are the (5 degrees) spacings between adjacent longitudes. Spacing in irregular grids depends on the location along the coordinate. Grids such as Gaussian grids have uneven spacing in latitude (points cluster near the equator) and so are irregular. Grids are rectangular if the number of elements in any dimension is not a function of any other dimension. For example, a T42 Gaussian latitude-longitude grid is rectangular because there are the same number of longitudes (128) for each of the (64) latitudes. Grids are non-rectangular if the elements in any dimension depend on another dimension. Non-rectangular grids present many special challenges to analysis software like NCO. Grids are structured if they are represented as functions of two horizontal spatial dimensions. For example, grids with latitude and longitude dimensions are structured, and so are curvilinear grids with along-track and cross-track dimensions. A grid with a single dimension is unstructured. For example, icosohedral grids are usually unstructured, as are MPAS grids. Wrapped coordinates (Wrapped Coordinates), such as longitude, are independent of these grid properties (regularity, rectangularity, structure). wrapped coordinates The preferred NCO technique to analyze data on non-standard coordinate grids is to create a region mask with ncap2, and then to use the mask within ncap2 for variable-specific processing, and/or with other operators (e.g., ncwa, ncdiff) for entire file processing. Before describing the construction of masks, let us review how irregularly gridded geoscience data are described. Say that latitude and longitude are stored as R-dimensional arrays and the product of the dimension sizes is the total number of elements N in the other variables. Geoscience applications tend to use

R=1,

R=2, and

R=3. If the grid is has no simple representation (e.g., discontinuous) then it makes sense to store all coordinates as 1D arrays with the same size as the number of grid points. These gridpoints can be completely independent of all the other (own weight, area, etc.). R=1: lat(number_of_gridpoints) and lon(number_of_gridpoints) If the horizontal grid is time-invariant then R=2 is common: R=2: lat(south_north,east_west) and lon(south_north,east_west) The Weather and Research Forecast (WRF) model uses R=3: R=3: lat(time,south_north,east_west), lon(time,south_north,east_west) and so supports grids that change with time. Grids with R > 1 often use missing values to indicated empty points. For example, so-called &textldquo;staggered grids&textrdquo; will use fewer east_west points near the poles and more near the equator. netCDF only accepts rectangular arrays so space must be allocated for the maximum number of east_west points at all latitudes. Then the application writes missing values into the unused points near the poles. We demonstrate the ncap2 analysis technique for irregular regions by constructing a mask for an R=2 grid. We wish to find, say, the mean temperature within [lat_min,lat_max] and [lon_min,lon_max]:

ncap2 -s 'mask_var= (lat >= lat_min && lat <= lat_max) && \
                    (lon >= lon_min && lon <= lon_max);' in.nc out.nc

Arbitrarily shaped regions can be defined by more complex conditional statements. Once defined, masks can be applied to specific variables, and to entire files:

ncap2 -s 'temperature_avg=(temperature*mask_var).avg()' in.nc out.nc
ncwa -a lat,lon -m mask_var -w area in.nc out.nc

Crafting such commands on the command line is possible though unwieldy. In such cases, a script is often cleaner and allows you to document the procedure: cat > ncap2.in << 'EOF' mask_var = (lat >= lat_min && lat <= lat_max) && (lon >= lon_min && > lon <= lon_max); if(mask_var.total() > 0){ // Check that mask contains some valid values temperature_avg=(temperature*mask_var).avg(); // Average temperature temperature_max=(temperature*mask_var).max(); // Maximum temperature } EOF ncap2 -S ncap2.in in.nc out.nc http://foehn.colorado.edu/wrfout_to_cf/wrfout_to_cf.ncl ncl 'file_in="wrfout.nc"' 'file_out="wrfpost.nc"' wrfout_to_cf.ncl ncl 'file_in="wrfout_d02_2013-10-04_20:00:00"' 'file_out="wrfout_d02_2013-10-04_20:00:00_cf.nc"' wrfout_to_cf.ncl ncl 'file_in="wrfout_v2_Lambert"' 'file_out="wrfout_v2_Lambert.nc"' wrfout_to_cf.ncl WRF Grids like those produced by the WRF model are complex because one must use global metadata to determine the grid staggering and offsets to translate XLAT and XLONG into real latitudes, longitudes, and missing points. The WRF grid documentation should describe this. For WRF files creating regional masks looks, in general, like

mask_var = (XLAT >= lat_min && XLAT <= lat_max) && (XLONG >= lon_min && XLONG <= lon_max);

A few notes: Irregular regions are the union of arrays of lat/lon min/max&textrsquo;s. The mask procedure is identical for all R. <a name="bln_ntp"></a>  <a name="bil_int"></a>  Bilinear interpolationGSL special functionsIrregular gridsncap2 netCDF Arithmetic Processor Bilinear interpolation As of version 4.0.0 NCO has internal routines to perform bilinear interpolation on gridded data sets. In mathematics, bilinear interpolation is an extension of linear interpolation for interpolating functions of two variables on a regular grid. The idea is to perform linear interpolation first in one direction, and then again in the other direction. Suppose we have an irregular grid of data temperature[lat,lon], with co-ordinate vars lat[lat], lon[lon]. We wish to find the temperature at an arbitary point [X,Y] within the grid. If we can locate lat_min,lat_max and lon_min,lon_max such that lat_min <= X <= lat_max and lon_min <= Y <= lon_max then we can interpolate in two dimensions the temperature at [X,Y]. The general form of the ncap2 interpolation function is

var_out=bilinear_interp(grid_in,grid_out,grid_out_x,grid_out_y,grid_in_x,grid_in_y)

where grid_in Input function data. Usually a two dimensional variable. It must be of size grid_in_x.size()*grid_in_y.size() grid_out This variable is the shape of var_out. Usually a two dimensional variable. It must be of size grid_out_x.size()*grid_out_y.size() grid_out_x X output values grid_out_y Y output values grid_in_x X input values values. Must be monotonic (increasing or decreasing). grid_in_y Y input values values. Must be monotonic (increasing or decreasing).

Prior to calculations all arguments are converted to type NC_DOUBLE. After calculations var_out is converted to the input type of grid_in. Suppose the first part of an ncap2 script is defdim("X",4); defdim("Y",5); // Temperature T_in[$X,$Y]= {100, 200, 300, 400, 500, 101, 202, 303, 404, 505, 102, 204, 306, 408, 510, 103, 206, 309, 412, 515.0 }; // Coordinate variables x_in[$X]={0.0,1.0,2.0,3.01}; y_in[$Y]={1.0,2.0,3.0,4.0,5}; Now we interpolate with the following variables: defdim("Xn",3); defdim("Yn",4); T_out[$Xn,$Yn]=0.0; x_out[$Xn]={0.0,0.02,3.01}; y_out[$Yn]={1.1,2.0,3,4}; var_out=bilinear_interp(T_in,T_out,x_out,y_out,x_in,y_in); print(var_out); // 110, 200, 300, 400, // 110.022, 200.04, 300.06, 400.08, // 113.3, 206, 309, 412 ; It is possible to interpolate a single point:

var_out=bilinear_interp(T_in,0.0,3.0,4.99,x_in,y_in);
print(var_out);
// 513.920594059406

Wrapping and Extrapolation &linebreak; The function bilinear_interp_wrap() takes the same arguments as bilinear_interp() but performs wrapping (Y) and extrapolation (X) for points off the edge of the grid. If the given range of longitude is say (25-335) and we have a point at 20 degrees, then the endpoints of the range are used for the interpolation. This is what wrapping means. For wrapping to occur Y must be longitude and must be in the range (0,360) or (-180,180). There are no restrictions on the longitude (X) values, though typically these are in the range (-90,90). This ncap2 script illustrates both wrapping and extrapolation of end points: defdim("lat_in",6); defdim("lon_in",5); // Coordinate input vars lat_in[$lat_in]={-80,-40,0,30,60.0,85.0}; lon_in[$lon_in]={30, 110, 190, 270, 350.0}; T_in[$lat_in,$lon_in]= {10,40,50,30,15, 12,43,52,31,16, 14,46,54,32,17, 16,49,56,33,18, 18,52,58,34,19, 20,55,60,35,20.0 }; defdim("lat_out",4); defdim("lon_out",3); // Coordinate variables lat_out[$lat_out]={-90,0,70,88.0}; lon_out[$lon_out]={0,190,355.0}; T_out[$lat_out,$lon_out]=0.0; T_out=bilinear_interp_wrap(T_in,T_out,lat_out,lon_out,lat_in,lon_in); print(T_out); // 13.4375, 49.5, 14.09375, // 16.25, 54, 16.625, // 19.25, 58.8, 19.325, // 20.15, 60.24, 20.135 ; <a name="gsl"></a>  GSL special functionsGSL interpolationBilinear interpolationncap2 netCDF Arithmetic Processor GSL special functions GSL As of version 3.9.6 (released January, 2009), NCO can link to the GNU Scientific Library (GSL). ncap2 can access most GSL special functions including Airy, Bessel, error, gamma, beta, hypergeometric, and Legendre functions and elliptical integrals. GSL must be version 1.4 or later. To list the GSL functions available with your NCO build, use ncap2 -f | grep ^gsl. The function names used by ncap2 mirror their GSL names. The NCO wrappers for GSL functions automatically call the error-handling version of the GSL function when available These are the GSL standard function names postfixed with _e. NCO calls these functions automatically, without the NCO command having to specifically indicate the _e function suffix. . This allows NCO to return a missing value when the GSL library encounters a domain error or a floating-point exception. The slow-down due to calling the error-handling version of the GSL numerical functions was found to be negligible (please let us know if you find otherwise). gamma function gsl_sf_gamma Consider the gamma function.&linebreak; The GSL function prototype is &linebreak; int gsl_sf_gamma_e(const double x, gsl_sf_result * result) The ncap2 script would be: lon_in[lon]={-1,0.1,0,2,0.3}; lon_out=gsl_sf_gamma(lon_in); lon_out= _, 9.5135, 4.5908, 2.9915 The first value is set to _FillValue since the gamma function is undefined for negative integers. If the input variable has a missing value then this value is used. Otherwise, the default double fill value is used (defined in the netCDF header netcdf.h as NC_FILL_DOUBLE = 9.969e+36). Bessel function gsl_sf_bessel_Jn Consider a call to a Bessel function with GSL prototype&linebreak; int gsl_sf_bessel_Jn_e(int n, double x, gsl_sf_result * result) An ncap2 script would be

lon_out=gsl_sf_bessel_Jn(2,lon_in);  
lon_out=0.11490, 0.0012, 0.00498, 0.011165

This computes the Bessel function of order n=2 for every value in lon_in. The Bessel order argument, an integer, can also be a non-scalar variable, i.e., an array. n_in[lon]={0,1,2,3}; lon_out=gsl_sf_bessel_Jn(n_in,0.5); lon_out= 0.93846, 0.24226, 0.03060, 0.00256 Arguments to GSL wrapper functions in ncap2 must conform to one another, i.e., they must share the same sub-set of dimensions. For example: three_out=gsl_sf_bessel_Jn(n_in,three_dmn_var_dbl) is valid because the variable three_dmn_var_dbl has a lon dimension, so n_in in can be broadcast to conform to three_dmn_var_dbl. However time_out=gsl_sf_bessel_Jn(n_in,time) is invalid. Elliptic integrals Consider the elliptical integral with prototype int gsl_sf_ellint_RD_e(double x, double y, double z, gsl_mode_t mode, gsl_sf_result * result)

three_out=gsl_sf_ellint_RD(0.5,time,three_dmn_var_dbl);

The three arguments are all conformable so the above ncap2 call is valid. The mode argument in the function prototype controls the convergence of the algorithm. It also appears in the Airy Function prototypes. It can be set by defining the environment variable GSL_PREC_MODE. If unset it defaults to the value GSL_PREC_DOUBLE. See the GSL manual for more details.

export GSL_PREC_MODE=0 // GSL_PREC_DOUBLE
export GSL_PREC_MODE=1 // GSL_PREC_SINGLE
export GSL_PREC_MODE=2 // GSL_PREC_APPROX

The ncap2 wrappers to the array functions are slightly different. Consider the following GSL prototype &linebreak; int gsl_sf_bessel_Jn_array(int nmin, int nmax, double x, double *result_array)

b1=lon.double();
x=0.5;
status=gsl_sf_bessel_Jn_array(1,4,x,&b1);
print(status);
b1=0.24226,0.0306,0.00256,0.00016;

This calculates the Bessel function of x=0.5 for n=1 to 4. The first three arguments are scalar values. If a non-scalar variable is supplied as an argument then only the first value is used. The final argument is the variable where the results are stored (NB: the & indicates this is a call by reference). This final argument must be of type double and must be of least size nmax-nmin+1. If either of these conditions is not met then then the function returns an error message. The function/wrapper returns a status flag. Zero indicates success. Consider another array function &linebreak; int gsl_sf_legendre_Pl_array(int lmax, double x, double *result_array); Legendre polynomial gsl_sf_legendre_Pl

a1=time.double();
x=0.3;
status=gsl_sf_legendre_Pl_array(a1.size()-1, x,&a1);  
print(status);

This call calculates P_l(0.3) for l=0..9. Note that |x|<=1, otherwise there will be a domain error. See the GSL documentation for more details. The GSL functions implemented in NCO are listed in the table below. This table is correct for GSL version 1.10. To see what functions are available on your build run the command ncap2 -f |grep ^gsl . To see this table along with the GSL C-function prototypes look at the spreadsheet doc/nco_gsl.ods. &linebreak; &linebreak; GSL NAME I NCAP FUNCTION CALL gsl_sf_airy_Ai_e Y gsl_sf_airy_Ai(dbl_expr) gsl_sf_airy_Bi_e Y gsl_sf_airy_Bi(dbl_expr) gsl_sf_airy_Ai_scaled_e Y gsl_sf_airy_Ai_scaled(dbl_expr) gsl_sf_airy_Bi_scaled_e Y gsl_sf_airy_Bi_scaled(dbl_expr) gsl_sf_airy_Ai_deriv_e Y gsl_sf_airy_Ai_deriv(dbl_expr) gsl_sf_airy_Bi_deriv_e Y gsl_sf_airy_Bi_deriv(dbl_expr) gsl_sf_airy_Ai_deriv_scaled_e Y gsl_sf_airy_Ai_deriv_scaled(dbl_expr) gsl_sf_airy_Bi_deriv_scaled_e Y gsl_sf_airy_Bi_deriv_scaled(dbl_expr) gsl_sf_airy_zero_Ai_e Y gsl_sf_airy_zero_Ai(uint_expr) gsl_sf_airy_zero_Bi_e Y gsl_sf_airy_zero_Bi(uint_expr) gsl_sf_airy_zero_Ai_deriv_e Y gsl_sf_airy_zero_Ai_deriv(uint_expr) gsl_sf_airy_zero_Bi_deriv_e Y gsl_sf_airy_zero_Bi_deriv(uint_expr) gsl_sf_bessel_J0_e Y gsl_sf_bessel_J0(dbl_expr) gsl_sf_bessel_J1_e Y gsl_sf_bessel_J1(dbl_expr) gsl_sf_bessel_Jn_e Y gsl_sf_bessel_Jn(int_expr,dbl_expr) gsl_sf_bessel_Jn_array Y status=gsl_sf_bessel_Jn_array(int,int,double,&var_out) gsl_sf_bessel_Y0_e Y gsl_sf_bessel_Y0(dbl_expr) gsl_sf_bessel_Y1_e Y gsl_sf_bessel_Y1(dbl_expr) gsl_sf_bessel_Yn_e Y gsl_sf_bessel_Yn(int_expr,dbl_expr) gsl_sf_bessel_Yn_array Y gsl_sf_bessel_Yn_array gsl_sf_bessel_I0_e Y gsl_sf_bessel_I0(dbl_expr) gsl_sf_bessel_I1_e Y gsl_sf_bessel_I1(dbl_expr) gsl_sf_bessel_In_e Y gsl_sf_bessel_In(int_expr,dbl_expr) gsl_sf_bessel_In_array Y status=gsl_sf_bessel_In_array(int,int,double,&var_out) gsl_sf_bessel_I0_scaled_e Y gsl_sf_bessel_I0_scaled(dbl_expr) gsl_sf_bessel_I1_scaled_e Y gsl_sf_bessel_I1_scaled(dbl_expr) gsl_sf_bessel_In_scaled_e Y gsl_sf_bessel_In_scaled(int_expr,dbl_expr) gsl_sf_bessel_In_scaled_array Y staus=gsl_sf_bessel_In_scaled_array(int,int,double,&var_out) gsl_sf_bessel_K0_e Y gsl_sf_bessel_K0(dbl_expr) gsl_sf_bessel_K1_e Y gsl_sf_bessel_K1(dbl_expr) gsl_sf_bessel_Kn_e Y gsl_sf_bessel_Kn(int_expr,dbl_expr) gsl_sf_bessel_Kn_array Y status=gsl_sf_bessel_Kn_array(int,int,double,&var_out) gsl_sf_bessel_K0_scaled_e Y gsl_sf_bessel_K0_scaled(dbl_expr) gsl_sf_bessel_K1_scaled_e Y gsl_sf_bessel_K1_scaled(dbl_expr) gsl_sf_bessel_Kn_scaled_e Y gsl_sf_bessel_Kn_scaled(int_expr,dbl_expr) gsl_sf_bessel_Kn_scaled_array Y status=gsl_sf_bessel_Kn_scaled_array(int,int,double,&var_out) gsl_sf_bessel_j0_e Y gsl_sf_bessel_J0(dbl_expr) gsl_sf_bessel_j1_e Y gsl_sf_bessel_J1(dbl_expr) gsl_sf_bessel_j2_e Y gsl_sf_bessel_j2(dbl_expr) gsl_sf_bessel_jl_e Y gsl_sf_bessel_jl(int_expr,dbl_expr) gsl_sf_bessel_jl_array Y status=gsl_sf_bessel_jl_array(int,double,&var_out) gsl_sf_bessel_jl_steed_array Y gsl_sf_bessel_jl_steed_array gsl_sf_bessel_y0_e Y gsl_sf_bessel_Y0(dbl_expr) gsl_sf_bessel_y1_e Y gsl_sf_bessel_Y1(dbl_expr) gsl_sf_bessel_y2_e Y gsl_sf_bessel_y2(dbl_expr) gsl_sf_bessel_yl_e Y gsl_sf_bessel_yl(int_expr,dbl_expr) gsl_sf_bessel_yl_array Y status=gsl_sf_bessel_yl_array(int,double,&var_out) gsl_sf_bessel_i0_scaled_e Y gsl_sf_bessel_I0_scaled(dbl_expr) gsl_sf_bessel_i1_scaled_e Y gsl_sf_bessel_I1_scaled(dbl_expr) gsl_sf_bessel_i2_scaled_e Y gsl_sf_bessel_i2_scaled(dbl_expr) gsl_sf_bessel_il_scaled_e Y gsl_sf_bessel_il_scaled(int_expr,dbl_expr) gsl_sf_bessel_il_scaled_array Y status=gsl_sf_bessel_il_scaled_array(int,double,&var_out) gsl_sf_bessel_k0_scaled_e Y gsl_sf_bessel_K0_scaled(dbl_expr) gsl_sf_bessel_k1_scaled_e Y gsl_sf_bessel_K1_scaled(dbl_expr) gsl_sf_bessel_k2_scaled_e Y gsl_sf_bessel_k2_scaled(dbl_expr) gsl_sf_bessel_kl_scaled_e Y gsl_sf_bessel_kl_scaled(int_expr,dbl_expr) gsl_sf_bessel_kl_scaled_array Y status=gsl_sf_bessel_kl_scaled_array(int,double,&var_out) gsl_sf_bessel_Jnu_e Y gsl_sf_bessel_Jnu(dbl_expr,dbl_expr) gsl_sf_bessel_Ynu_e Y gsl_sf_bessel_Ynu(dbl_expr,dbl_expr) gsl_sf_bessel_sequence_Jnu_e N gsl_sf_bessel_sequence_Jnu gsl_sf_bessel_Inu_scaled_e Y gsl_sf_bessel_Inu_scaled(dbl_expr,dbl_expr) gsl_sf_bessel_Inu_e Y gsl_sf_bessel_Inu(dbl_expr,dbl_expr) gsl_sf_bessel_Knu_scaled_e Y gsl_sf_bessel_Knu_scaled(dbl_expr,dbl_expr) gsl_sf_bessel_Knu_e Y gsl_sf_bessel_Knu(dbl_expr,dbl_expr) gsl_sf_bessel_lnKnu_e Y gsl_sf_bessel_lnKnu(dbl_expr,dbl_expr) gsl_sf_bessel_zero_J0_e Y gsl_sf_bessel_zero_J0(uint_expr) gsl_sf_bessel_zero_J1_e Y gsl_sf_bessel_zero_J1(uint_expr) gsl_sf_bessel_zero_Jnu_e N gsl_sf_bessel_zero_Jnu gsl_sf_clausen_e Y gsl_sf_clausen(dbl_expr) gsl_sf_hydrogenicR_1_e N gsl_sf_hydrogenicR_1 gsl_sf_hydrogenicR_e N gsl_sf_hydrogenicR gsl_sf_coulomb_wave_FG_e N gsl_sf_coulomb_wave_FG gsl_sf_coulomb_wave_F_array N gsl_sf_coulomb_wave_F_array gsl_sf_coulomb_wave_FG_array N gsl_sf_coulomb_wave_FG_array gsl_sf_coulomb_wave_FGp_array N gsl_sf_coulomb_wave_FGp_array gsl_sf_coulomb_wave_sphF_array N gsl_sf_coulomb_wave_sphF_array gsl_sf_coulomb_CL_e N gsl_sf_coulomb_CL gsl_sf_coulomb_CL_array N gsl_sf_coulomb_CL_array gsl_sf_coupling_3j_e N gsl_sf_coupling_3j gsl_sf_coupling_6j_e N gsl_sf_coupling_6j gsl_sf_coupling_RacahW_e N gsl_sf_coupling_RacahW gsl_sf_coupling_9j_e N gsl_sf_coupling_9j gsl_sf_coupling_6j_INCORRECT_e N gsl_sf_coupling_6j_INCORRECT gsl_sf_dawson_e Y gsl_sf_dawson(dbl_expr) gsl_sf_debye_1_e Y gsl_sf_debye_1(dbl_expr) gsl_sf_debye_2_e Y gsl_sf_debye_2(dbl_expr) gsl_sf_debye_3_e Y gsl_sf_debye_3(dbl_expr) gsl_sf_debye_4_e Y gsl_sf_debye_4(dbl_expr) gsl_sf_debye_5_e Y gsl_sf_debye_5(dbl_expr) gsl_sf_debye_6_e Y gsl_sf_debye_6(dbl_expr) gsl_sf_dilog_e N gsl_sf_dilog gsl_sf_complex_dilog_xy_e N gsl_sf_complex_dilog_xy_e gsl_sf_complex_dilog_e N gsl_sf_complex_dilog gsl_sf_complex_spence_xy_e N gsl_sf_complex_spence_xy_e gsl_sf_multiply_e N gsl_sf_multiply gsl_sf_multiply_err_e N gsl_sf_multiply_err gsl_sf_ellint_Kcomp_e Y gsl_sf_ellint_Kcomp(dbl_expr) gsl_sf_ellint_Ecomp_e Y gsl_sf_ellint_Ecomp(dbl_expr) gsl_sf_ellint_Pcomp_e Y gsl_sf_ellint_Pcomp(dbl_expr,dbl_expr) gsl_sf_ellint_Dcomp_e Y gsl_sf_ellint_Dcomp(dbl_expr) gsl_sf_ellint_F_e Y gsl_sf_ellint_F(dbl_expr,dbl_expr) gsl_sf_ellint_E_e Y gsl_sf_ellint_E(dbl_expr,dbl_expr) gsl_sf_ellint_P_e Y gsl_sf_ellint_P(dbl_expr,dbl_expr,dbl_expr) gsl_sf_ellint_D_e Y gsl_sf_ellint_D(dbl_expr,dbl_expr,dbl_expr) gsl_sf_ellint_RC_e Y gsl_sf_ellint_RC(dbl_expr,dbl_expr) gsl_sf_ellint_RD_e Y gsl_sf_ellint_RD(dbl_expr,dbl_expr,dbl_expr) gsl_sf_ellint_RF_e Y gsl_sf_ellint_RF(dbl_expr,dbl_expr,dbl_expr) gsl_sf_ellint_RJ_e Y gsl_sf_ellint_RJ(dbl_expr,dbl_expr,dbl_expr,dbl_expr) gsl_sf_elljac_e N gsl_sf_elljac gsl_sf_erfc_e Y gsl_sf_erfc(dbl_expr) gsl_sf_log_erfc_e Y gsl_sf_log_erfc(dbl_expr) gsl_sf_erf_e Y gsl_sf_erf(dbl_expr) gsl_sf_erf_Z_e Y gsl_sf_erf_Z(dbl_expr) gsl_sf_erf_Q_e Y gsl_sf_erf_Q(dbl_expr) gsl_sf_hazard_e Y gsl_sf_hazard(dbl_expr) gsl_sf_exp_e Y gsl_sf_exp(dbl_expr) gsl_sf_exp_e10_e N gsl_sf_exp_e10 gsl_sf_exp_mult_e Y gsl_sf_exp_mult(dbl_expr,dbl_expr) gsl_sf_exp_mult_e10_e N gsl_sf_exp_mult_e10 gsl_sf_expm1_e Y gsl_sf_expm1(dbl_expr) gsl_sf_exprel_e Y gsl_sf_exprel(dbl_expr) gsl_sf_exprel_2_e Y gsl_sf_exprel_2(dbl_expr) gsl_sf_exprel_n_e Y gsl_sf_exprel_n(int_expr,dbl_expr) gsl_sf_exp_err_e Y gsl_sf_exp_err(dbl_expr,dbl_expr) gsl_sf_exp_err_e10_e N gsl_sf_exp_err_e10 gsl_sf_exp_mult_err_e N gsl_sf_exp_mult_err gsl_sf_exp_mult_err_e10_e N gsl_sf_exp_mult_err_e10 gsl_sf_expint_E1_e Y gsl_sf_expint_E1(dbl_expr) gsl_sf_expint_E2_e Y gsl_sf_expint_E2(dbl_expr) gsl_sf_expint_En_e Y gsl_sf_expint_En(int_expr,dbl_expr) gsl_sf_expint_E1_scaled_e Y gsl_sf_expint_E1_scaled(dbl_expr) gsl_sf_expint_E2_scaled_e Y gsl_sf_expint_E2_scaled(dbl_expr) gsl_sf_expint_En_scaled_e Y gsl_sf_expint_En_scaled(int_expr,dbl_expr) gsl_sf_expint_Ei_e Y gsl_sf_expint_Ei(dbl_expr) gsl_sf_expint_Ei_scaled_e Y gsl_sf_expint_Ei_scaled(dbl_expr) gsl_sf_Shi_e Y gsl_sf_Shi(dbl_expr) gsl_sf_Chi_e Y gsl_sf_Chi(dbl_expr) gsl_sf_expint_3_e Y gsl_sf_expint_3(dbl_expr) gsl_sf_Si_e Y gsl_sf_Si(dbl_expr) gsl_sf_Ci_e Y gsl_sf_Ci(dbl_expr) gsl_sf_atanint_e Y gsl_sf_atanint(dbl_expr) gsl_sf_fermi_dirac_m1_e Y gsl_sf_fermi_dirac_m1(dbl_expr) gsl_sf_fermi_dirac_0_e Y gsl_sf_fermi_dirac_0(dbl_expr) gsl_sf_fermi_dirac_1_e Y gsl_sf_fermi_dirac_1(dbl_expr) gsl_sf_fermi_dirac_2_e Y gsl_sf_fermi_dirac_2(dbl_expr) gsl_sf_fermi_dirac_int_e Y gsl_sf_fermi_dirac_int(int_expr,dbl_expr) gsl_sf_fermi_dirac_mhalf_e Y gsl_sf_fermi_dirac_mhalf(dbl_expr) gsl_sf_fermi_dirac_half_e Y gsl_sf_fermi_dirac_half(dbl_expr) gsl_sf_fermi_dirac_3half_e Y gsl_sf_fermi_dirac_3half(dbl_expr) gsl_sf_fermi_dirac_inc_0_e Y gsl_sf_fermi_dirac_inc_0(dbl_expr,dbl_expr) gsl_sf_lngamma_e Y gsl_sf_lngamma(dbl_expr) gsl_sf_lngamma_sgn_e N gsl_sf_lngamma_sgn gsl_sf_gamma_e Y gsl_sf_gamma(dbl_expr) gsl_sf_gammastar_e Y gsl_sf_gammastar(dbl_expr) gsl_sf_gammainv_e Y gsl_sf_gammainv(dbl_expr) gsl_sf_lngamma_complex_e N gsl_sf_lngamma_complex gsl_sf_taylorcoeff_e Y gsl_sf_taylorcoeff(int_expr,dbl_expr) gsl_sf_fact_e Y gsl_sf_fact(uint_expr) gsl_sf_doublefact_e Y gsl_sf_doublefact(uint_expr) gsl_sf_lnfact_e Y gsl_sf_lnfact(uint_expr) gsl_sf_lndoublefact_e Y gsl_sf_lndoublefact(uint_expr) gsl_sf_lnchoose_e N gsl_sf_lnchoose gsl_sf_choose_e N gsl_sf_choose gsl_sf_lnpoch_e Y gsl_sf_lnpoch(dbl_expr,dbl_expr) gsl_sf_lnpoch_sgn_e N gsl_sf_lnpoch_sgn gsl_sf_poch_e Y gsl_sf_poch(dbl_expr,dbl_expr) gsl_sf_pochrel_e Y gsl_sf_pochrel(dbl_expr,dbl_expr) gsl_sf_gamma_inc_Q_e Y gsl_sf_gamma_inc_Q(dbl_expr,dbl_expr) gsl_sf_gamma_inc_P_e Y gsl_sf_gamma_inc_P(dbl_expr,dbl_expr) gsl_sf_gamma_inc_e Y gsl_sf_gamma_inc(dbl_expr,dbl_expr) gsl_sf_lnbeta_e Y gsl_sf_lnbeta(dbl_expr,dbl_expr) gsl_sf_lnbeta_sgn_e N gsl_sf_lnbeta_sgn gsl_sf_beta_e Y gsl_sf_beta(dbl_expr,dbl_expr) gsl_sf_beta_inc_e N gsl_sf_beta_inc gsl_sf_gegenpoly_1_e Y gsl_sf_gegenpoly_1(dbl_expr,dbl_expr) gsl_sf_gegenpoly_2_e Y gsl_sf_gegenpoly_2(dbl_expr,dbl_expr) gsl_sf_gegenpoly_3_e Y gsl_sf_gegenpoly_3(dbl_expr,dbl_expr) gsl_sf_gegenpoly_n_e N gsl_sf_gegenpoly_n gsl_sf_gegenpoly_array Y gsl_sf_gegenpoly_array gsl_sf_hyperg_0F1_e Y gsl_sf_hyperg_0F1(dbl_expr,dbl_expr) gsl_sf_hyperg_1F1_int_e Y gsl_sf_hyperg_1F1_int(int_expr,int_expr,dbl_expr) gsl_sf_hyperg_1F1_e Y gsl_sf_hyperg_1F1(dbl_expr,dbl_expr,dbl_expr) gsl_sf_hyperg_U_int_e Y gsl_sf_hyperg_U_int(int_expr,int_expr,dbl_expr) gsl_sf_hyperg_U_int_e10_e N gsl_sf_hyperg_U_int_e10 gsl_sf_hyperg_U_e Y gsl_sf_hyperg_U(dbl_expr,dbl_expr,dbl_expr) gsl_sf_hyperg_U_e10_e N gsl_sf_hyperg_U_e10 gsl_sf_hyperg_2F1_e Y gsl_sf_hyperg_2F1(dbl_expr,dbl_expr,dbl_expr,dbl_expr) gsl_sf_hyperg_2F1_conj_e Y gsl_sf_hyperg_2F1_conj(dbl_expr,dbl_expr,dbl_expr,dbl_expr) gsl_sf_hyperg_2F1_renorm_e Y gsl_sf_hyperg_2F1_renorm(dbl_expr,dbl_expr,dbl_expr,dbl_expr) gsl_sf_hyperg_2F1_conj_renorm_e Y gsl_sf_hyperg_2F1_conj_renorm(dbl_expr,dbl_expr,dbl_expr,dbl_expr) gsl_sf_hyperg_2F0_e Y gsl_sf_hyperg_2F0(dbl_expr,dbl_expr,dbl_expr) gsl_sf_laguerre_1_e Y gsl_sf_laguerre_1(dbl_expr,dbl_expr) gsl_sf_laguerre_2_e Y gsl_sf_laguerre_2(dbl_expr,dbl_expr) gsl_sf_laguerre_3_e Y gsl_sf_laguerre_3(dbl_expr,dbl_expr) gsl_sf_laguerre_n_e Y gsl_sf_laguerre_n(int_expr,dbl_expr,dbl_expr) gsl_sf_lambert_W0_e Y gsl_sf_lambert_W0(dbl_expr) gsl_sf_lambert_Wm1_e Y gsl_sf_lambert_Wm1(dbl_expr) gsl_sf_legendre_Pl_e Y gsl_sf_legendre_Pl(int_expr,dbl_expr) gsl_sf_legendre_Pl_array Y status=gsl_sf_legendre_Pl_array(int,double,&var_out) gsl_sf_legendre_Pl_deriv_array N gsl_sf_legendre_Pl_deriv_array gsl_sf_legendre_P1_e Y gsl_sf_legendre_P1(dbl_expr) gsl_sf_legendre_P2_e Y gsl_sf_legendre_P2(dbl_expr) gsl_sf_legendre_P3_e Y gsl_sf_legendre_P3(dbl_expr) gsl_sf_legendre_Q0_e Y gsl_sf_legendre_Q0(dbl_expr) gsl_sf_legendre_Q1_e Y gsl_sf_legendre_Q1(dbl_expr) gsl_sf_legendre_Ql_e Y gsl_sf_legendre_Ql(int_expr,dbl_expr) gsl_sf_legendre_Plm_e Y gsl_sf_legendre_Plm(int_expr,int_expr,dbl_expr) gsl_sf_legendre_Plm_array Y status=gsl_sf_legendre_Plm_array(int,int,double,&var_out) gsl_sf_legendre_Plm_deriv_array N gsl_sf_legendre_Plm_deriv_array gsl_sf_legendre_sphPlm_e Y gsl_sf_legendre_sphPlm(int_expr,int_expr,dbl_expr) gsl_sf_legendre_sphPlm_array Y status=gsl_sf_legendre_sphPlm_array(int,int,double,&var_out) gsl_sf_legendre_sphPlm_deriv_array N gsl_sf_legendre_sphPlm_deriv_array gsl_sf_legendre_array_size N gsl_sf_legendre_array_size gsl_sf_conicalP_half_e Y gsl_sf_conicalP_half(dbl_expr,dbl_expr) gsl_sf_conicalP_mhalf_e Y gsl_sf_conicalP_mhalf(dbl_expr,dbl_expr) gsl_sf_conicalP_0_e Y gsl_sf_conicalP_0(dbl_expr,dbl_expr) gsl_sf_conicalP_1_e Y gsl_sf_conicalP_1(dbl_expr,dbl_expr) gsl_sf_conicalP_sph_reg_e Y gsl_sf_conicalP_sph_reg(int_expr,dbl_expr,dbl_expr) gsl_sf_conicalP_cyl_reg_e Y gsl_sf_conicalP_cyl_reg(int_expr,dbl_expr,dbl_expr) gsl_sf_legendre_H3d_0_e Y gsl_sf_legendre_H3d_0(dbl_expr,dbl_expr) gsl_sf_legendre_H3d_1_e Y gsl_sf_legendre_H3d_1(dbl_expr,dbl_expr) gsl_sf_legendre_H3d_e Y gsl_sf_legendre_H3d(int_expr,dbl_expr,dbl_expr) gsl_sf_legendre_H3d_array N gsl_sf_legendre_H3d_array gsl_sf_legendre_array_size N gsl_sf_legendre_array_size gsl_sf_log_e Y gsl_sf_log(dbl_expr) gsl_sf_log_abs_e Y gsl_sf_log_abs(dbl_expr) gsl_sf_complex_log_e N gsl_sf_complex_log gsl_sf_log_1plusx_e Y gsl_sf_log_1plusx(dbl_expr) gsl_sf_log_1plusx_mx_e Y gsl_sf_log_1plusx_mx(dbl_expr) gsl_sf_mathieu_a_array N gsl_sf_mathieu_a_array gsl_sf_mathieu_b_array N gsl_sf_mathieu_b_array gsl_sf_mathieu_a N gsl_sf_mathieu_a gsl_sf_mathieu_b N gsl_sf_mathieu_b gsl_sf_mathieu_a_coeff N gsl_sf_mathieu_a_coeff gsl_sf_mathieu_b_coeff N gsl_sf_mathieu_b_coeff gsl_sf_mathieu_ce N gsl_sf_mathieu_ce gsl_sf_mathieu_se N gsl_sf_mathieu_se gsl_sf_mathieu_ce_array N gsl_sf_mathieu_ce_array gsl_sf_mathieu_se_array N gsl_sf_mathieu_se_array gsl_sf_mathieu_Mc N gsl_sf_mathieu_Mc gsl_sf_mathieu_Ms N gsl_sf_mathieu_Ms gsl_sf_mathieu_Mc_array N gsl_sf_mathieu_Mc_array gsl_sf_mathieu_Ms_array N gsl_sf_mathieu_Ms_array gsl_sf_pow_int_e N gsl_sf_pow_int gsl_sf_psi_int_e Y gsl_sf_psi_int(int_expr) gsl_sf_psi_e Y gsl_sf_psi(dbl_expr) gsl_sf_psi_1piy_e Y gsl_sf_psi_1piy(dbl_expr) gsl_sf_complex_psi_e N gsl_sf_complex_psi gsl_sf_psi_1_int_e Y gsl_sf_psi_1_int(int_expr) gsl_sf_psi_1_e Y gsl_sf_psi_1(dbl_expr) gsl_sf_psi_n_e Y gsl_sf_psi_n(int_expr,dbl_expr) gsl_sf_synchrotron_1_e Y gsl_sf_synchrotron_1(dbl_expr) gsl_sf_synchrotron_2_e Y gsl_sf_synchrotron_2(dbl_expr) gsl_sf_transport_2_e Y gsl_sf_transport_2(dbl_expr) gsl_sf_transport_3_e Y gsl_sf_transport_3(dbl_expr) gsl_sf_transport_4_e Y gsl_sf_transport_4(dbl_expr) gsl_sf_transport_5_e Y gsl_sf_transport_5(dbl_expr) gsl_sf_sin_e N gsl_sf_sin gsl_sf_cos_e N gsl_sf_cos gsl_sf_hypot_e N gsl_sf_hypot gsl_sf_complex_sin_e N gsl_sf_complex_sin gsl_sf_complex_cos_e N gsl_sf_complex_cos gsl_sf_complex_logsin_e N gsl_sf_complex_logsin gsl_sf_sinc_e N gsl_sf_sinc gsl_sf_lnsinh_e N gsl_sf_lnsinh gsl_sf_lncosh_e N gsl_sf_lncosh gsl_sf_polar_to_rect N gsl_sf_polar_to_rect gsl_sf_rect_to_polar N gsl_sf_rect_to_polar gsl_sf_sin_err_e N gsl_sf_sin_err gsl_sf_cos_err_e N gsl_sf_cos_err gsl_sf_angle_restrict_symm_e N gsl_sf_angle_restrict_symm gsl_sf_angle_restrict_pos_e N gsl_sf_angle_restrict_pos gsl_sf_angle_restrict_symm_err_e N gsl_sf_angle_restrict_symm_err gsl_sf_angle_restrict_pos_err_e N gsl_sf_angle_restrict_pos_err gsl_sf_zeta_int_e Y gsl_sf_zeta_int(int_expr) gsl_sf_zeta_e Y gsl_sf_zeta(dbl_expr) gsl_sf_zetam1_e Y gsl_sf_zetam1(dbl_expr) gsl_sf_zetam1_int_e Y gsl_sf_zetam1_int(int_expr) gsl_sf_hzeta_e Y gsl_sf_hzeta(dbl_expr,dbl_expr) gsl_sf_eta_int_e Y gsl_sf_eta_int(int_expr) gsl_sf_eta_e Y gsl_sf_eta(dbl_expr) <a name="gsl_int"></a>  GSL interpolationGSL least-squares fittingGSL special functionsncap2 netCDF Arithmetic Processor GSL interpolation GSL As of version 3.9.9 (released July, 2009), NCO has wrappers to the GSL interpolation functions. Given a set of data points (x1,y1)...(xn, yn) the GSL functions computes a continuous interpolating function Y(x) such that Y(xi) = yi. The interpolation is piecewise smooth, and its behavior at the end-points is determined by the type of interpolation used. For more information consult the GSL manual. Interpolation with ncap2 is a two stage process. In the first stage, a RAM variable is created from the chosen interpolating function and the data set. This RAM variable holds in memory a GSL interpolation object. In the second stage, points along the interpolating function are calculated. If you have a very large data set or are interpolating many sets then consider deleting the RAM variable when it is redundant. Use the command ram_delete(var_nm). A simple example x_in[$lon]={1.0,2.0,3.0,4.0}; y_in[$lon]={1.1,1.2,1.5,1.8}; // Ram variable is declared and defined here gsl_interp_cspline(&ram_sp,x_in,y_in); x_out[$lon_grd]={1.1,2.0,3.0,3.1,3.99}; y_out=gsl_spline_eval(ram_sp,x_out); y2=gsl_spline_eval(ram_sp,1.3); y3=gsl_spline_eval(ram_sp,0.0); ram_delete(ram_sp); print(y_out); // 1.10472, 1.2, 1.4, 1.42658, 1.69680002 print(y2); // 1.12454 print(y3); // '_' Note in the above example y3 is set to &textrsquo;missing value&textrsquo; because 0.0 isn&textrsquo;t within the input X range. GSL Interpolation Types&linebreak; All the interpolation functions have been implemented. These are:&linebreak; gsl_interp_linear() &linebreak; gsl_interp_polynomial() &linebreak; gsl_interp_cspline()&linebreak; gsl_interp_cspline_periodic()&linebreak; gsl_interp_akima() &linebreak; gsl_interp_akima_periodic() &linebreak; &linebreak; &linebreak; Evaluation of Interpolating Types &linebreak; Implemented &linebreak; gsl_spline_eval() &linebreak; Not implemented &linebreak; gsl_spline_deriv()&linebreak; gsl_spline_deriv2()&linebreak; gsl_spline_integ()&linebreak; <a name="ncap_lsqf"></a>  GSL least-squares fittingGSL statisticsGSL interpolationncap2 netCDF Arithmetic Processor GSL least-squares fitting Least Squares fitting is a method of calculating a straight line through a set of experimental data points in the XY plane. Data may be weighted or unweighted. For more information please refer to the GSL manual. These GSL functions fall into three categories:&linebreak; A) Fitting data to Y=c0+c1*X&linebreak; B) Fitting data (through the origin) Y=c1*X&linebreak; C) Multi-parameter fitting (not yet implemented)&linebreak; Section A &linebreak; status=gsl_fit_linear (data_x,stride_x,data_y,stride_y,n,&co,&c1,&cov00,&cov01,&cov11,&sumsq) Input variables: data_x, stride_x, data_y, stride_y, n &linebreak; From the above variables an X and Y vector both of length &textrsquo;n&textrsquo; are derived. If data_x or data_y is less than type double then it is converted to type double. It is up to you to do bounds checking on the input data. For example if stride_x=3 and n=8 then the size of data_x must be at least 24 Output variables: c0, c1, cov00, cov01, cov11,sumsq &linebreak; The &textrsquo;&&textrsquo; prefix indicates that these are call-by-reference variables. If any of the output variables don&textrsquo;t exist prior to the call then they are created on the fly as scalar variables of type double. If they already exist then their existing value is overwritten. If the function call is successful then status=0. status= gsl_fit_wlinear(data_x,stride_x,data_w,stride_w,data_y,stride_y,n,&co,&c1,&cov00,&cov01,&cov11,&chisq) Similar to the above call except it creates an additional weighting vector from the variables data_w, stride_w, n data_y_out=gsl_fit_linear_est(data_x,c0,c1,cov00,cov01,cov11) This function calculates y values along the line Y=c0+c1*X &linebreak; &linebreak; Section B &linebreak; status=gsl_fit_mul(data_x,stride_x,data_y,stride_y,n,&c1,&cov11,&sumsq) Input variables: data_x, stride_x, data_y, stride_y, n &linebreak; From the above variables an X and Y vector both of length &textrsquo;n&textrsquo; are derived. If data_x or data_y is less than type double then it is converted to type double. &linebreak; Output variables: c1,cov11,sumsq &linebreak; status= gsl_fit_wmul(data_x,stride_x,data_w,stride_w,data_y,stride_y,n,&c1,&cov11,&sumsq) Similar to the above call except it creates an additional weighting vector from the variables data_w, stride_w, n data_y_out=gsl_fit_mul_est(data_x,c0,c1,cov11) This function calculates y values along the line Y=c1*X &linebreak; The below example shows gsl_fit_linear() in action defdim("d1",10); xin[d1]={1,2,3,4,5,6,7,8,9,10.0}; yin[d1]={3.1,6.2,9.1,12.2,15.1,18.2,21.3,24.0,27.0,30.0}; gsl_fit_linear(xin,1,yin,1,$d1.size,&c0,&c1,&cov00,&cov01,&cov11,&sumsq); print(c0); // 0.2 print(c1); // 2.98545454545 defdim("e1",4); xout[e1]={1.0,3.0,4.0,11}; yout[e1]=0.0; yout=gsl_fit_linear_est(xout,c0,c1,cov00,cov01,cov11,sumsq); print(yout); // 3.18545454545, 9.15636363636, 12.1418181818, 33.04 The following code does linear regression of sst(time,lat,lon) for each time-step&linebreak; // Declare variables c0[$lat, $lon]=0.; // Intercept c1[$lat, $lon]=0.; // Slope sdv[$lat, $lon]=0.; // Standard deviation covxy[$lat, $lon]=0.; // Covariance for (i=0;i<$lat.size;i++) // Loop over lat { for (j=0;j<$lon.size;j++) // Loop over lon { // Linear regression function gsl_fit_linear(time,1,sst(:, i, j),1,$time.size,&tc0,&tc1,&cov00,&cov01,&cov11,&sumsq); c0(i,j)=tc0; // Output results c1(i,j)=tc1; // Output results // Covariance function covxy(i,j)=gsl_stats_covariance(time,1,$time.size,double(sst(:,i,j)),1,$time.size); // Standard deviation function sdv(i,j)=gsl_stats_sd(sst(:,i,j),1,$time.size); } } // slope (c1) missing values are set to '0', change to -999. (variable c0 intercept value) where(c0 == -999) c1=-999; <a name="ncap_stat"></a>  GSL statisticsGSL random number generationGSL least-squares fittingncap2 netCDF Arithmetic Processor GSL statistics Wrappers for most of the GSL Statistical functions have been implemented. The GSL function names include a type specifier (except for type double functions). To obtain the equivalent NCO name simply remove the type specifier; then depending on the data type the appropriate GSL function is called. The weighed statistical functions e.g., gsl_stats_wvariance() are only defined in GSL for floating-point types; so your data must of type float or double otherwise ncap2 will emit an error message. To view the implemented functions use the shell command ncap2 -f|grep _stats GSL Functions

short gsl_stats_max (short data[], size_t stride, size_t n);
double gsl_stats_int_mean (int data[], size_t stride, size_t n);
double gsl_stats_short_sd_with_fixed_mean (short data[], size_t stride, size_t n, double mean);
double gsl_stats_wmean (double w[], size_t wstride, double data[], size_t stride, size_t n);
double gsl_stats_quantile_from_sorted_data (double sorted_data[], size_t stride, size_t n, double f) ;

Equivalent ncap2 wrapper functions

short gsl_stats_max (var_data, data_stride, n);
double gsl_stats_mean (var_data, data_stride, n);
double gsl_stats_sd_with_fixed_mean (var_data, data_stride, n, var_mean);
double gsl_stats_wmean (var_weight, weight_stride, var_data, data_stride, n, var_mean);
double gsl_stats_quantile_from_sorted_data (var_sorted_data, data_stride, n, var_f) ;

GSL has no notion of missing values or dimensionality beyond one. If your data has missing values which you want ignored in the calculations then use the ncap2 built in aggregate functions(Methods and functions). The GSL functions operate on a vector of values created from the var_data/stride/n arguments. The ncap wrappers check that there is no bounding error with regard to the size of the data and the final value in the vector. a1[time]={1,2,3,4,5,6,7,8,9,10}; a1_avg=gsl_stats_mean(a1,1,10); print(a1_avg); // 5.5 a1_var=gsl_stats_variance(a1,4,3); print(a1_var); // 16.0 // bounding error, vector attempts to access element a1(10) a1_sd=gsl_stats_sd(a1,5,3); For functions with the signature func_nm(var_data,data_stride,n), one may omit the second or third arguments. The default value for stride is 1. The default value for n is 1+(data.size()-1)/stride.

// Following statements are equvalent
n2=gsl_stats_max(a1,1,10)
n2=gsl_stats_max(a1,1);
n2=gsl_stats_max(a1);

// Following statements are equvalent
n3=gsl_stats_median_from_sorted_data(a1,2,5);
n3=gsl_stats_median_from_sorted_data(a1,2);

// Following statements are NOT equvalent
n4=gsl_stats_kurtosis(a1,3,2);
n4=gsl_stats_kurtosis(a1,3); //default n=4

The following example illustrates some of the weighted functions. The data are randomly generated. In this case the value of the weight for each datum is either 0.0 or 1.0

defdim("r1",2000);
data[r1]=1.0;

// Fill with random numbers [0.0,10.0)
data=10.0*gsl_rng_uniform(data);

// Create a weighting variable
weight=(data>4.0);

wmean=gsl_stats_wmean(weight,1,data,1,$r1.size);
print(wmean);

wsd=gsl_stats_wsd(weight,1,data,1,$r1.size);
print(wsd);

// number of values in data that are greater than 4
weight_size=weight.total();
print(weight_size);

// print min/max of data 
dmin=data.gsl_stats_min();
dmax=data.gsl_stats_max();
print(dmin);print(dmax);

<a name="ncap_rng"></a>  GSL random number generationExamples ncap2GSL statisticsncap2 netCDF Arithmetic Processor GSL random number generation The GSL library has a large number of random number generators. In addition there are a large set of functions for turning uniform random numbers into discrete or continuous probabilty distributions. The random number generator algorithms vary in terms of quality numbers output, speed of execution and maximum number output. For more information see the GSL documentation. The algorithm and seed are set via environment variables, these are picked up by the ncap2 code. Setup &linebreak; The number algorithm is set by the environment variable GSL_RNG_TYPE. If this variable isn&textrsquo;t set then the default rng algorithm is gsl_rng_19937. The seed is set with the environment variable GSL_RNG_SEED. The following wrapper functions in ncap2 provide information about the chosen algorithm. &linebreak; gsl_rng_min() the minimum value returned by the rng algorithm. gsl_rng_max() the maximum value returned by the rng algorithm.

Uniformly Distributed Random Numbers gsl_rng_get(var_in) This function returns var_in with integers from the chosen rng algorithm. The min and max values depend uoon the chosen rng algorthm. gsl_rng_uniform_int(var_in) This function returns var_in with random integers from 0 to n-1. The value n must be less than or equal to the maximum value of the chosen rng algorithm. gsl_rng_uniform(var_in) This function returns var_in with double-precision numbers in the range [0.0,1). The range includes 0.0 and excludes 1.0. gsl_rng_uniform_pos(var_in) This function returns var_in with double-precision numbers in the range (0.0,1), excluding both 0.0 and 1.0.

Below are examples of gsl_rng_get() and gsl_rng_uniform_int() in action.

export GSL_RNG_TYPE=ranlux
export GSL_RNG_SEED=10
ncap2 -v -O -s 'a1[time]=0;a2=gsl_rng_get(a1);' in.nc foo.nc 
// 10 random numbers from the range 0 - 16777215
// a2=9056646, 12776696, 1011656, 13354708, 5139066, 1388751, 11163902, 7730127, 15531355, 10387694 ;

ncap2 -v -O -s 'a1[time]=21;a2=gsl_rng_uniform_int(a1).sort();' in.nc foo.nc
// 10 random numbers from the range 0 - 20
a2 = 1, 1, 6, 9, 11, 13, 13, 15, 16, 19 ;

The following example produces an ncap2 runtime error. This is because the chose rng algorithm has a maximum value greater than NC_MAX_INT=2147483647; the wrapper functions to gsl_rng_get() and gsl_rng_uniform_int() return variable of type NC_INT. Please be aware of this when using random number distribution functions functions from the GSL library which return unsigned int. Examples of these are gsl_ran_geometric() and gsl_ran_pascal().

export GSL_RNG_TYPE=mt19937
ncap2 -v -O -s 'a1[time]=0;a2=gsl_rng_get(a1);' in.nc foo.nc

To find the maximum value of the chosen rng algorithm use the following code snippet.

ncap2 -v -O -s 'rng_max=gsl_rng_max();print(rng_max)' in.nc foo.nc

Random Number Distributions &linebreak; The GSL library has a rich set of random number disribution functions. The library also provides cumulative distribution functions and inverse cumulative distribution functions sometimes referred to a quantile functions. To see whats available on your build use the shell command ncap2 -f|grep -e _ran -e _cdf. The following examples all return variables of type NC_INT &linebreak;

defdim("out",15);
a1[$out]=0.5;
a2=gsl_ran_binomial(a1,30).sort();
//a2 = 10, 11, 12, 12, 13, 14, 14, 15, 15, 16, 16, 16, 16, 17, 22 ;
a3=gsl_ran_geometric(a2).sort();
//a2 = 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 3, 4, 5 ;
a4=gsl_ran_pascal(a2,50);
//a5 = 37, 40, 40, 42, 43, 45, 46, 49, 52, 58, 60, 62, 62, 65, 67 ;

The following all return variables of type NC_DOUBLE;

defdim("b1",1000);
b1[$b1]=0.8;
b2=gsl_ran_exponential(b1);
b2_avg=b2.avg();
print(b2_avg);
// b2_avg = 0.756047976787

b3=gsl_ran_gaussian(b1);
b3_avg=b3.avg();
b3_rms=b3.rms();
print(b3_avg);
// b3_avg = -0.00903446534258;
print(b3_rms);
// b3_rms = 0.81162979889;

b4[$b1]=10.0;
b5[$b1]=20.0;
b6=gsl_ran_flat(b4,b5);
b6_avg=b6.avg();
print(b6_avg);
// b6_avg=15.0588129413

<a name="ncap_emp"></a>  Examples ncap2Intrinsic mathematical methodsGSL random number generationncap2 netCDF Arithmetic Processor Examples ncap2 See the ncap.in and ncap2.in scripts released with NCO for more complete demonstrations of ncap2 functionality (script available on-line at http://nco.sf.net/ncap2.in). Define new attribute new for existing variable one as twice the existing attribute double_att of variable att_var: ncap2 -s 'one@new=2*att_var@double_att' in.nc out.nc Average variables of mixed types (result is of type double):

ncap2 -s 'average=(var_float+var_double+var_int)/3' in.nc out.nc

Multiple commands may be given to ncap2 in three ways. First, the commands may be placed in a script which is executed, e.g., tst.nco. Second, the commands may be individually specified with multiple -s arguments to the same ncap2 invocation. Third, the commands may be chained into a single -s argument to ncap2. Assuming the file tst.nco contains the commands a=3;b=4;c=sqrt(a^2+b^2);, then the following ncap2 invocations produce identical results:

ncap2 -v -S tst.nco in.nc out.nc
ncap2 -v -s 'a=3' -s 'b=4' -s 'c=sqrt(a^2+b^2)' in.nc out.nc
ncap2 -v -s 'a=3;b=4;c=sqrt(a^2+b^2)' in.nc out.nc

The second and third examples show that ncap2 does not require that a trailing semi-colon ; be placed at the end of a -s argument, although a trailing semi-colon ; is always allowed. However, semi-colons are required to separate individual assignment statements chained together as a single -s argument. <a name="xmp_grw"></a>  growing dimensions dimensions, growing ncap2 may be used to &textldquo;grow&textrdquo; dimensions, i.e., to increase dimension sizes without altering existing data. Say in.nc has ORO(lat,lon) and the user wishes a new file with new_ORO(new_lat,new_lon) that contains zeros in the undefined portions of the new grid.

defdim("new_lat",$lat.size+1); // Define new dimension sizes
defdim("new_lon",$lon.size+1);
new_ORO[$new_lat,$new_lon]=0.0f; // Initialize to zero
new_ORO(0:$lat.size-1,0:$lon.size-1)=ORO; // Fill valid data

The commands to define new coordinate variables new_lat and new_lon in the output file follow a similar pattern. One would might store these commands in a script grow.nco and then execute the script with

ncap2 -v -S grow.nco in.nc out.nc

<a name="flg"></a>  flags Imagine you wish to create a binary flag based on the value of an array. The flag should have value 1.0 where the array exceeds 1.0, and value 0.0 elsewhere. This example creates the binary flag ORO_flg in out.nc from the continuous array named ORO in in.nc.

ncap2 -s 'ORO_flg=(ORO > 1.0)' in.nc out.nc

Suppose your task is to change all values of ORO which equal 2.0 to the new value 3.0:

ncap2 -s 'ORO_msk=(ORO==2.0);ORO=ORO_msk*3.0+!ORO_msk*ORO' in.nc out.nc

mask This creates and uses ORO_msk to mask the subsequent arithmetic operation. Values of ORO are only changed where ORO_msk is true, i.e., where ORO equals 2.0 &linebreak; Using the where statement the above code simplifies to :

ncap2 -s 'where(ORO == 2.0) ORO=3.0;' in.nc foo.nc

<a name="cvr"></a>  This example uses ncap2 to compute the covariance of two variables. Let the variables u and v be the horizontal wind components. covariance The covariance of u and v is defined as the time mean product of the deviations of u and v from their respective time means. Symbolically, the covariance $[\wndznl^{\prime} \wndmrd^{\prime}] = [\wndznl\wndmrd]-[\wndznl][\wndmrd]$ where $[\xxx]$ denotes the time-average of~$\xxx$, $[\xxx] \equiv {1 \over \tau} \int_{\tm=0}^{\tm=\tau} \xxx(\tm) \,\dfr\tm$ and $\xxx^{\prime}$ @clear flg

[

u'v'] = [uv]-[u][v] where

[

x] denotes the time-average of

x and

x' denotes the deviation from the time-mean. The covariance tells us how much of the correlation of two signals arises from the signal fluctuations versus the mean signals. eddy covariance Sometimes this is called the eddy covariance. We will store the covariance in the variable uprmvprm.

ncwa -O -a time -v u,v in.nc foo.nc # Compute time mean of u,v
ncrename -O -v u,uavg -v v,vavg foo.nc # Rename to avoid conflict
ncks -A -v uavg,vavg foo.nc in.nc # Place time means with originals
ncap2 -O -s 'uprmvprm=u*v-uavg*vavg' in.nc in.nc # Covariance
ncra -O -v uprmvprm in.nc foo.nc # Time-mean covariance

The mathematically inclined will note that the same covariance would be obtained by replacing the step involving ncap2 with

ncap2 -O -s 'uprmvprm=(u-uavg)*(v-vavg)' foo.nc foo.nc # Covariance

As of NCO version 3.1.8 (December, 2006), ncap2 can compute averages, and thus covariances, by itself:

ncap2 -s 'uavg=u.avg($time);vavg=v.avg($time);uprmvprm=u*v-uavg*vavg' \
      -s 'uprmvrpmavg=uprmvprm.avg($time)' in.nc foo.nc

We have not seen a simpler method to script and execute powerful arithmetic than ncap2. globbing shell quotes extended regular expressions regular expressions ncap2 utilizes many meta-characters (e.g., $, ?, ;, (), []) that can confuse the command-line shell if not quoted properly. The issues are the same as those which arise in utilizing extended regular expressions to subset variables (Subsetting Files). The example above will fail with no quotes and with double quotes. This is because shell globbing tries to interpolate the value of $time from the shell environment unless it is quoted:

ncap2 -s 'uavg=u.avg($time)'  in.nc foo.nc # Correct (recommended)
ncap2 -s  uavg=u.avg('$time') in.nc foo.nc # Correct (and dangerous)
ncap2 -s  uavg=u.avg($time)   in.nc foo.nc # Wrong ($time = '')
ncap2 -s "uavg=u.avg($time)"  in.nc foo.nc # Wrong ($time = '')

Without the single quotes, the shell replaces $time with an empty string. The command ncap2 receives from the shell is uavg=u.avg(). This causes ncap2 to average over all dimensions rather than just the time dimension, and unintended consequence. We recommend using single quotes to protect ncap2 command-line scripts from the shell, even when such protection is not strictly necessary. Expert users may violate this rule to exploit the ability to use shell variables in ncap2 command-line scripts (CCSM Example). In such cases it may be necessary to use the shell backslash character \ to protect the ncap2 meta-character. appending data time-averaging ncks ncwa ncra degenerate dimension -b A dimension of size one is said to be degenerate. Whether a degenerate record dimension is desirable or not depends on the application. Often a degenerate time dimension is useful, e.g., for concatenating, though it may cause problems with arithmetic. Such is the case in the above example, where the first step employs ncwa rather than ncra for the time-averaging. Of course the numerical results are the same with both operators. The difference is that, unless -b is specified, ncwa writes no time dimension to the output file, while ncra defaults to keeping time as a degenerate (size 1) dimension. Appending u and v to the output file would cause ncks to try to expand the degenerate time axis of uavg and vavg to the size of the non-degenerate time dimension in the input file. Thus the append (ncks -A) command would be undefined (and should fail) in this case. -C Equally important is the -C argument (Subsetting Coordinate Variables) to ncwa to prevent any scalar time variable from being written to the output file. Knowing when to use ncwa -a time rather than the default ncra for time-averaging takes, well, time. <a name="mth"></a>  Intrinsic mathematical methodsOperator precedence and associativityExamples ncap2ncap2 netCDF Arithmetic Processor Intrinsic mathematical methods ncap2 supports the standard mathematical functions supplied with most operating systems. addition subtraction multiplication division exponentiation power modulus + (addition) - (subtraction) * (multiplication) / (division) ^ (power) % (modulus) Standard calculator notation is used for addition +, subtraction -, multiplication *, division /, exponentiation ^, and modulus %. The available elementary mathematical functions are: abs acosh acos asinh asin atanh atan ceil cosh cos erfc erf exp floor gamma ln log10 log nearbyint pow rint round sinh sin sqrt tanh tan trunc mathematical functions nearest integer function (inexact) nearest integer function (exact) rounding functions truncation function absolute value arccosine function arcsine function arctangent function ceiling function complementary error function cosine function error function exponentiation function floor function gamma function hyperbolic arccosine function hyperbolic arcsine function hyperbolic arctangent function hyperbolic cosine function hyperbolic sine function hyperbolic tangent logarithm, base 10 logarithm, natural power function sine function square root function abs(x) Absolute value Absolute value of $x$, $|x|$. Absolute value of x. Example: abs$(-1) = 1$

abs(-1) = 1

acos(x) Arc-cosine Arc-cosine of x where x is specified in radians. Example: acos$(1.0) = 0.0$

acos(1.0) = 0.0

acosh(x) Hyperbolic arc-cosine Hyperbolic arc-cosine of x where x is specified in radians. Example: acosh$(1.0) = 0.0$

acosh(1.0) = 0.0

asin(x) Arc-sine Arc-sine of x where x is specified in radians. Example: asin$(1.0) = 1.57079632679489661922$

asin(1.0) = 1.57079632679489661922

asinh(x) Hyperbolic arc-sine Hyperbolic arc-sine of x where x is specified in radians. Example: asinh$(1.0) = 0.88137358702$

asinh(1.0) = 0.88137358702

atan(x) Arc-tangent Arc-tangent of x where x is specified in radians between $-\pi/2$ and $\pi/2$.

-pi/2

and

pi/2

. Example: atan$(1.0) = 0.78539816339744830961$

atan(1.0) = 0.78539816339744830961

atan2(y,x) Arc-tangent2 Arc-tangent of y/x

:Example atan2(1,3) =  0.321689857

atanh(x) Hyperbolic arc-tangent Hyperbolic arc-tangent of x where x is specified in radians between $-\pi/2$ and $\pi/2$.

-pi/2

and

pi/2

. Example: atanh$(3.14159265358979323844) = 1.0$

atanh(3.14159265358979323844) = 1.0

ceil(x) Ceil Ceiling of x. Smallest integral value not less than argument. Example: ceil$(0.1) = 1.0$

ceil(0.1) = 1.0

cos(x) Cosine Cosine of x where x is specified in radians. Example: cos$(0.0) = 1.0$

cos(0.0) = 1.0

cosh(x) Hyperbolic cosine Hyperbolic cosine of x where x is specified in radians. Example: cosh$(0.0) = 1.0$

cosh(0.0) = 1.0

erf(x) Error function Error function of x where x is specified between $-1$ and $1$.

-1

and

1

. Example: erf$(1.0) = 0.842701$

erf(1.0) = 0.842701

erfc(x) Complementary error function Complementary error function of x where x is specified between $-1$ and $1$.

-1

and

1

. Example: erfc$(1.0) = 0.15729920705$

erfc(1.0) = 0.15729920705

exp(x) Exponential Exponential of x, $e^{x}$.

e^x

. Example: exp$(1.0) = 2.71828182845904523536$

exp(1.0) = 2.71828182845904523536

floor(x) Floor Floor of x. Largest integral value not greater than argument. Example: floor$(1.9) = 1$

floor(1.9) = 1

gamma(x) Gamma function Gamma function of x, $\Gamma(x)$.

Gamma(x)

. The well-known and loved continuous factorial function. Example: gamma$(0.5) = \sqrt{\pi}$

gamma(0.5) = sqrt(pi)

gamma_inc_P(x) Incomplete Gamma function Incomplete Gamma function of parameter a and variable x, $P(a,x)$.

gamma_inc_P(a,x)

. One of the four incomplete gamma functions. Example: gamma\_inc\_P$(1,1) = 1-\me^{-1}$

gamma_inc_P(1,1) = 1-1/e

ln(x) Natural Logarithm Natural logarithm of x, $\ln(x)$.

ln(x)

. Example: ln$(2.71828182845904523536) = 1.0$

ln(2.71828182845904523536) = 1.0

log(x) Natural Logarithm Exact synonym for ln(x). log10(x) Base 10 Logarithm Base 10 logarithm of x, $\log_{10}(x)$.

log10(x)

. Example: log$(10.0) = 1.0$

log(10.0) = 1.0

nearbyint(x) Round inexactly Nearest integer to x is returned in floating-point format. inexact conversion No exceptions are raised for inexact conversions. Example: nearbyint$(0.1) = 0.0$

nearbyint(0.1) = 0.0

pow(x,y) Power promotion automatic type conversion Value of x is raised to the power of y. Exceptions are raised for domain errors. Due to type-limitations in the C language pow function, integer arguments are promoted (Type Conversion) to type NC_FLOAT before evaluation. Example: pow$(2,3) = 8$

pow(2,3) = 8

rint(x) Round exactly Nearest integer to x is returned in floating-point format. Exceptions are raised for inexact conversions. Example: rint$(0.1) = 0.0$

rint(0.1) = 0

round(x) Round Nearest integer to x is returned in floating-point format. Round halfway cases away from zero, regardless of current IEEE rounding direction. Example: round$(0.5) = 1.0$

round(0.5) = 1.0

sin(x) Sine Sine of x where x is specified in radians. Example: sin$(1.57079632679489661922) = 1.0$

sin(1.57079632679489661922) = 1.0

sinh(x) Hyperbolic sine Hyperbolic sine of x where x is specified in radians. Example: sinh$(1.0) = 1.1752$

sinh(1.0) = 1.1752

sqrt(x) Square Root Square Root of x, $\sqrt{x}$.

sqrt(x)

. Example: sqrt$(4.0) = 2.0$

sqrt(4.0) = 2.0

tan(x) Tangent Tangent of x where x is specified in radians. Example: tan$(0.78539816339744830961) = 1.0$

tan(0.78539816339744830961) = 1.0

tanh(x) Hyperbolic tangent Hyperbolic tangent of x where x is specified in radians. Example: tanh$(1.0) = 0.761594155956$

tanh(1.0) = 0.761594155956

trunc(x) Truncate Nearest integer to x is returned in floating-point format. Round halfway cases toward zero, regardless of current IEEE rounding direction. Example: trunc$(0.5) = 0.0$

trunc(0.5) = 0.0

The complete list of mathematical functions supported is platform-specific. Functions mandated by ANSI C are guaranteed to be present and are indicated with an asterisk ANSI C float precision quadruple-precision single-precision double-precision long double NC_DOUBLE ANSI C compilers are guaranteed to support double-precision versions of these functions. These functions normally operate on netCDF variables of type NC_DOUBLE without having to perform intrinsic conversions. For example, ANSI compilers provide sin for the sine of C-type double variables. The ANSI standard does not require, but many compilers provide, an extended set of mathematical functions that apply to single (float) and quadruple (long double) precision variables. Using these functions (e.g., sinf for float, sinl for long double), when available, is (presumably) more efficient than casting variables to type double, performing the operation, and then re-casting. NCO uses the faster intrinsic functions when they are available, and uses the casting method when they are not. . and are indicated with an asterisk. -f --prn_fnc_tbl --fnc_tbl Use the -f (or fnc_tbl or prn_fnc_tbl) switch to print a complete list of functions supported on your platform. Linux Linux supports more of these intrinsic functions than other OSs. <a name="xmp_ncap"></a>  <a name="xmp_ncap2"></a>  <a name="ncap_opts"></a>  Operator precedence and associativityID QuotingIntrinsic mathematical methodsncap2 netCDF Arithmetic Processor Operator precedence and associativity This page lists the ncap2 operators in order of precedence (highest to lowest). Their associativity indicates in what order operators of equal precedence in an expression are applied. Operator Description Associativity ++ -- Postfix Increment/Decrement Right to Left () Parentheses (function call) . Method call ++ -- Prefix Increment/Decrement Right to Left + - Unary Plus/Minus ! Logical Not ^ Power of Operator Right to Left * / % Multiply/Divide/Modulus Left To Right + - Addition/Subtraction Left To Right >> << Fortran style array clipping Left to Right < <= Less than/Less than or equal to Left to Right > >= Greater than/Greater than or equal to == != Equal to/Not equal to Left to Right && Logical AND Left to Right || Logical OR Left to Right ?: Ternary Operator Right to Left = Assignment Right to Left += -= Addition/subtraction assignment *= /= Multiplication/division assignment <a name="ncap_nmc"></a>  ID Quotingmake_bounds() functionOperator precedence and associativityncap2 netCDF Arithmetic Processor ID Quoting ID Quoting In this section a name refers to a variable, attribute, or dimension name. The allowed characters in a valid netCDF name vary from release to release. (See end section). To use metacharacters in a name, or to use a method name as a variable name, the name must be quoted wherever it occurs. The default NCO name is specified by the regular expressions:

DGT:     ('0'..'9');
LPH:     ( 'a'..'z' | 'A'..'Z' | '_' );
name:    (LPH)(LPH|DGT)+

The first character of a valid name must be alphabetic or the underscore. Subsequent characters must be alphanumeric or underscore, e.g., a1, _23, hell_is_666. The valid characters in a quoted name are specified by the regular expressions:

LPHDGT:  ( 'a'..'z' | 'A'..'Z' | '_' | '0'..'9');
name:    (LPHDGT|'-'|'+'|'.'|'('|')'|':' )+  ;

Quote a variable:&linebreak; &textrsquo;avg&textrsquo; , &textrsquo;10_+10&textrsquo;,&textrsquo;set_miss&textrsquo; &textrsquo;+-90field&textrsquo; , &textrsquo;&textndash;test&textrsquo;=10.0d&linebreak; &linebreak; Quote an attribute:&linebreak; &textrsquo;three&arobase;10&textrsquo;, &textrsquo;set_mss&arobase;+10&textrsquo;, &textrsquo;666&arobase;hell&textrsquo;, &textrsquo;t1&arobase;+units&textrsquo;="kelvin" &linebreak; &linebreak; Quote a dimension: &linebreak; &textrsquo;$10&textrsquo;, &textrsquo;$t1&textndash;&textrsquo;, &textrsquo;$&textndash;odd&textrsquo;, c1[&textrsquo;$10&textrsquo;,&textrsquo;$t1&textndash;&textrsquo;]=23.0d &linebreak; The following comments are from the netCDF library definitions and detail the naming conventions for each release. netcdf-3.5.1 &linebreak; netcdf-3.6.0-p1 &linebreak; netcdf-3.6.1 &linebreak; netcdf-3.6.2 &linebreak; /* * ( [a-zA-Z]|[0-9]|'_'|'-'|'+'|'.'|'|':'|'@'|'('|')' )+ * Verify that name string is valid CDL syntax, i.e., all characters are * alphanumeric, '-', '_', '+', or '.'. * Also permit ':', '@', '(', or ')' in names for chemists currently making * use of these characters, but don't document until ncgen and ncdump can * also handle these characters in names. */ netcdf-3.6.3&linebreak; netcdf-4.0 Final 2008/08/28&linebreak; /* * Verify that a name string is valid syntax. The allowed name * syntax (in RE form) is: * * ([a-zA-Z_]|{UTF8})([^\x00-\x1F\x7F/]|{UTF8})* * * where UTF8 represents a multibyte UTF-8 encoding. Also, no * trailing spaces are permitted in names. This definition * must be consistent with the one in ncgen.l. We do not allow '/' * because HDF5 does not permit slashes in names as slash is used as a * group separator. If UTF-8 is supported, then a multi-byte UTF-8 * character can occur anywhere within an identifier. We later * normalize UTF-8 strings to NFC to facilitate matching and queries. */ <a name="make_bounds"></a>  make_bounds() functionsolar_zenith_angle functionID Quotingncap2 netCDF Arithmetic Processor make_bounds() function make_bounds() function The ncap2 custom function &textrsquo;make_bounds()&textrsquo; takes any monotonic 1D coordinate variable with regular or irregular (e.g., Gaussian) spacing and creates a bounds variable. <bounds_var_out>=make_bounds( <coordinate_var_in>, <dim in>, <string>) 1st Argument The name of the input coordinate variable. 2nd Argument The second dimension of the output variable, referenced as a dimension (i.e., the name preceded by a dollarsign) not as a string name. The size of this dimension should always be 2. If the dimension does not yet exist create it first using defdim(). 3rd Argument This optional string argument will be placed in the "bounds" attribute that will be created in the input coordinate variable. Normally this is the name of the bounds variable:

Typical usage:

defdim("nv",2);
longitude_bounds=make_bounds(longitude,$nv,"longitude_bounds");

Another common CF convention:

defdim("nv",2);
climatology_bounds=make_bounds(time,$nv,"climatology_bounds");

solar_zenith_angle functionmake_bounds() functionncap2 netCDF Arithmetic Processor solar_zenith_angle function solar_zenith_angle function <zenith_out>=solar_zenith_angle( <time_in>, <latitude in>) This function takes two arguments, mean local solar time and latitude. Calculation and output is done with type NC_DOUBLE. The calendar attribute for <time_in> in is NOT read and is assumed to be Gregorian (this is the calendar that UDUnits uses). As part of the calculation <time_in> is converted to days since start of year. For some input units e.g., seconds, this function may produce gobbledygook. The output <zenith_out> is in degrees. For more details of the algorithm used please examine the function solar_geometry() in fmc_all_cls.cc. Note that this routine does not account for the equation of time, and so can be in error by the angular equivalent of up to about fifteen minutes time depending on the day of year.

my_time[time]={10.50, 11.0, 11.50, 12.0, 12.5, 13.0, 13.5, 14.0, 14.50, 15.00};  
my_time&arobase;units="hours since 2017-06-21";

// Assume we are at Equator
latitude=0.0;

// 32.05428, 27.61159, 24.55934, 23.45467, 24.55947, 27.61184, 32.05458, 37.39353, 43.29914, 49.55782 ;
zenith=solar_zenith_angle(my_time,latitude);

ncatted netCDF Attribute Editorncbo netCDF Binary Operatorncap2 netCDF Arithmetic ProcessorReference Manual

ncatted netCDF Attribute Editor attributes attribute names editing attributes ncatted SYNTAX

ncatted [-a att_dsc] [-a &dots;] [-D dbg]
[-H] [-h] [--hdr_pad nbr] [--hpss] 
[-l path] [-O] [-o output-file] [-p path]
[-R] [-r] [--ram_all] [-t] input-file [[output-file]]

DESCRIPTION ncattget ncatted edits attributes in a netCDF file. If you are editing attributes then you are spending too much time in the world of metadata, and ncatted was written to get you back out as quickly and painlessly as possible. ncatted can append, create, delete, modify, and overwrite attributes (all explained below). ncatted allows each editing operation to be applied to every variable in a file. This saves time when changing attribute conventions throughout a file. ncatted is for writing attributes. To read attribute values in plain text, use ncks -m -M, or define something like ncattget as a shell command (Filters for ncks). history -h Because repeated use of ncatted can considerably increase the size of the history global attribute (History Attribute), the -h switch is provided to override automatically appending the command to the history global attribute in the output-file. performance operator speed speed execution time According to the netCDF User Guide, altering metadata in netCDF files does not incur the penalty of recopying the entire file when the new metadata occupies less space than the old metadata. Thus ncatted may run much faster (at least on netCDF3 files) if judicious use of header padding (Metadata Optimization) was made when producing the input-file. Similarly, using the --hdr_pad option with ncatted helps ensure that future metadata changes to output-file occur as swiftly as possible. missing values data, missing _FillValue When ncatted is used to change the _FillValue attribute, it changes the associated missing data self-consistently. If the internal floating-point representation of a missing value, e.g., 1.0e36, differs between two machines then netCDF files produced on those machines will have incompatible missing values. This allows ncatted to change the missing values in files from different machines to a single value so that the files may then be concatenated, e.g., by ncrcat, without losing information. Missing Values, for more information. To master ncatted one must understand the meaning of the structure that describes the attribute modification, att_dsc specified by the required option -a or --attribute. This option is repeatable and may be used multiple time in a single ncatted invocation to increase the efficiency of altering multiple attributes. Each att_dsc contains five elements. This makes using ncatted somewhat complicated, though powerful. The att_dsc fields are in the following order:&linebreak; att_dsc = att_nm, var_nm, mode, att_type, att_val&linebreak; att_nm Attribute name. Example: units As of NCO 4.5.1 (July, 2015), ncatted accepts regular expressions (Subsetting Files) for attribute names (it has &textldquo;always&textrdquo; accepted regular expressions for variable names). Regular expressions will select all matching attribute names. var_nm Variable name. Example: pressure, '^H2O'. extended regular expressions regular expressions pattern matching wildcards Regular expressions (Subsetting Files) are accepted and will select all matching variable (and/or group) names. The names global and group have special meaning. mode Edit mode abbreviation. Example: a. See below for complete listing of valid values of mode. att_type Attribute type abbreviation. Example: c. See below for complete listing of valid values of att_type. att_val Attribute value. Example: pascal.

There should be no empty space between these five consecutive arguments. The description of these arguments follows in their order of appearance. The value of att_nm is the name of the attribute to edit. The meaning of this should be clear to all ncatted users. Both att_nm and var_nm may be specified as regular expressions. If att_nm is omitted (i.e., left blank) and Delete mode is selected, then all attributes associated with the specified variable will be deleted. global attributes attributes, global The value of var_nm is the name of the variable containing the attribute (named att_nm) that you want to edit. There are three very important and useful exceptions to this rule. The value of var_nm can also be used to direct ncatted to edit global attributes, or to repeat the editing operation for every group or variable in a file. A value of var_nm of global indicates that att_nm refers to a global (i.e., root-level) attribute, rather than to a particular variable&textrsquo;s attribute. This is the method ncatted supports for editing global attributes. A value of var_nm of group indicates that att_nm refers to all groups, rather than to a particular variable&textrsquo;s or group&textrsquo;s attribute. The operation will proceed to edit group metadata for every group. Finally, if var_nm is left blank, then ncatted attempts to perform the editing operation on every variable in the file. This option may be convenient to use if you decide to change the conventions you use for describing the data. As of NCO 4.6.0 (May, 2016), ncatted accepts the -t (or long-option equivalent --typ_mch or --type_match) option. This causes ncatted to perform the editing operation only on variables that are the same type as the specified attribute. <a name="mode"></a>  <a name="att_mode"></a>  <a name="att_edit"></a>  <a name="att_append"></a>  <a name="att_create"></a>  <a name="att_delete"></a>  <a name="att_modify"></a>  <a name="att_nappend"></a>  <a name="att_overwrite"></a>  <a name="att_prepend"></a>  The value of mode is a single character abbreviation (a, c, d, m, n, o, or p) standing for one of seven editing modes:&linebreak; attribute, edit attribute, append attribute, create attribute, delete attribute, modify attribute, nappend attribute, overwrite attribute, prepend a Append. Append value att_val to current var_nm attribute att_nm value att_val, if any. If var_nm does not already have an existing attribute att_nm, it is created with the value att_val. c Create. Create variable var_nm attribute att_nm with att_val if att_nm does not yet exist. If var_nm already has an attribute att_nm, there is no effect, so the existing attribute is preserved without change. d Delete. Delete current var_nm attribute att_nm. If var_nm does not have an attribute att_nm, there is no effect. If att_nm is omitted (left blank), then all attributes associated with the specified variable are automatically deleted. When Delete mode is selected, the att_type and att_val arguments are superfluous and may be left blank. m Modify. Change value of current var_nm attribute att_nm to value att_val. If var_nm does not have an attribute att_nm, there is no effect. n Nappend. Append value att_val to var_nm attribute att_nm value att_val if att_nm already exists. If var_nm does not have an attribute att_nm, there is no effect. In other words, if att_nm already exists, Nappend behaves like Append otherwise it does nothing. The mnenomic is &textldquo;non-create append&textrdquo;. Nappend mode was added to ncatted in version 4.6.0 (May, 2016). o Overwrite. Write attribute att_nm with value att_val to variable var_nm, overwriting existing attribute att_nm, if any. This is the default mode. p Prepend. Prepend value att_val to var_nm attribute att_nm value att_val if att_nm already exists. If var_nm does not have an attribute att_nm, there is no effect. Prepend mode was added to ncatted in version 5.0.5 (January, 2022).

<a name="att_typ"></a>  The value of att_type is a single character abbreviation (f, d, l, i, s, c, b, u) or a short string standing for one of the twelve primitive netCDF data types:&linebreak; f Float. Value(s) specified in att_val will be stored as netCDF intrinsic type NC_FLOAT. d Double. Value(s) specified in att_val will be stored as netCDF intrinsic type NC_DOUBLE. i, l Integer or (its now deprecated synonym) Long. Value(s) specified in att_val will be stored as netCDF intrinsic type NC_INT. s Short. Value(s) specified in att_val will be stored as netCDF intrinsic type NC_SHORT. c Char. Value(s) specified in att_val will be stored as netCDF intrinsic type NC_CHAR. b Byte. Value(s) specified in att_val will be stored as netCDF intrinsic type NC_BYTE. ub Unsigned Byte. Value(s) specified in att_val will be stored as netCDF intrinsic type NC_UBYTE. us Unsigned Short. Value(s) specified in att_val will be stored as netCDF intrinsic type NC_USHORT. u, ui, ul Unsigned Int. Value(s) specified in att_val will be stored as netCDF intrinsic type NC_UINT. ll, int64 Int64. Value(s) specified in att_val will be stored as netCDF intrinsic type NC_INT64. ull, uint64 Uint64. Value(s) specified in att_val will be stored as netCDF intrinsic type NC_UINT64. sng, string String. Value(s) specified in att_val will be stored as netCDF intrinsic type NC_STRING. Note that ncatted handles type NC_STRING attributes correctly beginning with version 4.3.3 released in July, 2013. Earlier versions fail when asked to handle NC_STRING attributes.

In Delete mode the specification of att_type is optional (and is ignored if supplied). The value of att_val is what you want to change attribute att_nm to contain. The specification of att_val is optional in Delete (and is ignored) mode. Attribute values for all types besides NC_CHAR must have an attribute length of at least one. Thus att_val may be a single value or one-dimensional array of elements of type att_type. If the att_val is not set or is set to empty space, and the att_type is NC_CHAR, e.g., -a units,T,o,c,"" or -a units,T,o,c,, then the corresponding attribute is set to have zero length. When specifying an array of values, it is safest to enclose att_val in single or double quotes, e.g., -a levels,T,o,s,"1,2,3,4" or -a levels,T,o,s,'1,2,3,4'. The quotes are strictly unnecessary around att_val except when att_val contains characters which would confuse the calling shell, such as spaces, commas, and wildcard characters. Perl ASCII NCO processing of NC_CHAR attributes is a bit like Perl in that it attempts to do what you want by default (but this sometimes causes unexpected results if you want unusual data storage). printf() \n (ASCII LF, linefeed) characters, special \t (ASCII HT, horizontal tab) If the att_type is NC_CHAR then the argument is interpreted as a string and it may contain C-language escape sequences, e.g., \n, which NCO will interpret before writing anything to disk. NCO translates valid escape sequences and stores the appropriate ASCII code instead. Since two byte escape sequences, e.g., \n, represent one-byte ASCII codes, e.g., ASCII 10 (decimal), the stored string attribute is one byte shorter than the input string length for each embedded escape sequence. The most frequently used C-language escape sequences are \n (for linefeed) and \t (for horizontal tab). These sequences in particular allow convenient editing of formatted text attributes. \a (ASCII BEL, bell) \b (ASCII BS, backspace) \f (ASCII FF, formfeed) \r (ASCII CR, carriage return) \v (ASCII VT, vertical tab) \\ (ASCII \, backslash) The other valid ASCII codes are \a, \b, \f, \r, \v, and \\. ncks netCDF Kitchen Sink, for more examples of string formatting (with the ncks -s option) with special characters. \' (protected end quote) \" (protected double quote) \? (protected question mark) \\ (protected backslash) ' (end quote) " (double quote) ? (question mark) \ (backslash) special characters ASCII Analogous to printf, other special characters are also allowed by ncatted if they are &textldquo;protected&textrdquo; by a backslash. The characters ", ', ?, and \ may be input to the shell as \", \', \?, and \\. NCO simply strips away the leading backslash from these characters before editing the attribute. No other characters require protection by a backslash. Backslashes which precede any other character (e.g., 3, m, $, |, &, &arobase;, %, {, and }) will not be filtered and will be included in the attribute. strings NUL-termination NUL C language 0 (NUL) Note that the NUL character \0 which terminates C language strings is assumed and need not be explicitly specified. If \0 is input, it is translated to the NUL character. However, this will make the subsequent portion of the string, if any, invisible to C standard library string functions. And that may cause unintended consequences. Because of these context-sensitive rules, one must use ncatted with care in order to store data, rather than text strings, in an attribute of type NC_CHAR. Note that ncatted interprets character attributes (i.e., attributes of type NC_CHAR) as strings. <a name="xmp_ncatted"></a>  EXAMPLES Append the string Data version 2.0.\n to the global attribute history:

ncatted -a history,global,a,c,'Data version 2.0\n' in.nc

Note the use of embedded C language printf()-style escape sequences. Change the value of the long_name attribute for variable T from whatever it currently is to &textldquo;temperature&textrdquo;:

ncatted -a long_name,T,o,c,temperature in.nc

<a name="MPAS"></a>  <a name="typ_mch"></a>  <a name="type_match"></a>  MPAS _FillValue Many model and observational datasets use missing values that are not annotated in the standard manner. For example, at the time (2015&textndash;2018) of this writing, the MPAS ocean and ice models use

-9.99999979021476795361e+33

as the missing value, yet do not store a _FillValue attribute with any variables. To prevent arithmetic from treating these values as normal, designate this value as the _FillValue attribute:

ncatted    -a _FillValue,,o,d,-9.99999979021476795361e+33 in.nc
ncatted -t -a _FillValue,,o,d,-9.99999979021476795361e+33 in.nc
ncatted -t -a _FillValue,,o,d,-9.99999979021476795361e+33 \
           -a _FillValue,,o,f,1.0e36 -a _FillValue,,o,i,-999 in.nc

The first example adds the attribute to all variables. The -t switch causes the second example to add the attribute only to double precision variables. This is often more useful, and can be used to provide distinct missing value attributes to each numeric type, as in the third example. <a name="NaNf"></a>  <a name="NaN"></a>  <a name="nan"></a>  NaN NaNf IEEE NaN, NaNf Not-a-Number NCO arithmetic operators may not work as expected on IEEE NaN (short for Not-a-Number) and NaN-like numbers such as positive infinity and negative infinity NaN is a special floating point value (not a string). Arithmetic comparisons to NaN and NaN-like numbers always return False, contrary to the behavior of all other numbers. This behavior is difficult to intuit, yet IEEE 754 mandates it. To correctly handle NaNs during arithmetic, code must use special math library macros (e.g., isnormal()) to determine whether any operand requires special treatment. If so, additional logic must be added to correctly perform the arithmetic. This is in addition to the normal handling incurred to correctly handle missing values. Handling field and missing values (either or both of which may be NaN) in binary operators thus incurs four-to-eight extra code paths. Each code path slows down arithmetic relative to normal numbers. This makes supporting NaN arithmetic costly and inefficient. Hence NCO supports NaN only to the extent necessary to replace it with a normal number. Although using NaN for the missing value (or any value) in datasets is legal in netCDF, we discourage it. We recommend avoiding NaN entirely.. One way to work-around this problem is to change IEEE NaNs to normal missing values. As of NCO 4.1.0 (March, 2012), ncatted works with NaNs (though none of NCO&textrsquo;s arithmetic operators do). This limited support enables users to change NaN to a normal number before performing arithmetic or propagating a NaN-tainted dataset. First set the missing value (i.e., the value of the _FillValue attribute) for the variable(s) in question to the IEEE NaN value.

ncatted -a _FillValue,,o,f,NaN in.nc

Then change the missing value from the IEEE NaN value to a normal IEEE number, like 1.0e36 (or to whatever the original missing value was).

ncatted -a _FillValue,,m,f,1.0e36 in.nc

Some NASA MODIS datasets provide a real-world example.

ncatted -O -a _FillValue,,m,d,1.0e36 -a missing_value,,m,d,1.0e36 \
        MODIS_L2N_20140304T1120.nc MODIS_L2N_20140304T1120_noNaN.nc

Delete all existing units attributes:

ncatted -a units,,d,, in.nc

The value of var_nm was left blank in order to select all variables in the file. The values of att_type and att_val were left blank because they are superfluous in Delete mode. global attribute global attributes attributes, global Delete all attributes associated with the tpt variable, and delete all global attributes

ncatted -a ,tpt,d,, -a ,global,d,, in.nc

The value of att_nm was left blank in order to select all attributes associated with the variable. To delete all global attributes, simply replace tpt with global in the above. units Modify all existing units attributes to meter second-1:

ncatted -a units,,m,c,'meter second-1' in.nc

units Add a units attribute of kilogram kilogram-1 to all variables whose first three characters are H2O:

ncatted -a units,'^H2O',c,c,'kilogram kilogram-1' in.nc

Remove the _FillValue attribute from lat and lon variables.

ncatted -O -a _FillValue,'[lat]|[lon]',d,, in.nc

Overwrite the quanta attribute of variable energy to an array of four integers.

ncatted -a quanta,energy,o,s,'010,101,111,121' in.nc

extended regular expressions regular expressions pattern matching wildcards As of NCO 3.9.6 (January, 2009), ncatted accepts extended regular expressions as arguments for variable names, and, since NCO 4.5.1 (July, 2015), for attribute names.

ncatted -a isotope,'^H2O*',c,s,'18' in.nc
ncatted -a '.?_iso19115$','^H2O*',d,, in.nc

The first example creates isotope attributes for all variables whose names contain H2O. The second deletes all attributes whose names end in _iso19115 from all variables whose names contain H2O. See Subsetting Files for more details on using regular expressions. groups As of NCO 4.3.8 (November, 2013), ncatted accepts full and partial group paths in names of attributes, variables, dimensions, and groups.

# Overwrite units attribute of specific 'lon' variable
ncatted -O -a units,/g1/lon,o,c,'degrees_west' in_grp.nc
# Overwrite units attribute of all 'lon' variables
ncatted -O -a units,lon,o,c,'degrees_west' in_grp.nc
# Delete units attribute of all 'lon' variables
ncatted -O -a units,lon,d,, in_grp.nc
# Overwrite units attribute with new type for specific 'lon' variable
ncatted -O -a units,/g1/lon,o,sng,'degrees_west' in_grp.nc
# Add new_att attribute to all variables
ncatted -O -a new_att,,c,sng,'new variable attribute' in_grp.nc
# Add new_grp_att group attribute to all groups
ncatted -O -a new_grp_att,group,c,sng,'new group attribute' in_grp.nc
# Add new_grp_att group attribute to single group
ncatted -O -a g1_grp_att,g1,c,sng,'new group attribute' in_grp.nc
# Add new_glb_att global attribute to root group
ncatted -O -a new_glb_att,global,c,sng,'new global attribute' in_grp.nc

Note that regular expressions work well in conjuction with group path support. In other words, the variable name (including group path component) and the attribute names may both be extended regular expressions. Demonstrate input of C-language escape sequences (e.g., \n) and other special characters (e.g., \")

ncatted -h -a special,global,o,c,
'\nDouble quote: \"\nTwo consecutive double quotes: \"\"\n
Single quote: Beyond my shell abilities!\nBackslash: \\\n
Two consecutive backslashes: \\\\\nQuestion mark: \?\n' in.nc

Note that the entire attribute is protected from the shell by single quotes. These outer single quotes are necessary for interactive use, but may be omitted in batch scripts. standard input pipes shell xargs Although ncatted accepts multiple -a att_dst options simultaneously, modifying lengthy commands can become unwieldy. To preserve simplicity in storing/modifying multiple attribute edits, consider storing the options separately in a text file and assembling them at run-time to generate and submit the correct command. One such method uses the xargs command to intermediate between an on-disk list attributes to change and the ncatted command. For example, use an intermediate file named options.txt to store one option per line thusly cat > opt.txt << EOF -a institution,global,m,c,\"Super Cool University\" -a source,global,c,c,\"My Awesome Radar\" -a contributors,global,c,c,\"Enrico Fermi, Galileo Galilei, Leonardo Da Vinci\" ... EOF The backslashes preserve the whitespace in the individual attributes for correct parsing by the shell. Simply substituting the expansion of this file through xargs directly on the command line fails to work (why?). However, a simple workaround is to use xargs to construct the command string, and execute that string with eval: opt=$(cat opt.txt | xargs) cmd="ncatted -O ${opt} in.nc out.nc" eval $cmd This procedure can by modified to employ more complex option pre-processing using other tools such as Awk, Perl, or Python. <a name="ncbo"></a>  <a name="ncdiff"></a>  <a name="ncadd"></a>  <a name="ncsub"></a>  <a name="ncsubtract"></a>  <a name="ncmult"></a>  <a name="ncmultiply"></a>  <a name="ncdivide"></a>

ncbo netCDF Binary Operatorncchecker netCDF Compliance Checkerncatted netCDF Attribute EditorReference Manual

ncbo netCDF Binary Operator ncbo ncdiff ncadd ncsub ncsubtract ncmult ncmultiply ncdivide binary operations addition subtraction multiplication adding data subtracting data multiplying data dividing data SYNTAX

ncbo [-3] [-4] [-5] [-6] [-7] [-A] [-C] [-c] [--cmp cmp_sng]
[--cnk_byt sz_byt] [--cnk_csh sz_byt] [--cnk_dmn nm,sz_lmn]
[--cnk_map map] [--cnk_min sz_byt] [--cnk_plc plc] [--cnk_scl sz_lmn]
[-D dbg] [-d dim,[min][,[max][,[stride]]] [-F] [--fl_fmt fl_fmt]
[-G gpe_dsc] [-g grp[,&dots;]] [--glb ...] [-H] [-h] [--hdr_pad nbr] [--hpss]
[-L dfl_lvl] [-l path] [--no_cll_msr] [--no_frm_trm] [--no_tmp_fl] 
[-O] [-o file_3] [-p path] [--qnt ...] [--qnt_alg alg_nm]
[-R] [-r] [--ram_all] [-t thr_nbr] [--unn] [-v var[,&dots;]]
[-X ...] [-x] [-y op_typ]
file_1 file_2 [file_3]

DESCRIPTION ncbo performs binary operations on variables in file_1 and the corresponding variables (those with the same name) in file_2 and stores the results in file_3. The binary operation operates on the entire files (modulo any excluded variables). Missing Values, for treatment of missing values. One of the four standard arithmetic binary operations currently supported must be selected with the -y op_typ switch (or long options --op_typ or --operation). add subtract multiply divide + - * / -y op_typ --operation op_typ --op_typ op_typ alternate invocations The valid binary operations for ncbo, their definitions, corresponding values of the op_typ key, and alternate invocations are: Addition Definition: file_3 = file_1 + file_2&linebreak; Alternate invocation: ncadd&linebreak; op_typ key values: add, +, addition&linebreak; Examples: ncbo --op_typ=add 1.nc 2.nc 3.nc, ncadd 1.nc 2.nc 3.nc&linebreak; Subtraction Definition: file_3 = file_1 - file_2&linebreak; Alternate invocations: ncdiff, ncsub, ncsubtract&linebreak; op_typ key values: sbt, -, dff, diff, sub, subtract, subtraction&linebreak; Examples: ncbo --op_typ=- 1.nc 2.nc 3.nc, ncdiff 1.nc 2.nc 3.nc&linebreak; Multiplication Definition: file_3 = file_1 * file_2&linebreak; Alternate invocations: ncmult, ncmultiply&linebreak; op_typ key values: mlt, *, mult, multiply, multiplication&linebreak; Examples: ncbo --op_typ=mlt 1.nc 2.nc 3.nc, ncmult 1.nc 2.nc 3.nc&linebreak; Division Definition: file_3 = file_1 / file_2&linebreak; Alternate invocation: ncdivide&linebreak; op_typ key values: dvd, /, divide, division&linebreak; Examples: ncbo --op_typ=/ 1.nc 2.nc 3.nc, ncdivide 1.nc 2.nc 3.nc&linebreak;

Care should be taken when using the shortest form of key values, i.e., +, -, *, and /. Some of these single characters may have special meanings to the shell naked characters A naked (i.e., unprotected or unquoted) * is a wildcard character. A naked - may confuse the command line parser. A naked + and / are relatively harmless.. Bash shell Place these characters inside quotes to keep them from being interpreted (globbed) by the shell The widely used shell Bash correctly interprets all these special characters even when they are not quoted. That is, Bash does not prevent NCO from correctly interpreting the intended arithmetic operation when the following arguments are given (without quotes) to ncbo: --op_typ=+, --op_typ=-, --op_typ=*, and --op_typ=/. globbing shell quotes For example, the following commands are equivalent

ncbo --op_typ=* 1.nc 2.nc 3.nc # Dangerous (shell may try to glob)
ncbo --op_typ='*' 1.nc 2.nc 3.nc # Safe ('*' protected from shell)
ncbo --op_typ="*" 1.nc 2.nc 3.nc # Safe ('*' protected from shell)
ncbo --op_typ=mlt 1.nc 2.nc 3.nc
ncbo --op_typ=mult 1.nc 2.nc 3.nc
ncbo --op_typ=multiply 1.nc 2.nc 3.nc
ncbo --op_typ=multiplication 1.nc 2.nc 3.nc
ncmult 1.nc 2.nc 3.nc # First do 'ln -s ncbo ncmult'
ncmultiply 1.nc 2.nc 3.nc # First do 'ln -s ncbo ncmultiply'

No particular argument or invocation form is preferred. Users are encouraged to use the forms which are most intuitive to them. alias ln -s symbolic links Normally, ncbo will fail unless an operation type is specified with -y (equivalent to --op_typ). You may create exceptions to this rule to suit your particular tastes, in conformance with your site&textrsquo;s policy on symbolic links to executables (files of a different name point to the actual executable). For many years, ncdiff was the main binary file operator. As a result, many users prefer to continue invoking ncdiff rather than memorizing a new command (ncbo -y sbt) which behaves identically to the original ncdiff command. However, from a software maintenance standpoint, maintaining a distinct executable for each binary operation (e.g., ncadd) is untenable, and a single executable, ncbo, is desirable. To maintain backward compatibility, therefore, NCO automatically creates a symbolic link from ncbo to ncdiff. Thus ncdiff is called an alternate invocation of ncbo. ncbo supports many additional alternate invocations which must be manually activated. Should users or system adminitrators decide to activate them, the procedure is simple. For example, to use ncadd instead of ncbo --op_typ=add, simply create a symbolic link from ncbo to ncadd The command to do this is ln -s -f ncbo ncadd. The alternatate invocations supported for each operation type are listed above. Alternatively, users may always define ncadd as an alias to ncbo --op_typ=add The command to do this is alias ncadd='ncbo --op_typ=add'. It is important to maintain portability in NCO scripts. Therefore we recommend that site-specfic invocations (e.g., ncadd) be used only in interactive sessions from the command-line. For scripts, we recommend using the full invocation (e.g., ncbo --op_typ=add). This ensures portability of scripts between users and sites. <a name="brd_var"></a>  broadcasting variables rank ncbo operates (e.g., adds) variables in file_2 with the corresponding variables (those with the same name) in file_1 and stores the results in file_3. broadcasting variables Variables in file_1 or file_2 are broadcast to conform to the corresponding variable in the other input file if necessaryPrior to NCO version 4.3.1 (May, 2013), ncbo would only broadcast variables in file_2 to conform to file_1. Variables in file_1 were never broadcast to conform to the dimensions in file_2.. Now ncbo is completely symmetric with respect to file_1 and file_2, i.e., $\rm{file}_1 - \rm{file}_2 = -(\rm{file}_2-\rm{file}_1)$. @clear flg

file_1 - file_2 = - (file_2 - file_1. Broadcasting a variable means creating data in non-existing dimensions by copying data in existing dimensions. For example, a two dimensional variable in file_2 can be subtracted from a four, three, or two (not one or zero) dimensional variable (of the same name) in file_1. anomalies This functionality allows the user to compute anomalies from the mean. In the future, we will broadcast variables in file_1, if necessary to conform to their counterparts in file_2. rank Thus, presently, the number of dimensions, or rank, of any processed variable in file_1 must be greater than or equal to the rank of the same variable in file_2. Of course, the size of all dimensions common to both file_1 and file_2 must be equal. When computing anomalies from the mean it is often the case that file_2 was created by applying an averaging operator to a file with initially the same dimensions as file_1 (often file_1 itself). In these cases, creating file_2 with ncra rather than ncwa will cause the ncbo operation to fail. For concreteness say the record dimension in file_1 is time. If file_2 was created by averaging file_1 over the time dimension with the ncra operator (rather than with the ncwa operator), then file_2 will have a time dimension of size 1 rather than having no time dimension at all degenerate dimension -b This is because ncra collapses the record dimension to a size of 1 (making it a degenerate dimension), but does not remove it, while, unless -b is given, ncwa removes all averaged dimensions. In other words, by default ncra changes variable size though not rank, while, ncwa changes both variable size and rank.. In this case the input files to ncbo, file_1 and file_2, will have unequally sized time dimensions which causes ncbo to fail. To prevent this from occurring, use ncwa to remove the time dimension from file_2. See the example below. coordinate variable NC_CHAR ncbo never operates on coordinate variables or variables of type NC_CHAR or NC_STRING. This ensures that coordinates like (e.g., latitude and longitude) are physically meaningful in the output file, file_3. This behavior is hardcoded. CF conventions ncbo applies special rules to some CF-defined (and/or NCAR CCSM or NCAR CCM fields) such as ORO. See CF Conventions for a complete description. Finally, we note that ncflint (ncflint netCDF File Interpolator) is designed for file interpolation. As such, it also performs file subtraction, addition, multiplication, albeit in a more convoluted way than ncbo. <a name="grp_brd"></a>  <a name="brd_grp"></a>  <a name="gb"></a>  <a name="GB"></a>  broadcasting groups Beginning with NCO version 4.3.1 (May, 2013), ncbo supports group broadcasting. Group broadcasting means processing data based on group patterns in the input file(s) and automatically transferring or transforming groups to the output file. Consider the case where file_1 contains multiple groups each with the variable v1, while file_2 contains v1 only in its top-level (i.e., root) group. Then ncbo will replicate the group structure of file_1 in the output file, file_3. Each group in file_3 contains the output of the corresponding group in file_1 operating on the data in the single group in file_2. An example is provided below. <a name="xmp_ncbo"></a>  <a name="xmp_ncdiff"></a>  EXAMPLES Say files 85_0112.nc and 86_0112.nc each contain 12 months of data. Compute the change in the monthly averages from 1985 to 1986:

ncbo   86_0112.nc 85_0112.nc 86m85_0112.nc
ncdiff 86_0112.nc 85_0112.nc 86m85_0112.nc
ncbo --op_typ=sub 86_0112.nc 85_0112.nc 86m85_0112.nc
ncbo --op_typ='-' 86_0112.nc 85_0112.nc 86m85_0112.nc

These commands are all different ways of expressing the same thing. broadcasting rank The following examples demonstrate the broadcasting feature of ncbo. Say we wish to compute the monthly anomalies of T from the yearly average of T for the year 1985. First we create the 1985 average from the monthly data, which is stored with the record dimension time.

ncra 85_0112.nc 85.nc
ncwa -O -a time 85.nc 85.nc

The second command, ncwa, gets rid of the time dimension of size 1 that ncra left in 85.nc. Now none of the variables in 85.nc has a time dimension. A quicker way to accomplish this is to use ncwa from the beginning:

ncwa -a time 85_0112.nc 85.nc

We are now ready to use ncbo to compute the anomalies for 1985:

ncdiff -v T 85_0112.nc 85.nc t_anm_85_0112.nc

Each of the 12 records in t_anm_85_0112.nc now contains the monthly deviation of T from the annual mean of T for each gridpoint. Say we wish to compute the monthly gridpoint anomalies from the zonal annual mean. A zonal mean is a quantity that has been averaged over the longitudinal (or x) direction. First we use ncwa to average over longitudinal direction lon, creating 85_x.nc, the zonal mean of 85.nc. Then we use ncbo to subtract the zonal annual means from the monthly gridpoint data:

ncwa -a lon 85.nc 85_x.nc
ncdiff 85_0112.nc 85_x.nc tx_anm_85_0112.nc

This examples works assuming 85_0112.nc has dimensions time and lon, and that 85_x.nc has no time or lon dimension. broadcasting groups Group broadcasting simplifies evaluation of multiple models against observations. Consider the input file cmip5.nc which contains multiple top-level groups cesm, ecmwf, and giss, each of which contains the surface air temperature field tas. We wish to compare these models to observations stored in obs.nc which contains tas only in its top-level (i.e., root) group. It is often the case that many models and/or model simulations exist, whereas only one observational dataset does. We evaluate the models and obtain the bias (difference) between models and observations by subtracting obs.nc from cmip5.nc. Then ncbo &textldquo;broadcasts&textrdquo; (i.e., replicates) the observational data to match the group structure of cmip5.nc, subtracts, and then stores the results in the output file, bias.nc which has the same group structure as cmip5.nc.

% ncbo -O cmip5.nc obs.nc bias.nc
% ncks -H -v tas -d time,3 bias.nc
/cesm/tas
time[3] tas[3]=-1 
/ecmwf/tas
time[3] tas[3]=0 
/giss/tas
time[3] tas[3]=1

<a name="csn_anm"></a>  As a final example, say we have five years of monthly data (i.e., 60 months) stored in 8501_8912.nc and we wish to create a file which contains the twelve month seasonal cycle of the average monthly anomaly from the five-year mean of this data. The following method is just one permutation of many which will accomplish the same result. First use ncwa to create the five-year mean:

ncwa -a time 8501_8912.nc 8589.nc

Next use ncbo to create a file containing the difference of each month&textrsquo;s data from the five-year mean:

ncbo 8501_8912.nc 8589.nc t_anm_8501_8912.nc

Now use ncks to group together the five January anomalies in one file, and use ncra to create the average anomaly for all five Januarys. These commands are embedded in a shell loop so they are repeated for all twelve months: Bash Shell Bourne Shell C Shell for idx in {1..12}; do # Bash Shell (version 3.0+) idx=`printf "%02d" ${idx}` # Zero-pad to preserve order ncks -F -d time,${idx},,12 t_anm_8501_8912.nc foo.${idx} ncra foo.${idx} t_anm_8589_${idx}.nc done for idx in 01 02 03 04 05 06 07 08 09 10 11 12; do # Bourne Shell ncks -F -d time,${idx},,12 t_anm_8501_8912.nc foo.${idx} ncra foo.${idx} t_anm_8589_${idx}.nc done foreach idx (01 02 03 04 05 06 07 08 09 10 11 12) # C Shell ncks -F -d time,${idx},,12 t_anm_8501_8912.nc foo.${idx} ncra foo.${idx} t_anm_8589_${idx}.nc end Note that ncra understands the stride argument so the two commands inside the loop may be combined into the single command ncra -F -d time,${idx},,12 t_anm_8501_8912.nc foo.${idx} Finally, use ncrcat to concatenate the 12 average monthly anomaly files into one twelve-record file which contains the entire seasonal cycle of the monthly anomalies:

ncrcat t_anm_8589_??.nc t_anm_8589_0112.nc

ncchecker netCDF Compliance Checkerncclimo netCDF Climatology Generatorncbo netCDF Binary OperatorReference Manual

ncchecker netCDF Compliance Checker CF ncchecker SYNTAX

ncchecker 
[-D dbg] [-i drc_in]
[--tests=tst_lst]
[-x] [-v var[,&dots;]] [--version]
[input-files]

DESCRIPTION <a name="DIWG"></a>  DIWG As of version 5.2.2 (March, 2024), NCO comes with the ncchecker script. This command checks files for compliance with best practices rules and recommendations from various data and metadata standards bodies. These include the Climate & Forecast (CF) Metadata Conventions and the NASA Dataset Interoperability Working Group (DIWG) recommendations. Only a small subset (six tests) of CF or DIWG recommendations are currently supported. The number of tests implemented, or, equivalently, of recommendations checked, is expected to grow. ncchecker reads each data file in input-files, in drc_in, or piped through standard input. It performs the checks requested in the --tests=tst_lst option, if any (otherwise it performs all tests), and writes the results to stdout. The command supports some standard NCO options, including increasing the verbosity level with -D dbg_lvl, excluding variables with -x -v var_lst, variable subsetting with -v var_lst, and printing the version with --version. The output contains counts of the location and number of failed tests, or prints &textldquo;SUCCESS&textrdquo; for tests with no failures. <a name="xmp_ncchecker"></a>  EXAMPLES ncchecker in1.nc in2.nc # Run all tests on two files ncchecker -v var1,var2 in1.nc # Check only two variables ncchecker *.nc # Glob input files via wildcard ls *.nc | ncchecker # Input files via stdin ncchecker --dbg=2 *.nc # Debug ncchecker ncchecker --tests=nan,mss *.nc # Select only two tests ncchecker --tests=xtn,tm,nan,mss,chr,bnd *.nc # Change test ordering ncchecker file:///Users/zender/in_zarr4#mode=nczarr,file # Check Zarr object(s) <a name="ncclimo"></a>  <a name="ncsplit"></a>  <a name="ncsplit"></a>

ncclimo netCDF Climatology Generatorncecat netCDF Ensemble Concatenatorncchecker netCDF Compliance CheckerReference Manual

ncclimo netCDF Climatology Generator climo climatology ncclimo SYNTAX

ncclimo [-3] [-4] [-5] [-6] [-7] 
[-a wnt_md] [-C clm_md] [-c caseid] [--cmp cmp_sng]
[-d dbg_lvl] [--d2f] [--dpf=dpf] [--dpt_fl=dpt_fl] [-E yr_prv] [-e yr_end]
[-f fml_nm] [--fl_fmt=fl_fmt] [--glb_avg] [--glb_stt=glb_stt] 
[-h hst_nm] [-i drc_in] [-j job_nbr] [-L dfl_lvl] [-l lnk_flg]
[-m mdl_nm] [--mth_end=mth_end] [--mth_srt=mth_srt]
[-n nco_opt] [--no_cll_msr] [--no_frm_trm] [--no_ntv_tms] [--no_stg_grd] [--no_stdin] 
[-O drc_rgr] [-o drc_out] [-P prc_typ] [-p par_typ] [--qnt=qnt_prc]
[-R rgr_opt] [-r rgr_map] [-S yr_prv] [-s yr_srt]
[--seasons=csn_lst] [--sgs_frc=sgs_frc] [--split] [--sum_scl=sum_scl]
[-t thr_nbr] [--tpd=tpd] [--uio] [-v var_lst] [--var_xtr=var_xtr] [--version] 
[--vrt_out=vrt_fl] [--vrt_xtr=vrt_xtr]
[-X drc_xtn] [-x drc_prv] [--xcl_var]
[-Y rgr_xtn] [-y rgr_prv] [--ypf=ypf_max]
[input-files]

DESCRIPTION In climatology generation mode, ncclimo ingests &textldquo;raw&textrdquo; data consisting of interannual sets of files, each containing sub-daily (diurnal), daily, monthly, or yearly averages, and from these produces climatological daily, monthly, seasonal, and/or annual means. Alternatively, in timeseries reshaping (aka &textldquo;splitter&textrdquo;) mode, ncclimo will subset and temporally split the input raw data timeseries into per-variable files spanning the entire period. ncclimo can optionally (call ncremap to) regrid all output files in either mode. Unlike the rest of NCO, ncclimo and ncremap are shell scripts, not compiled binariesThis means that newer (including user-modified) versions of ncclimo work fine without re-compiling NCO. Re-compiling is only necessary to take advantage of new features or fixes in the NCO binaries, not to improve ncclimo. One may download and give executable permissions to the latest source at https://github.com/nco/nco/tree/master/data/ncclimo without re-installing the rest of NCO.. <a name="HDF5_USE_FILE_LOCKING"></a>  HDF5_USE_FILE_LOCKING As of NCO 4.9.2 (February, 2020), the ncclimo and ncremap scripts export the environment variable HDF5_USE_FILE_LOCKING with a value of FALSE. This prevents failures of these operators that can occur with some versions of the underlying HDF library that attempt to lock files on file systems that cannot, or do not, support it. There are five (usually) required options (-c, -s, -e, -i, and -o)) to generate climatologies, and many more options are available to customize the processing. Options are similar to ncremap options. Standard ncclimo usage for climatology generation looks like

ncclimo            -c caseid -s srt_yr -e end_yr -i drc_in -o drc_out
ncclimo -m mdl_nm  -c caseid -s srt_yr -e end_yr -i drc_in -o drc_out
ncclimo -v var_lst -c caseid -s srt_yr -e end_yr -i drc_in -o drc_out
ncclimo --case=caseid --start=srt_yr --end=end_yr --input=drc_in --output=drc_out

In climatology generation mode, ncclimo constructs the list of input filenames from the arguments to the caseid, date, and model-type options. As of NCO version 4.9.4 (September, 2020), ncclimo can produce climatologies of high-frequency input data supplied via standard input, positional command-line options, or directory contents, all input methods traditionally supported only in splitter mode. Instead of using the caseid option to help generate the input filenames as it does for normal (monthly) climos, ncclimo uses the caseid option, when provided, to rename the output files for high-frequency climos. # Generate diurnal climos from high-frequency CMIP6 timeseries cd ${drc_in};ls ${caseid}*.h4.nc | ncclimo --clm_md=hfc \ -c ${caseid} --yr_srt=2001 --yr_end=2002 --drc_out=${HOME} standard input stdin ncclimo automatically switches to timeseries reshaping mode if it receives a list of files from stdin, or, alternatively, placed as positional arguments (after the last command-line option), or if neither of these is done and no caseid is specified, in which case it assumes all *.nc files in drc_in constitute the input file list. # Split monthly timeseries into CMIP-like timeseries cd ${drc_in};ls ${caseid}*.h4.nc | ncclimo -v=T \ --ypf=1 --yr_srt=56 --yr_end=76 --drc_out=${HOME} # Split high-frequency timeseries into CMIP-like timeseries cd ${drc_in};ls ${caseid}*.h4.nc | ncclimo --clm_md=hfs -v=T \ --ypf=1 --yr_srt=56 --yr_end=76 --drc_out=${HOME} Options for ncclimo and ncremap come in both short (single-letter) and long forms. The handful of long-option synonyms for each option allows the user to imbue the commands with a level of verbosity and precision that suits her taste. A complete description of all options is given below, in alphabetical order of the short option letter. Long option synonyms are given just after the letter. When invoked without options, ncclimo and ncremap print a succinct table of all options and some examples. All valid options for both operators are listed in their command syntax above but, for brevity, options that ncclimo passes straight through to ncremap are only fully described in the table of ncremap options. <a name="winter_mode"></a>  <a name="wnt_md"></a>  <a name="dec_md"></a>  <a name="dcm_md"></a>  <a name="december_mode"></a>  <a name="djf"></a>  <a name="DJF"></a>  <a name="jfd"></a>  <a name="JFD"></a>  -a wnt_md djf DJF JFD jfd --dec_md --dcm_md --december_mode --wnt_md --winter_mode dec_md -a dec_md (--dec_md, --dcm_md, --december_mode, --dec_mode) Winter mode aka December mode determines the start and end months of the climatology and the type of NH winter seasonal average. Two valid arguments are jfd (default, or synonyms sdd and JFD) and djf (or synonyms scd and DJF). DJF-mode is the same as SCD-mode which stands for &textldquo;Seasonally Continuous December&textrdquo;. The first month used is December of the year before the start year specified with -s. The last month is November of the end year specified with -e. In DJF-mode the Northern Hemisphere winter seasonal climatology will be computed with sets of the three consecutive months December, January, and February (DJF) where the calendar year of the December months is always one less than the calendar year of January and February. JFD-mode is the same as SDD-mode which stands for &textldquo;Seasonally Discontinuous December&textrdquo;. The first month used is January of the specified start year. The last month is December of the end year specified with -e. In JFD-mode the Northern Hemisphere winter seasonal climatology will be computed with sets of the three non-consecutive months January, February, and December (JFD) from each calendar year. <a name="clm_md"></a>  -C clm_md clm_md --clm_md --climatology_mode --mode --climatology -C clm_md (--clm_md, --climatology_mode, --mode, --climatology) Climatology mode. Valid values for clm_md are ann (or synonyms annual, annual, yearly, or year) for annual-mode climatologies, dly (or synonyms daily, doy, or day) for daily-mode climatologies, hfc (or synonyms high_frequency_climo or hgh_frq_clm) for high-frequency (diurnally resolved) climos, hfs (or synonyms high_frequency_splitter or hgh_frq_spl) for high-frequency splitting, and mth (or synonyms month or monthly) for monthly climotologies. The value indicates the timespan of each input file for annual and monthly climatologies. The default mode is mth, which means input files are monthly averages. Use ann when the input files are a series of annual means (a common temporal resolution for ice-sheet simulations). The value dly is used only input files whose temporal resolution is daily or finer, and when the desired output is a day-of-year climatology where the means are output for each day of a 365 day year. Day-of-year climatologies are uncommon, yet useful for showing daily variability. The value hfc indicates a high-frequency climatology where the output will be a traditional set of climatological monthly, seasonal, or annual means similar to monthly climos, except that each file will have the same number of timesteps-per-day as the input data to resolve the diurnal cycle. The value hfs indicates a high-frequency splitting operation where an interannual input timeseries will be split into regular size segments of a given number of years, similar to CMIP timeseries. The climatology generator and splitter do not require that daily-mode input files begin or end on daily boundaries. These tools hyperslab the input files using the date information required to performed their analysis. This facilitates analyzing datasets with varying numbers of days per input file. stdin crontab nohup Python Explicitly specifying --clm_md=mth serves a secondary purpose, namely invoking the default setting on systems that control stdin. When ncclimo detects that stdin is not attached to the terminal (keyboard) it automatically expects a list of files on stdin. Some environments, however, hijack stdin for their purposes and thereby confuse ncclimo into expecting a list argument. Users have encountered this issue when attempting to run ncclimo in Python parallel environments, via inclusion in crontab, and in nohup-mode (whatever that is!). In such cases, explicitly specify --clm_md=mth (or ann or day) to persuade ncclimo to compute a normal climatology. <a name="caseid"></a>  -c caseid caseid --caseid --case -c caseid (--case, --caseid, --case_id) Simulation name, or any input filename for non-CESM&textrsquo;ish files. The use of caseid is required in climate generation mode (unless equivalent information is provided through other options), where caseid is used to construct both input and output filenames. For CESM&textrsquo;ish input files like famipc5_ne30_v0.3_00001.cam.h0.1980-01.nc, specify -c famipc5_ne30_v0.3_00001. The .cam. and .h0. bits are added internally to produce the input filenames. Modify these via the and options if needed. For input files named slightly differently than standard CESM&textrsquo;ish names, supply the filename (excluding the path component) as the caseid and then ncclimo will attempt to parse that by matching to a database of regular expressions known to be favored by various other datasets. These expressions are all various formats of dates at the end of the filenames, and adhere to the general convention prefix[.-]YYYY[-]MM[-]DD[-]SSSSS.suffix. The particular formats currently supported, as of NCO version 5.1.6 (May, 2023) are: prefix_YYYYMM.suffix, prefix.YYYY-MM.suffix, prefix.YYYY-MM-01.suffix, and prefix.YYYY-MM-01-00000.suffix. For example, input files like merra2_198001.nc (i.e., the six digits that precede the suffix are YYYYMM-format), specify -c merra2_198001.nc and the prefix (merra2) will be automatically abstracted and used to template and generate all the filenames based on the specified yr_srt and yr_end. ncclimo -c merra2_198001.nc --start=1980 --end=1999 --drc_in=${drc} ncclimo -c cesm_1980-01.nc --start=1980 --end=1999 --drc_in=${drc} ncclimo -c eamxx_1980-01-00000.nc --start=1980 --end=1999 --drc_in=${drc} Please tell us any common dataset filename regular expressions that you would like added to ncclimo&textrsquo;s internal database. The --caseid=caseid option is not mandatory in the High-Frequency-Splitter (clm_md=hfs) and High-Frequency-Climatology (clm_md=hfc) modes. Those modes expect all input filenames to be entered from the command-line so there is no internal need to create filenames from the caseid variable. Instead, when caseid is specified in a high-freqency mode, its value is used to name the output files in a similar manner to the -f fml_nm option. -D dbg_lvl dbg_lvl --dbg_lvl --debug_level -D dbg_lvl (--dbg_lvl, --dbg, --debug, --debug_level) Specifies a debugging level similar to the rest of NCO. If

dbg_lvl = 1, ncclimo prints more extensive diagnostics of its behavior. If

dbg_lvl = 2, ncclimo prints the commands it would execute at any higher or lower debugging level, but does not execute these commands. If

dbg_lvl > 2, ncclimo prints the diagnostic information, executes all commands, and passes-through the debugging level to the regridder (ncks) for additional diagnostics. d2f --d2f --d2s --dbl_flt --dbl_sgl --double_float ncpdq Precision --d2f (--d2f, --d2s, --dbl_flt, --dbl_sgl, --double_float) This switch (which takes no argument) causes ncclimo to invoke ncremap with the same switch, so that ncremap converts all double precision non-coordinate variables to single precision in the regridded file. This switch has no effect on files that are not regridded. To demote the precision in such files, use ncpdq to apply the dbl_flt packing map to the file directly. <a name="dpf"></a>  --dpf=dpf dpf --dpf --days-per-file --dpf=dpf (--dpf, --days_per_file) The number of days-per-file in files ingested by ncclimo. It can sometimes be difficult for ncclimo to infer the number of days-per-file in high-frequency input files, i.e., those with 1 or more timesteps-per-day. In such cases, users may override the inferred value by explicitly specifying --dpf=dpf. --dpt_fl=dpt_fl dpt_fl --mpas_fl --mpas_depth --depth_file add_depth.py Depth MPAS --dpt_fl=dpt_fl (--dpt_fl, --depth_file, --mpas_fl, --mpas_depth) The --dpt_fl=dpt_fl triggers the addition of a depth coordinate to MPAS ocean datasets that will undergo regridding. ncclimo passes this option through to ncremap, and this option has no effect when ncclimo does not invoke ncremap. The ncremap documentation contains the full description of this option. <a name="end_yr"></a>  <a name="yr_end"></a>  -e end_yr end_yr --end_yr --end_year --year_end --end -e end_yr (--end_yr, --yr_end, --end_year, --year_end, --end) End year (example: 2000). By default, the last month is December of the specified end year. If -a scd is specified, the last month used is November of the specified end year. <a name="fml_nm"></a>  -f fml_nm fml_nm --fml_nm --fml --family --family_name -f fml_nm (--fml_nm, --fml, --family, --family_name) Family name (nickname) of output files. In climate generation mode, output climo file names are constructed by default with the same caseid as the input files. The fml_nm, if supplied, replaces caseid in output climo names, which are of the form fml_nm_XX_YYYYMM_YYYYMM.nc where XX is the month or seasonal abbreviation. Use -f fml_nm to simplify long names, avoid overlap, etc. Example values of fml_nm are control, experiment, and (for a single-variable climo) FSNT. In timeseries reshaping mode, fml_nm will be used, if supplied, as an additional string in the output filename. For example, specifying -f control would cause T_000101_000912.nc to be instead named T_control_000101_000912.nc. <a name="hst_nm"></a>  -h hst_nm hst_nm --history_name --hst_nm --history -h hst_nm (--hst_nm, --history_name, --history) History volume name of file used to generate climatologies. This referring to the hst_nm character sequence used to construct input file names: caseid.mdl_nm.hst_nm.YYYY-MM.nc. By default input climo file names are constructed from the caseid of the input files, together with the model name mdl_nm (specified with -m) and the date range. Use -h hst_nm to specify alternative history volumes. Examples include h0 (default, works for CAM, CLM/CTSM/ELM), h1, and h (for CISM). <a name="drc_in"></a>  -i drc_in drc_in --drc_in --in_drc --dir_in --input -i drc_in (--drc_in, --in_drc, --dir_in, --input) Directory containing all monthly mean files to read as input to the climatology. The use of drc_in is mandatory in climate generation mode and is optional in timeseries reshaping mode. In timeseries reshaping mode, ncclimo uses all netCDF files (meaning files with suffixes .nc, .nc3, .nc4, .nc5, .nc6, .nc7, .cdf, .hdf, .he5, or .h5) in drc_in to create the list of input files when no list is provided through stdin or as positional arguments to the command-line. <a name="job_nbr"></a>  <a name="job_nbr_ncclimo"></a>  -j job_nbr job_nbr --job_nbr --job_number --jobs -j job_nbr (--job_nbr, --job_number, --jobs) The job_nbr parameter controls the parallelism granularity of both timeseries reshaping (aka splitting) and climatology generation. These modes parallelize over different types of tasks, so we describe the effects of job_nbr separately, first for climatologies, then for splitting. However, for both modes, job_nbr specifies the total number of simultaneous processes to run in parallel either on the local node for Background parallelism, or across all the nodes for MPI parallelism (i.e., job_nbr is the simultaneous total across all nodes, it is not the simultaneous number per node). For climatology generation, job_nbr specifies the number of averaging tasks to perform simultaneously on the local node for Background parallelism, or spread across all nodes for MPI-parallelism. By default ncclimo sets

job_nbr = 12 for background parallelism mode. This number ensures that monthly averages for all individual months complete more-or-less simultaneously, so that all seasonal averages can then be computed. However, many nodes are too small to simultaneously average multiple distinct months (January, February, etc.). Hence job_nbr may be set to any factor of 12, i.e., 1, 2, 3, 4, 6, or 12. For Background parallelism, setting

job_nbr = 4 causes four-months to be averaged at one time. After three batches of four-months complete, the climatology generator then moves on to seasonal averaging and regridding. In MPI-mode, ncclimo defaults to

job_nbr = nd_nbr unless the user explicitly sets job_nbr to a different value. For the biggest jobs, when a single-month nearly exhausts the RAM on a node, this default value

job_nbr = nd_nbr ensures that each node gets only one job at a time. To override this default for MPI-parallelism, set

job_nbr >= nd_nbr otherwise some nodes will be idle for the entire time. If a node can handle averaging three distinct months simultaneously, then try

job_nbr = 3*nd_nbr. Never set

job_nbr > 12 in climatology mode, since there are at most only twelve jobs that can be performed in parallel. For splitting, job_nbr specifies the number of simultaneous subsetting processes to spawn during parallel execution for both Background and MPI-parallelism. In both parallelism modes ncclimo spawns processes in batches of job_nbr jobs, then waits for those processes to complete. Once a batch finishes, ncclimo spawns the next batch. For Background-parallelism, all jobs are spawned to the local node. For MPI-parallelism, all jobs are spawned in round-robin fashion to all available nodes until job_nbr jobs are running. Rinse, lather, repeat until all variables have been split. The splitter chooses its default value of job_nbr based on on the parallelism mode. For Background parallelism, job_nbr defaults to the number of variables to be split, so that not specifying job_nbr results in launching var_nbr simultaneous splitter tasks. This scales well to over a hundred variables in our tests At least one known environment (the E3SM-Unified Anaconda environment at NERSC) prevents users from spawning scores of processes and may report OpenBLAS/pthread or RLIMIT_NPROC-related errors. A solution seems to be executing ulimit -u unlimited. In practice, splitting timeseries consumes minimal memory, since ncrcat (which underlies the splitter) only holds one record (timestep) of a variable in memory Memory Requirements. However, if splitting consumes so much RAM (e.g., because variables are large and/or the number of jobs is large) that a single node can perform only one or a few subsetting jobs at a time, then it is reasonable to use MPI-mode to split the datasets. For MPI-parallelism, job_nbr defaults to the number of nodes requested. This helps prevent users from overloading nodes with too many jobs. Usually, however, nodes can usually subset (and then regrid, if requested) multiple variables simultaneously. In summary, the splitter defaults to

job_nbr = var_nbr in Background mode, and to

job_nbr = node_nbr in MPI mode. Subject to the availability of adequate RAM, expand the number of jobs-per-node by increasing job_nbr until overall throughput peaks. The main throughput bottleneck in timeseries reshaping mode is I/O. Increasing job_nbr may reduce throughput once the maximum I/O bandwidth of the node is reached, due to contention for I/O resources. Regridding requires math that can relieve some I/O contention and allows for some throughput gains with increasing job_nbr. One strategy that seems sensible is to set job_nbr equal to the number of nodes times the number of cores per node, and increase or decrease as necessary until throughput peaks. -L --dfl_lvl --dfl --deflate -L (--dfl_lvl, --dfl, --deflate) Activate deflation (i.e., lossless compress, see Deflation) with the -L dfl_lvl short option (or with the same argument to the --dfl_lvl or --deflate long options). Specify deflation level dfl_lvl on a scale from no deflation (dfl_lvl = 0, the default) to maximum deflation (dfl_lvl = 9). <a name="lnk_flg"></a>  -l --lnk_flg --link_flag --no_amwg_links --no_amwg --amwg_links -l (--lnk_flg, --link_flag) --no_amwg_link (--no_amwg_link, --no_amwg_links, --no_amwg, --no_AMWG_link, --no_AMWG_links) --amwg_link (--amwg_link, --amwg_links, --AMWG_link, --AMWG_links) These options turn-on or turn-off the linking of E3SM/ACME-climo to AMWG-climo filenames. AMWG omits the YYYYMM components of climo filenames, resulting in shorter names. By default ncclimo symbolically links the full E3SM/ACME filename (which is always) created to a file with the shorter (AMWG) name whose creation is optional. AMWG diagnostics scripts can produce plots directly from the linked AMWG filenames. The -l (and --lnk_flg and --link_flag long-option synonmyms) are true options that require an argument of either Yes or No. The remaining synonyms are switches that take no arguments. The --amwg_link switch and its synonyms cause the creation of symbolic links with AMWG filenames. The --no_amwg_link switch and its synonyms prevent the creation of symbolic links with AMWG filenames. If you do not need AMWG filenames, turn-off linking to reduce file proliferation in the output directories. <a name="mdl_nm"></a>  -m mdl_nm mdl_nm --model_name --mdl --model --mdl_nm -m mdl_nm (--mdl_nm, --mdl, --model_name, --model) Model name (as embedded in monthly input filenames). Default is cam. Other options are clm2, ocn, ice, cism, cice, pop. <a name="nco_opt"></a>  -n nco_opt nco_opt --nco_opt --nco_options --nco -n nco_opt (nco_opt, nco, nco_options) Specifies a string of options to pass-through unaltered to ncks. nco_opt defaults to --no_tmp_fl. Note that ncclimo passes its nco_opt to ncremap. This can cause unexpected results, so use the front-end options to ncclimo when possible, rather than attempting to subvert them with nco_opt. <a name="drc_rgr"></a>  -O drc_rgr drc_rgr --drc_rgr --rgr_drc --dir_rgr --regrid -O drc_rgr (--drc_rgr, --rgr_drc, --dir_rgr, --regrid) Directory to hold regridded climo files. Regridded climos are placed in drc_out unless a separate directory for them is specified with -O (NB: capital &textldquo;O&textrdquo;). <a name="no_cll_msr"></a>  --no_cll_msr --no_cll --no_cell_measures --no_area cell_measures attribute --no_cll_msr (--no_cll_msr, --no_cll, --no_cell_measures, --no_area) This switch (which takes no argument) controls whether ncclimo and ncremap add measures variables to the extraction list along with the primary variable and other associated variables. See CF Conventions for a detailed description. <a name="no_frm_trm"></a>  --no_frm_trm --no_frm --no_formula_terms formula_terms attribute --no_frm_trm (--no_frm_trm, --no_frm, --no_formula_terms) This switch (which takes no argument) controls whether ncclimo and ncremap add formula variables to the extraction list along with the primary variable and other associated variables. See CF Conventions for a detailed description. <a name="hms_avg"></a>  <a name="rgn_avg"></a>  <a name="glb_avg"></a>  <a name="global_average"></a>  <a name="regional_average"></a>  --glb_avg --global_average --rgn_avg --regional_average --hms_avg --hemispheric_average global average regional average hemispheric average --glb_avg (--glb_avg, --global_average) (deprecated) --rgn_avg (--rgn_avg, --region_average) When introduced in NCO version 4.9.1 (released December, 2019), this switch (which takes no argument) caused the splitter to output global horizontally spatially averaged timeseries files instead of raw, native-grid timeseries. This switch changed behavior in NCO version 5.1.1 (released November, 2022). It now causes the splitter to output three horizontally spatially averaged timeseries. First is the global average (as before), next is the northern hemisphere average, followed by the southern hemisphere average. The three timeseries are now saved in a two-dimensional (time by region) array with a &textldquo;region dimension&textrdquo; named rgn. The region names are stored in the variable named region_name. As of NCO version 5.2.3 (released March, 2024), this switch works with sub-gridscale fractions, such as are common in surface models like ELM and CLM. The correct weights for (SGS) fraction will automatically be applied so long as ncclimo is invoked with -P prc_typ. Otherwise the field containing the (SGS) fraction must be supplied with --sgs_frc=sgs_frc. This switch only has effect in timeseries splitting mode. This is useful, for example, to quickly diagnose the behavior of ongoing model simulations prior to a full-blown analysis. Thus the spatial mean files will be in the same location and have the same name as the native grid timeseries would have been and had, respectively. Note that this switch does not alter the capability of also outputting the full regridded timeseries, if requested, at the same time. Because the switch now outputs global and regional averages, the best practice is to invoke with --rgn_avg instead of --glb_avg. NCO version 5.2.8 (released September, 2024) superseded this switch by introducing support for more general global/regional statistics timeseries output via the --glb_stt/--rgn_stt options. <a name="hms_stt"></a>  <a name="rgn_stt"></a>  <a name="glb_stt"></a>  <a name="global_statistic"></a>  <a name="regional_statistic"></a>  <a name="foo"></a>  <a name="foo"></a>  <a name="foo"></a>  <a name="foo"></a>  --sum_scl --sum_scale --scl_fct --scale_factor --sum_scl --glb_stt --global_statistic --rgn_stt --regional_statistic --hms_stt global statistic regional statistic hemispheric statistic sum scale scale factor --sum_scl --glb_stt (--glb_stt, --global_statistic) --rgn_stt (--rgn_stt, --region_statistic) NCO version 5.2.8 (released September, 2024) introduced the --glb_stt/--rgn_stt options (or long options equivalents --hms_stt, --regional_statistic, and --global_statistic) to support more general global/regional statistics timeseries output. These options allow the user to choose which statistic, sums or averages, to output with global/regional timeseries for all variables. Set rgn_stt to avg, average, or mean to output timeseries of the global/regional mean statistic. Set rgn_stt to sum, total, ttl, or integral to output timeseries of the global/regional sum. The option --rgn_stt=avg is equivalent setting the --rgn_avg switch (which may eventually be deprecated). When invoked with --rgn_stt=sum the averaged field is multiplied by the sum of the area variable. For area-intensive fields (e.g., fluxes per unit area) this results in the total net flux over the area. However, the field must employ the same areal units as the area variable for this to be true. For example, fields given in inverse square meters would need to employ an area variable in square meters. Unfortunately, many people love non-SI units so that is rarely the case! For example, ELM and CLM archive area in a field named area whose units are square kilometers, so a scale factor of one million is needed to correct the sum for many variables. EAM and CAM also archive area in a field named area, though in unis of inverse steradians, which would require a different scale factor to match the sums of area-intensive fields. That is why ncclimo introduced a second new option --sum_scl=sum_scl, in NCO version 5.2.8 (released September, 2024). The long option equivalents are --scl_fct, --sum_scale, and --scale_factor. When rgn_stt is sum, the sum_scl scale factor multiplies the integrated field value, which allows the user to generate timeseries in the desired units for any field. Consider these prototypical examples to generate global timeseries of common geophysical statistics from ESM output: # Timeseries of global GPP in grams/s for ELM/CLM: ncclimo -P elm --split --rgn_stt=sum --sum_scl=1.0e6 -v GPP ... # Timeseries of global GPP in GT C/yr for ELM/CLM: ncclimo -P elm --split --rgn_stt=sum --sum_scl=1.0e6*3600*24*365/1.0e12 -v GPP ... # Timeseries of global column vapor in kg for EAM/CAM: ncclimo -P eam --split --rgn_stt=sum --sum_scl=6.37122e6^2 -v TMQ ... All three examples set rgn_stt to sum in order to activate the sum_scl factor. The first example scales (multiplies) the mean of all global timeseries (here, GPP for concreteness) by one million. This factor converts the ELM or CLM area variable from square kilometers to square meters, appropriate to to integrating fields like GPP whose fluxes are per square meter. The output timeseries of GPP would then be in gC s-1. The second example sets the scale factor to convert the global GPP statistic to units of GT C yr-1. The third example shows how to convert areal sums in sterradians (which EAM and CAM use for area) to square meters. This factor converts atmospheric variables from global mean mass per square meter to global total mass. The --glb_stt=sum/--sum_scl procedure is model- and variable-specific and we are open to suggestions to make it more useful. <a name="no_ntv_tms"></a>  <a name="no_ntv"></a>  <a name="no_native"></a>  --no_ntv_tms --no_ntv --no_native --remove_native --no_ntv_tms (--no_ntv_tms, --no_ntv, --no_native, --remove_native) This switch (which takes no argument) controls whether the splitter retains native grid split files, which it does by default, or deletes them. ncclimo can split model output from multi-variable native grid files into per-variable timeseries files and regrid those onto a so-called analysis grid. That is the typical format in which Model Intercomparison Projects (MIPs) request and disseminate contributions. When the data producer has no use for the split timeseries on the native grid, he/she can invoke this flag to cause ncclimo to delete the native grid timeseries (not the raw native grid datafiles). This functionality is implemented by first creating the native grid timeseries, regridding it, and then overwriting the native grid timeseries with the regridded timeseries. Thus the regridded files will be in the same location and have the same name as the native grid timeseries would have been and had, respectively. <a name="no_stg_grd"></a>  <a name="no_stg"></a>  slat slon w_stag --no_stg_grd --no_stg --no_stagger --no_staggered_grid --no_stg_grd (--no_stg_grd, --no_stg, --no_stagger, --no_staggered_grid) This switch (which takes no argument) controls whether regridded output will contain the staggered grid coordinates slat, slon, and w_stag (Regridding). By default the staggered grid is output for all files regridded from a Cap (aka FV) grid, except when the regridding is performed as part of splitting (reshaping) into timeseries. <a name="drc_out"></a>  -o drc_out drc_out --drc_out --out_drc --dir_out --output -o drc_out (--drc_out, --out_drc, --dir_out, --output) Directory to hold computed (output) native grid climo files. Regridded climos are also placed here unless a separate directory for them is specified with -O (NB: capital &textldquo;O&textrdquo;). <a name="par_typ"></a>  <a name="par_typ_ncclimo"></a>  -p par_typ par_typ --par_typ --par_md --parallel_type --parallel_mode --parallel -p par_typ (--par_typ, --par_md, --parallel_type, --parallel_mode, --parallel) Specifies the parallelism mode desired. The options are serial mode (-p srl, -p serial, or -p nil), background mode parallelism (-p bck or -p background)), and MPI parallelism (-p mpi or -p MPI). The default is background-mode parallelism. The default par_typ is background, which means ncclimo spawns up to twelve (one for each month) parallel processes at a time. See discussion below under Memory Considerations. <a name="qnt_prc"></a>  --qnt=qnt_prc ppc_prc qnt_prc --ppc --ppc_prc --qnt_prc --precision --qnt --quantize --qnt=qnt_prc (--ppc, --ppc_prc, --precision, --qnt, --quantize) Decimal Significant Digits Number of Significant Digits Bit-Grooming Specifies the precision of the Precision-Preserving Compression algorithm (Precision-Preserving Compression). A positive integer is interpreted as the Number of Significant Digits for the Bit-Grooming algorithm, and is equivalent to specifying --qnt default=qnt_prc to a binary operator. A positive or negative integer preceded by a period, e.g., .-2 is interpreted as the number of Decimal Significant Digits for the rounding algorithm and is equivalent to specifying --qnt default=.qnt_prc to a binary operator. This option applies one precision algorithm and a uniform precision for the entire file. To specify variable-by-variable precision options, pass the desired options as a quoted string directly with -n nco_opt, e.g., -n '--qnt FSNT,TREFHT=4 --qnt CLOUD=2'. <a name="rgr_opt"></a>  -R rgr_opt rgr_opt --rgr_opt --regrid_options -R rgr_opt (rgr_opt, regrid_options) Specifies a string of options to pass-through unaltered to ncks. rgr_opt defaults to -O --no_tmp_fl. <a name="rgr_map"></a>  -r rgr_map rgr_map --rgr_map --regrid_map --map -r rgr_map (--rgr_map, --regrid_map, --map) Regridding map. Unless -r is specified ncclimo produces only a climatology on the native grid of the input datasets. The rgr_map specifies how to (quickly) transform the native grid into the desired analysis grid. ncclimo will (call ncremap to) apply the given map to the native grid climatology and produce a second climatology on the analysis grid. Options intended exclusively for the regridder may be passed as arguments to the -R switch. See below the discussion on regridding. <a name="mth_srt"></a>  <a name="srt_mth"></a>  <a name="mth_end"></a>  <a name="end_mth"></a>  --mth_srt --srt_mth --start_month --month_start --mth_srt=mth_srt (--mth_srt, --srt_mth, --month_start, --start_month) --mth_end --end_mth --end_month --month_end --mth_end=mth_end (--mth_end, --end_mth, --month_end, --end_month) Start month (example: 4), and end month (example: 11). The starting month of monthly timeseries extracted by the splitter defaults to January of the specified start year, and the ending month defaults to December of the specified end year. As of NCO version 4.9.8, released in March, 2021, the splitter mode of ncclimo accepts user-specified start and end months with the --mth_srt and --mth_end options, respectively. Months are input as one-based integers so January is 1 and December is is 12. To extract 14-month timeseries from individual monthly input files one could use, e.g.,

ncclimo --yr_srt=1 --yr_end=2 --mth_srt=4 --mth_end=5 ...

Note that mth_srt and mth_end only affect the splitter, and that they play no role in climatology generation. <a name="srt_yr"></a>  <a name="yr_srt"></a>  -s srt_yr srt_yr --srt_yr --start_year --year_start --start -s srt_yr (--srt_yr, --yr_srt, --start_year, --year_start, --start) Start year (example: 1980). By default, the first month used is January of the specified start year. If -a scd is specified, the first month used will be December of the year before the start year (to allow for contiguous DJF climos). <a name="csn_lst"></a>  --seasons=csn_lst csn_lst --seasons --csn --csn_lst --seasons=csn_lst (--seasons, --csn_lst, --csn) Seasons for ncclimo to compute in monthly climatology generation mode. The list of seasons, csn_lst, is a comma-separated, case-insensitive, unordered subset of the abbreviations for the eleven (so far) defined seasons: jfm, amj, jas, ond, on, fm, djf, mam, jja, son, and ann. By default csn_lst=mam,jja,son,djf. Moreover, ncclimo automatically computes the climatological annual mean, ANN, is always computed when MAM, JJA, SON, and DJF are all requested (which is the default). The ANN computed automatically is the time-weighted average of the four seasons, rather than as the time-weighted average of the twelve monthly climatologies. Users who need ANN but not DJF, MAM, JJA, and SON should instead explicitly specify ANN as a season in csn_lst. The ANN computed as a season is the time-weighted average of the twelve monthly climatologies, rather than the time-weighted average of four seasonal climatologies. Specifying the four seasons and ANN in csn_lst (e.g., csn_lst=mam,jja,son,djf,ann) is legal though redundant and wasteful. It cause ANN to be computed twice, first as the average of the twelve monthly climatologies, then as the average of the four seasons. The special value csn_lst=none turns-off computation of seasonal (and annual) climatologies.

ncclimo --seasons=none ...            # Produce only monthly climos
ncclimo --seasons=mam,jja,son,djf ... # Monthly + MAM,JJA,SON,DJF,ANN
ncclimo --seasons=jfm,jas,ann ...     # Monthly + JFM,JAS,ANN
ncclimo --seasons=fm,on ...           # Monthly + FM,ON

<a name="split"></a>  <a name="splitter"></a>  <a name="tms_flg"></a>  <a name="timeseries"></a>  --split --splitter --tms_flg --timeseries --split (--split, --splitter, --tms_flg, --timeseries) This switch (which takes no argument) explicitly instructs ncclimo to split the input multi-variable raw datasets into per-variable timeseries spanning the entire period. The --split switch, and its synonyms --splitter, --tms_flg, and --timeseries, were introduced in NCO version 5.0.4 (released December, 2021). Previously, the splitter was automatically invoked whenever the input files were provided via stdin, globbing, or positional command-line arguments, with some exceptions. That older method became ambiguous and untenable once it was decided to also allow climos to be generated from files provided via stdin, globbing, or positional command-line arguments. Now there are three methods to invoke the splitter: 1) Use the --split flag: this is the most explicit way to invoke the splitter. 2) Select clm_md=hfs: the high-frequency splitter mode by definition invokes the splitter, so a more explicit option than this is not necessary. 3) Set the years-per-file option, e.g., --ypf=25: the ypf_max option is only useful to the splitter, and has thus been used in many scripts. Since this option still causes the splitter to be invoked, those will perform as before the API change. These three splitter invocations methods are non-exclusive, i.e., more than one can be used, and there is no harm in doing so. While the API change in version 5.0.4 does proscribe the former practice of passively invoking the splitter by simply piping files to stdin or similar, it enables much more flexibility for future features, including the possibility of automatically generating timeseries filenames for the splitter, and of piping files to stdin or similar for climo generation. <a name="no_stdin"></a>  Chrysalis Common Workflow Language Azure CI SLURM CWL --no_stdin --no_inp_std --no_standard_input --no_redirect --no_stdin (--no_stdin, --no_inp_std, --no_redirect, --no_standard_input) First introduced in NCO version 4.8.0 (released May, 2019), this switch (which takes no argument) disables checking standard input (aka stdin) for input files. This is useful because ncclimo and ncremap may mistakenly expect input to be provided on stdin in environments that use stdin for other purposes. Some non-interactive environments (e.g., crontab, nohup, Azure CI, CWL), may use standard input for their own purposes, and thus confuse NCO into thinking that you provided the input files names via the stdin mechanism. In such cases users may disable the automatic checks for standard input by explicitly invoking the --no_stdin flag. This switch is usually not required for jobs in an interactive shell. Interactive SLURM shells can also commandeer stdin, as is the case on the DOE machine named Chrysalis. This behavior appears to vary depending on the SLURM implementation. ncclimo --no_stdin -v T -s 2000 -e 2001 --ypf=10 -i in -o out -t thr_nbr thr_nbr --thr_nbr --thr --threads --thread_number -t thr_nbr (--thr_nbr, --thr, --thread_number, --threads) Specifies the number of threads used per regridding process (OpenMP Threading). The NCO regridder scales well to 8&textndash;16 threads. However, regridding with the maximum number of threads can interfere with climatology generation in parallel climatology mode (i.e., when

par_typ = mpi or bck). Hence ncclimo defaults to thr_nbr=2. <a name="tpd"></a>  --tpdtpd_out --tpd --tpd_out --timesteps_per_day tpd --tpd=tpd (--tpd_out, --tpd, --timesteps_per_day) Normally, the number of timesteps-per-day in files ingested by ncclimo. It can sometimes be difficult for ncclimo to infer the number of timesteps-per-day in high-frequency input files, i.e., those with 1 or more timesteps-per-day. In such cases, users may override the inferred value by explicitly specifying --tpd=tpd. The value of tpd_out in daily-average climatology mode clm_md=dly (which is generally not used outside of ice-sheet models) is different, and actually refers to the number of timesteps per day that ncclimo will output, regardless of its value in the input files. Hence in daily-average mode (only), we refer to this variable as tpd_out. The climatology output from input files at daily or sub-daily resolution is, by default, averaged to daily resolution, i.e., tpd_out=1. If the number of timesteps per day in each input file is tpd_in, then the user may select any value of tpd_out that is smaller than and integrally divides tpd_in. For example, an input timeseries with tpd_in=8 (i.e., 3-hourly resolution), can be used to produce climatological output at 3, 6, or 12-hourly resolution by setting tpd_out to 8, 4, or 2, respectively. This option only takes effect in daily-average climatology mode. For full generality, the --tpd option should probably be split into separate options --tpd_in and --tpd_out. However, because it is unlikely that anyone will need to specify these to different values, we leave only one option. If this hinders you, please let us know and we will split the options. -v var_lst var_lst --var_lst --var --vars --variables --variable_list -v var_lst (--var_lst, --var, --vars, --variables, --variable_list) Variables to subset or to split. Same behavior as Subsetting Files. The use of var_lst is optional in clim-generation mode. We suggest using this feature to test whether an ncclimo command, especially one that is lengthy and/or time-consuming, works as intended on one or a few variables with, e.g., -v T,FSNT before generating the full climatology (by omitting this option). Invoking this switch was required in the original splitter released in version 4.6.5 (March, 2017), and became optional as of version 4.6.6 (May, 2017). This option is recommended in timeseries reshaping mode to prevent inadvertently copying the results of an entire model simulation. Regular expressions are allowed so, e.g., PREC.? extracts the variables PRECC,PRECL,PRECSC,PRECSL if present. Currently in reshaping mode all matches to a regular expression are placed in the same output file. We hope to remove this limitation in the future. <a name="var_xtr"></a>  var_xtr --var_xtr --var_extra --extra_variables --variables_extra --var_xtr=var_xtr (--var_xtr, --var_xtr, --var_extra, --variables_extra, --extra_variables) The --var_xtr option causes ncclimo to include the extra variables list in var_xtr in every timeseries split from the raw data. This is useful when extra variables are desired in timeseries. There are no limits on the extra variables&textmdash;they may be of any rank and may be timeseries themselves. One useful application of this option, is to ensure that the area variable is included with each timeseries, e.g., --var_xtr=area. --version --vrs --config --configuration --cnf --version (--version, --vrs, --config, --configuration, --cnf) This switch (which takes no argument) causes the operator to print its version and configuration. This includes the copyright notice, URLs to the BSD and NCO license, directories from which the NCO scripts and binaries are running, and the locations of any separate executables that may be used by the script. <a name="xcl_var"></a>  --xcl_var --xcl --exclude --exclude_variables --xcl_var (--xcl_var, --xcl, --exclude, --exclude_variables) This flag (which takes no argument) changes var_lst, as set by the --var_lst option, from an extraction list to an exclusion list so that variables in var_lst will not be processed, and variables not in var_lst will be processed. Thus the option -v var_lst must also be present for this flag to take effect. Variables explicitly specified for exclusion by --xcl --vars=var_lst[,&dots;] need not be present in the input file. Previously, this switch has always woked in climo mode. As of NCO version 5.2.5 (July, 2024), this switch also works in timeseries mode. <a name="ypf"></a>  --ypf_max ypf_max ypf_max --ypf_max --ypf_max ypf_max (--ypf, --years, --years_per_file) Specifies the maximum number of years-per-file output by ncclimo&textrsquo;s splitting operation. When ncclimo subsets and splits a collection of input files spanning a timerseries, it places each subset variable in its own output file. The maximum length, in years, of each output file is ypf_max, which defaults to ypf_max=50. If an input timeseries spans 237 years and ypf_max=50, then ncclimo will generate four output files of length 50 years and one output file of length 37 years. Note that invoking this option causes ncclimo to enter timeseries reshaping mode. In fact, one must use --ypf to turn-on splitter mode when the input files are specified by using the -i drc_in method. Otherwise it would be ambiguous whether to generate a climatology from or to split the input files.

Timeseries Reshaping mode, aka Splitting This section of the ncclimo documentation applies only to resphaping mode, whereas all subsequent sections apply to climatology generation mode. In splitter mode, ncclimo reshapes the input so that the outputs are continuous timeseries of each variable taken from all input files. As of NCO version 5.0.4 (released December, 2021), ncclimo enters splitter mode when invoked with the --split switch (or its synonyms --splitter, --tms_flg, or --timeseries) or with the --ypf_max option. Then ncclimo will create per-variable timeseries from the list of files supplied via stdin, or, alternatively, placed as positional arguments (after the last command-line option), or if neither of these is done and no caseid is specified, in which case it assumes all *.nc files in drc_in constitute the input file list. These examples invoke reshaping mode in the four possible ways:

# Pipe list to stdin
cd $drc_in;ls *mdl*000[1-9]*.nc | ncclimo --split -v T,Q,RH -s 1 -e 9 -o $drc_out
# Redirect list from file to stdin
cd $drc_in;ls *mdl*000[1-9]*.nc > foo;ncclimo --split -v T,Q,RH -s 1 -e 9 -o $drc_out < foo
# List as positional arguments
ncclimo --split -v T,Q,RH -s 1 -e 9 -o $drc_out $drc_in/*mdl*000[1-9]*.nc
# Glob directory
ncclimo --split -v T,Q,RH -s 1 -e 9 -i $drc_in -o $drc_out

Assuming each input file is a monthly average comprising the variables T, Q, and RH, then the output will be files T_000101_000912.nc, Q_000101_000912.nc, and RH_000101_000912.nc. When necessary, the output is split into segments each containing no more than ypf_max (default 50) years of input, i.e., T_000101_005012.nc, T_005101_009912.nc, T_010001_014912.nc, etc. MPAS-O/SI/LI considerations MPAS ocean and ice models currently have their own (non-CESM&textrsquo;ish) naming convention that guarantees output files have the same names for all simulations. By default ncclimo analyzes the &textldquo;timeSeriesStatsMonthly&textrdquo; analysis member output (tell us if you want options for other analysis members). ncclimo and ncremap recognize input files as being MPAS-style when invoked with -P mpas or with the more expressive synonym --prc_typ=mpas. The the generic -P mpas invocation works for generating climatologies for any MPAS model. However, some regridder options are model-specific and therefore it is smarter to specify which MPAS model produced the input data with -P mpasatmosphere, (or -P mpasa for short), -P mpasocean, (or -P mpaso for short), -P mpasseaice, (or -P mpassi for short), or -P mali, like this: ncclimo -P mpasa -c $case -s 1980 -e 1983 -i $drc_in -o $drc_out # MPAS-A ncclimo -P mpaso -c $case -s 1980 -e 1983 -i $drc_in -o $drc_out # MPAS-O ncclimo -P mpassi -c $case -s 1980 -e 1983 -i $drc_in -o $drc_out # MPAS-SI ncclimo -P mali -c $case -s 1980 -e 1983 -i $drc_in -o $drc_out # MPAS-LI As of June 2024 and NCO version 5.2.5, ncclimo updated its MPAS dataset filename construction option. Previously it constructed MPAS monthly datasets names like this: $mdl_nm.hist.am.timeSeriesStatsMonthly.$YYYY-$MM-01.nc. where mdl_nm is the canonical MPAS component name, e.g., mpaso. This yielded names consistent with E3SM v1 output like mpaso.hist.am.timeSeriesStatsMonthly.0001-02-01.nc, and mpascice.hist.am.timeSeriesStatsMonthly.0001-02-01.nc. Now ncclimo prepends the caseid, if present, to the filename. This yields names consistent with E3SM v2 and v3 output like v2.LR.historical_0101.mpaso.hist.am.timeSeriesStatsMonthly.0001-02-01.nc, and v2.LR.historical_0101.mpassi.hist.am.timeSeriesStatsMonthly.0001-02-01.nc. To read MPAS filenames with other patterns, simply pipe the filenames to ncclimo: ls *mpas*hist | ncclimo .... Raw output data from all MPAS models does not contain missing value attributes We submitted pull-requests to implement the _FillValue attribute in all MPAS-ocean output in July, 2020. The status of this PR may be tracked at https://github.com/MPAS-Dev/MPAS-Model/pull/677. Once this PR is merged to master, we will do the same for the MPAS-Seaice and MPAS-Landice models.. These attributes must be manually added before sending the data as input to ncclimo or ncremap. We recommend that simulation producers annotate all floating point variables with the appropriate _FillValue prior to invoking ncclimo. Run something like this once in the history-file directory: for fl in `ls hist.*` ; do ncatted -O -t -a _FillValue,,o,d,-9.99999979021476795361e+33 ${fl} done If/when MPAS-O/I generates the _FillValue attributes itself, this step can and should be skipped. All other ncclimo features like regridding (below) are invoked identically for MPAS as for CAM/CLM users although under-the-hood ncclimo does do some special pre-processing (dimension permutation, metadata annotation) for MPAS. A five-year oEC60to30 MPAS-O climo with regridding to T62 takes less than 10 minutes on the machine rhea. Annual climos Not all model or observed history files are created as monthly means. To create a climatological annual mean from a series of annual mean inputs, select ncclimo&textrsquo;s annual climatology mode with the -C ann option: ncclimo -C ann -m cism -h h -c caseid -s 1851 -e 1900 -i drc_in -o drc_out The options -m mdl_nm and -h hst_nm (that default to cam and h0, respectively) tell ncclimo how to construct the input filenames. The above formula names the files caseid.cism.h.1851-01-01-00000.nc, caseid.cism.h.1852-01-01-00000.nc, and so on. Annual climatology mode produces a single output file (or two if regridding is selected), and in all other respects behaves the same as monthly climatology mode. Regridding Climos and Other Files ncclimo will (optionally) regrid during climatology generation and produce climatology files on both native and analysis grids. This regridding is virtually free, because it is performed on idle nodes/cores after monthly climatologies have been computed and while seasonal climatologies are being computed. This load-balancing can save half-an-hour on ne120 datasets. To regrid, simply pass the desired mapfile name with -r map.nc, e.g., -r maps/map_ne120np4_to_fv257x512_aave.20150901.nc. Although this should not be necessary for normal use, you may pass any options specific to regridding with -R opt1 opt2. Specifying -O drc_rgr (NB: uppercase O) causes ncclimo to place the regridded files in the directory drc_rgr. These files have the same names as the native grid climos from which they were derived. There is no namespace conflict because they are in separate directories. These files also have symbolic links to their AMWG filenames. If -O drc_rgr is not specified, ncclimo places all regridded files in the native grid climo output directory, drc_out, specified by -o drc_out (NB: lowercase o). To avoid namespace conflicts when both climos are stored in the same directory, the names of regridded files are suffixed by the destination geometry string obtained from the mapfile, e.g., *_climo_fv257x512_bilin.nc. These files also have symbolic links to their AMWG filenames.

ncclimo -c amip_xpt -s 1980 -e 1983 -i drc_in -o drc_out
ncclimo -c amip_xpt -s 1980 -e 1983 -i drc_in -o drc_out -r map_fl
ncclimo -c amip_xpt -s 1980 -e 1983 -i drc_in -o drc_out -r map_fl -O drc_rgr

The above commands perform a climatology without regridding, then with regridding (all climos stored in drc_out), then with regridding and storing regridded files separately. Paths specified by drc_in, drc_out, and drc_rgr may be relative or absolute. An alternative to regridding during climatology generation is to regrid afterwards with ncremap, which has more special features built-in for regridding. To use ncremap to regrid a climatology in drc_out and place the results in drc_rgr, use something like

ncremap -I drc_out -m map.nc -O drc_rgr
ls drc_out/*climo* | ncremap -m map.nc -O drc_rgr

See ncremap netCDF Remapper for more details (including MPAS!). <a name="yr_end_prv"></a>  <a name="yr_srt_prv"></a>  <a name="drc_xtn"></a>  <a name="drc_prv"></a>  <a name="drc_rgr_xtn"></a>  <a name="drc_rgr_prv"></a>  <a name="clm_bnr"></a>  <a name="clm_xtn"></a>  Extended Climatologies incremental climatology (climo) binary climatology (climo) extended climatology (climo) previous climatology (climo) current climatology (climo) ncclimo supports two methods for generating extended climatologies: Binary and Incremental. Both methods lengthen a climatology without requiring access to all the raw monthly data spanning the time period. The binary method combines, with appropriate weighting, two previously computed climatologies into a single climatology. No raw monthly data are employed. The incremental method computes a climatology from raw monthly data and (with appropriate weighting) combines that with a previously computed climatology that ends the month prior to raw data. The incremental method was introduced in NCO version 4.6.1 (released August, 2016), and the binary method was introduced in NCO version 4.6.3 (released December, 2016). Both methods, binary and incremental, compute the so-called &textldquo;extended climo&textrdquo; as a weighted mean of two shorter climatologies, called the &textldquo;previous&textrdquo; and &textldquo;current&textrdquo; climos. The incremental method uses the original monthly input to compute the curent climo, which must immediately follow in time the previous climo which has been pre-computed. The binary method use pre-computed climos for both the previous and current climos, and these climos need not be sequential nor chronological. Both previous and current climos for both binary and incremental methods may be of any length (in years); their weights will be automatically adjusted in computing the extended climo. The use of pre-computed climos permits ongoing simulations (or lengthy observations) to be analyzed in shorter segments combined piecemeal, instead of requiring all raw, native-grid data to be simultaneously accessible. Without extended climatology capability, generating a one-hundred year climatology requires that one-hundred years of monthly data be available on disk. Disk-space requirements for large datasets may make this untenable. Extended climo methods permits a one-hundred year climo to be generated as the weighted mean of, say, the current ten year climatology (weighted at 10%) combined with the pre-computed climatology of the previous 90-years (weighted at 90%). The 90-year climo could itself have been generated incrementally or binary-wise, and so on. Climatologies occupy at most 17/(12N) the amount of space of N years of monthly data, so the extended methods vastly reduce disk-space requirements. Incremental mode is selected by specifying -S, the start year of the pre-computed, previous climo. The argument to -S) is the previous climo start year. That, together with the current climo end year, determines the extended climo range. ncclimo assumes that the previous climo ends the month before the current climo begins. In incremental mode, ncclimo first generates the current climatology from the current monthly input files then weights that current climo with the previous climo to produce the extended climo. Binary mode is selected by specifying both -S and -E, the end year of the pre-computed, previous climo. In binary mode, the previous and current climatologies can be of any length, and from any time-period, even overlapping. Most users will run extended clmos the same way they run regular climos in terms of parallelism and regridding, although that is not required. Both climos must treat Decembers same way (or else previous climo files will not be found), and if subsetting (i.e., -v var_lst) is performed, then the subset must remain the same, and if nicknames (i.e., -f fml_nm) are employed, then the nickname must remain the same. As of 20161129, the climatology_bounds attributes of extended climos are incorrect. This is a work in progress... Options: -E yr_end_prv yr_end_prv --yr_end_prv --prv_yr_end --previous_end -E yr_end_prv (--yr_end_prv, --prv_yr_end, --previous_end) The ending year of the previous climo. This argument is required to trigger binary climatologies, and should not be used for incremental climatologies. -S yr_srt_prv yr_srt_prv --yr_srt_prv --prv_yr_srt --previous_start -S yr_srt_prv (--yr_srt_prv, --prv_yr_srt, --previous_start) The starting year of the previous climo. This argument is required to trigger incremental climatologies, and is also mandatory for binary climatologies. -X drc_xtn drc_xtn --drc_xtn --xtn_drc --extended -X drc_xtn (--drc_xtn, --xtn_drc, --extended) Directory in which the extended native grid climo files will be stored for an extended climatology. Default value is drc_prv. Unless a separate directory is specified (with -Y) for the extended climo on the analysis grid, it will be stored in drc_xtn, too. -x drc_prv drc_prv --drc_prv --prv_drc --previous -x drc_prv (--drc_prv, --prv_drc, --previous) Directory in which the previous native grid climo files reside for an incremental climatology. Default value is drc_out. Unless a separate directory is specified (with -y) for the previous climo on the analysis grid, it is assumed to reside in drc_prv, too. -Y drc_rgr_xtn drc_rgr_xtn --drc_rgr_xtn --drc_xtn_rgr --extended_regridded --regridded_extended -Y drc_rgr_xtn (--drc_rgr_xtn, --drc_xtn_rgr, --extended_regridded, --regridded_extended) Directory in which the extended analysis grid climo files will be stored in an incremental climatology. Default value is drc_xtn. -y drc_rgr_prv drc_rgr_prv --drc_rgr_prv --drc_prv_rgr --regridded_previous --previous_regridded -y drc_rgr_prv (--drc_rgr_prv, --drc_prv_rgr, --regridded_previous, --previous_regridded) Directory in which the previous climo on the analysis grid resides in an incremental climatology. Default value is drc_prv.

Incremental method climatologies can be as simple as providing a start year for the previous climo, e.g.,

ncclimo -v FSNT,AODVIS -c caseid -s 1980 -e 1981 -i raw -o clm -r map.nc
ncclimo -v FSNT,AODVIS -c caseid -s 1982 -e 1983 -i raw -o clm -r map.nc -S 1980

By default ncclimo stores all native and analysis grid climos in one directory so the above &textldquo;just works&textrdquo;. There are no namespace clashes because all climos are for distinct years, and regridded files have a suffix based on their grid resolution. However, there can be only one set of AMWG filename links due to AMWG filename convention. Thus AMWG filename links, if any, point to the latest extended climo in a given directory. Many researchers segregate (with -O drc_rgr) native-grid from analysis-grid climos. Incrementally generated climos must be consistent in this regard. In other words, all climos contributing to an extended climo must have their native-grid and analysis-grid files in the same (per-climo) directory, or all climos must segregate their native from their analysis grid files. Do not segregate the grids in one climo, and combine them in another. Such climos cannot be incrementally aggregated. Thus incrementing climos can require from zero to four additional options that specify all the previous and extended climatologies for both native and analysis grids. The example below constructs the current climo in crr, then combines the weighted average of that with the previous climo in prv, and places the resulting extended climatology in xtn. Here the native and analysis climos are combined in one directory per climo:

ncclimo -v FSNT,AODVIS -c caseid -s 1980 -e 1981 -i raw -o prv -r map.nc
ncclimo -v FSNT,AODVIS -c caseid -s 1982 -e 1983 -i raw -o clm -r map.nc \
        -S 1980 -x prv -X xtn

If the native and analysis grid climo directories are segregated, then those directories must be specified, too:

ncclimo -v FSNT,AODVIS -c caseid -s 1980 -e 1981 -i raw -o prv -O rgr_prv -r map.nc
ncclimo -v FSNT,AODVIS -c caseid -s 1982 -e 1983 -i raw -o clm -O rgr -r map.nc \
        -S 1980 -x prv -X xtn -y rgr_prv -Y rgr_xtn

ncclimo does not know whether a pre-computed climo is on a native grid or an analysis grid, i.e., whether it has been regridded. In binary mode, ncclimo may be pointed to two pre-computed native grid climatologies, or to two pre-computed analysis grid climatologies. In other words, it is not necessary to maintain native grid climatologies for use in creating extended climatologies. It is sufficient to generate climatologies on the analysis grid, and feed them to ncclimo in binary mode, without a mapping file:

ncclimo -c caseid -S 1980 -E 1981 -x prv -s 1980 -e 1981 -i crr -o clm

Coupled Runs ncclimo works on all E3SM/ACME and CESM models. It can simultaneously generate climatologies for a coupled run, where climatologies mean both native and regridded monthly, seasonal, and annual averages as per E3SM/ACME specifications (which mandate the inclusion of certain helpful metadata and provenance information). Here are template commands for a recent simulation: caseid=20160121.A_B2000ATMMOD.ne30_oEC.titan.a00 drc_in=/scratch/simulations/$caseid/run drc_out=${DATA}/acme map_atm=${DATA}/maps/map_ne30np4_to_fv129x256_aave.20150901.nc map_lnd=$map_atm map_ocn=${DATA}/maps/map_oEC60to30_to_t62_bilin.20160301.nc map_ice=$map_ocn ncclimo -p mpi -c $caseid -m cam -s 2 -e 5 -i $drc_in -r $map_atm -o $drc_out/atm ncclimo -c $caseid -m clm2 -s 2 -e 5 -i $drc_in -r $map_lnd -o $drc_out/lnd ncclimo -p mpi -m mpaso -s 2 -e 5 -i $drc_in -r $map_ocn -o $drc_out/ocn ncclimo -m mpassi -s 2 -e 5 -i $drc_in -r $map_ice -o $drc_out/ice Atmosphere and ocean model output is typically larger than land and ice model output. These commands recognize that by using different parallelization strategies that may (rhea standard queue) or may not (cooley, or rhea&textrsquo;s bigmem queue) be required, depending on the fatness of the analysis nodes, as explained below. Memory Considerations It is important to employ the optimal ncclimo parallelization strategy for your computer hardware resources. Select from the three available choices with the switch. The options are serial mode (-p srl, -p serial, or -p nil), background mode parallelism (-p bck, or -p background), and MPI parallelism (-p mpi or -p MPI). The default is background-mode parallelism. This is appropriate for lower resolution (e.g., ne30L30) simulations on most nodes at high-performance computer centers. Use (or at least start with) serial mode on personal laptops/workstations. Serial mode requires twelve times less RAM than the parallel modes, and is much less likely to deadlock or cause OOM (out-of-memory) conditions on your personal computer. If the available RAM (plus swap) is

< 12*4*

sizeof(monthly input file), then try serial mode first (12 is the optimal number of parallel processes for monthly climos, the computational overhead is a factor of four). EAM-SE ne30L30 output is about 1 GB/month so each month requires about 4 GB of RAM. EAM-SE ne30L72 output (with LINOZ) is about 10 GB/month so each month requires about 40 GB RAM. EAM-SE ne120 output is about 12 GB/month so each month requires about 48 GB RAM. The computer does not actually use all this memory at one time, and many kernels compress RAM usage to below what top reports, so the actual physical usage is hard to pin-down, but may be a factor of 2.5&textndash;3.0 (rather than a factor of four) times the size of the input file. For instance, my 16 GB 2014 MacBookPro successfully runs an ne30L30 climatology (that requests 48 GB RAM) in background mode. However the laptop is slow and unresponsive for other uses until it finishes (in 6&textndash;8 minutes) the climos. Experiment and choose the parallelization option that performs best. Serial-mode, as its name implies, uses one core at a time for climos, and proceeds sequentially from months to seasons to annual climatologies. Serial mode means that climos are performed serially, while regridding still employs OpenMP threading (up to 16 cores) on platforms that support it. By design each month and each season is independent of the others, so all months can be computed in parallel, then each season can be computed in parallel (using monthly climatologies), from which annual average is computed. Background parallelization mode exploits this parallelism and executes the climos in parallel as background processes on a single node, so that twelve cores are simultaneously employed for monthly climatologies, four for seasonal, and one for annual. The optional regridding will employ, by default, up to two cores per process. The MPI parallelism mode executes the climatologies on different nodes so that up to (optimally) twelve nodes compute monthly climos. The full memory of each node is available for each individual climo. The optional regridding employs, by default, up to eight cores per node in MPI-mode. MPI-mode or serial-mode must be used to process ne30L72 and ne120L30 climos on all but the fattest DOE nodes. An ne120L30 climo in background mode on rhea (i.e., on one 128 GB compute node) fails due to OOM. (Unfortunately OOM errors do not produce useful return codes so if your climo processes die without printing useful information, the cause may be OOM). However the same climo in background-mode succeeds when executed on a single big-memory (1 TB) node on rhea (use -lpartition=gpu, as shown below). Or MPI-mode can be used for any climatology. The same ne120L30 climo will also finish blazingly fast in background mode on cooley (i.e., on one 384 GB compute node), so MPI-mode is unnecessary on cooley. In general, the fatter the memory, the better the performance. Single, Dedicated Nodes at LCFs The basic approach above (running the script from a standard terminal window) that works well for small cases can be unpleasantly slow on login nodes of LCFs and for longer or higher resolution (e.g., ne120) climatologies. As a baseline, generating a climatology of 5 years of ne30 (~1x1 degree) EAM-SE output with ncclimo takes 1&textndash;2 minutes on rhea (at a time with little contention), and 6&textndash;8 minutes on a 2014 MacBook Pro. To make things a bit faster at LCFs, request a dedicated node (this only makes sense on supercomputers or clusters with job-schedulers). On rhea or titan, which use the PBS scheduler, do this with

# Standard node (128 GB), PBS scheduler
qsub -I -A CLI115 -V -l nodes=1 -l walltime=00:10:00 -N ncclimo
# Bigmem node (1 TB), PBS scheduler
qsub -I -A CLI115 -V -l nodes=1 -l walltime=00:10:00 -lpartition=gpu -N ncclimo

The equivalent requests on cooley or mira (Cobalt scheduler) and cori or titan (SLURM scheduler) are:

# Cooley node (384 GB) with Cobalt
qsub -I -A HiRes_EarthSys --nodecount=1 --time=00:10:00 --jobname=ncclimo 
# Cori node (128 GB) with SLURM
salloc  -A acme --nodes=1 --partition=debug --time=00:10:00 --job-name=ncclimo

Flags used and their meanings: -I Submit in interactive mode. This returns a new terminal shell rather than running a program. --time How long to keep this dedicated node for. Unless you kill the shell created by the qsub command, the shell will exist for this amount of time, then die suddenly. In the above examples, 10 minutes is requested. -l nodes=1 PBS syntax (e.g., on rhea) for nodes. --nodecount 1 Cobalt syntax (e.g., on cooley) for nodes. --nodes=1 SLURM syntax (e.g., on cori or edison) for nodes. These scheduler-dependent variations request a quantity of nodes. Request 1 node for Serial or Background-mode, and up to 12 nodes for MPI-mode parallelism. In all cases ncclimo will use multiple cores per node if available. -V Export existing environmental variables into the new interactive shell. This may not actually be needed. -q name Queue name. This is needed for locations like edison that have multiple queues with no default queue. -A Name of account to charge for time used.

Acquiring a dedicated node is useful for any workflow, not just creating climos. This command returns a prompt once nodes are assigned (the prompt is returned in your home directory so you may then have to cd to the location you meant to run from). Then run your code with the basic ncclimo invocation. The is faster because the node is exclusively dedicated to ncclimo. Again, ne30L30 climos only require < 2 minutes, so the 10 minutes requested in the example is excessive and conservative. Tune it with experience. 12 node MPI-mode Jobs The above parallel approaches will fail when a single node lacks enough RAM (plus swap) to store all twelve monthly input files, plus extra RAM for computations. One should employ MPI multinode parallelism -p mpi on nodes with less RAM than

12*3*

sizeof(input). The longest an ne120 climo will take is less than half an hour (~25 minutes on edison or rhea), so the simplest method to run MPI jobs is to request 12-interactive nodes using the above commands (though remember to add -p mpi), then execute the script at the command line. It is also possible, and sometimes preferable, to request non-interactive compute nodes in a batch queue. Executing an MPI-mode climo (on machines with job scheduling and, optimally, 12 nodes) in a batch queue can be done in two commands. First, write an executable file which calls the ncclimo script with appropriate arguments. We do this below by echoing to a file, ncclimo.pbs. echo "ncclimo -p mpi -c $caseid -s 1 -e 20 -i $drc_in -o $drc_out" > ncclimo.pbs The only new argument here is -p mpi that tells ncclimo to use MPI parallelism. Then execute this command file with a 12 node non-interactive job:

qsub -A CLI115 -V -l nodes=12 -l walltime=00:30:00 -j oe -m e -N ncclimo \
     -o ncclimo.out ncclimo.pbs

This script adds new flags: -j oe (combine output and error streams into standard error), -m e (send email to the job submitter when the job ends), -o ncclimo.out (write all output to ncclimo.out). The above commands are meant for PBS schedulers like on rhea. Equivalent commands for cooley/mira (Cobalt) and cori/edison (SLURM) are # Cooley (Cobalt scheduler) /bin/rm -f ncclimo.err ncclimo.out echo '#!/bin/bash' > ncclimo.cobalt echo "ncclimo -p mpi -c $caseid -s 1 -e 20 -i $drc_in -o $drc_out" >> ncclimo.cobalt chmod a+x ncclimo.cobalt qsub -A HiRes_EarthSys --nodecount=12 --time=00:30:00 --jobname ncclimo \ --error ncclimo.err --output ncclimo.out --notify zender@uci.edu ncclimo.cobalt # Cori/Edison (SLURM scheduler) echo "ncclimo -p mpi -c $caseid -s 1 -e 20 -i $drc_in -o $drc_out -r $map_fl" \ > ncclimo.pbs chmod a+x ncclimo.slurm sbatch -A acme --nodes=12 --time=03:00:00 --partition=regular --job-name=ncclimo \ --mail-type=END --error=ncclimo.err --output=ncclimo.out ncclimo.slurm Notice that Cobalt and SLURM require the introductory shebang-interpreter line (#!/bin/bash) which PBS does not need. Set only the scheduler batch queue parameters mentioned above. In MPI-mode, ncclimo determines the appropriate number of tasks-per-node based on the number of nodes available and script internals (like load-balancing for regridding). Hence do not set a tasks-per-node parameter with scheduler configuration parameters as this could cause conflicts. What does ncclimo do? For monthly climatologies (e.g., JAN), ncclimo passes the list of all relevant January monthly files to NCO&textrsquo;s ncra command, which averages each variable in these monthly files over their time-dimension (if it exists) or copies the value from the first month unchanged (if no time-axis exists). Seasonal climos are then created by taking the average of the monthly climo files using ncra. To account for differing numbers of days per month, the ncra -w flag is used, followed by the number of days in the relevant months. For example, the MAM climo is computed with ncra -w 31,30,31 MAR_climo.nc APR_climo.nc MAY_climo.nc MAM_climo.nc (details about file names and other optimization flags have been stripped here to make the concept easier to follow). The annual (ANN) climo is then computed as a weighted average of the seasonal climos. Assumptions, Approximations, and Algorithms (AAA) Employed: A climatology embodies many algorithmic choices, and regridding from the native to the analysis grid involves still more choices. A separate method should reproduce the ncclimo and NCO answers to round-off precision if it implements the same algorithmic choices. For example, ncclimo agrees to round-off with AMWG diagnostics when making the same (sometimes questionable) choices. The most important choices have to do with converting single- to double-precision (SP and DP, respectively), treatment of missing values, and generation/application of regridding weights. For concreteness and clarity we describe the algorithmic choices made in processing a EAM-SE monthly output into a climatological annual mean (ANN) and then regridding that. Other climatologies (e.g., daily to monthly, or annual-to-climatological) involve similar choices. E3SM/ACME (and CESM) computes fields in DP and outputs history (not restart) files as monthly means in SP. The NCO climatology generator (ncclimo) processes these data in four stages. Stage N accesses input only from stage

N-1, never from stage

N-2 or earlier. Thus the (on-disk) files from stage N determine the highest precision achievable by stage

N+1. The general principal is to perform math (addition, weighting, normalization) in DP and output results to disk in the same precision in which they were input from disk (usually SP). In Stage 1, NCO ingests Stage 0 monthly means (raw EAM-SE output), converts SP input to DP, performs the average across all years, then converts the answer from DP to SP for storage on-disk as the climatological monthly mean. In Stage 2, NCO ingests Stage 1 climatological monthly means, converts SP input to DP, performs the average across all months in the season (e.g., DJF), then converts the answer from DP to SP for storage on-disk as the climatological seasonal mean. In Stage 3, NCO ingests Stage 2 climatological seasonal means, converts SP input to DP, performs the average across all four seasons (DJF, MAM, JJA, SON), then converts the answer from DP to SP for storage on-disk as the climatological annual mean. Stage 2 weights each input month by its number of days (e.g., 31 for January), and Stage 3 weights each input season by its number of days (e.g., 92 for MAM). E3SM/ACME runs EAM-SE with a 365-day calendar, so these weights are independent of year and never change. The treatment of missing values in Stages 1&textndash;3 is limited by the lack of missing value tallies provided by Stage 0 (model) output. Stage 0 records a value as missing if it is missing for the entire month, and present if the value is valid for one or more timesteps. Stage 0 does not record the missing value tally (number of valid timesteps) for each spatial point. Thus a point with a single valid timestep during a month is weighted the same in Stages 1&textndash;4 as a point with 100% valid timesteps during the month. The absence of tallies inexorably degrades the accuracy of subsequent statistics by an amount that varies in time and space. On the positive side, it reduces the output size (by a factor of two) and complexity of analyzing fields that contain missing values. Due to the ambiguous nature of missing values, it is debatable whether they merit efforts to treat them more exactly. The vast majority of fields undergo three promotion/demotion cycles between EAM-SE and ANN. No promotion/demotion cycles occur for history fields that EAM-SE outputs in DP rather than SP, nor for fields without a time dimension. Typically these fields are grid coordinates (e.g., longitude, latitude) or model constants (e.g., $\cod$ @clear flg CO2 mixing ratio). NCO never performs any arithmetic on grid coordinates or non-time-varying input, regardless of whether they are SP or DP. Instead, NCO copies these fields directly from the first input file. Stage 4 uses a mapfile to regrid climos from the native to the desired analysis grid. E3SM/ACME currently uses mapfiles generated by ESMF_RegridWeightGen (ERWG) and by TempestRemap. The algorithmic choices, approximations, and commands used to generate mapfiles from input gridfiles are separate issues. We mention only some of these issues here for brevity. Input gridfiles used by E3SM/ACME until ~20150901, and by CESM (then and currently, at least for Gaussian grids) contained flaws that effectively reduced their precision, especially at regional scales, and especially for Gaussian grids. E3SM/ACME (and CESM) mapfiles continue to approximate grids as connected by great circles, whereas most analysis grids (and some models) use great circles for longitude and small circles for latitude. The great circle assumption may be removed in the future. Constraints imposed by ERWG during weight-generation ensure that global integrals of fields undergoing conservative regridding are exactly conserved. Application of weights from the mapfile to regrid the native data to the analysis grid is straightforward. Grid fields (e.g., latitude, longitude, area) are not regridded. Instead they are copied (and area is reconstructed if absent) directly from the mapfile. NCO ingests all other native grid (source) fields, converts SP to DP, and accumulates destination gridcell values as the sum of the DP weight (from the sparse matrix in the mapfile) times the (usually SP-promoted-to-DP) source values. Fields without missing values are then stored to disk in their original precision. Fields with missing values are treated (by default) with what NCO calls the &textldquo;conservative&textrdquo; algorithm. This algorithm uses all valid data from the source grid on the destination grid once and only once. Destination cells receive the weighted valid values of the source cells. This is conservative because the global integrals of the source and destination fields are equal. See ncremap netCDF Remapper for more description of the conservative and of the optional (&textldquo;renormalized&textrdquo;) algorithm. <a name="xmp_ncclimo"></a>  EXAMPLES <a name="merra2"></a>  How to create a climo from a collection of monthly non-CESM&textrsquo;ish files? This is a two-step procedure: First be sure the names are arranged with a YYYYMM-format date preceding the suffix (usually .nc). Then give any monthly input filename to ncclimo. Consider the MERRA2 collection, for example. As retrieved from NASA, MERRA2 files have names like svc_MERRA2_300.tavgM_2d_aer_Nx.200903.nc4. While the sub-string 200903 is easy to recognize as a month in YYYYMM format, other parts (specifically the 300 code) of the filename also change with date. We can use Bash regular expressions to extract dates and create symbolic links to simpler filenames with regularly patterned YYYYMM strings like merra2_200903.nc4: for fl in `ls *.nc4` ; do # Convert svc_MERRA2_300.tavgM_2d_aer_Nx.YYYYMM.nc4 to merra2_YYYYMM.nc4 sfx_out=`expr match "${fl}" '.*_Nx.$.*.nc4$'` fl_out="merra2_${sfx_out}" ln -s ${fl} ${fl_out} done Then call ncclimo with any standard format filename, e.g., merra2_200903.nc4, as as the caseid:

ncclimo -c merra2_200903.nc4 -s 1980 -e 2016 -i $drc_in -o $drc_out

<a name="ecmwf_ifs"></a>  In the default monthly climo generation mode, ncclimo expects each input file to contain one single record that is the monthly average of all fields. Another example of of wrangling observed datasets into a CESMish format is ECMWF Integrated Forecasting System (IFS) output that contains twelve months per file, rather than the one month per file that ncclimo expects. for yr in {1979..2016}; do # Convert ifs_YYYY01-YYYY12.nc to ifs_YYYYMM.nc yyyy=`printf "%04d" $yr` for mth in {1..12}; do mm=`printf "%02d" $mth` ncks -O -F -d time,${mth} ifs_${yyyy}01-${yyyy}12.nc ifs_${yyyy}${mm}.nc done done Then call ncclimo with ifs_197901.nc as caseid:

ncclimo -c ifs_197901.nc -s 1979 -e 2016 -i $drc_in -o $drc_out

ncclimo does not recognize all combinations imaginable of records per file and files per year. However, support can be added for the most prevalent combinations so that ncclimo, rather than the user, does any necessary data wrangling. Contact us if there is a common input data format you would like supported as a custom option. Often one wishes to create a climatology of a single variable. The -f fml_nm option to ncclimo makes this easy. Consider a series of single-variable climos for the fields FSNT, and FLNT ncclimo -v FSNT -f FSNT -c amip_xpt -s 1980 -e 1983 -i drc_in -o drc_out ncclimo -v FLNT -f FLNT -c amip_xpt -s 1980 -e 1983 -i drc_in -o drc_out These climos use the -f option and so their output files will have no namespace conflicts. Moreover, the climatologies can be generated in parallel. <a name="ncecat"></a>

ncecat netCDF Ensemble Concatenatornces netCDF Ensemble Statisticsncclimo netCDF Climatology GeneratorReference Manual

ncecat netCDF Ensemble Concatenator concatenation ensemble concatenation ncecat SYNTAX

ncecat [-3] [-4] [-5] [-6] [-7] [-A] [-C] [-c] [--cmp cmp_sng]
[--cnk_byt sz_byt] [--cnk_csh sz_byt] [--cnk_dmn nm,sz_lmn]
[--cnk_map map] [--cnk_min sz_byt] [--cnk_plc plc] [--cnk_scl sz_lmn]
[-D dbg] [-d dim,[min][,[max][,[stride]]] [-F] [--fl_fmt fl_fmt]
[-G gpe_dsc] [-g grp[,&dots;]] [--gag] [--glb ...]
[-H] [-h] [--hdf] [--hdr_pad nbr] [--hpss] 
[-L dfl_lvl] [-l path] [-M] [--md5_digest] [--mrd] [-n loop]
[--no_cll_msr] [--no_frm_trm] [--no_tmp_fl] 
[-O] [-o output-file] [-p path] [--qnt ...] [--qnt_alg alg_nm]
[-R] [-r] [--ram_all] [-t thr_nbr] [-u ulm_nm] [--unn]
[-v var[,&dots;]] [-X ...] [-x] 
[input-files] [output-file]

DESCRIPTION ncecat aggregates an arbitrary number of input files into a single output file using using one of two methods. Record AGgregation (RAG), the traditional method employed on (flat) netCDF3 files and still the default method, stores input-files as consecutive records in the output-file. Group AGgregation (GAG) stores input-files as top-level groups in the netCDF4 output-file. Record Aggregation (RAG) makes numerous assumptions about the structure of input files whereas Group Aggregation (GAG) makes none. Both methods are described in detail below. Since ncecat aggregates all the contents of the input files, it can easily produce large output files so it is often helpful to invoke subsetting simultaneously (Subsetting Files). <a name="rag"></a>  record aggregation RAG RAG makes each variable (except coordinate variables) in each input file into a single record of the same variable in the output file. Coordinate variables are not concatenated, they are instead simply copied from the first input file to the output-file. All input-files must contain all extracted variables (or else there would be &textldquo;gaps&textrdquo; in the output file). A new record dimension is the glue which binds together the input file data. The new record dimension is defined in the root group of the output file so it is visible to all sub-groups. Its name is, by default, &textldquo;record&textrdquo;. unlimited dimension record dimension -u ulm_nm --ulm_nm ulm_nm --rcd_nm ulm_nm This default name can be overridden with the -u ulm_nm short option (or the --ulm_nm or rcd_nm long options). Each extracted variable must be constant in size and rank across all input-files. record dimension hyperslab The only exception is that ncecat allows files to differ in the record dimension size if the requested record hyperslab (Hyperslabs) resolves to the same size for all files. CMIP This allows easier gluing/averaging of unequal length timeseries from simulation ensembles (e.g., the CMIP archive). fixed dimension fix record dimension Classic (i.e., all netCDF3 and NETCDF4_CLASSIC) output files can contain only one record dimension. ncecat makes room for the new glue record dimension by changing the pre-existing record dimension, if any, in the input files into a fixed dimension in the output file. netCDF4 output files may contain any number of record dimensions, so ncecat need not and does not alter the record dimensions, if any, of the input files as it copies them to the output file. <a name="gag"></a>  group aggregation GAG Group AGgregation (GAG) stores input-files as top-level groups in the output-file. No assumption is made about the size or shape or type of a given object (variable or dimension or group) in the input file. The entire contents of the extracted portion of each input file is placed in its own top-level group in output-file, which is automatically made as a netCDF4-format file. GAG has two methods to specify group names for the output-file. The -G option, or its long-option equivalent --gpe, takes as argument a group path editing description gpe_dsc of where to place the results. Each input file needs a distinct output group name to avoid namespace conflicts in the output-file. Hence ncecat automatically creates unique output group names based on either the input filenames or the gpe_dsc arguments. When the user provides gpe_dsc (i.e., with -G), then the output groups are formed by enumerating sequential two-digit numeric suffixes starting with zero, and appending them to the specified group path (Group Path Editing). When gpe_dsc is not provided (i.e., user requests GAG with --gag instead of -G), then ncecat forms the output groups by stripping the input file name of any type-suffix (e.g., .nc), and all but the final component of the full filename.

ncecat --gag 85.nc 86.nc 87.nc 8587.nc # Output groups 85, 86, 87
ncecat -G 85_ a.nc b.nc c.nc 8589.nc # Output groups 85_00, 85_01, 85_02
ncecat -G 85/ a.nc b.nc c.nc 8589.nc # Output groups 85/00, 85/01, 85/02

With both RAG and GAG the output-file size is the sum of the sizes of the extracted variables in the input files. Statistics vs Concatenation, for a description of the distinctions between the various statistics tools and concatenators. multi-file operators standard input stdin As a multi-file operator, ncecat will read the list of input-files from stdin if they are not specified as positional arguments on the command line (Large Numbers of Files). -M --no_glb_mtd --suppress_global_metadata history provenance metadata, global Suppress global metadata copying. By default NCO&textrsquo;s multi-file operators copy the global metadata from the first input file into output-file. This helps to preserve the provenance of the output data. However, the use of metadata is burgeoning and sometimes one encounters files with excessive amounts of extraneous metadata. Extracting small bits of data from such files leads to output files which are much larger than necessary due to the automatically copied metadata. ncecat supports turning off the default copying of global metadata via the -M switch (or its long option equivalents, --no_glb_mtd and --suppress_global_metadata). climate model Consider five realizations, 85a.nc, 85b.nc, &dots; 85e.nc of 1985 predictions from the same climate model. Then ncecat 85?.nc 85_ens.nc glues together the individual realizations into the single file, 85_ens.nc. If an input variable was dimensioned [lat,lon], it will by default have dimensions [record,lat,lon] in the output file. A restriction of ncecat is that the hyperslabs of the processed variables must be the same from file to file. Normally this means all the input files are the same size, and contain data on different realizations of the same variables. ncpdq packing unpacking add_offset scale_factor Concatenating a variable packed with different scales across multiple datasets is beyond the capabilities of ncecat (and ncrcat, the other concatenator (Concatenation). ncecat does not unpack data, it simply copies the data from the input-files, and the metadata from the first input-file, to the output-file. This means that data compressed with a packing convention must use the identical packing parameters (e.g., scale_factor and add_offset) for a given variable across all input files. Otherwise the concatenated dataset will not unpack correctly. The workaround for cases where the packing parameters differ across input-files requires three steps: First, unpack the data using ncpdq. Second, concatenate the unpacked data using ncecat, Third, re-pack the result with ncpdq. <a name="xmp_ncecat"></a>  EXAMPLES Consider a model experiment which generated five realizations of one year of data, say 1985. You can imagine that the experimenter slightly perturbs the initial conditions of the problem before generating each new solution. Assume each file contains all twelve months (a seasonal cycle) of data and we want to produce a single file containing all the seasonal cycles. Here the numeric filename suffix denotes the experiment number (not the month):

ncecat 85_01.nc 85_02.nc 85_03.nc 85_04.nc 85_05.nc 85.nc
ncecat 85_0[1-5].nc 85.nc
ncecat -n 5,2,1 85_01.nc 85.nc

These three commands produce identical answers. Specifying Input Files, for an explanation of the distinctions between these methods. The output file, 85.nc, is five times the size as a single input-file. It contains 60 months of data. <a name="ncecat_rnm"></a>  One often prefers that the (new) record dimension have a more descriptive, context-based name than simply &textldquo;record&textrdquo;. This is easily accomplished with the -u ulm_nm switch. To add a new record dimension named &textldquo;time&textrdquo; to all variables

ncecat -u time in.nc out.nc

To glue together multiple files with a new record variable named &textldquo;realization&textrdquo;

ncecat -u realization 85_0[1-5].nc 85.nc

Users are more likely to understand the data processing history when such descriptive coordinates are used. <a name="dmn_rcd_rm"></a>  record dimension fixed dimension fix record dimension --mk_rec_dmn dim Consider a file with an existing record dimension named time. and suppose the user wishes to convert time from a record dimension to a non-record dimension. This may be useful, for example, when the user has another use for the record variable. The simplest method is to use ncks --fix_rec_dmn, and another possibility is to use ncecat followed by ncwa: degenerate dimension

ncecat in.nc out.nc # Convert time to non-record dimension
ncwa -a record in.nc out.nc # Remove new degenerate record dimension

The second step removes the degenerate record dimension. See ncpdq netCDF Permute Dimensions Quickly and ncks netCDF Kitchen Sink for other methods of of changing variable dimensionality, including the record dimension. <a name="nces"></a>  <a name="ncea"></a>

nces netCDF Ensemble Statisticsncflint netCDF File Interpolatorncecat netCDF Ensemble ConcatenatorReference Manual

nces netCDF Ensemble Statistics averaging data ensemble average nces SYNTAX

nces [-3] [-4] [-5] [-6] [-7] [-A] [-C] [-c] [--cb y1,y2,m1,m2,tpd]
[--cmp cmp_sng] [--cnk_byt sz_byt] [--cnk_csh sz_byt] [--cnk_dmn nm,sz_lmn]
[--cnk_map map] [--cnk_min sz_byt] [--cnk_plc plc] [--cnk_scl sz_lmn]
[-D dbg] [-d dim,[min][,[max][,[stride]]] [-F]
[-G gpe_dsc] [-g grp[,&dots;]] [--glb ...]
[-H] [-h] [--hdf] [--hdr_pad nbr] [--hpss] 
[-L dfl_lvl] [-l path] [-n loop]
[--no_cll_msr] [--no_frm_trm] [--no_tmp_fl] [--nsm_fl|grp] [--nsm_sfx sfx]
[-O] [-o output-file] [-p path] [--qnt ...] [--qnt_alg alg_nm]
[-R] [-r] [--ram_all] [--rth_dbl|flt] [-t thr_nbr] [--unn]
[-v var[,&dots;]] [-w wgt] [-X ...] [-x] [-y op_typ]
[input-files] [output-file]

DESCRIPTION nces performs gridpoint statistics (including, but not limited to, averages) on variables across an arbitrary number (an ensemble) of input-files and/or of input groups within each file. Each file (or group) receives an equal weight by default. nces was formerly (until NCO version 4.3.9, released December, 2013) known as ncea (netCDF Ensemble Averager)The old ncea command was deprecated in NCO version 4.3.9, released December, 2013. NCO will attempt to maintain back-compatibility and work as expected with invocations of ncea for as long as possible. Please replace ncea by nces in all future work.. ensemble For example, nces will average a set of files or groups, weighting each file or group evenly by default. This is distinct from ncra, which performs statistics only over the record dimension(s) (e.g., time), and weights each record in each record dimension evenly. The file or group is the logical unit of organization for the results of many scientific studies. Often one wishes to generate a file or group which is the statistical product (e.g., average) of many separate files or groups. This may be to reduce statistical noise by combining the results of a large number of experiments, or it may simply be a step in a procedure whose goal is to compute anomalies from a mean state. In any case, when one desires to generate a file whose statistical properties are influenced by all the inputs, then use nces. weights weighted average As of NCO version 4.9.4, released in July, 2020, nces accepts user-specified weights with the -w (or long-option equivalent --wgt, --wgt_var, or --weight) switch. The user must specify one weight per input file on the command line, or the name of a (scalar or degenerate 1-D array) variable in each input file that contains a single value to weight that file. When no weight is specified, nces weights each file (e.g., ensemble) in the input-files equally. Variables in the output-file are the same size as the variable hyperslab in each input file or group, and each input file or group must be the same size after hyperslabbing As of NCO version 4.4.2 (released February, 2014) nces allows hyperslabs in all dimensions so long as the hyperslabs resolve to the same size. The fixed (i.e., non-record) dimensions should be the same size in all ensemble members both before and after hyperslabbing, although the hyperslabs may (and usually do) change the size of the dimensions from the input to the output files. Prior to this, nces was only guaranteed to work on hyperslabs in the record dimension that resolved to the same size. record dimension hyperslab nces does allow files to differ in the input record dimension size if the requested record hyperslab (Hyperslabs) resolves to the same size for all files. nces recomputes the record dimension hyperslab limits for each input file so that coordinate limits may be used to select equal length timeseries from unequal length files. IPCC AR4 CMIP This simplifies analysis of unequal length timeseries from simulation ensembles (e.g., the CMIP3 IPCC AR4 archive). <a name="nsm_fl"></a>  <a name="nsm_grp"></a>  <a name="nsm_sfx"></a>  --nsm_fl --nsm_grp --ensemble_file --ensemble_group --nsm_sfx --ensemble_suffix nces works in one of two modes, file ensembles or group ensembles. File ensembles are the default (equivalent to the old ncea) and may also be explicitly specified by the --nsm_fl or --ensemble_file switches. To perform statistics on ensembles of groups, a newer feature, use --nsm_grp or --ensemble_group. Members of a group ensemble are groups that share the same structure, parent group, and nesting level. Members must be leaf groups, i.e., not contain any sub-groups. Their contents usually have different values because they are realizations of replicated experiments. In group ensemble mode nces computes the statistics across the ensemble, which may span multiple input files. Files may contain members of multiple, distinct ensembles. However, all ensembles must have at least one member in the first input file. Group ensembles behave as an unlimited dimension of datasets: they may contain an arbitrary and extensible number of realizations in each file, and may be composed from multiple files. Output statistics in group ensemble mode are stored in the parent group by default. If the ensemble members are /cesm/cesm_01 and /cesm/cesm_02, then the computed statistic will be in /cesm in the output file. The --nsm_sfx option instructs nces to instead store output in a new child group of the parent created by attaching the suffix to the parent group&textrsquo;s name, e.g., --nsm_sfx='_avg' would store results in the output group /cesm/cesm_avg:

nces --nsm_grp                  mdl1.nc mdl2.nc mdl3.nc out.nc
nces --nsm_grp --nsm_sfx='_avg' mdl1.nc mdl2.nc mdl3.nc out.nc

Statistics vs Concatenation, for a description of the distinctions between the statistics tools and concatenators. multi-file operators standard input stdin As a multi-file operator, nces will read the list of input-files from stdin if they are not specified as positional arguments on the command line (Large Numbers of Files). Like ncra and ncwa, nces treats coordinate variables as a special case. Coordinate variables are assumed to be the same in all ensemble members, so nces simply copies the coordinate variables that appear in ensemble members directly to the output file. This has the same effect as averaging the coordinate variable across the ensemble, yet does not incur the time- or precision- penalties of actually averaging them. ncra and ncwa allow coordinate variables to be processed only by the linear average operation, regardless of the arithmetic operation type performed on the non-coordinate variables (Operation Types). Thus it can be said that the three operators (ncra, ncwa, and nces) all average coordinate variables (even though nces simply copies them). All other requested arithmetic operations (e.g., maximization, square-root, RMS) are applied only to non-coordinate variables. In these cases the linear average of the coordinate variable will be returned. <a name="xmp_ncea"></a>  <a name="xmp_nces"></a>  EXAMPLES Consider a model experiment which generated five realizations of one year of data, say 1985. Imagine that the experimenter slightly perturbs the initial conditions of the problem before generating each new solution. Assume each file contains all twelve months (a seasonal cycle) of data and we want to produce a single file containing the ensemble average (mean) seasonal cycle. Here the numeric filename suffix denotes the realization number (not the month):

nces 85_01.nc 85_02.nc 85_03.nc 85_04.nc 85_05.nc 85.nc
nces 85_0[1-5].nc 85.nc
nces -n 5,2,1 85_01.nc 85.nc

These three commands produce identical answers. Specifying Input Files, for an explanation of the distinctions between these methods. The output file, 85.nc, is the same size as the inputs files. It contains 12 months of data (which might or might not be stored in the record dimension, depending on the input files), but each value in the output file is the average of the five values in the input files. In the previous example, the user could have obtained the ensemble average values in a particular spatio-temporal region by adding a hyperslab argument to the command, e.g.,

nces -d time,0,2 -d lat,-23.5,23.5 85_??.nc 85.nc

In this case the output file would contain only three slices of data in the time dimension. These three slices are the average of the first three slices from the input files. Additionally, only data inside the tropics is included. As of NCO version 4.3.9 (released December, 2013) nces also works with groups (rather than files) as the fundamental unit of the ensemble. Consider two ensembles, /ecmwf and /cesm stored across three input files mdl1.nc, mdl2.nc, and mdl3.nc. Ensemble members would be leaf groups with names like /ecmwf/01, /ecmwf/02 etc. and /cesm/01, /cesm/02, etc. These commands average both ensembles:

nces --nsm_grp mdl1.nc mdl2.nc mdl3.nc out.nc
nces --nsm_grp --nsm_sfx='_min' --op_typ=min -n 3,1,1 mdl1.nc out.nc
nces --nsm_grp -g cesm -v tas -d time,0,3 -n 3,1,1 mdl1.nc out.nc

nces --nsm_grp mdl1.nc mdl2.nc mdl3.nc out.nc
nces --nsm_grp --nsm_sfx='_min' --op_typ=min -n 3,1,1 mdl1.nc out.nc
nces --nsm_grp -g cesm -v tas -d time,0,3 -n 3,1,1 mdl1.nc out.nc

The first command stores averages in the output groups /cesm and /ecmwf, while the second stores minima in the output groups /cesm/cesm_min and /ecmwf/ecmwf_min: The third command demonstrates that sub-setting and hyperslabbing work as expected. Note that each input file may contain different numbers of members of each ensemble, as long as all distinct ensembles contain at least one member in the first file. weights weighted average As of NCO version 4.9.4, released in July, 2020, nces accepts user-specified weights with the -w (or long-option equivalent --wgt, --wgt_var, or --weight) switch:

# Construct input variables with values of 1 and 2
ncks -O -M -v one ~/nco/data/in.nc ~/1.nc
ncrename -O -v one,var ~/1.nc
ncap2 -O -s 'var=2' ~/1.nc ~/2.nc

# Three methods of weighting input files unevenly
# 1. Old-method: specify input files multiple times
# 2. New-method: specify one weight per input file
# 3. New-method: specify weight variable in each input file
nces -O ~/1.nc ~/2.nc ~/2.nc ~/out.nc # Clumsy, limited to integer weights
nces -O -w 1,2 ~/1.nc ~/2.nc ~/out.nc # Flexible, works for any weight
nces -O -w var ~/1.nc ~/2.nc ~/out.nc # Flexible, works for any weight
# All three methods produce same answer: var=(1*1+2*2)/3=5/3=1.67
ncks ~/out.nc

ncflint netCDF File Interpolatorncks netCDF Kitchen Sinknces netCDF Ensemble StatisticsReference Manual

ncflint netCDF File Interpolator interpolation adding data multiplying data addition ncflint SYNTAX

ncflint [-3] [-4] [-5] [-6] [-7] [-A] [-C] [-c]  [--cmp cmp_sng]
[--cnk_byt sz_byt] [--cnk_csh sz_byt] [--cnk_dmn nm,sz_lmn]
[--cnk_map map] [--cnk_min sz_byt] [--cnk_plc plc] [--cnk_scl sz_lmn]
[-D dbg] [-d dim,[min][,[max][,[stride]]] [--fl_fmt fl_fmt]
[-F] [--fix_rec_crd] [-G gpe_dsc] [-g grp[,&dots;]] [--glb ...]
[-H] [-h] [--hdr_pad nbr] [--hpss] 
[-i var,val3] [-L dfl_lvl] [-l path] [-N]
[--no_cll_msr] [--no_frm_trm] [--no_tmp_fl] 
[-O] [-o file_3] [-p path] [--qnt ...] [--qnt_alg alg_nm] 
[-R] [-r] [--ram_all] [-t thr_nbr] [--unn] [-v var[,&dots;]]
[-w wgt1[,wgt2]] [-X ...] [-x]
file_1 file_2 [file_3]

DESCRIPTION ncflint creates an output file that is a linear combination of the input files. This linear combination is a weighted average, a normalized weighted average, or an interpolation of the input files. Coordinate variables are not acted upon in any case, they are simply copied from file_1. There are two conceptually distinct methods of using ncflint. The first method is to specify the weight each input file contributes to the output file. In this method, the value val3 of a variable in the output file file_3 is determined from its values val1 and val2 in the two input files according to $val3 = wgt1 \times val1 + wgt2 \times val2$ @clear flg

val3 = wgt1*val1 + wgt2*val2 . Here at least wgt1, and, optionally, wgt2, are specified on the command line with the -w (or --weight or --wgt_var) switch. -w wgt1[,wgt2] --weight wgt1[,wgt2] --wgt_var wgt1[,wgt2] If only wgt1 is specified then wgt2 is automatically computed as

wgt2 = 1 − wgt1. Note that weights larger than 1 are allowed. Thus it is possible to specify

wgt1 = 2 and

wgt2 = -3. One can use this functionality to multiply all values in a given file by a constant. normalization -N --nrm --normalize As of NCO version 4.6.1 (July, 2016), the -N switch (or long-option equivalents --nrm or --normalize) implements a variation of this method. This switch instructs ncflint to internally normalize the two supplied (or one supplied and one inferred) weights so that $wgt1 = wgt1 / (wgt1 + wgt2)$ and $wgt2 = wgt2 / (wgt1 + wgt2)$ @clear flg

wgt1 = wgt1/(wgt1 + wgt2 and

wgt2 = wgt2/(wgt1 + wgt2 and . This allows the user to input integral weights, say, and to delegate the chore of normalizing them to ncflint. Be careful that -N means what you think, since the same switch means something quite different in ncwa. The second method of using ncflint is to specify the interpolation option with -i (or with the --ntp or --interpolate long options). This is the inverse of the first method in the following sense: When the user specifies the weights directly, ncflint has no work to do besides multiplying the input values by their respective weights and adding together the results to produce the output values. It makes sense to use this when the weights are known a priori. arrival value Another class of problems has the arrival value (i.e., val3) of a particular variable var known a priori. In this case, the implied weights can always be inferred by examining the values of var in the input files. This results in one equation in two unknowns, wgt1 and wgt2: $val3 = wgt1 \times val1 + wgt2 \times val2$ @clear flg

val3 = wgt1*val1 + wgt2*val2 . Unique determination of the weights requires imposing the additional constraint of normalization on the weights:

wgt1 + wgt2 = 1. Thus, to use the interpolation option, the user specifies var and val3 with the -i option. ncflint then computes wgt1 and wgt2, and uses these weights on all variables to generate the output file. Although var may have any number of dimensions in the input files, it must represent a single, scalar value. degenerate dimension Thus any dimensions associated with var must be degenerate, i.e., of size one. If neither -i nor -w is specified on the command line, ncflint defaults to weighting each input file equally in the output file. This is equivalent to specifying -w 0.5 or -w 0.5,0.5. Attempting to specify both -i and -w methods in the same command is an error. ncflint does not interpolate variables of type NC_CHAR and NC_STRING. This behavior is hardcoded. By default ncflint interpolates or multiplies record coordinate variables (e.g., time is often stored as a record coordinate) not other coordinate variables (e.g., latitude and longitude). This is because ncflint is often used to time-interpolate between existing files, but is rarely used to spatially interpolate. Sometimes however, users wish to multiply entire files by a constant that does not multiply any coordinate variables. The --fix_rec_crd switch was implemented for this purpose in NCO version 4.2.6 (March, 2013). It prevents ncflint from multiplying or interpolating any coordinate variables, including record coordinate variables. missing values _FillValue Depending on your intuition, ncflint may treat missing values unexpectedly. Consider a point where the value in one input file, say val1, equals the missing value mss_val_1 and, at the same point, the corresponding value in the other input file val2 is not misssing (i.e., does not equal mss_val_2). There are three plausible answers, and this creates ambiguity. Option one is to set

val3 = mss_val_1. The rationale is that ncflint is, at heart, an interpolator and interpolation involving a missing value is intrinsically undefined. ncflint currently implements this behavior since it is the most conservative and least likely to lead to misinterpretation. Option two is to output the weighted valid data point, i.e., $val3 = wgt2 \times val2$ @clear flg

val3 = wgt2*val2 . The rationale for this behavior is that interpolation is really a weighted average of known points, so ncflint should weight the valid point. Option three is to return the unweighted valid point, i.e.,

val3 = val2. This behavior would appeal to those who use ncflint to estimate data using the closest available data. When a point is not bracketed by valid data on both sides, it is better to return the known datum than no datum at all. The current implementation uses the first approach, Option one. If you have strong opinions on this matter, let us know, since we are willing to implement the other approaches as options if there is enough interest. <a name="xmp_ncflint"></a>  EXAMPLES Although it has other uses, the interpolation feature was designed to interpolate file_3 to a time between existing files. Consider input files 85.nc and 87.nc containing variables describing the state of a physical system at times

time = 85 and

time = 87. Assume each file contains its timestamp in the scalar variable time. Then, to linearly interpolate to a file 86.nc which describes the state of the system at time at time = 86, we would use

ncflint -i time,86 85.nc 87.nc 86.nc

Say you have observational data covering January and April 1985 in two files named 85_01.nc and 85_04.nc, respectively. Then you can estimate the values for February and March by interpolating the existing data as follows. Combine 85_01.nc and 85_04.nc in a 2:1 ratio to make 85_02.nc:

ncflint -w 0.667 85_01.nc 85_04.nc 85_02.nc
ncflint -w 0.667,0.333 85_01.nc 85_04.nc 85_02.nc

Multiply 85.nc by 3 and by −2 and add them together to make tst.nc:

ncflint -w 3,-2 85.nc 85.nc tst.nc

null operation This is an example of a null operation, so tst.nc should be identical (within machine precision) to 85.nc. multiplication file multiplication scaling Multiply all the variables except the coordinate variables in the file emissions.nc by by 0.8:

ncflint --fix_rec_crd -w 0.8,0.0 emissions.nc emissions.nc scaled_emissions.nc

The use of --fix_rec_crd ensures, e.g., that the time coordinate, if any, is not scaled (i.e., multiplied). Add 85.nc to 86.nc to obtain 85p86.nc, then subtract 86.nc from 85.nc to obtain 85m86.nc

ncflint -w 1,1 85.nc 86.nc 85p86.nc
ncflint -w 1,-1 85.nc 86.nc 85m86.nc
ncdiff 85.nc 86.nc 85m86.nc

Thus ncflint can be used to mimic some ncbo operations. broadcasting variables However this is not a good idea in practice because ncflint does not broadcast (ncbo netCDF Binary Operator) conforming variables during arithmetic. Thus the final two commands would produce identical results except that ncflint would fail if any variables needed to be broadcast. units Rescale the dimensional units of the surface pressure prs_sfc from Pascals to hectopascals (millibars)

ncflint -C -v prs_sfc -w 0.01,0.0 in.nc in.nc out.nc
ncatted -a units,prs_sfc,o,c,millibar out.nc

ncks netCDF Kitchen Sinkncpdq netCDF Permute Dimensions Quicklyncflint netCDF File InterpolatorReference Manual

ncks netCDF Kitchen Sink kitchen sink printing files contents printing variables ncks SYNTAX

ncks [-3] [-4] [-5] [-6] [-7] [-A] [-a] [--area_wgt] [-b fl_bnr]
[-C] [-c] [--cdl] [--chk_bnd] [--chk_chr] [--chk_map] [--chk_mss] [--chk_nan] [--chk_xtn]
[--cmp cmp_sng] [--cnk_byt sz_byt] [--cnk_csh sz_byt] [--cnk_dmn nm,sz_lmn]
[--cnk_map map] [--cnk_min sz_byt] [--cnk_plc plc] [--cnk_scl sz_lmn]
[-D dbg] [-d dim,[min][,[max][,[stride]]]
[-F] [--fix_rec_dmn dim] [--fl_fmt fl_fmt] [--fmt_val format]
[-G gpe_dsc] [-g grp[,&dots;]] [--glb ...] [--grp_xtr_var_xcl]
[-H] [-h] [--hdn] [--hdr_pad nbr] [--hpss] [--hrz fl_hrz] [--jsn] [--jsn_fmt lvl] 
[-L dfl_lvl] [-l path]
[-M] [-m] [--map map-file] [--md5] [--mk_rec_dmn dim]
[--no_blank] [--no_cll_msr] [--no_frm_trm] [--no_tmp_fl] 
[-O] [-o output-file] [-P] [-p path] [--prn_fl print-file]
[-Q] [-q] [--qnt ...] [--qnt_alg alg_nm]
[-R] [-r] [--rad] [--ram_all] [--rgr ...] [--rnr=wgt]
[-s format] [--s1d] [-u] [--unn] [-V] [-v var[,&dots;]] [--vrt vrt-file] 
[-X ...] [-x] [--xml] input-file [[output-file]]

DESCRIPTION ncextr The nickname &textldquo;kitchen sink&textrdquo; is a catch-all because ncks combines most features of ncdump and nccopy with extra features to extract, hyperslab, multi-slab, sub-set, and translate into one versatile utility. ncks extracts (a subset of the) data from input-file, regrids it according to map-file if specified, then writes in netCDF format to output-file, and optionally writes it in flat binary format to fl_bnr, and optionally prints it to screen. --trd --traditional traditional ncks prints netCDF input data in ASCII, CDL, JSON, or NcML/XML text formats to stdout, like (an extended version of) ncdump. By default ncks prints CDL format. Option -s (or long options --sng_fmt and --string) permits the user to format data using C-style format strings, while option --cdl outputs CDL, option --jsn (or json) outputs JSON, option --trd (or traditional) outputs &textldquo;traditional&textrdquo; format, and option --xml (or ncml) outputs NcML. The &textldquo;traditional&textrdquo; tabular format is intended to be easy to search for the data you want, one datum per screen line, with all dimension subscripts and coordinate values (if any) preceding the datum. ncks exposes many flexible controls over printed output, including CDL, JSON, and NcML. Options -a, --cdl, -F, --fmt_val, -H, --hdn, --jsn, -M, -m, -P, --prn_fl, -Q, -q, -s, --trd, -u, -V, and --xml (and their long option counterparts) control the presence of data and metadata and their formatted location and appearance when printed. global attributes attributes, global ncks extracts (and optionally creates a new netCDF file comprised of) only selected variables from the input file (similar to the old ncextr specification). Only variables and coordinates may be specifically included or excluded&textmdash;all global attributes and any attribute associated with an extracted variable are copied to the screen and/or output netCDF file. Options -c, -C, -v, and -x (and their long option synonyms) control which variables are extracted. ncks extracts hyperslabs from the specified variables (ncks implements the original nccut specification). Option -d controls the hyperslab specification. Input dimensions that are not associated with any output variable do not appear in the output netCDF. This feature removes superfluous dimensions from netCDF files. appending data merging files ncks will append variables and attributes from the input-file to output-file if output-file is a pre-existing netCDF file whose relevant dimensions conform to dimension sizes of input-file. The append features of ncks are intended to provide a rudimentary means of adding data from one netCDF file to another, conforming, netCDF file. If naming conflicts exist between the two files, data in output-file is usually overwritten by the corresponding data from input-file. Thus, when appending, the user should backup output-file in case valuable data are inadvertantly overwritten. If output-file exists, the user will be queried whether to overwrite, append, or exit the ncks call completely. Choosing overwrite destroys the existing output-file and create an entirely new one from the output of the ncks call. Append has differing effects depending on the uniqueness of the variables and attributes output by ncks: If a variable or attribute extracted from input-file does not have a name conflict with the members of output-file then it will be added to output-file without overwriting any of the existing contents of output-file. In this case the relevant dimensions must agree (conform) between the two files; new dimensions are created in output-file as required. global attributes attributes, global When a name conflict occurs, a global attribute from input-file will overwrite the corresponding global attribute from output-file. If the name conflict occurs for a non-record variable, then the dimensions and type of the variable (and of its coordinate dimensions, if any) must agree (conform) in both files. Then the variable values (and any coordinate dimension values) from input-file will overwrite the corresponding variable values (and coordinate dimension values, if any) in output-file Those familiar with netCDF mechanics might wish to know what is happening here: ncks does not attempt to redefine the variable in output-file to match its definition in input-file, ncks merely copies the values of the variable and its coordinate dimensions, if any, from input-file to output-file. . Since there can only be one record dimension in a file, the record dimension must have the same name (though not necessarily the same size) in both files if a record dimension variable is to be appended. If the record dimensions are of differing sizes, the record dimension of output-file will become the greater of the two record dimension sizes, the record variable from input-file will overwrite any counterpart in output-file and fill values will be written to any gaps left in the rest of the record variables (I think). In all cases variable attributes in output-file are superseded by attributes of the same name from input-file, and left alone if there is no name conflict. Some users may wish to avoid interactive ncks queries about whether to overwrite existing data. For example, batch scripts will fail if ncks does not receive responses to its queries. Options -O and -A are available to force overwriting existing files, and appending existing variables, respectively. Options specific to ncks The following summarizes features unique to ncks. Features common to many operators are described in Shared features. <a name="abc"></a>  <a name="alphabetize"></a>  alphabetization sort alphabetically --abc --alphabetize --no_abc --no_alphabetize --no-abc --no-alphabetize -a Switches -a, --abc, and --alphabetize turn-off the default alphbetization of extracted fields in ncks only. These switches are misleadingly named and were deprecated in ncks as of NCO version 4.7.1 (December, 2017). This is the default behavior so these switches are no-ops included only for completeness. By default, NCO extracts, prints, and writes specified output variables to disk in alphabetical order. This tends to make long output lists easier to search for particular variables. Again, no option is necessary to write output in alphabetical order. Until NCO version 4.7.1 (December, 2017), ncks used the -a, --abc, or --alphabetize switches to turn-off the default alphabetization. These names were counter-intuitive and needlessly confusing. As of NCO version 4.7.1, ncks uses the new switches --no_abc, --no-abc, --no_alphabetize, or --no-alphabetize, all of which are equivalent. The --abc and --alphabetize switches are now no-ops, i.e., they write the output in the unsorted order of the input. The -a switch is now completely deprecated in favor of the clearer long option switches. <a name="bnr"></a>  <a name="binary"></a>  binary format -b --fl_bnr --bnr --binary -b file Activate native machine binary output writing to binary file file. Also --fl_bnr and --binary-file. Writing packed variables in binary format is not supported. Metadata is never output to the binary file. Examine the netCDF output file to see the variables in the binary file. Use the -C switch, if necessary, to avoid wanting unwanted coordinates to the binary file:

% ncks -O -v one_dmn_rec_var -b bnr.dat -p ~/nco/data in.nc out.nc
% ls -l bnr.dat | cut -d ' ' -f 5 # 200 B contains time and one_dmn_rec_var
200
% ls -l bnr.dat
% ncks -C -O -v one_dmn_rec_var -b bnr.dat -p ~/nco/data in.nc out.nc
% ls -l bnr.dat | cut -d ' ' -f # 40 B contains one_dmn_rec_var only
40

<a name="cal"></a>  <a name="cln"></a>  <a name="date_format"></a>  <a name="dt_fmt"></a>  <a name="calendar"></a>  calendar dates date formats Gregorian dates --calendar --cln_lgb --prn_lgb --dt_fmt --date_format --datestamp --cal As of NCO version 4.6.5 (March, 2017), ncks can print human-legible calendar strings corresponding to time values with UDUnits-compatible date units of the form time-since-basetime, e.g., days since 2000-01-01 and a CF calendar attribute, if any. Enact this with the --calendar (also --cln, --prn_lgb, and --datestamp) option when printing in any mode. Invoking this option when

dbg_lvl >= 1 in CDL mode prints both the value and the calendar string (one in comments): zender@aerosol:~$ ncks -D 1 --cal -v tm_365 ~/nco/data/in.nc ... variables: double tm_365 ; tm_365:units = "days since 2013-01-01" ; // char tm_365:calendar = "365_day" ; // char data: tm_365 = "2013-03-01"; // double value: 59 ... zender@aerosol:~$ ncks -D 1 -v tm_365 ~/nco/data/in.nc ... tm_365 = 59; // calendar format: "2013-03-01" ... This option is similar to the ncdump -t option. As of NCO version 4.6.8 (August, 2017), ncks CDL printing supports finer-grained control of date formats with the --dt_fmt=dt_fmt (or --date_format) option. The dt_fmt is an enumerated integer from 0&textndash;3. Values

dt_fmt=0 or 1 correspond to the short format for dates that are the default. The value

dt_fmt=2 requests the &textldquo;regular&textrdquo; format for dates,

dt_fmt=3 requests the full ISO-8601 format with the &textldquo;T&textrdquo; separator and the comma: ncks -H -m -v time_bnds -C --dt_fmt=value ~/nco/data/in.nc # Value: Output: # 0,1 1964-03-13 09:08:16 # Default, short format # 2 1964-03-13 09:08:16.000000 # Regular format # 3 1964-03-13T09:08:16.000000 # ISO8601 'T' format Note that --dt_fmt automatically implies --cal makes that options superfluous. As of NCO version 4.9.4 (September, 2020), invoking the --dt_fmt option now applies equally well to JSON and XML output as to CDL output:

% ncks -d time,0 -v time --cdl --dt_fmt=3 ~/nco/data/in.nc
...
time = "1964-03-13T21:09:0.000000" ;
...
% ncks -d time,0 -v time --json --dt_fmt=3 ~/nco/data/in.nc
...
"data": ["1964-03-13T21:09:0.000000"]
...
% ncks -d time,0 -v time --xml --dt_fmt=3 ~/nco/data/in.nc
...
<ncml:values separator="*">1964-03-13T21:09:0.000000</ncml:values>
...

<a name="chk_map"></a>  <a name="area_wgt"></a>  <a name="frac_b_nrm"></a>  --area_wgt --chk_map --frac_b_nrm map-file --chk_map As of NCO version 4.9.0 (December, 2019), invoking --chk_map causes ncks to evaluate the quality of regridding weights in the map-file provided as input-file. This option works with map-files (not grid-files) in ESMF/CMIP6-compliant format (i.e., a sparse matrix variable named S and coordinates [xy][ab]_[cv]. When invoked with the additional --area_wgt option, the evaluation statistics are area-weighted and thus exactly represent the global-mean/min/max/mebs/rms/sdn biases expected when regridding a globally uniform field. This tool makes it easier to objectively assess weight-generation algorithms, and will hopefully assist in their improvement. Thanks to Mark Taylor of Saturday Night Live (SNL) and Paul Ullrich of UC Davis for this suggestion and early prototypes. $ ncks --chk_map map.nc # Unweighted statistics $ ncks --chk_map --dbg=2 map.nc # Additional diagnostics $ ncks --chk_map --area_wgt map.nc # Area-weighted statistics The map-checker performs numerous checks and reports numerous statistics, probably more than you care about. Be assured that each piece of provided information has in the past proved useful to developers of weight-generation and regridding algorithms. Most of the time, users can learn whether the examined map is of sufficient quality for their purposes by examing only a few of these statistics. Before defining these primary statistics, it is helpful to understand the meaning of the weight-array S (stored in a map-file as the variable S), and the terminology of rows and columns. A remapping (aka regridding) transforms a field on an input grid to an an output grid while conserving to the extent possible or desired the local and global properties of the field. The map S is a matrix of M rows and N columns of weights, where M is the number of gridcells (or degrees of freedom, DOFs) in the destination grid, and N is the number of gridcells (or DOFs) in the source grid. An individual weight S(m,n) represents the fractional contribution to destination gridcell m by source gridcell n. By convention the weights are normalized to sum to unity in each row (destination gridcell) that completely overlaps the input grid. Thus the weights in a single row are all equivalent to the fractional destination areas that the same destination gridcell (we will drop the DOF terminology hereafter for conciseness) receives from each source gridcell. Regardless of the values of the individual weights, it is intuitive that their row-sum should never exceed unity because that would be physically equivalent to an output gridcell receiving more than its own area from the source grid. Map-files typically store these row-sum statistics for each destination gridcell in the frac_b variable described further below. Likewise the weights in a single column represent the fractional destination areas that a single source gridcell contributes to every output gridcell. Each output gridcell in a column may have a different area so column-sums need not, and in general do not, sum to unity. However, a source gridcell ought to contribute to the destination grid a total area equal to its own area. Thus a constraint on column-sums is that their weights, themselves weighted by the destination gridcell area corresponding to each row, should sum exactly to the source gridcell area. In other words, the destination-area-weighted column-sum divided by the source gridcell area would be unity (in a perfect first order map) for every source gridcell that completely overlaps valid destination gridcells. Map-files typically store these area-weighted-column-sum-ratio statistics for each gridcell in the frac_a variable described further below. Storing the entire weight-matrix S is unnecessary because only a relative handful of gridcells in the source grid contribute to a given destination gridcell, and visa versa. Instead, map-files store only the non-zero S(m,n), and encode them as a sparse-matrix. Storing S as a sparse matrix rather than a full matrix reduces overall storage sizes by a factor on the order of the ratio of the product of the grid sizes to their sum, or about 10,000 for grids with horizontal resolution near one degree, and more for finer resolutions. The sparse-matrix representation is a one-dimensional array of weights S, together with two ancillary arrays, row and column, that contain the one-dimensional row and column indices, respectively, corresponding to the destination and source gridcells of the associated weight. By convention, map-files store the row and column indices using the 1-based convention in common use in the 1990s when regridding software was all written in Fortran. The map-checker prints cell locations with 1-based indices as well: % ncks --chk_map map_ne30np4_to_cmip6_180x360_nco.20190601.nc Characterization of map-file map_ne30np4_to_cmip6_180x360_nco.20190601.nc Cell triplet elements : [Fortran (1-based) index, center latitude, center longitude] Sparse matrix size n_s: 246659 Weight min S(190813): 5.1827201764857658e-25 from cell \ [33796,-45.7998,+136.437] to [15975,-45.5,+134.5] Weight max S( 67391): 1.0000000000000000e+00 from cell \ [33671,-54.4442,+189.645] to [12790,-54.5,+189.5] Ignored weights (S=0.0): 0 ... Here the map-file weights span twenty-five orders of magnitude. This may seem large though in practice is typical for high-resolution intersection grids. The Fortran-convention index of each weight extreme is followed by its geographic latitude and longitude. Reporting the locations of extrema, and of gridcells whose metrics miss their target values by more than a specificied tolerance, are prime map-checker features. As mentioned above, the two statistics most telling about map quality are the weighted column-sums frac_a and the row-sums frac_b. The short-hand names for what these metrics quantify are Conservation and Consistency, respectively. Conservation means the total fraction of an input gridcell that contributes to the output grid. For global input and output grids that completely tile the sphere, the entirety of each input gridcell should contribute (i.e., map to) the output grid. The same concept that applies locally to conservation of a gridcell value applies globally to the overall conservation of an input field. Thus a perfectly conservative mapping between global grids that tile the sphere would have

frac_a = 1.0 for every input gridcell, and for the mean of all input gridcells. The map-checker computes Conservation (frac_a) from the stored variables S, row, column, area_a, and area_b in the map-file, and then compares those values to the frac_a values (if any) on-disk, and warns of any disagreements As of version 5.1.1 (November 2022), the map checker diagnoses from the global attributes map_method, no_conserve, or noconserve (in that order, if present) whether the mapping weights are intended to be conservative (as opposed to, e.g., bilinear). Weights deemed non-conservative by design are no longer flagged with dire WARNING messages.. By definition, conservation is perfect to first order if the sum of the destination-gridcell-area-weighted weights (which is an area) equals the source gridcell area, and so their ratio (frac_a) is unity. Computing the area-weighted-column-sum-ratios and comparing those frac_a to the stored frac_a catches any discrepancies. The analysis sounds an alarm when discrepancies exceed a tolerance (currently 5.0e-16). More importantly, the map-checker reports the summary statistics of the computed frac_a metrics and their imputed errors, including the grid mean, minimum, maximum, mean-absolute bias, root-mean-square bias, and standard deviation. % ncks --chk_map map_ne30np4_to_cmip6_180x360_nco.20190601.nc ... Conservation metrics (column-sums of area_b-weighted weights normalized by area_a) and errors--- Perfect metrics for global Grid B are avg = min = max = 1.0, mbs = rms = sdn = 0.0: frac_a avg: 1.0000000000000000 = 1.0-0.0e+00 // Mean frac_a min: 0.9999999999991109 = 1.0-8.9e-13 // Minimum in grid A cell [45328,+77.3747,+225] frac_a max: 1.0000000000002398 = 1.0+2.4e-13 // Maximum in grid A cell [47582,+49.8351,+135] frac_a mbs: 0.0000000000000096 = 9.6e-15 // Mean absolute bias from 1.0 frac_a rms: 0.0000000000000167 = 1.7e-14 // RMS relative to 1.0 frac_a sdn: 0.0000000000000167 = 1.7e-14 // Standard deviation ... The values of the frac_a metric are generally imperfect (not 1.0) for global grids. The bias is the deviation from the target metric shown in the second floating-point column in each row above (e.g., 8.9e-13). These biases should be vanishingly small with respect to unity. Mean biases as large as 1.0e-08 may be considered acceptable for off-line analyses (i.e., a single regridding of raw data) though the acceptable tolerance should be more stringent for on-line use such as in a coupler where forward and reverse mappings may be applied tens of thousands of times. The mean biases for such on-line regridding should be close to 1.0e-15 in order for tens-of-thousands of repetitions to still conserve mass/energy to full double-precision. The minimum and maximum gridcell biases indicate the worst performing locations of the mapping. These are generally much (a few orders of magnitude) greater than the mean biases. Observe that the minimum and maximum biases in the examples above and below occur at longitudes that are multiples of 45 degrees. This is characteristic of mappings to/from for cube-square grids whose faces have edges, and thus additional complexity, at multiples of 45 degrees. This illustrates how intersection grid geometry influences biases. More complex, finer-scale structures, produce greater biases. The Root-Mean-Square (RMS) and standard deviation metrics characterize the distribution of biases throughout the entire intersection grid, and are thus complementary information to the minimum and maximum biases. Consistency expresses the total fraction of an output gridcell that receives contributions from the input grid. Thus Consistency is directly analogous to Conservation, only applied to the output grid. Conservation is the extent to which the mapping preserves the local and grid-wide integrals of input fields, while Consistency is the extent to which the mapping correctly aligns the input and output grids so that each destination cell receives the appropriate proportion of the input integrals. The mapping will produce an acceptably faithful reproduction of the input on the output grid only if all local and global Conservation and Consistency metrics meet the acceptable error tolerances. The map-checker computes the Consistency (frac_b) as row-sums of the weights stored in S and compares these to the stored values of frac_b. (Note how the definition of weights S(m,n) as the fractional contribution to destination gridcell m by source gridcell n makes calculation of frac_b almost trivial in comparison to frac_a). Nevertheless, frac_b in the file may differ from the computed row-sum for example if the map-file generator artificially limits the stored frac_b value for any cell to 1.0 for those row-sums that exceed 1.0. The map-checker raises an alarm when discrepancies between computed and stored frac_b exceed a tolerance (currently 5.0e-16). There are semi-valid reasons a map-generator might do this, so this does not necessarily indicate an error. The alarm simply informs the user that applying the weights will lead to a slightly different Consistency than indicated by the stored frac_b. As with frac_a, the values of frac_b are generally imperfect (not 1.0) for global grids: % ncks --chk_map map_ne30np4_to_cmip6_180x360_nco.20190601.nc ... Consistency metrics (row-sums of weights) and errors--- Perfect metrics for global Grid A are avg = min = max = 1.0, mbs = rms = sdn = 0.0: frac_b avg: 0.9999999999999999 = 1.0-1.1e-16 // Mean frac_b min: 0.9999999999985523 = 1.0-1.4e-12 // Minimum in grid B cell [59446,+75.5,+45.5] frac_b max: 1.0000000000004521 = 1.0+4.5e-13 // Maximum in grid B cell [63766,+87.5,+45.5] frac_b mbs: 0.0000000000000065 = 6.5e-15 // Mean absolute bias from 1.0 frac_b rms: 0.0000000000000190 = 1.9e-14 // RMS relative to 1.0 frac_b sdn: 0.0000000000000190 = 1.9e-14 // Standard deviation ... This example shows that frac_b has the greatest local errors at similar boundaries (multiples of 45 degrees longitude) as frac_a. It is typical for Conservation and Consistency to degrade in intricate areas of the intersection grid, and these areas occur at multiples of 45 degrees longitude for cubed-sphere mappings. The map-checker will produce area-weighted metrics when invoked with the --area_wgt flag, e.g., ncks --area_wgt in.nc. Area-weighted statistics show the exact local and global results to expect with real-world grids in which large consistency/conservation errors in small gridcells may be less important than smaller errors in larger gridcells. Global-weighted mean statistics will of course differ from unweighted statistics, although the minimum and maximum do not change: % ncks --area_wgt map_ne30np4_to_cmip6_180x360_nco.20190601.nc ... Conservation metrics (column-sums of area_b-weighted weights normalized by area_a) and errors--- Perfect metrics for global Grid B are avg = min = max = 1.0, mbs = rms = sdn = 0.0: frac_a avg: 1.0000000000000009 = 1.0+8.9e-16 // Area-weighted mean frac_a min: 0.9999999999999236 = 1.0-7.6e-14 // Minimum in grid A cell [12810,+3.44654,+293.25] frac_a max: 1.0000000000001146 = 1.0+1.1e-13 // Maximum in grid A cell [16203,-45.7267,+272.31] frac_a mbs: 0.0000000000000067 = 6.7e-15 // Area-weighted mean absolute bias from 1.0 frac_a rms: 0.0000000000000102 = 1.0e-14 // Area-weighted RMS relative to 1.0 frac_a sdn: 0.0000000000000103 = 1.0e-14 // Standard deviation Consistency metrics (row-sums of weights) and errors--- Perfect metrics for global Grid A are avg = min = max = 1.0, mbs = rms = sdn = 0.0: frac_b avg: 1.0000000000000047 = 1.0+4.7e-15 // Area-weighted mean frac_b min: 0.9999999999998442 = 1.0-1.6e-13 // Minimum in grid B cell [48415,+44.5,+174.5] frac_b max: 1.0000000000002611 = 1.0+2.6e-13 // Maximum in grid B cell [16558,-44.5,+357.5] frac_b mbs: 0.0000000000000065 = 6.5e-15 // Area-weighted mean absolute bias from 1.0 frac_b rms: 0.0000000000000129 = 1.3e-14 // Area-weighted RMS relative to 1.0 frac_b sdn: 0.0000000000000133 = 1.3e-14 // Standard deviation ... The examples above show no outstanding differences (besides rounding) between the unweighted and area-weighted statistics. The absence of degradation between the global unweighted statistics (further up the page) and the global weighted statistics (just above) demonstrates there are no important correlations between local weight biases and gridcell areas. The area-weighted mean frac_b statistic deserves special mention. Its value is the exact factor by which the mapping will shift the global mean of a spatially uniform input field. This metric is, therefore, first among equals when evaluating the quality of maps under consideration for use in time-stepping models where global conservation (e.g., of mass or energy) is crucial. --frac_b_nrm As of NCO version 4.9.2 (March, 2020), adding the --frac_b_nrm flag changes the map-checker into a read-write algorithm that first diagnoses the map-file statistics described above and then re-writes the weights (and weight-derived statistics frac_a and frac_b) to compensate or &textldquo;fix&textrdquo; issues that poor-quality input grids can cause. Input grids can and often do have regions that are not tiled by any portion of any input gridcell. For example, many FV ocean grids (such as MPAS) are empty (have no gridcells) in land regions beyond the coasts. Some FV ocean grids have gridcells everywhere and mask (i.e., screen-out) the non-ocean gridcells by setting the mask value to zero. Both these designs are perfectly legal. What is illegal, yet sometimes encountered in practice, is overlapping gridcells on the same input grid. Such an input grid is said to be self-overlapping. The surface topography dataset grid SCRIPgrid_1km-merge-10min_HYDRO1K-merge-nomask_c130402.nc (hereafter the HYDRO1K grid for short) used by E3SM and CESM is self-overlapping. Weight-generators that receive the same input location twice might (if they do not take precaustions to idenfity the issue, which no known weight-generators do) double-weight the self-overlapped region(s). In other words, self-overlapping input grids can lead weight-generators to produce values

frac_b >> 1.0. Applying these weights would lead to exaggerated values on the destination grid. The best solution to this issue is to adjust the input grid to avoid self-overlap. However, this solution may be difficult or impractical where the original data, producer, or algorithm are unavailable or unclear. In such cases, the --frac_b_nrm flag provides a workaround. Please understand that ncks --frac_b_nrm map.nc is designed to alter map.nc in-xsplace, so backup the original file first. % ncks --frac_b_nrm map_hydro1k_to_ne1024np4_nco.20200301.nc ... ... <a name="chk_bnd"></a>  <a name="check_bounds"></a>  --chk_bnd --check_bounds bounds CF DIWG --chk_bnd As of NCO version 5.2.0 (February, 2022), ncks can report all coordinates that lack a corresponding bounds attribute. This check complies with CF Conventions and with NASA&textrsquo;s Dataset Interoperability Working Group (https://wiki.earthdata.nasa.gov/display/ESDSWG/Dataset+Interoperability+Working+GroupDIWG). CF requires that coordinate variables that describe a continuous (not discrete) axis contain a &textldquo;bounds&textrdquo; attribute that points to a variable marking the edges of each gridcell (in time, space, or other dimensions). This option reports which coordinates lack the required bounds attribute, so that a file can be easily checked for compliance with the convention: $ ncks --chk_bnd in.nc ncks: WARNING nco_chk_bnd() reports coordinate Lat does not contain "bounds" attribute ncks: WARNING nco_chk_bnd() reports coordinate Lon does not contain "bounds" attribute ncks: INFO nco_chk_bnd() reports total number of coordinates without "bounds" attribute is 2 <a name="chk_chr"></a>  <a name="check_char"></a>  --chk_chr --check_char missing_value CF DIWG --chk_chr The identifiers in a netCDF file are the set of dimension, group, variable, and attribute names it contains. As of NCO version 5.1.8 (September, 2023), ncks can report all identifiers that violate the CF Convention that identifiers &textldquo;should begin with a letter and be composed of letters, digits, and underscores.&textrdquo; System or library-defined identifiers (such as _FillValue) are not subject to this (user-land) rule. NASA&textrsquo;s Dataset Interoperability Working Group (https://wiki.earthdata.nasa.gov/display/ESDSWG/Dataset+Interoperability+Working+GroupDIWG) supports this convention. This option reports which identifiers do not comply with this convention, so that a file can be easily checked for compliance with the DIWG recommendation and the underlying CF Convention: $ ncks --chk_chr ~/nco/data/in.nc ... ncks: WARNING nco_chk_chr() reports variable att_var_spc_chr attribute name "at_in_name@" is not CF-compliant ncks: WARNING nco_chk_chr() reports variable name "var_nm-dash" is not CF-compliant ncks: WARNING nco_chk_chr() reports variable var_nm-dash attribute name "att_nm-dash" is not CF-compliant ncks: INFO nco_chk_chr() reports total number of identifiers with CF non-compliant names is 26 <a name="chk_mss"></a>  <a name="check_missing_value"></a>  --chk_mss --check_missing_value missing_value CF DIWG --chk_mss As of NCO version 5.1.8 (September, 2023), ncks can report all variables and groups that contain a missing_value attribute. NASA&textrsquo;s Dataset Interoperability Working Group (https://wiki.earthdata.nasa.gov/display/ESDSWG/Dataset+Interoperability+Working+GroupDIWG) notes that the missing_value attribute has been semi-deprecated, and recommends that it should not be used in new Earth Science data products. This option reports which variables (and groups) contain a missing_value attribute, so that a file can be easily checked for compliance with the DIWG recommendation: $ ncks --chk_mss ~/nco/data/in.nc ncks: WARNING nco_chk_mss() reports variable fll_val_mss_val contains "missing_value" attribute ncks: WARNING nco_chk_mss() reports variable one_dmn_rec_var_missing_value contains "missing_value" attribute ... ncks: WARNING nco_chk_mss() reports variable rec_var_int_mss_val_flt contains "missing_value" attribute ncks: INFO nco_chk_mss() reports total number of variables and/or groups with "missing_value" attribute is 11 <a name="chk_nan"></a>  <a name="check_nan"></a>  --chk_nan --check_nan NaN NaNf DIWG --chk_nan As of NCO version 4.8.0 (May, 2019), ncks can locate NaN or NaNf in double- and single-precision floating-point variables, respectively. NCO prints the location of the first NaN (if any) encountered in each variable. NASA&textrsquo;s Dataset Interoperability Working Group (https://wiki.earthdata.nasa.gov/display/ESDSWG/Dataset+Interoperability+Working+GroupDIWG) notes that the missing_value attribute has been semi-deprecated, and recommends that it should not be used in new Earth Science data products. This option reports allows users to easily check whether all the floating point variables in a file comply with the DIWG recommendation: $ ncks --chk_nan ~/nco/data/in_4.nc ncks: WARNING nco_chk_nan() reports variable /nan_arr has first NaNf at hyperslab element 1 ncks: WARNING nco_chk_nan() reports variable /nan_scl has first NaNf at hyperslab element 0 ncks: INFO nco_chk_nan() reports total number of floating-point variables with NaN elements is 2 Thanks to Matthew Thompson of NASA for originally suggesting this feature. <a name="chk_xtn"></a>  <a name="check_extension"></a>  --chk_xtn --check_extension DIWG --chk_xtn A filename extension is the suffix that follows the final period . in a filename. For example, the suffix of in.file.nc is nc. NASA&textrsquo;s Dataset Interoperability Working Group (https://wiki.earthdata.nasa.gov/display/ESDSWG/Dataset+Interoperability+Working+GroupDIWG) recommends that &textldquo;files created with the HDF5, HDF-EOS5, or netCDF APIs should have filename extensions \"h5\", \"he5\", or \"nc\", respectively.&textrdquo; As of NCO version 5.1.9 (November, 2023), ncks can report all filenames that violate this DIWG recommendation. This option reports which filenames do not comply with this convention. If a file appears to be mis-labeled, e.g., the extension is .h5 but the file contents match HDF5-EOS structure, that will also be reported. zender@spectral:~$ ncks --chk_xtn ~/nco/data/in.nc zender@spectral:~$ ncks --chk_xtn ~/in.nc4 ncks: WARNING nco_chk_xtn() reports filename extension "nc4" is non-compliant ncks: HINT rename file with "nc" rather than "nc4" extension ncks: INFO nco_chk_xtn() reports total number of non-compliant filename extensions is 1 <a name="dmn_fix_mk"></a>  <a name="fix_rec_dmn"></a>  record dimension fixed dimension --fix_rec_dmn dim --no_rec_dmn dim --fix_rec_dmn Change record dimension dim in the input file into a fixed dimension in the output file. Also --no_rec_dmn. Before NCO version 4.2.5 (January, 2013), the syntax for --fix_rec_dmn did not permit or require the specification of the dimension name dim. This is because the feature only worked on netCDF3 files, which support only one record dimension, so specifying its name was unnecessary. netCDF4 files allow an arbitrary number of record dimensions, so the user must specify which record dimension to fix. The decision was made that starting with NCO version 4.2.5 (January, 2013), it is always required to specify the dimension name to fix regardless of the netCDF file type. This keeps the code simple, and is symmetric with the syntax for --mk_rec_dmn, described next. As of NCO version 4.4.0 (January, 2014), the argument all may be given to --fix_rec_dmn to convert all record dimensions to fixed dimensions in the output file. Previously, --fix_rec_dmn only allowed one option, the name of a single record dimension to be fixed. Now it is simple to simultaneously fix all record dimensions. This is useful (and nearly mandatory) when flattening netCDF4 files that have multiple record dimensions per group into netCDF3 files (which are limited to at most one record dimension) (Group Path Editing). <a name="hdn"></a>  <a name="hidden"></a>  hidden attributes special attributes --hdn --hidden _SOURCE_FORMAT _Format _DeflateLevel _Shuffle _Storage _ChunkSizes _Endianness _Fletcher32 _NOFILL _NCProperties _IsNetcdf4 _SuperblockVersion --hdn As of NCO version 4.4.0 (January, 2014), the --hdn or --hidden options print hidden (aka special) attributes. This is equivalent to ncdump -s. Hidden attributes include: _Format, _DeflateLevel, _Shuffle, _Storage, _ChunkSizes, _Endianness, _Fletcher32, and _NOFILL. Previously ncks ignored all these attributes in CDL/XML modes. Now it prints these attributes as appropriate in all modes. As of NCO version 4.4.6 (September, 2014), --hdn also prints the extended file format (i.e., the format of the file or server supplying the data) as _SOURCE_FORMAT. As of NCO version 4.6.1 (August, 2016), --hdn also prints the hidden attributes _NCProperties, _IsNetcdf4, and _SuperblockVersion for netCDF4 files so long as NCO is linked against netCDF library version 4.4.1 or later. Users are referred to the http://www.unidata.ucar.edu/software/netcdf/docsUnidata netCDF Documentation, or the man pages for ncgen or ncdump, for detailed descriptions of the meanings of these hidden attributes. <a name="cdl"></a>  <a name="hdp"></a>  <a name="hncgen"></a>  <a name="h4_ncgen"></a>  <a name="ncgen-hdf"></a>  hdp ncgen ncgen-hdf hncgen h4_ncgen ncdump --cdl CDL HDF HDF4 --cdl As of NCO version 4.3.3 (July, 2013), ncks can print extracted data and metadata to screen (i.e., stdout) as valid CDL (network Common data form Description Language). CDL is the human-readable &textldquo;lingua franca&textrdquo; of netCDF ingested by ncgen and excreted by ncdump. As of NCO version 4.6.9 (September, 2017), ncks prints CDL by default, and the &textldquo;traditional&textrdquo; mode must be explicitly selected with --trd. Compare ncks &textldquo;traditional&textrdquo; with CDL printing: zender@roulee:~$ ncks --trd -v one ~/nco/data/in.nc one: type NC_FLOAT, 0 dimensions, 1 attribute, chunked? no, compressed? no, packed? no one size (RAM) = 1*sizeof(NC_FLOAT) = 1*4 = 4 bytes one attribute 0: long_name, size = 3 NC_CHAR, value = one one = 1 zender@roulee:~$ ncks --cdl -v one ~/nco/data/in.nc netcdf in { variables: float one ; one:long_name = "one" ; data: one = 1 ; } // group / Users should note the NCO&textrsquo;s CDL mode outputs successively more verbose additional diagnostic information in CDL comments as the level of debugging increases from zero to two. For example printing the above with -D 2 yields zender@roulee:~$ ncks -D 2 --cdl -v one ~/nco/data/in.nc netcdf in { // ncgen -k classic -b -o in.nc in.cdl variables: float one ; // RAM size = 1*sizeof(NC_FLOAT) = 1*4 = 4 bytes, ID = 147 one:long_name = "one" ; // char data: one = 1 ; } // group / ncgen converts CDL-mode output into a netCDF file:

ncks -v one ~/nco/data/in.nc > ~/in.cdl
ncgen -k netCDF-4 -b -o ~/in.nc ~/in.cdl
ncks -v one ~/in.nc

The HDF4 version of ncgen, often named hncgen, h4_ncgen, or ncgen-hdf, (usually) converts netCDF3 CDL into an HDF file:

cd ~/nco/data
ncgen      -b -o hdf.hdf hdf.cdl # HDF ncgen is sometimes named...ncgen
ncgen      -b -o in.hdf  in.cdl  # Fails: Some valid netCDF CDL breaks HDF ncgen
hncgen     -b -o hdf.hdf hdf.cdl # HDF ncgen is hncgen in some RPM packages
h4_ncgen   -b -o hdf.hdf hdf.cdl # HDF ncgen is h4_ncgen in Anaconda packages
ncgen-hdf  -b -o hdf.hdf hdf.cdl # HDF ncgen is ncgen-hdf in some Debian packages
hdp dumpsds hdf.hdf              # ncdump/h5dump-equivalent for HDF4
h4_ncdump dumpsds hdf.hdf        # ncdump/h5dump-equivalent for HDF4

Note that HDF4 does not support netCDF-style groups, so the above commands fail when the input file contains groups. Only netCDF4 and HDF5 support groups. In our experience the HDF ncgen command, by whatever name installed, is not robust and fails on many valid netCDF3 CDL constructs. The HDF4 version of ncgen will definitely fail on the default NCO input file nco/data/in.cdl. The NCO source code distribution provides nco/data/hdf.cdl that works with the HDF4 version of ncgen, and can be used to test HDF files. <a name="dmn_rec_mk"></a>  <a name="mk_rec_dmn"></a>  record dimension fixed dimension fix record dimension --mk_rec_dmn dim --mk_rec_dmn dim Change existing dimension dim to a record dimension in the output file. This is the most straightforward way of changing a dimension to a/the record dimension, and works fine in most cases. See ncecat netCDF Ensemble Concatenator and ncpdq netCDF Permute Dimensions Quickly for other methods of changing variable dimensionality, including the record dimension. <a name="H"></a>  <a name="data"></a>  -H --data --hieronymus -H Toggle (turn-on or turn-off) default behavior of printing data (not metadata) to screen or copying data to disk. Also activated using --print or --prn. By default ncks prints all metadata but no data to screen when no netCDF output-file is specified. And if output-file is specified, ncks copies all metadata and all data to it. In other words, the printing/copying default is context-sensitive, and -H toggles the default behavior. Hence, use -H to turn-off copying data (not metadata) to an output file. (It is occasionally useful to write all metadata to a file, so that the file has allocated the required disk space to hold the data, yet to withold writing the data itself). And use -H to turn-on printing data (not metadata) to screen. Unless otherwise specified (with -s), each element of the data hyperslab prints on a separate line containing the names, indices, and, values, if any, of all of the variables dimensions. The dimension and variable indices refer to the location of the corresponding data element with respect to the variable as stored on disk (i.e., not the hyperslab).

% ncks --trd -C -v three_dmn_var in.nc
lat[0]=-90 lev[0]=100 lon[0]=0 three_dmn_var[0]=0 
lat[0]=-90 lev[0]=100 lon[1]=90 three_dmn_var[1]=1 
lat[0]=-90 lev[0]=100 lon[2]=180 three_dmn_var[2]=2 
...
lat[1]=90 lev[2]=1000 lon[1]=90 three_dmn_var[21]=21 
lat[1]=90 lev[2]=1000 lon[2]=180 three_dmn_var[22]=22 
lat[1]=90 lev[2]=1000 lon[3]=270 three_dmn_var[23]=23

Printing the same variable with the -F option shows the same variable indexed with Fortran conventions

% ncks -F -C -v three_dmn_var in.nc
lon(1)=0 lev(1)=100 lat(1)=-90 three_dmn_var(1)=0 
lon(2)=90 lev(1)=100 lat(1)=-90 three_dmn_var(2)=1 
lon(3)=180 lev(1)=100 lat(1)=-90 three_dmn_var(3)=2 
...

Printing a hyperslab does not affect the variable or dimension indices since these indices are relative to the full variable (as stored in the input file), and the input file has not changed. However, if the hyperslab is saved to an output file and those values are printed, the indices will change:

% ncks --trd -H -d lat,90.0 -d lev,1000.0 -v three_dmn_var in.nc out.nc
...
lat[1]=90 lev[2]=1000 lon[0]=0 three_dmn_var[20]=20 
lat[1]=90 lev[2]=1000 lon[1]=90 three_dmn_var[21]=21 
lat[1]=90 lev[2]=1000 lon[2]=180 three_dmn_var[22]=22 
lat[1]=90 lev[2]=1000 lon[3]=270 three_dmn_var[23]=23 
% ncks --trd -C -v three_dmn_var out.nc
lat[0]=90 lev[0]=1000 lon[0]=0 three_dmn_var[0]=20 
lat[0]=90 lev[0]=1000 lon[1]=90 three_dmn_var[1]=21 
lat[0]=90 lev[0]=1000 lon[2]=180 three_dmn_var[2]=22 
lat[0]=90 lev[0]=1000 lon[3]=270 three_dmn_var[3]=23

<a name="jsn"></a>  <a name="json"></a>  <a name="JSON"></a>  --jsn_fmt --jsn --json JSN JSON JavaScript --jsn, --json As of NCO version 4.6.2 (November, 2016), ncks can print extracted metadata and data to screen (i.e., stdout) as JSON, JavaScript Object Notation, defined http://www.json.orghere. ncks supports JSON output more completely, flexibly, and robustly than any other tool to our knowledge. With ncks one can translate entire netCDF3 and netCDF4 files into JSON, including metadata and data, using all NCO&textrsquo;s subsetting and hyperslabbing capabilities. NCO uses a JSON format we developed ourselves, during a year of discussion among interested researchers. Some refer to this format as NCO-JSON, to disambiguate it from other JSON formats for netCDF data. Other projects have since adopted, and some can generate, NCO-JSON. ERDDAP CF-JSON Projects that support NCO-JSON include ERDDAP (https://coastwatch.pfeg.noaa.gov/erddap/index.html, choose output filetype .ncoJson from this https://coastwatch.pfeg.noaa.gov/erddap/griddap/documentation.html#fileTypetable) and CF-JSON (http://cf-json.org). Behold JSON output in default mode: zender@aerosol:~$ ncks --jsn -v one ~/nco/data/in.nc { "variables": { "one": { "type": "float", "attributes": { "long_name": "one" }, "data": 1.0 } } } NCO converts to (using commonsense rules) and prints all NC_TYPEs as one of three atomic types distinguishable as JSON values: float, string, and int The JSON boolean atomic type is not (yet) supported as there is no obvious netCDF-equivalent to this type.. Floating-point types (NC_FLOAT and NC_DOUBLE) are printed with a decimal point and at least one signficant digit following the decimal point, e.g., 1.0 rather than 1. or 1. Integer types (e.g., NC_INT, NC_UINT64) are printed with no decimal point. String types (NC_CHAR and NC_STRING) are enclosed in double-quotes. The JSON specification allows many possible output formats for netCDF files. NCO developers implemented a working prototype in Octoboer, 2016 and, after discussing options with interested parties https://sourceforge.net/p/nco/discussion/9829/thread/8c4d7e72here, finalized the emitted JSON syntax a few weeks later. The resulting JSON backend supports three levels of pedanticness, ordered from more concise, flexible, and human-readable to more verbose, restrictive, and 1-to-1 reproducible. JSON-specific switches access these modes and other features. Each JSON configuration option automatically triggers JSON printing, so that specifying --json in addition to a JSON configuration option is redundant and unnecessary. Request a specific format level with the pedantic level argument to the --jsn_fmt lvl option. As of NCO version 4.6.3 (December, 2016), the option formerly known as --jsn_att_fmt was renamed simply --jsn_fmt. The more general name reflects the fact that the option controls all JSON formatting, not just attribute formatting. As of version 4.6.3, NCO defaults to demarcate inner dimensions of variable data with (nested) square brackets rather than printing data as an unrolled single dimensional array. An array with C-ordered dimensionality [2,3,4] prints as: % ncks --jsn -v three_dmn_var ~/nco/data/in.nc ... "data": [[[0.0, 1.0, 2.0, 3.0], [4.0, 5.0, 6.0, 7.0], [8.0, 9.0, 10.0,11.0]], [[12.0, 13.0, 14.0, 15.0], [16.0, 17.0, 18.0, 19.0], [20.0,21.0, 22.0, 23.0]]] ... % ncks --jsn_fmt=4 -v three_dmn_var ~/nco/data/in.nc ... "data": [0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, 21.0,22.0, 23.0] ... One can recover the former behavior (and omit the brackets) by adding four to the base pedantic level lvl (as shown above). Besides the potential offset of four, lvl may take one of three values between 0&textndash;2: •

lvl = 0 is the default mode, and is also explicitly selectable with --jsn_fmt=0. All values are output without the original NC_TYPE token. This allows attributes to print as JSON name-value pairs, rather than as more complex objects: % ncks --jsn_fmt=0 -v att_var ~/nco/data/in_grp.nc ... "att_var": { "shape": ["time"], "type": "float", "attributes": { "byte_att": [0, 1, 2, 127, -128, -127, -2, -1], "char_att": "Sentence one.\nSentence two.\n", "short_att": 37, "int_att": 73, "long_att": 73, "float_att": [73.0, 72.0, 71.0, 70.010, 69.0010, 68.010, 67.010], "double_att": [73.0, 72.0, 71.0, 70.010, 69.0010, 68.010, 67.0100010] }, "data": [10.0, 10.10, 10.20, 10.30, 10.40101, 10.50, 10.60, 10.70, 10.80, 10.990] ... This least pedantic mode produces the most easily read results, and suffices for many (most?) purposes. Any downstream parser is expected to assign an appropriate type as indicated by JSON syntax rules. Because the original attributes&textrsquo; NC_TYPE are not output, a downstream parser may not exactly reproduce the input file datatypes. For example, whether the original attribute string was stored as NC_CHAR or NC_STRING will be unknown to a downstream parser. Distinctions between NC_FLOAT and NC_DOUBLE are similarly lost, as are all distinctions among the integer types. In our experience, these distinctions are immaterial for attributes, which are intended for metadata not for large-scale storage. Type-distinctions can, however, significantly impact the size of variable data, responsible for nearly all the storage required by datasets. For instance, storing or transferring an NC_SHORT field as NC_DOUBLE would waste a factor of four in space or bandwidth. This is why NCO always prints the NC_TYPE of variable data. Downstream parsers can (but are not required to) take advantage of the variable&textrsquo;s NC_TYPE to choose the most efficient storage type. The Shape member of the variable object is an ordered array of dimension names such as "shape": ["lat","lon"], similar to its use in NcML. Each name corresponds to a previously defined Dimension object that, taken together, define the rank, shape, and size of the variable. Variables are assumed to be scalar by default. Hence the Shape member is mandatory for arrays, and is always omitted for scalars (by contrast, NcML requires an empty shape string to indicate scalars). •

lvl = 1 is a medium-pedantic level that prints all attributes as objects (with explicit types) unless the attribute type match the simplest default JSON value types. In other words, attributes of type NC_FLOAT, NC_CHAR, NC_SHORT, and NC_BYTE are printed as objects with an explicit type so that parsers do not use the default type. Attributes of type NC_DOUBLE, NC_STRING, and NC_INT are printed as JSON arrays, as in the

lvl = 0 above: % ncks --jsn_fmt=1 -v att_var ~/nco/data/in.nc ... "att_var": { "shape": ["time"], "type": "float", "attributes": { "byte_att": { "type": "byte", "data": [0, 1, 2, 127, -128, -127, -2, -1]}, "char_att": "Sentence one.\nSentence two.\n", "short_att": { "type": "short", "data": 37}, "int_att": 73, "long_att": 73, "float_att": [73.0, 72.0, 71.0, 70.010, 69.0010, 68.010, 67.010], "double_att": { "type": "double", "data": [73.0, 72.0, 71.0, 70.010, 69.0010, 68.010, 67.0100010]} }, "data": [10.0, 10.10, 10.20, 10.30, 10.40101, 10.50, 10.60, 10.70, 10.80, 10.990] ... The attributes of type NC_BYTE, NC_SHORT, and NC_DOUBLE are printed as JSON objects that comprise an NC_TYPE and a value list, because their values could conceivably not be representable, or would waste space, if interpreted as NC_INT or NC_FLOAT, respectively. All other attributes may be naturally mapped to the type indicated by the JSON syntax of the value, where numbers are assumed to correspond to NC_FLOAT for floating-point, NC_INT for integers, and NC_CHAR or NC_STRING for strings. This minimal increase in verbosity allows a downstream parser to re-construct the original dataset with nearly identical attributes types to the original. •

lvl = 2 is the most pedantic mode, and should be used when preserving all input types (e.g., to ensure exact reproducibility of the input file) is important. This mode always prints attributes as JSON objects with a type value so that any downstream parser can (though it need not) guarantee exact reproduction of the original dataset: % ncks --jsn_fmt=2 -v att_var ~/nco/data/in.nc ... "att_var": { "shape": ["time"], "type": "float", "attributes": { "byte_att": { "type": "byte", "data": [0, 1, 2, 127, -128, -127, -2, -1]}, "char_att": { "type": "char", "data": "Sentence one.\nSentence two.\n"}, "short_att": { "type": "short", "data": 37}, "int_att": { "type": "int", "data": 73}, "long_att": { "type": "int", "data": 73}, "float_att": { "type": "float", "data": [73.0, 72.0, 71.0, 70.010, 69.0010, 68.010, 67.010]}, "double_att": { "type": "double", "data": [73.0, 72.0, 71.0, 70.010, 69.0010, 68.010, 67.0100010]} }, "data": [10.0, 10.10, 10.20, 10.30, 10.40101, 10.50, 10.60, 10.70, 10.80, 10.990] ... jsonlint That ncks produces correct translations of for all supported datatypes may be verified by a JSON syntax checker command like jsonlint. Please let us know how to improve JSON features for your application. <a name="Metadata"></a>  <a name="M"></a>  -M --Mtd --Metadata metadata, global -M Turn-on printing to screen or turn-off copying global and group metadata. This includes file summary information and global and group attributes. Also --Mtd and --Metadata. By default ncks prints global metadata to screen if no netCDF output file and no variable extraction list is specified (with -v). Use -M to print global metadata to screen if a netCDF output is specified, or if a variable extraction list is specified (with -v). Use -M to turn-off copying of global and group metadata when copying, subsetting, or appending to an output file. <a name="prn_tbl"></a>  The various combinations of printing switches can be confusing. In an attempt to anticipate what most users want to do, ncks uses context-sensitive defaults for printing. Our goal is to minimize the use of switches required to accomplish the common operations. We assume that users creating a new file or overwriting (e.g., with -O) an existing file usually wish to copy all global and variable-specific attributes to the new file. In contrast, we assume that users appending (e.g., with -A an explicit variable list from one file to another usually wish to copy only the variable-specific attributes to the output file. The switches -H, -M, and -m switches are implemented as toggles which reverse the default behavior. The most confusing aspect of this is that -M inhibits copying global metadata in overwrite mode and causes copying of global metadata in append mode.

ncks                 in.nc        # Print  VAs and GAs
ncks          -v one in.nc        # Print  VAs not GAs
ncks    -M    -v one in.nc        # Print  GAs only
ncks       -m -v one in.nc        # Print  VAs only
ncks    -M -m -v one in.nc        # Print  VAs and GAs
ncks -O              in.nc out.nc # Copy   VAs and GAs
ncks -O       -v one in.nc out.nc # Copy   VAs and GAs
ncks -O -M    -v one in.nc out.nc # Copy   VAs not GAs
ncks -O    -m -v one in.nc out.nc # Copy   GAs not VAs
ncks -O -M -m -v one in.nc out.nc # Copy   only data (no atts)
ncks -A              in.nc out.nc # Append VAs and GAs
ncks -A       -v one in.nc out.nc # Append VAs not GAs
ncks -A -M    -v one in.nc out.nc # Append VAs and GAs
ncks -A    -m -v one in.nc out.nc # Append only data (no atts)
ncks -A -M -m -v one in.nc out.nc # Append GAs not VAs

where VAs and GAs denote variable and group/global attributes, respectively. <a name="-m"></a>  <a name="m"></a>  <a name="mtd"></a>  <a name="metadata"></a>  ncdump -m --mtd --metadata metadata -m Turn-on printing to screen or turn-off copying variable metadata. Using -m will print variable metadata to screen (similar to ncdump -h). This displays all metadata pertaining to each variable, one variable at a time. chunking compression deflation This includes information on the storage properties of the variable, such as whether it employs chunking, compression, or packing. Also activated using --mtd and --metadata. The ncks default behavior is to print variable metadata to screen if no netCDF output file is specified. Use -m to print variable metadata to screen if a netCDF output is specified. Also use -m to turn-off copying of variable metadata to an output file. <a name="no_blank"></a>  <a name="noblank"></a>  <a name="no-blank"></a>  --no_blank --noblank --no-blank blank missing values --no_blank Print numeric representation of missing values. As of NCO version 4.2.2 (October, 2012), NCO prints missing values as blanks (i.e., the underscore character _) by default. To enable the old behavior of printing the numeric representation of missing values (e.g., 1.0e36), use the --no_blank switch. Also activated using --noblank or --no-blank. <a name="P"></a>  <a name="prn"></a>  -P --print --prn -P Print data, metadata, and units to screen. The -P switch is a convenience abbreviation for -C -H -M -m -u. Also activated using --print or --prn. This set of switches is useful for exploring file contents. <a name="fl_prn"></a>  <a name="prn_fl"></a>  print file --fl_prn --prn_fl --prn_fl print-file Activate printing formatted output to file print-file. Also --print_file, --fl_prn, and --file_print. One can achieve the same result by redirecting stdout to a named file. However, it is slightly faster to print formatted output directly to a file than to stdout:

ncks --fl_prn=foo.txt --jsn in.nc

<a name="Q"></a>  <a name="quiet"></a>  -Q --quiet -Q Print quietly, meaning omit dimension names, indices, and coordinate values when printing arrays. Variable (not dimension) indices are printed. Variable names appear flush left in the output: zender@roulee:~$ ncks --trd -Q -v three_dmn_rec_var -C -H ~/nco/data/in.nc three_dmn_rec_var[0]=1 ... This helps locate specific variables in lists with many variables and different dimensions. See also the -V option, which omits all names and indices and prints only variable values. <a name="q"></a>  <a name="quench"></a>  -q --quench quench -q Quench (turn-off) all printing to screen. This overrides the setting of all print-related switches, equivalent to -H -M -m when in single-file printing mode. When invoked with -R (Retaining Retrieved Files), ncks automatically sets -q. This allows ncks to retrieve remote files without automatically trying to print them. Also --quench. <a name="rad"></a>  <a name="orphan"></a>  <a name="rph_dmn"></a>  --rad orphan dimensions --retain_all_dimensions --orphan_dimensions --rph_dmn --rad Retain all dimensions. When invoked with --rad (Retain All Dimensions), ncks copies each dimension in the input file to the output file, regardless of whether the dimension is utilized by any variables. Normally ncks discards &textldquo;orphan dimensions&textrdquo;, i.e., dimensions not referenced by any variables. This switch allows users to keep non-referenced dimensions in the workflow. When invoked in printing mode, causes orphaned dimensions to be printed (they are not printed by default). Also --retain_all_dimensions, --orphan_dimensions, and --rph_dmn. <a name="s"></a>  <a name="sng_fmt"></a>  <a name="string"></a>  -s --string --sng_fmt printf() C language -s format String format for text output. Accepts C language escape sequences and printf() formats. Also --string and --sng_fmt. This option is only intended for use with traditional (TRD) printing, and thus automatically invokes the --trd switch. <a name="fmt_val"></a>  <a name="val_fmt"></a>  <a name="value_format"></a>  --value_format --fmt_val --val_fmt printf() C language --fmt_val format Supply a printf()-style format for printed output, i.e., in CDL, JSON, TRD, or XML modes. Also --val_fmt and --value_format. One use for this option is to reduce the printed precision of floating point values:

# Default printing of original double precision values
# 0.0,0.1,0.12,0.123,0.1234,0.12345,0.123456,0.1234567,0.12345678,0.123456789
% ncks -C -v ppc_dbl ~/nco/data/in.nc
...
ppc_dbl = 0, 0.1, 0.12, 0.123, 0.1234, 0.12345, 0.123456, 0.1234567, 0.12345678, 0.123456789 ;
...
# Restrict printing to three digits after the decimal
% ncks --fmt_val=%.3f -C -v ppc_dbl ~/nco/data/in.nc
...
ppc_dbl = 0., 0.1, 0.12, 0.123, 0.123, 0.123, 0.123, 0.123, 0.123, 0.123 ;
...

The supplied format only applies to floating point variable values (NC_FLOAT or NC_DOUBLE), and not to other types or to attributes. For reference, the default printf() format for CDL, JSON, TRD, and XML modes is %#.7gf, %#.7g, %g, and %#.7g, respectively, for single-precision data, and, for double-precision data is %#.15g, %#.15g, %.12g, and %#.15g, respectively. NCO introduced this feature in version 4.7.3 (March, 2018). We would appreciate your feedback on whether and how to extend this feature to make it more useful. <a name="hrz"></a>  <a name="hrz_crd"></a>  <a name="hrz_fl"></a>  <a name="fl_hrz"></a>  <a name="s1d"></a>  <a name="restart"></a>  <a name="sparse"></a>  <a name="unpack_sparse"></a>  restart files sparse format S1D format --unpack_sparse --s1d --sparse --hrz --hrz_fl --hrz_crd PFT MEC multiple elevation classes elevation classes landunit topounit plant functional type --s1d, --sparse, --unpack_sparse, --hrz file As of NCO version 5.2.0, released in February, 2024, ncks can help analyze initial condition and restart datasets produced by the E3SM ELM and CESM CLM/CTSM land-surface models. Whereas gridded history datasets from these ESMs use a standard gridded data format, land-surface "restart files" employ a custom packing format that unwinds multi-dimensional data into sparse, 1-D (S1D) arrays that are not easily visualized. ncks can convert S1D files into gridded datasets where all dimensions are explicitly declared, rather than unrolled or "packed". Invoke this conversion feature with the --s1d option (or long option equivalents, --sparse or --unpacksparse) and, with --hrz_crd fl_hrz (e.g., --hrz_crd hrz.nc), point to the file that contains the horizontal coordinates (that restart files usually omit). The output file is the fully gridded input file, with no loss of information: ncks --s1d --hrz=elmv3_history.nc elmv3_restart.nc out.nc The output file contains all input variables placed on a lat-lon or unstructured grid, with new dimensions for Plant Funtional Type (PFT) and multiple elevation classes (MECs). <a name="ssh"></a>  <a name="scr"></a>  <a name="secret"></a>  --ssh --scr --secret hidden features --ssh, --secret Print summary of ncks hidden features. These hidden or secret features are used mainly by developers. They are not supported for general use and may change at any time. This demonstrates conclusively that I cannot keep a secret. Also --ssh and --scr. <a name="trd"></a>  <a name="traditional"></a>  --trd --traditional --trd, --traditional From 1995&textndash;2017 ncks dumped the ASCII text representation of netCDF files in what we now call &textldquo;traditional&textrdquo; mode. Much of this manual contains output printed in traditional mode, which places one value per line, with complete dimensional information. Traditional-mode metadata output includes lower-level information, such as RAM usage and internal variable IDs, than CDL. While this is useful for some developers and user, CDL has, over the years, become more useful than traditional mode for most users. As of NCO version 4.6.9 (September, 2017) CDL became the default printing mode. Traditional printing mode is accessed via the --trd option. <a name="units"></a>  <a name="u"></a>  -u --units -u, --units Toggle the printing of a variable&textrsquo;s units attribute, if any, with its values. Also --units. <a name="V"></a>  <a name="val_var"></a>  -V --val_var --no_dmn_var_nm --no_nm_prn -V Print variable values only. Do not print variable and dimension names, indices, and coordinate values when printing arrays. zender@roulee:~$ ncks --trd -V -v three_dmn_rec_var -C -H ~/nco/data/in.nc 1 ... See also the -Q option, which prints variable names and indices, but not dimension names, indices, or coordinate values when printing arrays. Using -V is the same as specifying -Q --no_nm_prn. <a name="xml"></a>  <a name="ncml"></a>  --xml --ncml ncdump XML NcML --xml, --ncml As of NCO version 4.3.3 (July, 2013), ncks can print extracted data and metadata to screen (i.e., stdout) as XML in NcML, the netCDF Markup Language. ncks supports XML more completely than ncdump -x. With ncks one can translate entire netCDF3 and netCDF4 files into NcML, including metadata and data, using all NCO&textrsquo;s subsetting and hyperslabbing capabilities. Compare ncks &textldquo;traditional&textrdquo; with XML printing: zender@roulee:~$ ncks --trd -v one ~/nco/data/in.nc one: type NC_FLOAT, 0 dimensions, 1 attribute, chunked? no, compressed? no, packed? no one size (RAM) = 1*sizeof(NC_FLOAT) = 1*4 = 4 bytes one attribute 0: long_name, size = 3 NC_CHAR, value = one one = 1 zender@roulee:~$ ncks --xml -v one ~/nco/data/in.nc <?xml version="1.0" encoding="UTF-8"?> <netcdf xmlns="http://www.unidata.ucar.edu/namespaces/netcdf/ncml-2.2" location="/home/zender/nco/data/in.nc"> <variable name="one" type="float" shape=""> <attribute name="long_name" separator="*" value="one" /> <values>1.</values> </variable> </netcdf> XML-mode prints variable metadata and, as of NCO version 4.3.7 (October, 2013), variable data and, as of NCO version 4.4.0 (January, 2014), hidden attributes. That ncks produces correct NcML translations of CDM files for all supported datatypes is verified by comparison to output from Unidata&textrsquo;s toolsUI Java program. Please let us know how to improve XML/NcML features. --xml_no_location --xml_spr_chr --xml_spr_nmr separator ncks provides additional options to configure NcML output: --xml_no_location, --xml_spr_chr, and --xml_spr_nmr. Every NcML configuration option automatically triggers NcML printing, so that specifying --xml in addition to a configuration option is redundant and unnecessary. The --xml_no_location switch prevents output of the NcML location element. By default the location element is printed with a value equal to the location of the input dataset, e.g., location="/home/zender/in.nc". The --xml_spr_chr and --xml_spr_nmr options customize the strings used as NcML separators for attributes and variables of character-type and numeric-type, respectively. Their default separators are * and &textldquo; &textrdquo; (a space): zender@roulee:~$ ncks --xml -d time,0,3 -v two_dmn_rec_var_sng in.nc ... <values separator="*">abc*bcd*cde*def</values> ... zender@roulee:~$ ncks --xml_spr_chr=', ' -v two_dmn_rec_var_sng in.nc ... <values separator=", ">abc, bcd, cde, def, efg, fgh, ghi, hij, jkl, klm</values> ... zender@roulee:~$ ncks --xml -v one_dmn_rec_var in.nc ... <values>1 2 3 4 5 6 7 8 9 10</values> ... zender@roulee:~$ ncks --xml_spr_nmr=', ' -v one_dmn_rec_var in.nc ... <values separator=", ">1, 2, 3, 4, 5, 6, 7, 8, 9, 10</values> ... Separator elements for strings are a thorny issue. One must be sure that the separator element is not mistaken as a portion of the string. NCO attempts to produce valid NcML and supplies the --xml_spr_chr option to work around any difficulties. NCO performs precautionary checks with strstr(val,spr) to identify presence of the separator string (spr) in data (val) and, when it detects a match, automatically switches to a backup separator string (*|*). However limitations of strstr() may lead to false negatives when the separator string occurs in data beyond the first string in multi-dimensional NC_CHAR arrays. Hence, results may be ambiguous to NcML parsers. If problems arise, use --xml_spr_chr to specify a multi-character separator that does not appear in the string array and that does not include an NcML formatting characters (e.g., commas, angles, quotes).

<a name="ncattget"></a>  <a name="nclst"></a>  <a name="ncvarlst"></a>  <a name="ncvardmnlst"></a>  <a name="ncdmnlst"></a>  <a name="ncdmnsz"></a>  <a name="ncgrplst"></a>  <a name="ncrecsz"></a>  <a name="ncmax"></a>  <a name="ncmdn"></a>  <a name="ncavg"></a>  <a name="ncrng"></a>  <a name="ncunits"></a>  <a name="alias"></a>  <a name="filters"></a>  <a name="filter"></a>  <a name="flt"></a>  * Filters for ncks::

Filters for ncksncks netCDF Kitchen Sinkncks netCDF Kitchen Sink Filters for ncks UNIX ncattget ncavg ncdmnlst ncvardmnlst ncvardmnlatlon ncdmnsz ncgrplst nclst nclst ncmax ncmdn ncmin ncrecsz ncrng nctypget ncunits .bashrc filters alias shell Bash shell Csh shell Sh shell bash We encourage the use of standard UNIX pipes and filters to narrow the verbose output of ncks into more precise targets. For example, to obtain an uncluttered listing of the variables in a file try

ncks --trd -m in.nc | grep -E ': type' | cut -f 1 -d ' ' | sed 's/://' | sort

A Bash user could alias the previous filter to the shell command ncvarlst as shown below. More complex examples could involve command line arguments. For example, a user may frequently be interested in obtaining the value of an attribute, e.g., for textual file examination or for passing to another shell command. Say the attribute is purpose, the variable is z, and the file is in.nc. In this example, ncks --trd -m -v z is too verbose so a robust grep and cut filter is desirable, such as

ncks --trd -M -m in.nc | grep -E -i "^z attribute [0-9]+: purpose" | cut -f 11- -d ' ' | sort

The filters are clearly too complex to remember on-the-fly so the entire procedure could be implemented as a shell command or function called, say, ncattget function ncattget { ncks --trd -M -m ${3} | grep -E -i "^${2} attribute [0-9]+: ${1}" | cut -f 11- -d ' ' | sort ; } The shell ncattget is invoked with three arugments that are, in order, the names of the attribute, variable, and file to examine. Global attributes are indicated by using a variable name of global. This definition yields the following results

% ncattget purpose z in.nc
Height stored with a monotonically increasing coordinate
% ncattget Purpose Z in.nc
Height stored with a monotonically increasing coordinate
% ncattget history z in.nc
% ncattget history global in.nc
History global attribute.

Note that case sensitivity has been turned off for the variable and attribute names (and could be turned on by removing the -i switch to grep). Furthermore, extended regular expressions may be used for both the variable and attribute names. The next two commands illustrate this by searching for the values of attribute purpose in all variables, and then for all attributes of the variable z:

% ncattget purpose .+ in.nc
1-D latitude coordinate referred to by geodesic grid variables
1-D longitude coordinate referred to by geodesic grid variables
...
% ncattget .+ Z in.nc
Height
Height stored with a monotonically increasing coordinate
meter

Extended filters are best stored as shell commands if they are used frequently. Shell commands may be re-used when they are defined in shell configuration files. These files are usually named .bashrc, .cshrc, and .profile for the Bash, Csh, and Sh shells, respectively. minimum maximum median mode range <a name="median"></a>  <a name="mode"></a>  <a name="range"></a>  # NB: Untested on Csh, Ksh, Sh, Zsh! Send us feedback! # Bash shell (/bin/bash), .bashrc examples # ncattget $att_nm $var_nm $fl_nm : What attributes does variable have? function ncattget { ncks --trd -M -m ${3} | grep -E -i "^${2} attribute [0-9]+: ${1}" | cut -f 11- -d ' ' | sort ; } # ncunits $att_val $fl_nm : Which variables have given units? function ncunits { ncks --trd -m ${2} | grep -E -i " attribute [0-9]+: units.+ ${1}" | cut -f 1 -d ' ' | sort ; } # ncavg $var_nm $fl_nm : What is mean of variable? function ncavg { ncwa -y avg -O -C -v ${1} ${2} ~/foo.nc ; ncks --trd -H -C -v ${1} ~/foo.nc | cut -f 3- -d ' ' ; } # ncavg $var_nm $fl_nm : What is mean of variable? function ncavg { ncap2 -O -C -v -s "foo=${1}.avg();print(foo)" ${2} ~/foo.nc | cut -f 3- -d ' ' ; } # ncdmnlst $fl_nm : What dimensions are in file? function ncdmnlst { ncks --cdl -m ${1} | cut -d ':' -f 1 | cut -d '=' -s -f 1 ; } # ncvardmnlst $var_nm $fl_nm : What dimensions are in a variable? function ncvardmnlst { ncks --trd -m -v ${1} ${2} | grep -E -i "^${1} dimension [0-9]+: " | cut -f 4 -d ' ' | sed 's/,//' ; } # ncvardmnlatlon $var_nm $fl_nm : Does variable contain both lat and lon dimensions? function ncvardmnlatlon { flg=`ncks -C -v ${1} -m ${2} | grep -E -i "${1}\(" | grep -E "lat.*lon|lon.*lat"` ; [[ ! -z "$flg" ]] && echo "Yes, ${1} has both lat and lon dimensions" || echo "No, ${1} does not have both lat and lon dimensions" } # ncdmnsz $dmn_nm $fl_nm : What is dimension size? function ncdmnsz { ncks --trd -m -M ${2} | grep -E -i ": ${1}, size =" | cut -f 7 -d ' ' | uniq ; } # ncgrplst $fl_nm : What groups are in file? function ncgrplst { ncks -m ${1} | grep 'group:' | cut -d ':' -f 2 | cut -d ' ' -f 2 | sort ; } # ncvarlst $fl_nm : What variables are in file? function ncvarlst { ncks --trd -m ${1} | grep -E ': type' | cut -f 1 -d ' ' | sed 's/://' | sort ; } # ncmax $var_nm $fl_nm : What is maximum of variable? function ncmax { ncwa -y max -O -C -v ${1} ${2} ~/foo.nc ; ncks --trd -H -C -v ${1} ~/foo.nc | cut -f 3- -d ' ' ; } # ncmax $var_nm $fl_nm : What is maximum of variable? function ncmax { ncap2 -O -C -v -s "foo=${1}.max();print(foo)" ${2} ~/foo.nc | cut -f 3- -d ' ' ; } # ncmdn $var_nm $fl_nm : What is median of variable? function ncmdn { ncap2 -O -C -v -s "foo=gsl_stats_median_from_sorted_data(${1}.sort());print(foo)" ${2} ~/foo.nc | cut -f 3- -d ' ' ; } # ncmin $var_nm $fl_nm : What is minimum of variable? function ncmin { ncap2 -O -C -v -s "foo=${1}.min();print(foo)" ${2} ~/foo.nc | cut -f 3- -d ' ' ; } # ncrng $var_nm $fl_nm : What is range of variable? function ncrng { ncap2 -O -C -v -s "foo_min=${1}.min();foo_max=${1}.max();print(foo_min,\"%f\");print(\" to \");print(foo_max,\"%f\")" ${2} ~/foo.nc ; } # ncmode $var_nm $fl_nm : What is mode of variable? function ncmode { ncap2 -O -C -v -s "foo=gsl_stats_median_from_sorted_data(${1}.sort());print(foo)" ${2} ~/foo.nc | cut -f 3- -d ' ' ; } # ncrecsz $fl_nm : What is record dimension size? function ncrecsz { ncks --trd -M ${1} | grep -E -i "^Root record dimension 0:" | cut -f 10- -d ' ' ; } # nctypget $var_nm $fl_nm : What type is variable? function nctypget { ncks --trd -m -v ${1} ${2} | grep -E -i "^${1}: type" | cut -f 3 -d ' ' | cut -f 1 -d ',' ; } # Csh shell (/bin/csh), .cshrc examples (derive others from Bash definitions): ncattget() { ncks --trd -M -m -v ${3} | grep -E -i "^${2} attribute [0-9]+: ${1}" | cut -f 11- -d ' ' | sort ; } ncdmnsz() { ncks --trd -m -M ${2} | grep -E -i ": ${1}, size =" | cut -f 7 -d ' ' | uniq ; } ncvarlst() { ncks --trd -m ${1} | grep -E ': type' | cut -f 1 -d ' ' | sed 's/://' | sort ; } ncrecsz() { ncks --trd -M ${1} | grep -E -i "^Record dimension:" | cut -f 8- -d ' ' ; } # Sh shell (/bin/sh), .profile examples (derive others from Bash definitions): ncattget() { ncks --trd -M -m ${3} | grep -E -i "^${2} attribute [0-9]+: ${1}" | cut -f 11- -d ' ' | sort ; } ncdmnsz() { ncks --trd -m -M ${2} | grep -E -i ": ${1}, size =" | cut -f 7 -d ' ' | uniq ; } ncvarlst() { ncks --trd -m ${1} | grep -E ': type' | cut -f 1 -d ' ' | sed 's/://' | sort ; } ncrecsz() { ncks --trd -M ${1} | grep -E -i "^Record dimension:" | cut -f 8- -d ' ' ; } <a name="xmp_ncks"></a>  EXAMPLES View all data in netCDF in.nc, printed with Fortran indexing conventions:

ncks -F in.nc

Copy the netCDF file in.nc to file out.nc.

ncks in.nc out.nc

Now the file out.nc contains all the data from in.nc. There are, however, two differences between in.nc and out.nc. history First, the history global attribute (History Attribute) will contain the command used to create out.nc. alphabetize output sort alphabetically -a Second, the variables in out.nc will be defined in alphabetical order. Of course the internal storage of variable in a netCDF file should be transparent to the user, but there are cases when alphabetizing a file is useful (see description of -a switch). <a name="xmp_att_glb_cpy"></a>  global attributes attributes, global subsetting exclusion extraction -v var --variable var -x --exclude --xcl Copy all global attributes (and no variables) from in.nc to out.nc:

ncks -A -x ~/nco/data/in.nc ~/out.nc

The -x switch tells NCO to use the complement of the extraction list (Subsetting Files). Since no extraction list is explicitly specified (with -v), the default is to extract all variables. The complement of all variables is no variables. -A --apn --append appending to files Without any variables to extract, the append (-A) command (Appending Variables) has only to extract and copy (i.e., append) global attributes to the output file. <a name="xmp_att_glb_cpy"></a>  Copy/append metadata (not data) from variables in one file to variables in a second file. When copying/subsetting/appending files (as opposed to printing them), the copying of data, variable metadata, and global/group metadata are now turned OFF by -H, -m, and -M, respectively. This is the opposite sense in which these switches work when printing a file. One can use these switches to easily replace data or metadata in one file with data or metadata from another:

# Extract naked (data-only) copies of two variables
ncks -h -M -m -O -C -v one,three_dmn_rec_var ~/nco/data/in.nc ~/out.nc
# Change values to be sure original values are not copied in following step
ncap2 -O -v -s 'one*=2;three_dmn_rec_var*=0' ~/nco/data/in.nc ~/in2.nc
# Append in2.nc metadata (not data!) to out.nc
ncks -A -C -H -v one,three_dmn_rec_var ~/in2.nc ~/out.nc

Variables in out.nc now contain data (not metadata) from in.nc and metadata (not data) from in2.nc. -s --string --sng_fmt printf() \n (linefeed) \t (horizontal tab) Print variable three_dmn_var from file in.nc with default notations. Next print three_dmn_var as an un-annotated text column. Then print three_dmn_var signed with very high precision. Finally, print three_dmn_var as a comma-separated list:

% ncks --trd -C -v three_dmn_var in.nc
lat[0]=-90 lev[0]=100 lon[0]=0 three_dmn_var[0]=0 
lat[0]=-90 lev[0]=100 lon[1]=90 three_dmn_var[1]=1 
...
lat[1]=90 lev[2]=1000 lon[3]=270 three_dmn_var[23]=23 
% ncks --trd -s '%f\n' -C -v three_dmn_var in.nc
0.000000
1.000000
...
23.000000
% ncks --trd -s '%+16.10f\n' -C -v three_dmn_var in.nc
   +0.0000000000
   +1.0000000000
...
  +23.0000000000
% ncks --trd -s '%f, ' -C -v three_dmn_var in.nc
0.000000, 1.000000, ..., 23.000000,

Programmers will recognize these as the venerable C language printf() formatting strings. The second and third options are useful when pasting data into text files like reports or papers. ncatted netCDF Attribute Editor, for more details on string formatting and special characters. --no_blank As of NCO version 4.2.2 (October, 2012), NCO prints missing values as blanks (i.e., the underscore character _) by default:

% ncks --trd -C -H -v mss_val in.nc
lon[0]=0 mss_val[0]=73 
lon[1]=90 mss_val[1]=_ 
lon[2]=180 mss_val[2]=73 
lon[3]=270 mss_val[3]=_ 
% ncks -s '%+5.1f, ' -H -C -v mss_val in.nc
+73.0, _, +73.0, _,

To print the numeric value of the missing value instead of a blank, use the --no_blank option. -Q --quiet -V --val_var --no_dmn_var_nm --no_nm_prn ncks prints in a verbose fashion by default and supplies a number of switches to pare-down (or even spruce-up) the output. The interplay of the -Q, -V, and (otherwise undocumented) --no_nm_prn switches yields most desired verbosities: % ncks -v three_dmn_rec_var -C -H ~/nco/data/in.nc time[0]=1 lat[0]=-90 lon[0]=0 three_dmn_rec_var[0]=1 % ncks -Q -v three_dmn_rec_var -C -H ~/nco/data/in.nc three_dmn_rec_var[0]=1 % ncks -V -v three_dmn_rec_var -C -H ~/nco/data/in.nc 1 % ncks -Q --no_nm_prn -v three_dmn_rec_var -C -H ~/nco/data/in.nc 1 % ncks --no_nm_prn -v three_dmn_rec_var -C -H ~/nco/data/in.nc 1 -90 0 1 One dimensional arrays of characters stored as netCDF variables are automatically printed as strings, whether or not they are NUL-terminated, e.g.,

ncks -v fl_nm in.nc

The %c formatting code is useful for printing multidimensional arrays of characters representing fixed length strings

ncks -s '%c' -v fl_nm_arr in.nc

core dump Using the %s format code on strings which are not NUL-terminated (and thus not technically strings) is likely to result in a core dump. <a name="xmp_xtr_xcl"></a>  subsetting exclusion extraction -x CF conventions coordinates attribute climatology attribute bounds attribute ancillary_variables attribute grid_mapping attribute Create netCDF out.nc containing all variables, and any associated coordinates, except variable time, from netCDF in.nc:

ncks -x -v time in.nc out.nc

As a special case of this, consider how to remove a variable such as time_bounds that is identified in a CF Convention (CF Conventions) compliant ancillary_variables, bounds, climatology, coordinates, or grid_mapping attribute. NCO subsetting assumes the user wants all ancillary variables, axes, bounds and coordinates associated with all extracted variables (Subsetting Coordinate Variables). Hence to exclude a ancillary_variables, bounds, climatology, coordinates, or grid_mapping variable while retaining the &textldquo;parent&textrdquo; variable (here time), one must use the -C switch:

ncks -C -x -v time_bounds in.nc out.nc

The -C switch tells the operator NOT to necessarily include all the CF ancillary variables, axes, bounds, and coordinates. Hence the output file will contain time and not time_bounds. Extract variables time and pressure from netCDF in.nc. If out.nc does not exist it will be created. Otherwise the you will be prompted whether to append to or to overwrite out.nc:

ncks -v time,pressure in.nc out.nc
ncks -C -v time,pressure in.nc out.nc

The first version of the command creates an out.nc which contains time, pressure, and any coordinate variables associated with pressure. The out.nc from the second version is guaranteed to contain only two variables time and pressure. Create netCDF out.nc containing all variables from file in.nc. Restrict the dimensions of these variables to a hyperslab. The specified hyperslab is: the fifth value in dimension time; the half-open range

lat > 0. in coordinate lat; the half-open range

lon < 330. in coordinate lon; the closed interval

0.3 <

band < 0.5 in coordinate band; and cross-section closest to 1000.&noeos; in coordinate lev. Note that limits applied to coordinate values are specified with a decimal point, and limits applied to dimension indices do not have a decimal point Hyperslabs.

ncks -d time,5 -d lat,,0.0 -d lon,330.0, -d band,0.3,0.5 
-d lev,1000.0 in.nc out.nc

wrapped coordinates Assume the domain of the monotonically increasing longitude coordinate lon is

0 <

lon < 360. Here, lon is an example of a wrapped coordinate. ncks will extract a hyperslab which crosses the Greenwich meridian simply by specifying the westernmost longitude as min and the easternmost longitude as max, as follows:

ncks -d lon,260.0,45.0 in.nc out.nc

For more details Wrapped Coordinates. <a name="ncpdq"></a>  <a name="ncpack"></a>  <a name="ncunpack"></a>

ncpdq netCDF Permute Dimensions Quicklyncra netCDF Record Averagerncks netCDF Kitchen SinkReference Manual

ncpdq netCDF Permute Dimensions Quickly ncpdq ncpack ncunpack reshape variables permute dimensions reverse dimensions re-order dimensions re-dimension packing unpacking SYNTAX

ncpdq [-3] [-4] [-5] [-6] [-7] [-A] [-a [-]dim[,&dots;]]
[-C] [-c] [--cmp cmp_sng]
[--cnk_byt sz_byt] [--cnk_csh sz_byt] [--cnk_dmn nm,sz_lmn]
[--cnk_map map] [--cnk_min sz_byt] [--cnk_plc plc] [--cnk_scl sz_lmn]
[-D dbg] [-d dim,[min][,[max][,[stride]]] [-F] [--fl_fmt fl_fmt]
[-G gpe_dsc] [-g grp[,&dots;]] [--glb ...]
[-H] [-h] [--hdf] [--hdr_pad nbr] [--hpss] 
[-L dfl_lvl] [-l path] [-M pck_map] [--mrd]
[--no_cll_msr] [--no_frm_trm] [--no_tmp_fl] 
[-O] [-o output-file] [-P pck_plc] [-p path]
[--qnt ...] [--qnt_alg alg_nm] [-R] [-r] [--ram_all] [-t thr_nbr]
[-U] [--unn] [-v var[,&dots;]] [-X ...] [-x]
input-file [output-file]

DESCRIPTION ncpdq performs one (not both) of two distinct functions per invocation: packing or dimension permutation. Without any options, ncpdq will pack data with default parameters. The -a option tells ncpdq to permute dimensions accordingly, otherwise ncpdq will pack data as instructed/controlled by the -M and -P options. ncpdq is optimized to perform these actions in a parallel fashion with a minimum of time and memory. The pdq may stand for &textldquo;Permute Dimensions Quickly&textrdquo;, &textldquo;Pack Data Quietly&textrdquo;, &textldquo;Pillory Dan Quayle&textrdquo;, or other silly uses. add_offset scale_factor ncap2 packing policy Packing and Unpacking Functions The ncpdq packing (and unpacking) algorithms are described in Methods and functions, and are also implemented in ncap2. ncpdq extends the functionality of these algorithms by providing high level control of the packing policy so that users can consistently pack (and unpack) entire files with one command. pck_plc -P pck_plc --pck_plc pck_plc --pack_policy pck_plc The user specifies the desired packing policy with the -P switch (or its long option equivalents, --pck_plc and --pack_policy) and its pck_plc argument. Four packing policies are currently implemented:&linebreak; Packing (and Re-Packing) Variables [default] Definition: Pack unpacked variables, re-pack packed variables&linebreak; Alternate invocation: ncpack&linebreak; pck_plc key values: all_new, pck_all_new_att&linebreak; Packing (and not Re-Packing) Variables Definition: Pack unpacked variables, copy packed variables&linebreak; Alternate invocation: none&linebreak; pck_plc key values: all_xst, pck_all_xst_att&linebreak; Re-Packing Variables Definition: Re-pack packed variables, copy unpacked variables&linebreak; Alternate invocation: none&linebreak; pck_plc key values: xst_new, pck_xst_new_att&linebreak; Unpacking Definition: Unpack packed variables, copy unpacked variables&linebreak; Alternate invocation: ncunpack&linebreak; pck_plc key values: upk, unpack, pck_upk&linebreak;

Equivalent key values are fully interchangeable. Multiple equivalent options are provided to satisfy disparate needs and tastes of NCO users working with scripts and from the command line. Regardless of the packing policy selected, ncpdq no longer (as of NCO version 4.0.4 in October, 2010) packs coordinate variables, or the special variables, weights, and other grid properties described in CF Conventions. Prior ncpdq versions treated coordinate variables and grid properties no differently from other variables. However, coordinate variables are one-dimensional, so packing saves little space on large files, and the resulting files are difficult for humans to read. ncpdq will, of course, unpack coordinate variables and weights, for example, in case some other, non-NCO software packed them in the first place. Concurrently, Gaussian and area weights and other grid properties are often used to derive fields in re-inflated (unpacked) files, so packing such grid properties causes a considerable loss of precision in downstream data processing. If users express strong wishes to pack grid properties, we will implement new packing policies. An immediate workaround for those needing to pack grid properties now, is to use the ncap2 packing functions or to rename the grid properties prior to calling ncpdq. We welcome your feedback. To reduce required memorization of these complex policy switches, ncpdq may also be invoked via a synonym or with switches that imply a particular policy. ncpack is a synonym for ncpdq and behaves the same in all respects. Both ncpdq and ncpack assume a default packing policy request of all_new. Hence ncpack may be invoked without any -P switch, unlike ncpdq. Similarly, ncunpack is a synonym for ncpdq except that ncpack implicitly assumes a request to unpack, i.e., -P pck_upk. -U --unpack Finally, the ncpdq -U switch (or its long option equivalents --unpack) requires no argument. It simply requests unpacking. Given the menagerie of synonyms, equivalent options, and implied options, a short list of some equivalent commands is appropriate. The following commands are equivalent for packing: ncpdq -P all_new, ncpdq --pck_plc=all_new, and ncpack. The following commands are equivalent for unpacking: ncpdq -P upk, ncpdq -U, ncpdq --pck_plc=unpack, and ncunpack. Equivalent commands for other packing policies, e.g., all_xst, follow by analogy. alias ln -s symbolic links Note that ncpdq synonyms are subject to the same constraints and recommendations discussed in the secion on ncbo synonyms (ncbo netCDF Binary Operator). That is, symbolic links must exist from the synonym to ncpdq, or else the user must define an alias. <a name="pck_map"></a>  <a name="hgh_sht"></a>  <a name="hgh_byt"></a>  <a name="flt_byt"></a>  <a name="nxt_lsr"></a>  <a name="dbl_flt"></a>  <a name="flt_dbl"></a>  packing map pck_map -M pck_map --pck_map pck_map --map pck_map The ncpdq packing algorithms must know to which type particular types of input variables are to be packed. The correspondence between the input variable type and the output, packed type, is called the packing map. The user specifies the desired packing map with the -M switch (or its long option equivalents, --pck_map and --map) and its pck_map argument. Six packing maps are currently implemented:&linebreak; hgh_sht hgh_byt flt_sht flt_byt nxt_lsr dbl_flt flt_dbl NC_DOUBLE NC_FLOAT NC_INT64 NC_UINT64 NC_INT NC_UINT NC_SHORT NC_USHORT NC_CHAR NC_BYTE NC_UBYTE Pack Floating Precisions to NC_SHORT [default] Definition: Pack floating precision types to NC_SHORT&linebreak; Map: Pack [NC_DOUBLE,NC_FLOAT] to NC_SHORT&linebreak; Types copied instead of packed: [NC_INT64,NC_UINT64,NC_INT,NC_UINT,NC_SHORT,NC_USHORT,NC_CHAR,NC_BYTE,NC_UBYTE]&linebreak; pck_map key values: flt_sht, pck_map_flt_sht&linebreak; Pack Floating Precisions to NC_BYTE Definition: Pack floating precision types to NC_BYTE&linebreak; Map: Pack [NC_DOUBLE,NC_FLOAT] to NC_BYTE&linebreak; Types copied instead of packed: [NC_INT64,NC_UINT64,NC_INT,NC_UINT,NC_SHORT,NC_USHORT,NC_CHAR,NC_BYTE,NC_UBYTE]&linebreak; pck_map key values: flt_byt, pck_map_flt_byt&linebreak; Pack Higher Precisions to NC_SHORT Definition: Pack higher precision types to NC_SHORT&linebreak; Map: Pack [NC_DOUBLE,NC_FLOAT,NC_INT64,NC_UINT64,NC_INT,NC_UINT] to NC_SHORT&linebreak; Types copied instead of packed: [NC_SHORT,NC_USHORT,NC_CHAR,NC_BYTE,NC_UBYTE]&linebreak; pck_map key values: hgh_sht, pck_map_hgh_sht&linebreak; Pack Higher Precisions to NC_BYTE Definition: Pack higher precision types to NC_BYTE&linebreak; Map: Pack [NC_DOUBLE,NC_FLOAT,NC_INT64,NC_UINT64,NC_INT,NC_UINT,NC_SHORT,NC_USHORT] to NC_BYTE&linebreak; Types copied instead of packed: [NC_CHAR,NC_BYTE,NC_UBYTE]&linebreak; pck_map key values: hgh_byt, pck_map_hgh_byt&linebreak; Pack to Next Lesser Precision Definition: Pack each type to type of next lesser size&linebreak; Map: Pack [NC_DOUBLE,NC_INT64,NC_UINT64] to NC_INT. Pack [NC_FLOAT,NC_INT,NC_UINT] to NC_SHORT. Pack [NC_SHORT,NC_USHORT] to NC_BYTE.&linebreak; Types copied instead of packed: [NC_CHAR,NC_BYTE,NC_UBYTE]&linebreak; pck_map key values: nxt_lsr, pck_map_nxt_lsr&linebreak; Pack Doubles to Floats Definition: Demote (via type-conversion, not packing) double-precision variables to single-precision&linebreak; Map: Demote NC_DOUBLE to NC_FLOAT. Types copied instead of packed: All except NC_DOUBLE&linebreak; pck_map key values: dbl_flt, pck_map_dbl_flt, dbl_sgl, pck_map_dbl_sgl&linebreak; The dbl_flt map was introduced in NCO version 4.7.7 (September, 2018). Promote Floats to Doubles Definition: Promote (via type-conversion, not packing) single-precision variables to double-precision&linebreak; Map: Promote NC_FLOAT to NC_DOUBLE. Types copied instead of packed: All except NC_FLOAT&linebreak; pck_map key values: flt_dbl, pck_map_flt_dbl, sgl_dbl, pck_map_sgl_dbl&linebreak; The flt_dbl map was introduced in NCO version 4.9.1 (December, 2019).

The default all_new packing policy with the default flt_sht packing map reduces the typical NC_FLOAT-dominated file size by about 50%. flt_byt packing reduces an NC_DOUBLE-dominated file by about 87%. d2f The &textldquo;packing map&textrdquo; pck_map_dbl_flt does a pure type-conversion (no packing is involved) from NC_DOUBLE to NC_FLOAT. The resulting variables are not packed, they are just single-precision floating point instead of double-precision floating point. This operation is irreversible, and no attributes are created, modified, or deleted for these variables. Note that coordinate and coordinate-like variables will not be demoted as best practices dictate maintaining coordinates in the highest possible precision. The &textldquo;packing map&textrdquo; pck_map_flt_dbl does a pure type-conversion (no packing is involved) from NC_FLOAT to NC_DOUBLE. The resulting variables are not packed, they are just double-precision floating point instead of single-precision floating point. This operation is irreversible, and no attributes are created, modified, or deleted for these variables. All single-precision variables, including coordinates, are promoted. Note that this map can double the size of a dataset. _FillValue _FillValue NUL The netCDF packing algorithm (Methods and functions) is lossy&textmdash;once packed, the exact original data cannot be recovered without a full backup. Hence users should be aware of some packing caveats: First, the interaction of packing and data equal to the _FillValue is complex. Test the _FillValue behavior by performing a pack/unpack cycle to ensure data that are missing stay missing and data that are not misssing do not join the Air National Guard and go missing. This may lead you to elect a new _FillValue. Second, ncpdq actually allows packing into NC_CHAR (with, e.g., flt_chr). However, the intrinsic conversion of signed char to higher precision types is tricky for values equal to zero, i.e., for NUL. Hence packing to NC_CHAR is not documented or advertised. Pack into NC_BYTE (with, e.g., flt_byt) instead. <a name="rvr"></a>  Dimension Permutation ncpdq re-shapes variables in input-file by re-ordering and/or reversing dimensions specified in the dimension list. The dimension list is a whitespace-free, comma separated list of dimension names, optionally prefixed by negative signs, that follows the -a (or long options --arrange, --permute, --re-order, or --rdr) switch. To re-order variables by a subset of their dimensions, specify these dimensions in a comma-separated list following -a, e.g., -a lon,lat. To reverse a dimension, prefix its name with a negative sign in the dimension list, e.g., -a -lat. Re-ordering and reversal may be performed simultaneously, e.g., -a lon,-lat,time,-lev. record dimension Users may specify any permutation of dimensions, including permutations which change the record dimension identity. The record dimension is re-ordered like any other dimension. concatenation record dimension This unique ncpdq capability makes it possible to concatenate files along any dimension. See Concatenation for a detailed example. record variable The record dimension is always the most slowly varying dimension in a record variable (C and Fortran Index Conventions). The specified re-ordering fails if it requires creating more than one record dimension amongst all the output variables This limitation, imposed by the netCDF storage layer, may be relaxed in the future with netCDF4.. Two special cases of dimension re-ordering and reversal deserve special mention. First, it may be desirable to completely reverse the storage order of a variable. To do this, include all the variable&textrsquo;s dimensions in the dimension re-order list in their original order, and prefix each dimension name with the negative sign. transpose Second, it may useful to transpose a variable&textrsquo;s storage order, e.g., from C to Fortran data storage order (C and Fortran Index Conventions). To do this, include all the variable&textrsquo;s dimensions in the dimension re-order list in reversed order. Explicit examples of these two techniques appear below. NB: fxm ncpdq documentation will evolve through Fall 2004. I will upload updates to documentation linked to by the NCO homepage. ncpdq is a powerful operator, and I am unfamiliar with the terminology needed to describe what ncpdq does. Sequences, sets, sheesh! I just know that it does ``The right thing'' according to my gut feelings. Now do you feel more comfortable using it? Let $\dmnvct(\xxx)$ represent the dimensionality of the variable $\xxx$. Dimensionality describes the order and sizes of dimensions. If $\xxx$ has rank $\dmnnbr$, then we may write $\dmnvct(\xxx)$ as the $\dmnnbr$-element vector $$ \dmnvct(\xxx) = [ \dmn_{1}, \dmn_{2}, \dmn_{3}, \ldots, \dmn_{\dmnidx-1}, \dmn_{\dmnidx}, \dmn_{\dmnidx+1}, \ldots, \dmn_{\dmnnbr-2}, \dmn_{\dmnnbr-1}, \dmn_{\dmnnbr} ] $$ where $\dmn_{\dmnidx}$ is the size of the $\dmnidx$'th dimension. The dimension re-order list specified with @samp{-a} is the $\rdrnbr$-element vector $$ \rdrvct = [ \rdr_{1}, \rdr_{2}, \rdr_{3}, \ldots, \rdr_{\rdridx-1}, \rdr_{\rdridx}, \rdr_{\rdridx+1}, \ldots, \rdr_{\rdrnbr-2}, \rdr_{\rdrnbr-1}, \rdr_{\rdrnbr} ] $$ There need be no relation between $\dmnnbr$ and $\rdrnbr$. Let the $\shrnbr$-element vector $\shrvct$ be the intersection (i.e., the ordered set of unique shared dimensions) of $\dmnvct$ and $\rdrvct$ Then $$ \eqalign{{\shrvct} &= \rdrvct \cap \dmnvct \cr &= [ \shr_{1}, \shr_{2}, \shr_{3}, \ldots, \shr_{\shridx-1}, \shr_{\shridx}, \shr_{\shridx+1}, \ldots, \shr_{\shrnbr-2}, \shr_{\shrnbr-1}, \shr_{\shrnbr} ]} $$ $\shrvct$ is empty if $\rdrvct \notin \dmnvct$. Re-ordering (or re-shaping) a variable means mapping the input state with dimensionality $\dmnvct(\xxx)$ to the output state with dimensionality $\dmnvctprm(\xxxprm)$. In practice, mapping occurs in three logically distinct steps. First, we tranlate the user input to a one-to-one mapping $\mpp$ between input and output dimensions, $\dmnvct \mapsto \dmnvctprm$. This tentative map is final unless external constraints (typically netCDF restrictions) impose themselves. Second, we check and, if necessary, refine the tentative mapping so that the re-shaped variables will co-exist in the same file without violating netCDF-imposed storage restrictions. This refined map specifies the final (output) dimensionality. Third, we translate the output dimensionality into one-dimensional memory offsets for each datum according to the @w{C language} convention for multi-dimensional array storage. Dimension reversal changes the ordering of data, though not the rank or dimensionality, and so is part of the third step. Dimensions $\rdr$ disjoint from $\dmnvct$ play no role in re-ordering. The first step taken to re-order a variable is to determine $\shrvct$. $\rdrvct$ is constant for all variables, whereas $\dmnvct$, and hence $\shrvct$, is variable-specific. $\shrvct$ is empty if $\rdrvct \notin \dmnvct$. This may be the case for some extracted variables. The user may explicitly specify the one-to-one mapping of input to output dimension order by supplying (with @samp{-a}) a re-order list $\rdrvct$ such that $\shrnbr = \dmnnbr$. In this case $\dmnsubnnnprm = \shrsubnnn$. The degenerate case occurs when $\dmnvct = \shrvct$. This produces the identity mapping $\dmnsubnnnprm = \dmnsubnnn$. The mapping of input to output dimension order is more complex when $\shrnbr \ne \dmnnbr$. In this case $\dmnsubnnnprm = \dmnsubnnn$ for the $\dmnnbr-\shrnbr$ dimensions $\dmnsubnnnprm \notin \shrvct$. For the $\shrnbr$ dimensions $\dmnsubnnnprm \in \shrvct$, $\dmnsubnnnprm = \shrsubsss$. <a name="xmp_ncpdq"></a>  EXAMPLES Pack and unpack all variables in file in.nc and store the results in out.nc:

ncpdq in.nc out.nc # Same as ncpack in.nc out.nc
ncpdq -P all_new -M flt_sht in.nc out.nc # Defaults
ncpdq -P all_xst in.nc out.nc
ncpdq -P upk in.nc out.nc # Same as ncunpack in.nc out.nc
ncpdq -U in.nc out.nc # Same as ncunpack in.nc out.nc

The first two commands pack any unpacked variable in the input file. They also unpack and then re-pack every packed variable. The third command only packs unpacked variables in the input file. If a variable is already packed, the third command copies it unchanged to the output file. The fourth and fifth commands unpack any packed variables. If a variable is not packed, the third command copies it unchanged. The previous examples all utilized the default packing map. Suppose you wish to archive all data that are currently unpacked into a form which only preserves 256 distinct values. Then you could specify the packing map pck_map as hgh_byt and the packing policy pck_plc as all_xst:

ncpdq -P all_xst -M hgh_byt in.nc out.nc

appending variables -A -v Many different packing maps may be used to construct a given file by performing the packing on subsets of variables (e.g., with -v) and using the append feature with -A (Appending Variables). Users may wish to unpack data packed with the HDF convention, and then re-pack it with the netCDF convention so that all their datasets use the same packing convention prior to intercomparison. ncl_convert2nc NCL

# One-step procedure: For NCO 4.4.0+, netCDF 4.3.1+
# 1. Convert, unpack, and repack HDF file into netCDF file
ncpdq --hdf_upk -P xst_new modis.hdf modis.nc # HDF4 files
ncpdq --hdf_upk -P xst_new modis.h5  modis.nc # HDF5 files

# One-step procedure: For NCO 4.3.7--4.3.9
# 1. Convert, unpack, and repack HDF file into netCDF file
ncpdq --hdf4 --hdf_upk -P xst_new modis.hdf modis.nc # HDF4
ncpdq        --hdf_upk -P xst_new modis.h5  modis.nc # HDF5

# Two-step procedure: For NCO 4.3.6 and earlier
# 1. Convert HDF file to netCDF file
ncl_convert2nc modis.hdf
# 2. Unpack using HDF convention and repack using netCDF convention
ncpdq --hdf_upk -P xst_new modis.nc modis.nc

NCO now Prior to NCO 4.4.0 and netCDF 4.3.1 (January, 2014), NCO requires the --hdf4 switch to correctly read HDF4 input files. For example, ncpdq --hdf4 --hdf_upk -P xst_new modis.hdf modis.nc. That switch is now obsolete, though harmless for backwards compatibility. Prior to version 4.3.7 (October, 2013), NCO lacked the software necessary to circumvent netCDF library flaws handling HDF4 files, and thus NCO failed to convert HDF4 files to netCDF files. In those cases, use the ncl_convert2nc command distributed with NCL to convert HDF4 files to netCDF. automatically detects HDF4 files. In this case it produces an output file modis.nc which preserves the HDF packing used in the input file. The ncpdq command first unpacks all packed variables using the HDF unpacking algorithm (as specified by --hdf_upk), and then repacks those same variables using the netCDF algorithm (because that is the only algorithm NCO packs with). As described above the --P xst_new packing policy only repacks variables that are already packed. Not-packed variables are copied directly without loss of precision ncpdq does not support packing data using the HDF convention. Although it is now straightforward to support this, we think it might sow more confusion than it reaps. Let us know if you disagree and would like NCO to support packing data with HDF algorithm.. Re-order file in.nc so that the dimension lon always precedes the dimension lat and store the results in out.nc:

ncpdq -a lon,lat in.nc out.nc
ncpdq -v three_dmn_var -a lon,lat in.nc out.nc

The first command re-orders every variable in the input file. The second command extracts and re-orders only the variable three_dmn_var. reverse dimensions Suppose the dimension lat represents latitude and monotonically increases increases from south to north. Reversing the lat dimension means re-ordering the data so that latitude values decrease monotonically from north to south. Accomplish this with

% ncpdq -a -lat in.nc out.nc
% ncks --trd -C -v lat in.nc
lat[0]=-90
lat[1]=90
% ncks --trd -C -v lat out.nc
lat[0]=90
lat[1]=-90

This operation reversed the latitude dimension of all variables. Whitespace immediately preceding the negative sign that specifies dimension reversal may be dangerous. long options quotes Quotes and long options can help protect negative signs that should indicate dimension reversal from being interpreted by the shell as dashes that indicate new command line switches.

ncpdq -a -lat in.nc out.nc # Dangerous? Whitespace before "-lat"
ncpdq -a '-lat' in.nc out.nc # OK. Quotes protect "-" in "-lat"
ncpdq -a lon,-lat in.nc out.nc # OK. No whitespace before "-"
ncpdq --rdr=-lat in.nc out.nc # Preferred. Uses "=" not whitespace

transpose reverse dimensions <a name="ncpdq_trn"></a>  <a name="transpose"></a>  To create the mathematical transpose of a variable, place all its dimensions in the dimension re-order list in reversed order. This example creates the transpose of three_dmn_var:

% ncpdq -a lon,lev,lat -v three_dmn_var in.nc out.nc
% ncks --trd -C -v three_dmn_var in.nc
lat[0]=-90 lev[0]=100 lon[0]=0 three_dmn_var[0]=0 
lat[0]=-90 lev[0]=100 lon[1]=90 three_dmn_var[1]=1 
lat[0]=-90 lev[0]=100 lon[2]=180 three_dmn_var[2]=2 
...
lat[1]=90 lev[2]=1000 lon[1]=90 three_dmn_var[21]=21 
lat[1]=90 lev[2]=1000 lon[2]=180 three_dmn_var[22]=22 
lat[1]=90 lev[2]=1000 lon[3]=270 three_dmn_var[23]=23 
% ncks --trd -C -v three_dmn_var out.nc
lon[0]=0 lev[0]=100 lat[0]=-90 three_dmn_var[0]=0
lon[0]=0 lev[0]=100 lat[1]=90 three_dmn_var[1]=12
lon[0]=0 lev[1]=500 lat[0]=-90 three_dmn_var[2]=4
...
lon[3]=270 lev[1]=500 lat[1]=90 three_dmn_var[21]=19
lon[3]=270 lev[2]=1000 lat[0]=-90 three_dmn_var[22]=11
lon[3]=270 lev[2]=1000 lat[1]=90 three_dmn_var[23]=23

reverse data To completely reverse the storage order of a variable, include all its dimensions in the re-order list, each prefixed by a negative sign. This example reverses the storage order of three_dmn_var:

% ncpdq -a -lat,-lev,-lon -v three_dmn_var in.nc out.nc
% ncks --trd -C -v three_dmn_var in.nc
lat[0]=-90 lev[0]=100 lon[0]=0 three_dmn_var[0]=0 
lat[0]=-90 lev[0]=100 lon[1]=90 three_dmn_var[1]=1 
lat[0]=-90 lev[0]=100 lon[2]=180 three_dmn_var[2]=2 
...
lat[1]=90 lev[2]=1000 lon[1]=90 three_dmn_var[21]=21 
lat[1]=90 lev[2]=1000 lon[2]=180 three_dmn_var[22]=22 
lat[1]=90 lev[2]=1000 lon[3]=270 three_dmn_var[23]=23 
% ncks --trd -C -v three_dmn_var out.nc
lat[0]=90 lev[0]=1000 lon[0]=270 three_dmn_var[0]=23
lat[0]=90 lev[0]=1000 lon[1]=180 three_dmn_var[1]=22
lat[0]=90 lev[0]=1000 lon[2]=90 three_dmn_var[2]=21
...
lat[1]=-90 lev[2]=100 lon[1]=180 three_dmn_var[21]=2
lat[1]=-90 lev[2]=100 lon[2]=90 three_dmn_var[22]=1
lat[1]=-90 lev[2]=100 lon[3]=0 three_dmn_var[23]=0

<a name="dmn_rcd_mk"></a>  <a name="mk_rcd_dmn"></a>  Creating a record dimension named, e.g., time, in a file which has no existing record dimension is simple with ncecat:

ncecat -O -u time in.nc out.nc # Create degenerate record dimension named "time"

record dimension Now consider a file with all dimensions, including time, fixed (non-record). Suppose the user wishes to convert time from a fixed dimension to a record dimension. This may be useful, for example, when the user wishes to append additional time slices to the data. As of NCO version 4.0.1 (April, 2010) the preferred method for doing this is with ncks:

ncks -O --mk_rec_dmn time in.nc out.nc # Change "time" to record dimension

Prior to 4.0.1, the procedure to change an existing fixed dimension into a record dimension required three separate commands, ncecat followed by ncpdq, and then ncwa. The recommended method is now to use ncks --fix_rec_dmn, yet it is still instructive to present the original procedure, as it shows how multiple operators can achieve the same ends by different means: degenerate dimension

ncecat -O in.nc out.nc # Add degenerate record dimension named "record"
ncpdq -O -a time,record out.nc out.nc # Switch "record" and "time"
ncwa -O -a record out.nc out.nc # Remove (degenerate) "record"

The first step creates a degenerate (size equals one) record dimension named (by default) record. The second step swaps the ordering of the dimensions named time and record. Since time now occupies the position of the first (least rapidly varying) dimension, it becomes the record dimension. The dimension named record is no longer a record dimension. The third step averages over this degenerate record dimension. Averaging over a degenerate dimension does not alter the data. The ordering of other dimensions in the file (lat, lon, etc.) is immaterial to this procedure. See ncecat netCDF Ensemble Concatenator and ncks netCDF Kitchen Sink for other methods of changing variable dimensionality, including the record dimension. <a name="ncra"></a>

ncra netCDF Record Averagerncrcat netCDF Record Concatenatorncpdq netCDF Permute Dimensions QuicklyReference Manual

ncra netCDF Record Averager averaging data record average record dimension running average ncra SYNTAX

ncra [-3] [-4] [-5] [-6] [-7] [-A] [-C] [-c]
[--cb y1,y2,m1,m2,tpd] [--cmp cmp_sng]
[--cnk_byt sz_byt] [--cnk_csh sz_byt] [--cnk_dmn nm,sz_lmn]
[--cnk_map map] [--cnk_min sz_byt] [--cnk_plc plc] [--cnk_scl sz_lmn]
[-D dbg] [-d dim,[min][,[max][,[stride][,[subcycle][,[interleave]]]]]
[-F] [--fl_fmt fl_fmt]
[-G gpe_dsc] [-g grp[,&dots;]] [--glb ...]
[-H] [-h] [--hdf] [--hdr_pad nbr] [--hpss] 
[-L dfl_lvl] [-l path] [--mro] [-N] [-n loop] 
[--no_cll_msr] [--no_cll_mth] [--no_frm_trm] [--no_tmp_fl] 
[-O] [-o output-file] [-p path] [--qnt ...] [--qnt_alg alg_nm]
[--prm_int] [--prw wgt_arr] [-R] [-r] [--ram_all] [--rec_apn] [--rth_dbl|flt]
[-t thr_nbr] [--unn] [-v var[,&dots;]] [-w wgt] [-X ...] [-x] [-y op_typ]
[input-files] [output-file]

DESCRIPTION ncra computes statistics (including, though not limited to, averages) of record variables across an arbitrary number of input-files. degenerate dimension record dimension The record dimension is, by default, retained as a degenerate (size 1) dimension in the output variables. Statistics vs Concatenation, for a description of the distinctions between the various statistics tools and concatenators. multi-file operators standard input stdin As a multi-file operator, ncra will read the list of input-files from stdin if they are not specified as positional arguments on the command line (Large Numbers of Files). Input files may vary in size, but each must have a record dimension. The record coordinate, if any, should be monotonic (or else non-fatal warnings may be generated). hyperslab Hyperslabs of the record dimension which include more than one file work correctly. stride ncra supports the stride argument to the -d hyperslab option (Hyperslabs) for the record dimension only, stride is not supported for non-record dimensions. operation types ncra always averages coordinate variables (e.g., time) regardless of the arithmetic operation type performed on non-coordinate variables (Operation Types). As of NCO version 4.4.9, released in May, 2015, ncra accepts user-specified weights with the -w (or long-option equivalent --wgt, --wgt_var, or --weight) switch. When no weight is specified, ncra weights each record (e.g., time slice) in the input-files equally. ncra does not attempt to see if, say, the time coordinate is irregularly spaced and thus would require a weighted average in order to be a true time-average. Specifying unequal weights is entirely the user&textrsquo;s responsibility. Weights specified with -w wgt may take one of two forms. In the first form, the wgt argument is a comma-separated list of values by which to weight each file (recall that files may have multiple timesteps). In this form the number of weights specified must equal the number of files specified in the input file list, or else the program will exit. In the second form, the wgt argument is the name of a weighting variable present in every input file. The variable may be a scalar or a one-dimensional record variable. Scalar weights are applied uniformly to the entire file (i.e., this produces the same arithmetic result as supplying the same value as a per-file weight option on the command-line). One-dimensional weights apply to each corresponding record (i.e., per-record weights), and are suitable for dynamically changing timesteps. By default, any weights specified (whether by value or by variable name) are normalized to unity by dividing each specified weight by the sum of all the weights. This means, for example, that, -w 0.25,0.75 is equivalent to -w 2.0,6.0 since both are equal when normalized. This behavior simplifies specifying weights based on countable items. For example, time-weighting monthly averages for March, April, and May to obtain a spring seasonal average can be done with -w 31,30,31 instead of -w 0.33695652173913043478,0.32608695652173913043,0.33695652173913043478. However, sometimes one wishes to use weights in &textldquo;dot-product mode&textrdquo;, i.e., multiply by the (non-normalized) weights. As of NCO version 4.5.2, released in July, 2015, ncra accepts the -N (or long-option equivalent --no_nrm_by_wgt) switch that prevents automatic weight normalization. When this switch is used, the weights will not be normalized (unless the user provides them as normalized), and the numerator of the weighted average will not be divided by the sum of the weights (which is one for normalized weights). <a name="prw"></a>  <a name="per_record_weights"></a>  --per_record_weights --prw weights weighted average per-record-weights record average As of NCO version 4.9.4, released in September, 2020, ncra supports the --per_record_weights (or --prw) flag to utilize the command-line weights separately specified by -w wgt_arr (or --wgt wgt_arr) for per-record weights instead of per-file-weights, where wgt_arr is a 1-D array of weights. This is useful when computing weighted averages with cyclically varying weights, since the weights given on the command line will be repeated for the length of the timeseries. Consider, for example, a CMIP6 timeseries of historical monthly mean emissions that one wishes to convert to a timeseries of annual-mean emissions. One can now weight each month by its number of days via:

ncra --per_record_weights --mro -d time,,,12,12 --wgt \
  31,28,31,30,31,30,31,31,30,31,30,31 ~/monthly.nc ~/annual.nc

Note that the twelve weights will be implicitly repeated throughtout the duration of the input file(s), which in this case may therefore specify an interannual monthly timeseries that is reduced to a timeseries of annual-means in the output. Bear these exceptions in mind when weighting input: First, ncra only applies weights if the arithmetic operation type is averaging (Operation Types), i.e., for timeseries mean and for timeseries mean absolute value. Weights are never applied for minimization, square-roots, etc. Second, ncra never weights coordinate variables (e.g., time) regardless of the weighting performed on non-coordinate variables. <a name="prm_ints"></a>  <a name="promote_ints"></a>  --promote_ints --prm_ints promotion Boolean values As of NCO version 4.9.4, released in September, 2020, ncra supports the --promote_ints (or prm_ints) flags to output statistics of integer-valued input variables in floating-point precision in the output file. By default, arithmetic operators such as ncra auto-promote integers to double-precision prior to arithmetic, then conduct the arithmetic, then demote the values back to integers for final output. The final stage (demotion) of this default behavior quantizes the mantissa of the values and prevents, e.g., retaining the statisitical means of Boolean (0 or 1-valued) input data as floating point data. The --promote_ints flag eliminates the demotion and causes the statistical means of integer (NC_BYTE, NC_SHORT, NC_INT, NC_INT64) inputs to be output as single-precision floating point (NC_FLOAT) variables. This allows useful arithmetic to be performed on Boolean values stored in the space-conserving NC_BYTE (single-byte) format.

ncra --prm_ints in*.nc out.nc

@c Old, and possibly future, documentation @html <a name="cb"></a>  <a name="c2b"></a>  <a name="clm_bnd"></a>  <a name="clm2bnd"></a>  @end html @cindex @code{--cb} @cindex @code{--c2b} @cindex @code{--clm_bnd} @cindex @code{--clm2bnd} As of @acronym{NCO} version 4.6.0 (May, 2016) @command{ncra} can honor the @acronym{CF} @code{climatology} and climatological statistics conventions described in @ref{CF Conventions}. This functionality only works when each input file contains only a single record (timestep). Currently this is opt-in with the @samp{--cb} flag (or long-option equivalent @samp{--clm_bnd}), or with the @samp{--c2b} flag (or its long-option equivalent @samp{--clm2bnd}) switches. Invoking @samp{--cb} causes @command{ncra} to: @enumerate @item Add a @code{climatology} attribute with value ``climatology_bounds'' the time coordinate, if necessary @item Remove the @code{bounds} attribute from the time coordinate, if necessary @item Output a variable named @code{climatology_bounds} with values that are minima/maxima of the input time coordinate @code{bounds} variable. @item Omit any input time coordinate @code{bounds} attribute and variable @item Ensure the @code{cell_methods} attribute for all variables is appropriate for climatologies within and over years. Climatologies within days will have incorrect units (the switch is currently opt-in so that incorrect units are not inadvertently generated). Please contact the authors if this functionality is important to you (The omission of climatologies within days is mainly a matter of trying to keep the switches and interface clean). @end enumerate Use the @samp{--c2b} flag (instead of @samp{--cb}) to convert the input @code{climatology} bounds to a non-climatology @code{bounds} in the output. In other words, use @samp{--c2b} when averaging sub-sampled climatologies together to produce a continuous (non-climatologically sub-sampled) mean. @example # Use --cb to average months into a climatological month ncra --cb 2014_01.nc 2015_01.nc 2016_01.nc clm_JAN.nc # Use --cb to average climatological months into a climatological season ncra --cb clm_DEC.nc clm_JAN.nc clm_FEB.nc clm_DJF.nc # Four seasons make a complete year so use --c2b ncra --c2b clm_DJF.nc clm_MAM.nc clm_JJA.nc clm_SON.nc clm_ANN.nc @end example Currently this functionality only works with climatologies within and over years (not within or over days). <a name="xmp_ncra"></a>  EXAMPLES Average files 85.nc, 86.nc, &dots; 89.nc along the record dimension, and store the results in 8589.nc: globbing NINTAP Processor CCM Processor

ncra 85.nc 86.nc 87.nc 88.nc 89.nc 8589.nc
ncra 8[56789].nc 8589.nc
ncra -n 5,2,1 85.nc 8589.nc

These three methods produce identical answers. Specifying Input Files, for an explanation of the distinctions between these methods. Fortran Assume the files 85.nc, 86.nc, &dots; 89.nc each contain a record coordinate time of length 12 defined such that the third record in 86.nc contains data from March 1986, etc. NCO knows how to hyperslab the record dimension across files. Thus, to average data from December, 1985 through February, 1986:

ncra -d time,11,13 85.nc 86.nc 87.nc 8512_8602.nc
ncra -F -d time,12,14 85.nc 86.nc 87.nc 8512_8602.nc

The file 87.nc is superfluous, but does not cause an error. The -F turns on the Fortran (1-based) indexing convention. stride The following uses the stride option to average all the March temperature data from multiple input files into a single output file

ncra -F -d time,3,,12 -v temperature 85.nc 86.nc 87.nc 858687_03.nc

Stride, for a description of the stride argument. Assume the time coordinate is incrementally numbered such that January,

1985 = 1

and December,

1989 = 60

. Assuming ?? only expands to the five desired files, the following averages June, 1985&textndash;June, 1989:

ncra -d time,6.,54. ??.nc 8506_8906.nc
ncra -y max -d time,6.,54. ??.nc 8506_8906.nc

The second example identifies the maximum instead of averaging. Operation Types, for a description of all available statistical operations. seasonal average average subcycle time-averaging ncra includes the powerful subcycle and multi-record output features (Subcycle). This example uses these features to compute and output winter (DJF) averages for all winter seasons beginning with year 1990 and continuing to the end of the input file:

ncra -O --mro -d time,"1990-12-01",,12,3 in.nc out.nc

The -w wgt option weights input data per-file when explicit numeric weights are given on the command-line, or per-timestep when the argument is a record variable that resides in the file:

ncra -w 31,31,28 dec.nc jan.nc feb.nc out.nc # Per-file weights
ncra -w delta_t in1.nc in2.nc in3.nc out.nc # Per-timestep weights

The first example weights the input differently per-file to produce correctly weighted winter seasonal mean statistics. The second example weights the input per-timestep to produce correctly weighted mean statistics. <a name="ncrcat"></a>

ncrcat netCDF Record Concatenatorncremap netCDF Remapperncra netCDF Record AveragerReference Manual

ncrcat netCDF Record Concatenator concatenation record concatenation ncrcat SYNTAX

ncrcat [-3] [-4] [-5] [-6] [-7] [-A] [-C] [-c] [--cmp cmp_sng]
[--cnk_byt sz_byt] [--cnk_csh sz_byt] [--cnk_dmn nm,sz_lmn]
[--cnk_map map] [--cnk_min sz_byt] [--cnk_plc plc] [--cnk_scl sz_lmn]
[-D dbg] [-d dim,[min][,[max][,[stride][,[subcycle][,[interleave]]]]]
[-F] [--fl_fmt fl_fmt]
[-G gpe_dsc] [-g grp[,&dots;]] [--glb ...]
[-H] [-h] [--hdr_pad nbr] [--hpss] 
[-L dfl_lvl] [-l path] [--md5_digest] [-n loop]
[--no_tmp_fl] [--no_cll_msr] [--no_frm_trm] [--no_tmp_fl] 
[-O] [-o output-file] [-p path] [--qnt ...] [--qnt_alg alg_nm]
[-R] [-r] [--ram_all] [--rec_apn] [-t thr_nbr]
[--unn] [-v var[,&dots;]] [-X ...] [-x] 
[input-files] [output-file]

DESCRIPTION ncrcat concatenates record variables across an arbitrary number of input-files. record dimension The final record dimension is by default the sum of the lengths of the record dimensions in the input files. Statistics vs Concatenation, for a description of the distinctions between the various statistics tools and concatenators. multi-file operators standard input stdin As a multi-file operator, ncrcat will read the list of input-files from stdin if they are not specified as positional arguments on the command line (Large Numbers of Files). Input files may vary in size, but each must have a record dimension. The record coordinate, if any, should be monotonic (or else non-fatal warnings may be generated). hyperslab Hyperslabs along the record dimension that span more than one file are handled correctly. stride ncra supports the stride argument to the -d hyperslab option for the record dimension only, stride is not supported for non-record dimensions. ncpdq packing unpacking add_offset scale_factor Concatenating a variable packed with different scales multiple datasets is beyond the capabilities of ncrcat (and ncecat, the other concatenator (Concatenation). ncrcat does not unpack data, it simply copies the data from the input-files, and the metadata from the first input-file, to the output-file. This means that data compressed with a packing convention must use the identical packing parameters (e.g., scale_factor and add_offset) for a given variable across all input files. Otherwise the concatenated dataset will not unpack correctly. The workaround for cases where the packing parameters differ across input-files requires three steps: First, unpack the data using ncpdq. Second, concatenate the unpacked data using ncrcat, Third, re-pack the result with ncpdq. ARM conventions ncrcat applies special rules to ARM convention time fields (e.g., time_offset). See ARM Conventions for a complete description. <a name="xmp_ncrcat"></a>  EXAMPLES Concatenate files 85.nc, 86.nc, &dots; 89.nc along the record dimension, and store the results in 8589.nc: globbing NINTAP Processor CCM Processor

ncrcat 85.nc 86.nc 87.nc 88.nc 89.nc 8589.nc
ncrcat 8[56789].nc 8589.nc
ncrcat -n 5,2,1 85.nc 8589.nc

These three methods produce identical answers. Specifying Input Files, for an explanation of the distinctions between these methods. Fortran Assume the files 85.nc, 86.nc, &dots; 89.nc each contain a record coordinate time of length 12 defined such that the third record in 86.nc contains data from March 1986, etc. NCO knows how to hyperslab the record dimension across files. Thus, to concatenate data from December, 1985&textndash;February, 1986:

ncrcat -d time,11,13 85.nc 86.nc 87.nc 8512_8602.nc
ncrcat -F -d time,12,14 85.nc 86.nc 87.nc 8512_8602.nc

The file 87.nc is superfluous, but does not cause an error. When ncra and ncrcat encounter a file which does contain any records that meet the specified hyperslab criteria, they disregard the file and proceed to the next file without failing. The -F turns on the Fortran (1-based) indexing convention. stride The following uses the stride option to concatenate all the March temperature data from multiple input files into a single output file

ncrcat -F -d time,3,,12 -v temperature 85.nc 86.nc 87.nc 858687_03.nc

Stride, for a description of the stride argument. Assume the time coordinate is incrementally numbered such that January, 1985 = 1 and December, 1989 = 60. Assuming ?? only expands to the five desired files, the following concatenates June, 1985&textndash;June, 1989:

ncrcat -d time,6.,54. ??.nc 8506_8906.nc

ncremap netCDF Remapperncrename netCDF Renamerncrcat netCDF Record ConcatenatorReference Manual

ncremap netCDF Remapper remap regrid ncremap SYNTAX

ncremap [-3] [-4] [-5] [-6] [-7]
[-a alg_typ] [--a2o] [--add_fll] [--alg_lst] [--area_dgn] [--cmp cmp_sng]
[-D dbg_lvl] [-d dst_fl] [--d2f] [--dpt] [--dpt_fl=dpt_fl]
[--dt_sng=dt_sng] [--esmf_typ=esmf_typ] 
[--fl_fmt=fl_fmt] [-G grd_sng] [-g grd_dst]
[-I drc_in] [-i input-file] [-j job_nbr] [-L dfl_lvl]
[-M] [-m map_fl] [--mpi_nbr=mpi_nbr] [--mpi_pfx=mpi_pfx] [--mpt_mss] [--msh_fl=msh_fl]
[--msk_apl] [--msk_dst=msk_dst] [--msk_out=msk_out] [--msk_src=msk_src] [--mss_val=mss_val]
[-n nco_opt] [--nm_dst=nm_dst] [--nm_src=nm_src] 
[--no_add_fll] [--no_cll_msr] [--no_frm_trm] [--no_permute] [--no_stdin] [--no_stg_grd]
[-O drc_out] [-o output-file] [-P prc_typ] [-p par_typ]
[--pdq=pdq_opt] [--qnt=qnt_opt] [--preserve=prs_stt] [--ps_nm=ps_nm]
[-R rgr_opt] [--rgn_dst] [--rgn_src] [--rnr_thr=rnr_thr] 
[--rrg_bb_wesn=bb_wesn] [--rrg_dat_glb=dat_glb] [--rrg_grd_glb=grd_glb]
[--rrg_grd_rgn=grd_rgn] [--rrg_rnm_sng=rnm_sng]
[-s grd_src] [--sgs_frc=sgs_frc] [--sgs_msk=sgs_msk] [--sgs_nrm=sgs_nrm]
[--skl=skl-file] [--stdin] [-T drc_tmp] [-t thr_nbr]
[-U] [-u unq_sfx] [--ugrid=ugrid-file] [--uio]
[-V rgr_var] [-v var_lst[,&dots;]] [--version] [--vrb=vrb_lvl] 
[--vrt_in=vrt_fl] [--vrt_out=vrt_fl] [--vrt_nm=vrt_nm] [--vrt_ntp=vrt_ntp] [--vrt_xtr=vrt_xtr]
[-W wgt_opt] [-w wgt_cmd] [-x xtn_lst[,&dots;]] [--xcl_var]
[--xtr_nsp=xtr_nsp] [--xtr_xpn=xtr_xpn]
[input-files] [output-file]

DESCRIPTION ncremap remaps the data file(s) in input-file, in drc_in, or piped through standard input, to the horizontal grid specified by (in descending order of precedence) map_fl, grd_dst, or dst_fl and stores the result in output-file(s). If a vertical grid vrt_fl is provided, ncremap will (also) vertically interpolate the input file(s) to that grid. When no input-file is provided, ncremap operates in &textldquo;map-only&textrdquo; mode where it exits after producing an annotated map-file. ncremap was introduced to NCO in version 4.5.4 (December, 2015). ncremap is a &textldquo;super-operator&textrdquo; that orchestrates the regridding features of several different programs including other NCO operators. Under the hood NCO applies pre-computed remapping weights or, when necessary, generates and infers grids, generates remapping weights itself or calls external programs to generate the weights, and then applies the weights (i.e., regrids). ncremap ESMF_RegridWeightGen GenerateOfflineMap GenerateOverlapMesh mbtempest mbconvert mbpart ERWG MOAB TempestRemap NCL Conda Unlike the rest of NCO, ncremap and ncclimo are shell scripts, not compiled binariesThis means that newer (including user-modified) versions of ncremap work fine without re-compiling NCO. Re-compiling is only necessary to take advantage of new features or fixes in the NCO binaries, not to improve ncremap. One may download and give executable permissions to the latest source at https://github.com/nco/nco/tree/master/data/ncremap without re-installing the rest of NCO.. <a name="HDF5_USE_FILE_LOCKING"></a>  HDF5_USE_FILE_LOCKING As of NCO 4.9.2 (February, 2020), the ncclimo and ncremap scripts export the environment variable HDF5_USE_FILE_LOCKING with a value of FALSE. This prevents failures of these operators that can occur with some versions of the underlying HDF library that attempt to lock files on file systems that cannot or do not support it. ncremap wraps the underlying regridder (ncks) and external executables to produce a friendly interface to regridding. Without any external dependencies, ncremap applies weights from a pre-exisiting map-file to a source data file to produce a regridded dataset. Source and destination datasets may be on any Swath, Curvilinear, Rectangular, or Unstructured Data (SCRUD) grid. ncremap will also use its own algorithms or, when requested, external programs ESMF&textrsquo;s ESMF_RegridWeightGen (ERWG) or MOAB&textrsquo;s mbconvert/mbpart/mbtempest, TempestRemap&textrsquo;s GenerateOverlapMesh/GenerateOfflineMap) to generate weights and mapfiles. In order to use the weight-generation options, either invoke an internal NCO weight-generation algorithm (e.g., --alg_typ=nco), or ensure that the desired external weight-generation package is installed and on your $PATH. The recommended way to obtain ERWG is as distributed in binary format. Many NCO users already have NCL on their system(s), and NCL usually comes with ERWG. Since about June, 2016, the Conda NCO package will also install ERWG Install the Conda NCO package with conda install -c conda-forge nco.. Then be sure the directory containing the ERWG executable is on your $PATH before using ncremap. As a fallback, ERWG may also be installed from source: https://earthsystemcog.org/projects/esmf/download_last_public. ncremap can also generate and utilize mapfiles created by TempestRemap, https://github.com/ClimateGlobalChange/tempestremap. Until about April, 2019, TempestRemap had to be built from source because there were no binary distributions of it. As of NCO version 4.8.0, released in May, 2019, the Conda NCO package automatically installs the new TempestRemap Conda package so building from source is not necessary. Please contact those projects for support on building and installing their software, which makes ncremap more functional and user-friendly. As of NCO version 5.0.2 from September, 2021, ncremap users can also use the MOAB regridding toolchain. MOAB and ERWG perform best in an MPI environment. One can easily obtain such an environment with Conda Install the Conda MPI versions of the ERWG and MOAB packages with conda install -c conda-forge moab=5.3.0=*mpich_tempest* esmf.. Please ensure you have the latest version of ERWG, MOAB, and/or TempestRemap before reporting any related problems to NCO. As mentioned above, ncremap orchestrates the regridding features of several different programs. ncremap runs most quickly when it is supplied with a pre-computed mapfile. However, ncremap will also (call other programs to) compute mapfiles when necessary and when given sufficient grid information. Thus it is helpful to understand when ncremap will and will not internally generate a mapfile. Supplying input data files and a pre-computed mapfile without any other grid information causes ncremap to regrid the data files without first pausing to internally generate a mapfile. On the other hand, supplying any grid information (i.e., using any of the -d, -G, -g, or -s switches described below), causes ncremap to internally (re-)generate the mapfile by combining the supplied and inferred grid information. A generated mapfile is given a default name unless a user-specified name is supplied with -m map_fl. Fields not regridded by ncremap Most people ultimately use ncremap to regrid data, yet not all data can or should be regridded in the sense of applying a sparse-matrix of weights to an input field to produce and output field. Certain fields (e.g., the longitude coordinate) specify the grid. These fields must be provided in order to compute the weights that are used to regrid. The regridded usually copies these fields &textldquo;as is&textrdquo; directly into regridded files, where they describe the destination grid, and replace or supercede the source grid information. Other fields are extensive grid properties (e.g., the number of cells adjacent to a given cell) that may apply only to the source (not the destination) grid, or be too difficult to re-compute for the destination grid. ncremap contains an internal database of fields that it will not propagate or regrid. First are variables with names identical to the coordinate names found in an ever-growing collection of publicly available geoscience datasets (CMIP, NASA, etc.):&linebreak; CO_Latitude CO_Longitude LAT LON LatitudeCornerpoints Latitude LongitudeCornerpoints Longitude S1_Latitude S1_Longitude TLAT TLONG TLON ULAT ULONG ULON XLAT_M XLAT XLONG_M XLONG area bounds_lat bounds_lon global_latitude0 global_longitude0 gridcell_area gw lat_bnds lat_vertices latitude0 latitude_bnds latitude latt_bounds latu_bounds lat lon_bnds lon_vertices longitude0 longitude_bnds longitude lont_bounds lonu_bounds lon nav_lat nav_lon slat slon w_stag area, gridcell_area, gw, LAT, lat, Latitude, latitude, nav_lat, global_latitude0, latitude0, slat, TLAT, ULAT, XLAT, XLAT_M, CO_Latitude, S1_Latitude, lat_bnds, lat_vertices, latt_bounds, latu_bounds, latitude_bnds, LatitudeCornerpoints, bounds_lat, LON, lon, Longitude, longitude, nav_lon, global_longitude0, longitude0, slon, TLON, TLONG, ULON, ULONG, XLONG, XLONG_M, CO_Longitude, S1_Longitude, lon_bnds, lon_vertices, lont_bounds, lonu_bounds, longitude_bnds, LongitudeCornerpoints, bounds_lon, and w_stag. Files produced by MPAS models may contain these variables that will not be regridded:&linebreak; angleEdge areaTriangle cellsOnCell cellsOnEdge cellsOnVertex dcEdge dvEdge edgeMask edgesOnCell edgesOnEdge edgesOnVertex indexToCellID indexToEdgeID indexToVertexID kiteAreasOnVertex latCell latEdge latVertex lonCell lonEdge lonVertex maxLevelEdgeTop meshDensity nEdgesOnCell nEdgesOnEdge vertexMask verticesOnCell verticesOnEdge weightsOnEdge xEdge yEdge zEdge xVertex yVertex zVertex angleEdge, areaTriangle, cellsOnCell, cellsOnEdge, cellsOnVertex, dcEdge, dvEdge, edgeMask, edgesOnCell, edgesOnEdge, edgesOnVertex, indexToCellID, indexToEdgeID, indexToVertexID, kiteAreasOnVertex, latCell, latEdge, latVertex, lonCell, lonEdge, lonVertex, maxLevelEdgeTop, meshDensity, nEdgesOnCell, nEdgesOnEdge, vertexMask, verticesOnCell, verticesOnEdge, weightsOnEdge, xEdge, yEdge, zEdge, xVertex, yVertex, and zVertex. Most of these fields that ncremap will not regrid are also fields that NCO size-and-rank-preserving operators will not modify, as described in CF Conventions. Options specific to ncremap The following summarizes features unique to ncremap. Features common to many operators are described in Shared features. <a name="tr_alg"></a>  <a name="alg_typ"></a>  <a name="neareststod"></a>  <a name="nstod"></a>  <a name="stod"></a>  <a name="nearestdtos"></a>  <a name="ndtos"></a>  <a name="dtos"></a>  <a name="nds"></a>  <a name="patch"></a>  <a name="patc"></a>  <a name="pch"></a>  <a name="bilinear"></a>  <a name="esmfbilin"></a>  <a name="bilin"></a>  <a name="blin"></a>  <a name="conserve"></a>  <a name="aave"></a>  <a name="esmfaave"></a>  <a name="traave"></a>  <a name="trbilin"></a>  <a name="trintbilin"></a>  <a name="trfv2"></a>  <a name="fv2se_flx"></a>  <a name="se2fv_flx"></a>  <a name="fv2se_stt"></a>  <a name="se2fv_stt"></a>  <a name="fv2se_alt"></a>  <a name="se2fv_alt"></a>  <a name="se2se"></a>  <a name="cs2cs"></a>  <a name="fv2fv"></a>  <a name="fv2fv_flx"></a>  <a name="fv2fv_stt"></a>  <a name="rll2rll"></a>  <a name="ncoaave"></a>  <a name="nco_con"></a>  <a name="nco_dwe"></a>  <a name="nco_idw"></a>  -a alg_typ alg_typ --alg_typ --algorithm --regrid_algorithm dwe idw neareststod nstod stod nsd nearestdtos ndtos dtos nds patch patc pch bilinear bilin esmfbilin blin aave esmfaave conserve conserve2nd -a alg_typ (--alg_typ, --algorithm, --regrid_algorithm) Specifies the interpolation algorithm for weight-generation for use by ESMF_RegridWeightGen (ERWG), MOAB, NCO, and/or TempestRemap. ncremap unbundles this algorithm choice from the rest of the weight-generator invocation syntax because users more frequently change interpolation algorithms than other options (that can be changed with -W wgt_opt). ncremap can invoke all seven ERWG weight generation algorithms, one NCO algorithm, and eight TempestRemap algorithms (with both TR and MOAB). <a name="alg_esmf"></a>  <a name="alg_erwg"></a>  The seven ERWG weight generation algorithms are: bilinear (acceptable abbreviations are: esmfbilin (preferred), bilin, blin, bln), conserve (or esmfaave (preferred), conservative, cns, c1, or aave), conserve2nd (or conservative2nd, c2, or c2nd) (NCO supports conserve2nd as of version 4.7.4 (April, 2018)), nearestdtos (or nds or dtos or ndtos), neareststod (or nsd or stod or nstod), and patch (or pch or patc). See ERWG documentation http://www.earthsystemmodeling.org/esmf_releases/public/ESMF_6_3_0rp1/ESMF_refdoc/node3.html#SECTION03020000000000000000here for detailed descriptions of ERWG algorithms. <a name="alg_nco"></a>  <a name="idw"></a>  <a name="dwe"></a>  <a name="nco_idw"></a>  <a name="nco_dwe"></a>  <a name="inverse_distance"></a>  <a name="inverse_distance_weighted"></a>  <a name="distance_weighted"></a>  <a name="nearest_neighbor"></a>  ncoaave nco_con nco_cns nco_conserve nco ncoidw nco_idw nco_dwe idw dwe inverse_distance_weighted distance_weighted nco_nearest_neighbor DWE IDW inverse-distance-weighted interpolation/extrapolation distance-weighted extrapolation nearest-neighbor extrapolation ncremap implements its own internal weight-generation algorithm as of NCO version 4.8.0 (May, 2019). The first NCO-native algorithm is a first-order conservative algorithm ncoaave that competes well in accuracy with similar algorithms (e.g., ERWG&textrsquo;s conservative algorithm esmfaave). This algorithm is built-in to NCO and requires no external software so it is NCO&textrsquo;s default weight generation algorithm. It works well for everyday use. The algorithm may also be explicitly invoked with nco_con (or nco_cns, nco_conservative, or simply nco). As of NCO version 4.9.4 (September, 2019) ncremap supports a second internal weight-generation algorithm based on inverse-distance-weighted (IDW) interpolation/extrapolation. IDW is similar to the ERWG nearestidavg extrapolation alorithm, and accepts the same two parameters as input: --xtr_xpn xtr_xpn sets the (absolute value of) the exponent used in inverse distance weighting (default is 2.0), and --xtr_nsp xtr_nsp sets the number of source points used in the extrapolation (default is 8). ncremap applies NCO&textrsquo;s IDW to the entire destination grid, not just to points with missing/masked values, whereas ERWG uses distance-weighted-extrapolation (DWE) solely for extrapolation to missing data points. Thus NCO&textrsquo;s IDW is more often used as an alternative to bilinear interpolation since it interpolates between known regions and extrapolates to unknown regions. ncremap --alg_typ=nco_idw -s src.nc -d dst.nc -m map.nc ncremap -a nco_idw --xtr_xpn=1.0 -s src.nc -d dst.nc -m map.nc ncremap -a nco_idw --xtr_nsp=1 -s src.nc -d dst.nc -m map.nc It promises to free users from the bondage of spherically-connected cell edges. We are working to make it geometrically exact for gridcells with small-circle edges, such as the latitude circles in @acronym{RLL} grids. <a name="moab"></a>  <a name="mbtr"></a>  <a name="MOAB"></a>  MOAB TempestRemap Tempest2 se2fv_flx fv2se_flx se2fv_stt fv2se_stt se2fv_alt fv2se_alt se2se cs2cs fv2fv fv2fv_flx fv2fv_stt rll2rll mono_se2fv conservative_monotone_se2fv monotr_fv2se conservative_monotone_fv2se highorder_se2fv accurate_conservative_nonmonotone_se2fv highorder_fv2se accurate_conservative_nonmonotone_fv2se intbilin_se2fv accurate_monotone_nonconservative_se2fv mono_fv2se conservative_monotone_fv2se_alt ncremap can invoke eight preconfigured TempestRemap weight-generation algorithms, and one generic algorithm (tempest) for which users should provide their own options. As of NCO version 4.7.2 (January, 2018), ncremap implemented the six E3SM-recommended TempestRemap mapping algorithms between FV and SE flux, state, and other variables. ncremap originated some (we hope) common-sense names for these algorithms (se2fv_flx, se2fv_stt, se2fv_alt, fv2se_flx, fv2se_stt, and fv2se_alt), and also allows more mathematically precise synonyms (shown below). As of NCO version 4.9.0 (December, 2019), ncremap added two further boutique mappings (fv2fv_flx and fv2fv_stt). As of NCO version 5.1.9 (November, 2023), ncremap added support for two brand new TempestRemap bilinear interpolation algorithms for FV grids. These are (trbilin for traditional bilinear interpolation, and trintbilin), for integrated bilinear or barycentric interpolation. As of NCO version 5.2.0 (February, 2024), ncremap added support for trfv2, a new second-order conservative algorithm. These newer TempestRemap algorithms are briefly described at https://acme-climate.atlassian.net/wiki/spaces/DOC/pages/1217757434/Mapping+file+algorithms+and+naming+convention. The -a tempest algorithm can be specified with the precise TempestRemap options as arguments to the -W (or --wgt_opt) option. Support for the named algorithms requires TempestRemap version 2.0.0 or later (some option combinations fail with earlier versions). The MOAB algorithms are identical TempestRemap algorithms However, mapping weights generated by Although MOAB and TempestRemap use the same numerical algorithms, they are likely to produce slightly different weights due to round-off differences. MOAB is heavily parallelized and computes and adds terms together in an unpredictable order compared to the serial TempestRemap.. Use the same algorithm names to select them. Passing the --mpi_nbr option to ncremap causes it to invoke the MOAB toolchain to compute weights for any TempestRemap algorithm (otherwise the TR toolchain is used). Generate and use the recommended weights to remap fluxes from FV to FV grids, for example, with ncremap -a traave --src_grd=src.g --dst_grd=dst.nc -m map.nc ncremap -m map.nc in.nc out.nc This causes ncremap to automatically invoke TempestRemap with the boutique options --in_type fv --in_np 1 --out_type fv --out_np 1 that are recommended by E3SM for conservative and monotone remapping of fluxes. Invoke MOAB to compute these weights by adding the --mpi_nbr=mpi_nbr option: ncremap --mpi_nbr=8 -a traave --src_grd=src.g --dst_grd=dst.nc -m map.nc This causes ncremap to automatically invoke multiple components of the MOAB toolchain: mbconvert -B -o PARALLEL=WRITE_PART -O PARALLEL=BCAST_DELETE \ -O PARTITION=TRIVIAL -O PARALLEL_RESOLVE_SHARED_ENTS \ "src.g" "src.h5m" mbconvert -B -o PARALLEL=WRITE_PART -O PARALLEL=BCAST_DELETE \ -O PARTITION=TRIVIAL -O PARALLEL_RESOLVE_SHARED_ENTS \ "dst.nc" "dst.h5m" mbpart 8 --zoltan RCB "src.h5m" "src_8p.h5m" mbpart 8 --zoltan RCB --recompute_rcb_box --scale_sphere \ --project_on_sphere 2 "dst.h5m" "dst_8p.h5m" mpirun -n 8 mbtempest --type 5 --weights \ --load "src_8p.h5m" --load "dst_8p.h5m" \ --method fv --order 1 --method fv --order 1 \ --file "map.nc" ncatted -O --gaa <command lines> map.nc map.nc The MOAB toolchain should produce a map-file identical, to rounding precision, to one produced by TR. When speed matters (i.e., large grids), and the algorithm is supported (e.g., traave), invoke MOAB, otherwise invoke TR. Galerkin methods cgll dgll mono --mono TempestRemap options have the following meanings: mono specifies a monotone remapping, i.e., one that does not generate any new extrema in the field variables. cgll indicates the input or output are represented by a continuous Galerkin method on Gauss-Lobatto-Legendre nodes. This is appropriate for spectral element datasets. (TempestRemap also supports, although NCO does not invoke, the dgll option for a discontinuous Galerkin method on Gauss-Lobatto-Legendre nodes.) It is equivalent to, yet simpler to remember and to invoke than ncremap -a tempest --src_grd=se.g --dst_grd=fv.nc -m map.nc \ -W '--in_type cgll --in_np 4 --out_type fv --mono' Specifying -a tempest without additional options in the -W clause causes TempestRemap to employ defaults. The default configuration requires both input and output grids to be FV, and produces a conservative, non-monotonic mapping. The -a traave option described below may produce more desirable results than this default for many users. Using -a tempest alone without other options for spectral element grids will lead to undefined and likely unintentional results. In other words, -a tempest is intended to be used in conjunction with a -W option clause to supply your own combination of TempestRemap options that does not duplicate one of the boutique option collections that already has its own name. The full list of supported canonical algorithm names, their synonyms, and boutique options passed to GenerateOfflineMap or to mbtempest follow. <a name="bug_moab"></a>  Caveat lector: As of September, 2021 MOAB-generated weights are only trustworthy for the traave algorithm (synonym for fv2fv_flx). The options for all other algorithms are implemented as indicated though they should be invoked for testing purposes only. High order and spectral element maps are completely unsupported. MOAB anticipates supporting more TempestRemap algorithms in the future. Always use the map-checker to test maps before use, e.g., with ncks --chk_map map.nc. <a name="alg_tr"></a>  <a name="alg_mbtr"></a>  <a name="alg_moab"></a>

traave (synonyms fv2fv_flx, fv2fv_mono, conservative_monotone_fv2fv), TR options: --in_type fv --in_np 1 --out_type fv --out_np 1&linebreak; MOAB options: --method fv --order 1 --method fv --order 1 trbilin (no synonyms), TR options: --in_type fv --out_type fv --method bilin&linebreak; MOAB options: --method fv --method fv --order 1 --order 1 --fvmethod bilin trintbilin (no synonyms), TR options: --in_type fv --out_type fv --method intbilin&linebreak; MOAB options: --method fv --method fv --order 1 --order 1 --fvmethod intbilin trfv2 (synonyms trfvnp2), TR options: --in_type fv --in_np 2 --out_type fv --out_np 1 --method normalize&linebreak; MOAB options: --method fv --order 2 --method fv --order 1 --fvmethod normalize se2fv_flx (synonyms mono_se2fv, conservative_monotone_se2fv) TR options: --in_type cgll --in_np 4 --out_type fv --mono&linebreak; MOAB options: --method cgll --order 4 --global_id GLOBAL_DOFS --method fv --monotonic 1 --global_id GLOBAL_ID fv2se_flx (synonyms monotr_fv2se, conservative_monotone_fv2se), TR options: --in_type cgll --in_np 4 --out_type fv --mono. For fv2se_flx the weights are generated with options identical to se2fv_flx, and then the transpose of the resulting weight matrix is employed.&linebreak; MOAB options: --method cgll --order 4 --method fv --monotonic 1 se2fv_stt (synonyms highorder_se2fv, accurate_conservative_nonmonotone_se2fv), TR options: --in_type cgll --in_np 4 --out_type fv&linebreak; MOAB options: --method cgll --order 4 --method fv fv2se_stt (synonyms highorder_fv2se, accurate_conservative_nonmonotone_fv2se), TR options: --in_type fv --in_np 2 --out_type cgll --out_np 4&linebreak; MOAB options: --method fv --order 2 --method cgll --order 4 se2fv_alt (synonyms intbilin_se2fv, accurate_monotone_nonconservative_se2fv), TR options: --in_type cgll --in_np 4 --out_type fv --method mono3 --noconserve&linebreak; MOAB options: --method cgll --order 4 --method fv --monotonic 3 --noconserve fv2se_alt (synonyms mono_fv2se, conservative_monotone_fv2se_alt), TR options: --in_type fv --in_np 1 --out_type cgll --out_np 4 --mono&linebreak; MOAB options: --method fv --order 1 --method cgll --order 4 --monotonic 1 se2se (synonyms cs2cs, conservative_monotone_se2se), TR options: --in_type cgll --in_np 4 --out_type cgll --out_np 4 --mono&linebreak; MOAB options: --method cgll --order 4 --method cgll --order 4 --monotonic 1 fv2fv (synonyms rll2rll), TR options: --in_type fv --in_np 2 --out_type fv&linebreak; MOAB options: --method fv --order 2 --method fv fv2fv_flx (synonyms traave fv2fv_mono, conservative_monotone_fv2fv), TR options: --in_type fv --in_np 1 --out_type fv --out_np 1&linebreak; MOAB options: --method fv --order 1 --method fv --order 1 fv2fv_stt (synonyms fv2fv_highorder, accurate_conservative_nonmonotone_fv2fv), TR options: --in_type fv --in_np 2 --out_type fv&linebreak; MOAB options: --method fv --order 2 --method fv

Thus these boutique options are specialized for SE grids with fourth order resolution (

np = 4). Full documentation of the E3SM-recommended boutique options for TempestRemap is https://acme-climate.atlassian.net/wiki/spaces/Docs/pages/178848194/Transition+to+TempestRemap+for+Atmosphere+gridshere (may require E3SM-authorization to view). Let us know if you would like other boutique TempestRemap option sets added as canonical options for ncremap. <a name="a2o"></a>  <a name="atm2ocn"></a>  <a name="b2l"></a>  <a name="big2ltl"></a>  <a name="l2s"></a>  <a name="lrg2sml"></a>  <a name="allow_no_overlap"></a>  GenerateOverlapMesh a2o --a2o --atm2ocn --b2l --big2ltl --l2s --lrg2sml --allow_no_overlap --a2o (--a2o, --atm2ocn, --b2l, --big2ltl, --l2s, --lrg2sml) Use one of these flags (that take no arguments) to cause TempestRemap to generate mapping weights from a source grid that has more coverage than the destination grid, i.e., the destination grid is a subset of the source. When computing the intersection of two meshes, TempestRemap uses an algorithm (in an executable named GenerateOverlapMesh) that expects the mesh with less coverage to be the first grid, and the grid with greater coverage to be the second, regardless of the mapping direction. By default, ncremap supplies the source grid first and the destination second, but this order causes GenerateOverlapMesh (which is agnostic about ordering for grids of equal coverage) to fail when the source grid covers regions not in the destination grid. For example, a global atmosphere grid has more coverage than a global ocean grid, so that remapping from atmosphere-to-ocean would require invoking the --atm2ocn switch:

# Use --a2o to generate weights for "big" to "little" remaps:
ncremap --a2o -a se2fv_flx --src_grd=atm_se_grd.nc \
              --dst_grd=ocn_fv_grd.nc -m atm2ocn.nc
# Otherwise, omit it:
ncremap       -a fv2se_flx --src_grd=ocn_fv_grd.nc \
              --dst_grd=atm_se_grd.nc -m map.nc
ncremap       -a se2fv_flx --src_grd=atm_se_grd.nc \
              --dst_grd=atm_fv_grd.nc -m map.nc
# Only necessary when generating, not applying, weights:
ncremap -m atm2ocn.nc in.nc out.nc

As shown in the second example above, remapping from global ocean-to-atmosphere grids does not require (and should not invoke) this switch. The third example shows that the switch is only needed when generating weights, not when applying them. The switch is never needed (and is ignored) when generating weights with ERWG (which constructs the intersection mesh with a different algorithm than TempestRemap). Attempting to remap a larger source grid to a subset destination grid without using --a2o causes GenerateOverlapMesh to emit an error (and a potential workaround) like this:

....Nearest target face 130767
....ERROR: No overlapping face found
....This may be caused by mesh B being a subset of mesh A
....Try swapping order of mesh A and B, or override with \
    --allow_no_overlap
....EXCEPTION (../src/OverlapMesh.cpp, Line 1738) Exiting

The --a2o switch and its synonyms are available in version 4.7.3 (March, 2018) and later. As of NCO version 4.9.9 (May, 2021), ncremap automatically transmits the flag --allow_no_overlap to GenerateOverlapMesh so that regional meshes that do not completely overlap may be intersected. This is thought to have no effect on global mappings. Please let us know if these capabilities do not work for you. <a name="add_fill_value"></a>  <a name="add_fll"></a>  <a name="fill_empty"></a>  <a name="fll_mpt"></a>  missing value _FillValue --add_fll --no_add_fll --add_fill_value --fll_mpt --no_fll_mpt --fill_empty --no_fill_empty --msk_apl --mask_apply --add_fll (--add_fll, --add_fill_value, --fll_mpt, --fill_empty) Introduced in NCO version 5.0.0 (released June, 2021), this switch (which takes no argument) causes the regridder to add a _FillValue attribute to fields with empty destination cells. The corresponding --no_add_fll switches, introduced in NCO version 5.1.1 (released November, 2022), do the opposite and prevent the regridder from adding a _FillValue attribute to fields with empty destination cells. Note that --add_fll adds an explicit _FillValue metadata attribute to fields that lack one only if the field contains empty destination cells (as described below). This option, by itself, does not directly change the values contained in empty gridcells. There are two varieties of empty destination cells: First are those cells with no non-zero weights from the source grids. If all source grid contributions to the a particular cell are zero, then no field will ever be mapped into that cell. For example, if an ocean model source grid only contains ocean gridcells (e.g., like MPAS-Ocean), then all continental interior gridcells in a destination grid will be empty. The second type of empty gridcell can occur in conjunction with sub-gridscale (SGS) fractions. All destination gridcells with SGS fraction equal to zero will always be empty. For example, sea-ice models often employ time-varying SGS fractions that are zero everywhere except where/when sea ice is present. These gridcells are disjoint from continental interior gridcells whose locations can be determined by mapping weights alone. When a contiguous geophysical field (e.g., air temperature) without a _FillValue is mapped to such a destination grid, the empty destination values are normally set to zero (because no source grid cells contribute). However, zero is a valid value for many geophysical fields. Use this switch to ensure that empty destination gridcells are always set to _FillValue. The default _FillValue will be used in the output file for input fields that lack an explicit _FillValue. This flag has no effect on fields that have any input values equal to an explicitly or implicitly defined _FillValue. The flag does affect fields that have valid input values everywhere on the source grid, yet for some reason (e.g., disjoint grids or zero sub-gridscale fractions) there are unmapped destination gridcells. ncremap ... # No renormalization/masking ncremap --sgs_frc=sgs --add_fll ... # Mask cells missing 100% ncremap --rnr=0.1 ... # Mask cells missing > 10% ncremap --rnr=0.1 --sgs_frc=sgs ... # Mask missing > 10% ncremap --rnr=0.1 --sgs_frc=sgs --add_fll ... # Mask missing > 90% or sgs < 10% ncremap -P mpas... # --add_fll implicit, mask where sgs=0.0 ncremap -P mpas... --no_add_fll # --add_fll explicitly turned-off, no masking ncremap -P mpas... --rnr=0.1 # Mask missing > 90% or sgs < 10% ncremap -P elm... # --add_fll not implicit, no masking Note that --add_fll is automatically triggered by --msk_apl to ensure that masked fields regridded with TempestRemap-generated map-files have _FillValues consistent with map-files generated by ESMF and NCO. <a name="alg_lst"></a>  --alg_lst=alg_lst alg_lst --alg_lst --algorithm_list --alg_lst=alg_lst (--alg_lst, --algorithm_list) As of NCO version 5.2.0 (February, 2024), ncremap supports --alg_lst=alg_lst, a comma-separated list of the algorithms that MWF-mode uses to create map-files. The default list is esmfaave,esmfbilin,ncoaave,ncoidw,traave,trbilin,trfv2,trintbilin. Each name in the list should be the primary name of an algorithm, not a synonym. For example, use esmfaave,traave not aave,fv2fv_flx (the latter are backward-compatible synonyms for the former). The algorithm list must be consistent with grid-types supplied: ESMF algorithms work with meshes in ESMF, SCRIP, or UGRID formats. NCO algorithms only work with meshes in SCRIP format. TempestRemap algorithms work with meshes in ESMF, Exodus, SCRIP, or UGRID formats. On output, ncremap inserts each algorithm name into the output map-file name in this format: map_nm_src_to_nm_dst_alg_typ.dt_sng.nc. % ncremap -P mwf --alg_lst=esmfnstod,ncoaave,ncoidw,traave,trbilin \ -s ocean.QU.240km.scrip.181106.nc -g ne11pg2.nc \ --nm_src=QU240 --nm_dst=ne11pg2 --dt_sng=20240201 ... % ls map* map_QU240_to_ne11pg2_esmfnstod.20240201.nc map_QU240_to_ne11pg2_ncoaave.20240201.nc map_QU240_to_ne11pg2_ncoidw.20240201.nc map_QU240_to_ne11pg2_traave.20240201.nc map_QU240_to_ne11pg2_trbilin.20240201.nc map_ne11pg2_to_QU240_esmfnstod.20240201.nc map_ne11pg2_to_QU240_ncoaave.20240201.nc map_ne11pg2_to_QU240_ncoidw.20240201.nc map_ne11pg2_to_QU240_traave.20240201.nc map_ne11pg2_to_QU240_trbilin.20240201.nc <a name="area_dgn"></a>  <a name="dgn_area"></a>  <a name="area_diagnose"></a>  <a name="diagnose_area"></a>  diagnose area --area_dgn --dgn_area --area_diagnose --diagnose_area --area_dgn (--area_dgn, --area_diagnose, --dgn_area, --diagnose_area) Introduced in NCO version 5.0.4 (released December, 2021), this switch (which takes no argument) causes the regridder to diagnose (rather than copy) the area of each gridcell to an inferred grid-file. By default, ncremap simply copies the area variable (whose name defaults to area and can be explicitly specified with -R '--rgr area_nm=name') into the grid_area variable of the inferred grid-file. When --area_dgn is invoked, ncremap instead computes the values of grid_area based on the cell boundaries in the input template file. ncremap --area_dgn -d dst.nc -g grid.nc Note that --area_dgn has no effect on any mapping weights subsequently generated from the grid-file because most weight-generators base their weights on internally computed cell areas (although ERWG has an option, --user_areas, to override this behavior). <a name="config"></a>  <a name="cnf"></a>  --version --vrs --config --configuration --cnf --version (--version, --vrs, --config, --configuration, --cnf) This switch (which takes no argument) causes the operator to print its version and configuration. This includes the copyright notice, URLs to the BSD and NCO license, directories from which the NCO scripts and binaries are running, and the locations of any separate executables that may be used by the script. <a name="d2f"></a>  d2f --d2f --d2s --dbl_flt --dbl_sgl --double_float ncpdq Precision --d2f (--d2f, --d2s, --dbl_flt, --dbl_sgl, --double_float) This switch (which takes no argument) demotes all double precision non-coordinate variables to single precision. Internally ncremap invokes ncpdq to apply the dbl_flt packing map to an intermediate version of the input file before regridding it. This switch has no effect on files that are not regridded. To demote the precision in such files, use ncpdq to apply the dbl_flt packing map to the file directly. Files without any double precision fields will be unaltered. <a name="dbg_lvl"></a>  -D dbg_lvl dbg_lvl --dbg_lvl --dbg --debug --debug_level -D dbg_lvl (--dbg_lvl, --dbg, --debug, --debug_level) Specifies a debugging level similar to the rest of NCO. If

dbg_lvl = 1, ncremap prints more extensive diagnostics of its behavior. If

dbg_lvl = 2, ncremap prints the commands it would execute at any higher or lower debugging level, but does not execute these commands. If

dbg_lvl > 2, ncremap prints the diagnostic information, executes all commands, and passes-through the debugging level to the regridder (ncks) for additional diagnostics. <a name="devnull"></a>  --devnull --dev_nll --dvn_flg --devnull=dvn_flg (--devnull, --dev_nll, --dvn_flg) The dvn_flg controls whether ncremap suppresses regridder output or sends it to /dev/null. The default value of dvn_flg is &textldquo;Yes&textrdquo;, so that ncremap prints little output to the terminal. Set dvn_flg to &textldquo;No&textrdquo; to allow the internal regridder executables (mainly ncks) to send their output to the terminal. <a name="dpt"></a>  <a name="dpt_fl"></a>  dpt --dpt --add_dpt --depth --add_depth --dpt_fl=dpt_fl dpt_fl --mpas_fl --mpas_depth --depth_file add_depth.py Depth MPAS --dpt (--dpt, --add_dpt, --depth, --add_depth) --dpt_fl=dpt_fl (--dpt_fl, --depth_file, --mpas_fl, --mpas_depth) The --dpt switch (which takes no argument) and the --dpt_fl=dpt_fl option which automatically sets the switch and also takes a filename argument, both control the addition of a depth coordinate to MPAS ocean datasets. Depth is the vertical distance below sea surface and, like pressure in the atmosphere, is an important vertical coordinate whose explicit values are often omitted from datasets yet may be computed from other variables (gridbox thickness, pressure difference) and grid information. Moreover, users are often more interested in the approximate depth, aka reference depth, of a given ocean layer independent of its horizontal position. To invoke either of these options first obtain and place the add_depth.py command on the executable path (i.e., $PATH), and use ncremap --config to verify that it is found. These options tell ncremap to invoke add_depth.py which uses the refBottomDepth variable in the current data file or, if specified, the dpt_fl, to create and add a depth coordinate to the current file (before regridding). As of NCO version 4.7.9 (February, 2019), the depth coordinate is an approximate, one-dimensional, globally uniform coordinate that neglects horizontal variations in depth that can occur near strong bathymetry or under ice shelves. Like its atmospheric counterpart in many models, the lev pressure-coordinate, depth is useful for plotting purposes and global studies. It would not be difficult to modify these options to add other depth information based on the 3D cell-thickness field to ocean files (please ask Charlie if interested in this). <a name="dst_fl"></a>  -d dst_fl dst_fl --dst_fl --destination_file --tpl --tpl_fl --template --template_file -d dst_fl (--dst_fl, --destination_file, --tpl, tpl_fl, --template_file, --template) Specifies a data file to serve as a template for inferring the destination grid. Currently dst_fl must be a data file (not a gridfile, SCRIP or otherwise) from which NCO can infer the destination grid. The more coordinate and boundary information and metadata the better NCO will do at inferring the grid. If dst_fl has cell boundaries then NCO will use those. If dst_fl has only cell-center coordinates (and no edges), then NCO will guess-at (for rectangular grids) or interpolate (for curvilinear grids) the edges. Unstructured grids must supply cell boundary information, as it cannot be interpolated or guessed-at. NCO only reads coordinate and grid data and metadata from dst_fl. dst_fl is not modified, and may have read-only permissions. <a name="dt_sng"></a>  --dt_sng=dt_sng dt_sng --dt_sng --date_string --dt_sng=dt_sng (--dt_sng, --date_string) Specifies the date-string use in the full name of map-files created in MWF mode. Map-file names include, by convention, a string to indicate the approximate date (and thus algorithm versions employed) of weight generation. ncremap uses the dt_sng argument to encode the date into output map-file names of this format: map_nm_src_to_nm_dst_alg_typ.dt_sng.nc. MWF mode defaults dt_sng to the current date in YYYYMMDD-format. <a name="esmf_typ"></a>  --esmf_typ=esmf_typ esmf_typ --esmf_typ --esmf_mth --esmf_extrap_type --esmf_extrap_method distance-weighted extrapolation --esmf_typ=esmf_typ (--esmf_typ, --esmf_mth, --esmf_extrap_type, --esmf_extrap_method) Specifies the extrapolation method used to compute unmapped destination point values with the ERWG weight generator. Valid values, their synonyms, and their meanings are neareststod (synonyms stod and nsd) which uses the nearest valid source value, nearestidavg (synonyms idavg and id) which uses an inverse distance-weighted (with an exponent of xtr_xpn) average of the nearest xtr_nsp valid source values, and none (synonyms nil and nowaydude) which forbids extrapolation. Default is

esmf_typ = none. The arguments to options --xtr_xpn=xtr_xpn (which defaults to 2.0) and --xtr_nsp=xtr_nsp (which defaults to 8) set the parameters that control the extrapolation nearestidavg algorithm. For more information on ERWG extrapolation, see documentation http://www.earthsystemmodeling.org/esmf_releases/last_built/ESMF_refdoc/node3.html#SECTION03022300000000000000here. NCO supports this feature as of version 4.7.4 (April, 2018). <a name="xtr_nsp"></a>  extrapolation --xtr_nsp=xtr_nsp xtr_nsp --xtr_nsp --esmf_pnt_src_nbr --esmf_extrap_num_src_pnts --xtr_nsp=xtr_nsp (--xtr_nsp, --esmf_pnt_src_nbr, --esmf_extrap_num_src_pnts) Specifies the number of source points to use in extrapolating unmapped destination point values with the ERWG weight generator. This option is only useful in conjunction with explicitly requested extrapolation types

esmf_typ = neareststod and

esmf_typ = nearestidavg. Default is

xtr_nsp = 8. For more information on ERWG extrapolation, see documentation http://www.earthsystemmodeling.org/esmf_releases/last_built/ESMF_refdoc/node3.html#SECTION03022300000000000000here. NCO supports this feature as of version 4.7.4 (April, 2018). <a name="xtr_xpn"></a>  --xtr_xpn=xtr_xpn xtr_xpn --xtr_xpn --esmf_pnt_src_nbr --esmf_extrap_num_src_pnts --xtr_xpn=xtr_xpn (--xtr_xpn, --esmf_pnt_src_nbr, --esmf_extrap_num_src_pnts) Specifies the number of source points to use in extrapolating unmapped destination point values with the ERWG weight generator. This option is only useful in conjunction with explicitly requested extrapolation types

esmf_typ = neareststod and

esmf_typ = nearestidavg. Default is

xtr_xpn = 2.0. For more information on ERWG extrapolation, see documentation http://www.earthsystemmodeling.org/esmf_releases/last_built/ESMF_refdoc/node3.html#SECTION03022300000000000000here. NCO supports this feature as of version 4.7.4 (April, 2018). <a name="grd_dst"></a>  -g grd_dst grd_dst --grd_dst --grid_dest --dest_grid --destination_grid infer -g grd_dst (--grd_dst, --grid_dest, --dest_grid, --destination_grid) Specifies the destination gridfile. An existing gridfile may be in any format accepted by the weight generator. NCO will use ERWG or TempestRemap to combine grd_dst with a source gridfile (either inferred from input-file, supplied with -s grd_src, or generated from -G grd_sng) to produce remapping weights. When grd_dst is used as input, it is not modified, and may have read-only permissions. When grd_dst is inferred from input-file or created from grd_sng, it will be generated in SCRIP format. <a name="fl_fmt_ncremap"></a>  --fl_fmt_ncremap --file_format_ncremap -3 -4 -5 -6 -7 As of NCO version 4.6.8 (August, 2017), ncremap supports most of the file format options that the rest of NCO has long supported (File Formats and Conversion). This includes short flags (e.g., -4) and key-value options (e.g., --fl_fmt=netcdf4) though not long-flags without values (e.g., --netcdf4). However, ncremap can only apply the full suite of file format options to files that it creates, i.e., regridded files. The weight generators (ERWG and TempestRemap) are limited in the file formats that they read and write. Currently (August, 2017), ERWG supports CLASSIC, 64BIT_OFFSET, and NETCDF4, while TempestRemap supports only CLASSIC. These can of course be converted to other formats using ncks (File Formats and Conversion). However, map-files produced in other non-CLASSIC formats can remap significantly larger grids than CLASSIC-format map-files. <a name="grd_sng"></a>  -G grd_sng &textndash;grd_sng --grd_sng --grid_generation --grid_gen --grid_string -G grd_sng (--grd_sng, --grid_generation, --grid_gen, --grid_string) Specifies, with together with other options, a source gridfile to createAs of version 4.7.6 (August, 2018)), NCO&textrsquo;s syntax for gridfile generation is much improved and streamlined, and is the syntax described here. This is also called &textldquo;Manual Grid-file Generation&textrdquo;. An earlier syntax (described at Grid Generation) accessed through ncks options still underlies the new syntax, though it is less user-friendly. Both old and new syntax work well and produce finer rectangular grids than any other software we know of.. ncremap creates the gridfile in SCRIP format by default, and then, should the requisite options for regridding be present, combines that with the destination grid (either inferred from input-file or supplied with -g grd_dst and generates mapping weights. Manual grid-file generation is not frequently used since ncremap can infer many grids directly from the input-file, and few users wish to keep track of SCRIP grids when they can be easily regenerated as intermediate files. This option also allows one to visually tune a grid by rapidly generating candidates and inspecting the results. If a desired grid-file is unavailable, and no dataset on that grid is available (so inferral cannot be used), then one must manually create a new grid. Users create new grids for many reasons including dataset intercomparisons, regional studies, and fine-tuned graphics. NCO and ncremap support manual generation of the most common rectangular grids as SCRIP-format grid-files. Create a grid by supplying ncremap with a grid-file name and &textldquo;grid-formula&textrdquo; (grd_sng) that contains, at a minimum, the grid-resolution. The grid-formula is a single hash-separated string of name-value pairs each representing a grid parameter. All parameters except grid resolution have reasonable defaults, so a grid-formula can be as simple as latlon=180,360: ncremap -g grd.nc -G latlon=180,360 The SCRIP-format grid-file grd.nc is a valid source or destination grid for ncremap and other regridders. Grid-file generation documentation in the NCO Users Guide at http://nco.sf.net/nco.html#grid describes all the grid parameters and contains many examples. Note that the examples in this section use grid generation API for ncremap version 4.7.6 (August, 2018) and later. Earlier versions can use the ncks API explained at Grid Generation in the Users Guide. The most useful grid parameters (besides resolution) are latitude type (lat_typ), longitude type (lon_typ), title (ttl), and, for regional grids, the SNWE bounding box (snwe). The three supported varieties of global rectangular grids are Uniform/equiangular (

lat_typ=uni), Cap/FV (

lat_typ=cap), and Gaussian (

lat_typ=gss). The four supported varieties of longitude types are the first (westernmost) gridcell centered at Greenwich (

lon_typ=grn_ctr), western edge at Greenwish (grn_wst), or at the Dateline (

lon_typ=180_ctr and

lon_typ=180_wst, respectively). Grids are global, uniform, and have their first longitude centered at Greenwich by default. The grid-formula for this is lat_typ=uni#lon_typ=grn_ctr. Some examples (remember, this API requires NCO 4.7.6+):

NCEP2 grid

ncremap -g grd.nc -G latlon=180,360 # 1x1 Uniform grid ncremap -g grd.nc -G latlon=180,360#lat_drc=n2s # 1x1 Uniform grid, N->S not S->N ncremap -g grd.nc -G latlon=180,360#lon_typ=grn_wst # 1x1 Uniform grid, Greenwich-west edge ncremap -g grd.nc -G latlon=129,256#lat_typ=cap # 1.4x1.4 FV grid ncremap -g grd.nc -G latlon=94,192#lat_typ=gss # T62 Gaussian grid ncremap -g grd.nc -G latlon=361,576#lat_typ=cap#lon_typ=180_ctr # MERRA2 FV grid ncremap -g grd.nc -G latlon=94,192#lat_typ=gss#lat_drc=n2s # NCEP2 T62 Gaussian grid Regional grids are a powerful tool in regional process analyses, and can be much smaller in size than global datasets. Regional grids are always uniform. Specify the rectangular bounding box, i.e., the outside edges of the region, in SNWE order: ncremap -g grd.nc -G ttl="Equi-Angular 1x1 Greenland grid"#latlon=30,90#snwe=55.0,85.0,-90.0,0.0 The grd_sng argument to -G or --grd_sng must be a single hash-separated string of name-value pairs, e.g., latlon=....#lat_typ=...#ttl="My title". ncremap will not correctly parse any other format, such as multiple separate name-value pairs without hashes. <a name="in_drc"></a>  -I in_drc in_drc stdin --in_drc --drc_in --dir_in --in_dir --input -I in_drc (--in_drc, --drc_in, --dir_in, --in_dir, input) Specifies the input directory, i.e., the directory which contains the input file(s). If in_fl is also specified, then the input filepath is constructed by appending a slash and the filename to the directory: in_drc/in_fl. Specifying in_drc without in_fl causes ncremap to attempt to remap every file in in_drc that ends with one of these suffixes: .nc, .nc3, .nc4, .nc5, .nc6, .nc7, .cdf, .hdf, .he5, or .h5. When multiple files are regridded, each output file takes the name of the corresponding input file. There is no namespace conflict because the input and output files are in separate directories. Note that ncremap can instead accept a list of input files through standard input (e.g., ls *.nc | ncremap ...) or as positional command-line arguments (e.g., ncremap in1.nc in2.nc ...). <a name="in_fl"></a>  -i in_fl in_fl --in_fl --in_file --input_file -i in_fl (--in_fl, --in_file, --input_file) Specifies the file containing data on the source grid to be remapped to the destination grid. When provided with the optional map_fl, ncremap only reads data from in_fl in order to regrid it. Without the optional map_fl or src_grd, ncremap will try to infer the source grid from in_fl, and so must read coordinate and metatdata information from in_fl. In this case the more coordinate and boundary information and metadata, the better NCO will do at inferring the source grid. If in_fl has cell boundaries then NCO will use those. If in_fl has only cell-center coordinates (and no edges), then NCO will guess (for rectangular grids) or interpolate (for curvilinear grids) the edges. Unstructured grids must supply cell boundary information, as it cannot be interpolated or guessed-at. in_fl is not modified, and may have read-only permissions. Note that ncremap can instead accept input file name(s) through standard input (e.g., ls *.nc | ncremap ...) or as positional command-line arguments (e.g., ncremap in1.nc in2.nc ...). When one or three-or-more positional arguments are given, they are all interpreted as input filename(s). Two positional arguments are interpreted as a single input-file and its corresponding output-file. <a name="job_nbr"></a>  <a name="job_nbr_ncremap"></a>  -j job_nbr job_nbr --job_nbr --job_number --job_number --jobs -j job_nbr (--job_nbr, --job_number, --jobs) Specifies the number of simultaneous regridding processes to spawn during parallel execution for both Background and MPI modes. In both parallel modes ncremap spawns processes in batches of job_nbr jobs, then waits for those processes to complete. Once a batch finishes, ncremap spawns the next batch. In Background mode, all jobs are spawned to the local node. In MPI mode, all jobs are spawned in round-robin fashion to all available nodes until job_nbr jobs are running. If regridding consumes so much RAM (e.g., because variables are large and/or the number of threads is large) that a single node can perform only one regridding job at a time, then a reasonable value for job_nbr is the number of nodes, node_nbr. Often, however, nodes can regrid multiple files simultaneously. It can be more efficient to spawn multiple jobs per node than to increase the threading per job because I/O contention for write access to a single file prevents threading from scaling indefinitely. By default

job_nbr = 2 in Background mode, and

job_nbr = node_nbr in MPI mode. This helps prevent users from overloading nodes with too many jobs. Subject to the availability of adequate RAM, expand the number of jobs per node by increasing job_nbr until, ideally, each core on the node is used. Remember that processes and threading are multiplicative in core use. Four jobs each with four threads each consumes sixteen cores. As an example, consider regridding 100 files with a single map. Say you have a five-node cluster, and each node has 16 cores and can simultaneously regrid two files using eight threads each. (One needs to test a bit to optimize these parameters.) Then an optimal (in terms of wallclock time) invocation would request five nodes with 10 simultaneous jobs of eight threads. On PBS or SLURM batch systems this would involve a scheduler command like qsub -l nodes=5 ... or sbatch --nodes=5 ..., respectively, followed by ncremap --par_typ=mpi --job_nbr=10 --thr_nbr=8 .... This job will likely complete between five and ten-times faster than a serial-mode invocation of ncremap to regrid the same files. The uncertainty range is due to unforeseeable, system-dependent load and I/O charateristics. Nodes that can simultaneously write to more than one file fare better with multiple jobs per node. Nodes with only one I/O channel to disk may be better exploited by utilizing more threads per process. <a name="map_mlt"></a>  -M --mlt_map --multimap --no_multimap --nomultimap -M (--mlt_map, --multimap, --no_multimap, --nomultimap) ncremap assumes that every input file is on a unique grid unless a source gridfile is specified (with -s grd_src) or multiple-mapfile generation is explicitly turned-off (with -M). The -M switch is a toggle, it requires and accepts no argument. Toggling -M tells ncremap to generate at most one mapfile regardless of the number of input files. If -M is not toggled (and neither -m map_fl nor -s grd_src is invoked) then ncremap will generate a new mapfile for each input file. Generating new mapfiles for each input file is necessary for processing batches of data on different grids (e.g., swath-like data), and slow, tedious, and unnecessary when batch processing data on the same grids. <a name="map_fl"></a>  -m map_fl map_fl --map_fl --map --map_file --rgr_map --regrid_map -m map_fl (--map_fl, --map, --map_file, --rgr_map, --regrid_map) Specifies a mapfile (i.e., weight-file) to remap the source to destination grid. If map_fl is specified in conjunction with any of the -d, -G, -g, or -s switches, then ncremap will name the internally generated mapfile map_fl. Otherwise (i.e., if none of the source-grid switches are used), ncremap assumes that map_fl is a pre-computed mapfile. In that case, the map_fl must be in SCRIP format, although it may have been produced by any application (usually ERWG or TempestRemap). If map_fl has only cell-center coordinates (and no edges), then NCO will guess-at or interpolate the edges. If map_fl has cell boundaries then NCO will use those. A pre-computed map_fl is not modified, and may have read-only permissions. The user will be prompted to confirm if a newly generated map-file named map_fl would overwrite an existing file. ncremap adds provenance information to any newly generated map-file whose name was specified with -m map_fl. This provenance includes a history attribute that contains the command invoking ncremap, and the map-generating command invoked by ncremap. <a name="mpi_pfx"></a>  <a name="mpi_prefix"></a>  <a name="srun_cmd"></a>  <a name="srun_command"></a>  <a name="mpi_nbr"></a>  <a name="mpi_number"></a>  <a name="tsk_nbr"></a>  <a name="task_number"></a>  --mpi_pfx=mpi_pfx mpi_pfx --mpi_pfx --mpi_prefix --srun_cmd --srun_command --mpi_pfx=mpi_pfx (--mpi_pfx, --mpi_prefix, --srun_cmd, --srun_command) --mpi_nbr=mpi_nbr mpi_nbr --mpi_nbr --mpi_number --tsk_nbr --task_nbr --mpi_nbr=mpi_nbr (--mpi_nbr, --mpi_number, --tsk_nbr, --task_number) The --mpi_pfx=mpi_pfx option specifies an appropriate job scheduler prefix for MPI-enabled weight-generation executables such as ESMF&textrsquo;s ESMF_RegridWeightGen and MOAB&textrsquo;s mbtempest. Other weight generators (ncks, GenerateOfflineMap) are unaffected by this option since they are not MPI-enabled. mpi_pfx defaults to mpirun -n ${mpi_nbr} on all machines except those whose $HOSTNAME matches an internal database of DOE-operated supercomputers where mpi_pfx usually defaults to srun -n ${mpi_nbr} When invoking --mpi_pfx, be sure to explicitly define the number of MPI tasks-per-node, e.g., ncremap --mpi_pfx='srun -n 16' ... ncremap --mpi_pfx='srun --mpi=pmi2 -n 4' ... The separate --mpi_nbr=mpi_nbr option specifies the number of tasks-per-node that MPI-enabled weight generators will request. It preserves the default job scheduler prefix (srun or mpirun): ncremap --mpi_nbr=4 ... # 16 MPI tasks-per-node for ERWG/mbtempest ncremap --mpi_nbr=16 ... # 4 MPI tasks-per-node for ERWG/mbtempest Thus --mpi_nbr=mpi_nbr can be used to create host-independent ncremap commands to facilitate benchmarking the scaling of weight-generators across hosts that work with the default value of mpi_pfx. The --mpi_pfx option will prevail and --mpi_nbr will be ignored if both are used in the same ncremap invocation. Note that mpi_pfx is only used internally by ncremap to exploit the MPI capabilities of select weight-generators. It is not used to control and does not affect the distribution of multiple ncremap commands among a cluster of nodes. <a name="msh_fl"></a>  --msh_fl=msh_fl msh_fl --msh_fl --msh --mesh --mesh_file --msh_fl=msh_fl (--msh_fl, --msh, --mesh, --mesh_file) Specifies a meshfile (aka intersection mesh, aka overlap mesh) that stores the grid formed by the intersection of the source and destination grids. If not specified then ncremap will name any internally generated meshfile with a temporary name and delete the file prior to exiting. NCO and TempestRemap support archiving the meshfile, and ERWG does not. NCO stores the meshfile in SCRIP format, while TempestRemap stores it in Exodus format (with a .g suffix). ncremap adds provenance information to any newly generated mesh-file whose name was specified with --msh_fl=msh_fl. This provenance includes a history attribute that contains the command invoking ncremap, and the map-generating command invoked by ncremap. <a name="mpt_mss"></a>  <a name="mask_apply"></a>  <a name="msk_app"></a>  <a name="fill_empty"></a>  missing value --add_fill_value --add_fll _FillValue --mpt_mss --mask_apply --msk_app --mpt_mss (--mpt_mss, --sgs_zro_mss, --empty_missing) Introduced in NCO version 5.1.9 (released November, 2023), this switch (which takes no argument) causes the regridder to set empty SGS gridcells to the missing value. Note that this switch works only in limited circumstances. First, it only applies to fields for which a valid sub-gridscale (SGS) distribution has been supplied. Second, it only applies to fields which have no missing values. The primary usage of this switch is for sea-ice model datasets. These datasets tend to be archived with a (SGS) fraction that is non-zero only when and where sea ice is present. The datasets also tend to be written with valid data throughout the ocean domain, regardless of whether sea-ice is present. Most sea-ice fields are usually zero in open-ocean areas (where

sgs_frc = 0.0), and non-zero where sea-ice exists. The --mpt_mss switch causes the regridder to set the open-ocean regions to the missing value. # Set open-ocean regions to missing values (not 0.0) in sea-ice output ncremap --mpt_mss -P mpasseaice -m map.nc in.nc out.nc <a name="msk_apl"></a>  <a name="mask_apply"></a>  <a name="msk_app"></a>  <a name="fill_empty"></a>  missing value --add_fill_value --add_fll _FillValue --msk_apl --mask_apply --msk_app --msk_apl (--msk_apl, --mask_apply, --msk_app) Introduced in NCO version 5.0.0 (released June, 2021), this switch (which takes no argument) causes the regridder to apply msk_out (i.e., mask_b) to variables after regridding. Some weight generators (e.g., TempestRemap) ignore masks and thus produce non-zero weights for masked destination cells, and/or from masked source cells. This flag causes regridded files produced with such map-files to adhere to the destination mask rules (though source mask rules may still be violated). This feature is especially useful in placing missing values (aka, _FillValue) in destination cells that should be empty, so that regridded files have _FillValue distributions identical with output from other weight-generators such as ESMF and NCO. ncremap --msk_apl -v FLNS -m map.nc in.nc out.nc ncremap --msk_apl --add_fll -v FLNS -m map.nc in.nc out.nc # Equivalent By itself, --msk_apl would only mask cells based on the mask_b field in the map-file. This is conceptually independent of the actual intersection mesh. However, --msk_apl automatically triggers --add_fll, which also masks fields based on the computed intersection mesh (i.e., --frac_b). This combinations ensures that masked fields regridded with TempestRemap-generated map-files have _FillValues consistent with map-files generated by ESMF and NCO. <a name="msk_dst"></a>  --msk_dst=msk_dst msk_dst --msk_dst --dst_msk --mask_destination --mask_dst --msk_dst=msk_dst (--msk_dst, --dst_msk, --mask_destination, --mask_dst) Specifies a template variable to use for the integer mask of the destination grid when inferring grid files and/or creating map-files (i.e., generating weights). Any variable on the same horizontal grid as a data file can serve as a mask template for that grid. The mask will be one (i.e., gridcells will participate in regridding) where msk_dst has valid, non-zero values in the data file from which NCO infers the destination grid. The mask will be zero (i.e., gridcells will not participate in regridding) where msk_nm has a missing value or is zero. A typical example of this option would be to use Sea-surface Temperature (SST) as a template variable for an ocean mask because SST is often defined only over ocean, and missing values might denote locations to which regridded quantities should never be placed. The special value

msk_dst = none prevents the regridder from inferring and treating any variable (even one named, e.g., mask) in a source file as a mask variable. This guarantees that all points in the inferred destination grid will be unmasked. msk_dst, msk_out, and msk_src are related yet distinct: msk_dst is the mask template variable in the destination file (whose grid will be inferred), msk_out is the name to give the destination mask (usually mask_b in the map-file) in regridded data files, and msk_src is the mask template variable in the source file (whose grid will be inferred). msk_src and msk_dst only affect inferred grid files for the source and destination grids, respectively, whereas msk_out only affects regridded files. <a name="msk_out"></a>  --msk_out=msk_out msk_out --msk_out --out_msk --mask_destination --mask_out --msk_out=msk_out (--msk_out, --out_msk, --mask_destination, --mask_out) Use of this option tells ncremap to include a variable named msk_out in any regridded file. The variable msk_out will contain the integer-valued regridding mask on the destination grid. The mask will be one (i.e., fields may have valid values in this gridcell) or zero (i.e., fields will have missing values in this gridcell). By default, ncremap does not output the destination mask to the regridded file. This option changes that default behavior and causes ncremap to ingest the default destination mask variable contained in the map-file. ERWG generates SCRIP-format map-files that contain the destination mask in the variable named mask_b. SCRIP generates map-files that contain the destination mask in the variable named dst_grid_imask. The msk_out option works with map-files that adhere to either of these conventions. Tempest generates map-files that do not typically contain the destination mask, and so the msk_out option has no effect on files that Tempest regrids. msk_dst, msk_out, and msk_src are related yet distinct: msk_dst is the mask template variable in the destination file (whose grid will be inferred), msk_out is the name to give the destination mask (usually mask_b in the map-file) in regridded data files, and msk_src is the mask template variable in the source file (whose grid will be inferred). msk_src and msk_dst only affect inferred grid files for the source and destination grids, respectively, whereas msk_out only affects regridded files. <a name="msk_src"></a>  --msk_src=msk_src msk_src --msk_src --src_msk --mask_source --mask_src --msk_src=msk_src (--msk_src, --src_msk, --mask_source, --mask_src) Specifies a template variable to use for the integer mask of the source grid when inferring grid files and/or creating map-files (i.e., generating weights). Any variable on the same horizontal grid as a data file can serve as a mask template for that grid. The mask will be one (i.e., gridcells will participate in regridding) where msk_src has valid, non-zero values in the data file from which NCO infers the source grid. The mask will be zero (i.e., gridcells will not participate in regridding) where msk_nm has a missing value or is zero. A typical example of this option would be to use Sea-surface Temperature (SST) as a template variable for an ocean mask because SST is often defined only over ocean, and missing values might denote locations from which regridded quantities should emanate. The special value

msk_src = none prevents the regridder from inferring and treating any variable (even one named, e.g., mask) in a source file as a mask variable. This guarantees that all points in the inferred source grid will be unmasked. msk_dst, msk_out, and msk_src are related yet distinct: msk_dst is the mask template variable in the destination file (whose grid will be inferred), msk_out is the name to give the destination mask (usually mask_b in the map-file) in regridded data files, and msk_src is the mask template variable in the source file (whose grid will be inferred). msk_src and msk_dst only affect inferred grid files for the source and destination grids, respectively, whereas msk_out only affects regridded files. <a name="--mss_val"></a>  <a name="mss_val_ncremap"></a>  --mss_val=mss_val mss_val --mss_val --fll_val --missing_value --fill_value --mss_val=mss_val (--mss_val, --fll_val, --missing_value, --fill_value) Specifies the numeric value that indicates missing data when processing MPAS datasets, i.e., when -P mpas is invoked. The default missing value is -9.99999979021476795361e+33 which is correct for the MPAS ocean and sea-ice models. Currently (January, 2018) the MPAS land-ice model uses -1.0e36 for missing values. Hence this option is usually invoked as --mss_val=-1.0e36 to facilitate processing of MPAS land-ice datasets. <a name="nco_opt"></a>  -n nco_opt nco_opt --nco_opt --nco_options --nco -n nco_opt (--nco_opt, --nco_options, --nco) Specifies a string of options to pass-through unaltered to ncks. nco_opt defaults to -O --no_tmp_fl. <a name="nm_dst"></a>  --nm_dst=nm_dst nm_dst --nm_dst --name_dst --nm_sht_dst --name_short_destination --nm_dst=nm_dst (--nm_dst, --name_dst, --name_short_destination, --nm_sht_dst) Specifies the short name for the destination grid to use in the full name of map-files created in MWF mode. Map-file names include, by convention, shortened versions of both the source and destination grids. ncremap uses the nm_dst argument to encode the destination grid name into the output map-file name of this format: map_nm_src_to_nm_dst_alg_typ.dt_sng.nc. MWF mode requires this argument, there is no default. <a name="nm_src"></a>  --nm_src=nm_src nm_src --nm_src --name_src --nm_sht_src --name_short_source --nm_src=nm_src (--nm_src, --name_src, --name_short_source, --nm_sht_src) Specifies the short name for the source grid to use in the full name of map-files created in MWF mode. Map-file names include, by convention, shortened versions of both the source and destination grids. ncremap uses the nm_dst argument to encode the source grid name into the output map-file name of this format: map_nm_src_to_nm_dst_alg_typ.dt_sng.nc. MWF mode requires this argument, there is no default. --no_cll_msr --no_cll --no_cell_measures --no_area cell_measures attribute --no_cll_msr (--no_cll_msr, --no_cll, --no_cell_measures, --no_area) This switch (which takes no argument) controls whether ncclimo and ncremap add measures variables to the extraction list along with the primary variable and other associated variables. See CF Conventions for a detailed description. --no_frm_trm --no_frm --no_formula_terms formula_terms attribute --no_frm_trm (--no_frm_trm, --no_frm, --no_formula_terms) This switch (which takes no argument) controls whether ncclimo and ncremap add formula variables to the extraction list along with the primary variable and other associated variables. See CF Conventions for a detailed description. slat slon w_stag --no_stg_grd --no_stg --no_stagger --no_staggered_grid --no_stg_grd (--no_stg_grd, --no_stg, --no_stagger, --no_staggered_grid) This switch (which takes no argument) controls whether regridded output will contain the staggered grid coordinates slat, slon, and w_stag (Regridding). Originally, the staggered grid was output for all files regridded from a Cap (aka FV) grid, except when the regridding was performed as part of splitting (reshaping) into timeseries. As of (roughly, I forget) NCO version 4.9.4, released in July, 2020, outputging the staggered grid information is turned-off for all workflows and must be proactively turned-on (with --stg_grd). Thus the --no_stg_grd switch is obsolete and is intened only to preserve backward-compatibility of previous workflows. <a name="out_drc"></a>  -O out_drc out_drc --out_drc --drc_out --dir_out --out_dir --output -O out_drc (--out_drc, --drc_out, --dir_out, --out_dir, --output) Specifies the output directory, i.e., the directory name to contain the output file(s). If out_fl is also specified, then the output filepath is constructed by appending a slash and the filename to the directory: out_drc/out_fl. Specifying out_drc without out_fl causes ncremap to name each output file the same as the corresponding input file. There is no namespace conflict because the input and output files will be in separate directories. <a name="out_fl"></a>  -o out_fl &textndash;out_fl --out_fl --output_file --out_file -o out_fl (--out_fl, --output_file, --out_file) Specifies the output filename, i.e., the name of the file to contain the data from in_fl remapped to the destination grid. If out_fl already exists it will be overwritten. Specifying out_fl when there are multiple input files (i.e., from using -I in_drc or standard input) generates an error (output files will be named the same as input files). Two positional arguments are interpreted as a single input-file and its corresponding output-file. <a name="mpas"></a>  <a name="prc_typ"></a>  <a name="rrg"></a>  <a name="eamxx"></a>  <a name="scream"></a>  -P prc_typ prc_typ --prc_typ --prm_typ --procedure -P prc_typ (--prc_typ, --prm_typ, --procedure) Specifies the permutation mode desired. As of NCO version 4.5.5 (February, 2016), one can tell ncremap to invoke special processing procedures for different types of input data. For instance, to automatically permute the dimensions in the data file prior to regridding for a limited (though growing) number of data-file types that encounter the ncremap limitation concerning dimension ordering. Valid procedure types include airs for NASA AIRS satellite data, eam or cam for DOE EAM and NCAR CAM model data, eamxx for DOE EAMxx (aka, SCREAM) model data, elm or clm for DOE ELM and NCAR CLM model data, cice for CICE ice model data (must be on 2D grids), cism for NCAR CISM land ice model data, mpasa, or mpasatmosphere for MPAS atmosphere model data, mpascice, mpasseaice, or mpassi for MPAS sea-ice model data, mpaso or mpasocean for MPAS ocean model data, mod04 for Level 2 MODIS MOD04 product, mwf for making all weight-files for a pair of grids, sgs for datasets containing sub-gridscale (SGS) data (such as CLM/CTSM/ELM land model data and CICE/MPAS-Seaice sea-ice model data), and nil (for none). The default prc_typ is nil, which means ncremap does not perform any special procedures prior to regridding. The AIRS procedure calls ncpdq to permute dimensions from their order in the input file to this order: StdPressureLev,GeoTrack,GeoXTrack. The ELM, CLM, and CICE procedures set idiosyncratic model values and then invoke the Sub-gridscale (SGS) procedure (see below). The MOD04 procedure unpacks input data. The EAMxx procedures permute input data dimensions into this order prior to horizontal regridding: ilev,lev,dim2,ncol, and cause the vertical interpolation routine to look for surface pressure under the name ps instead of PS. The MPAS procedures permute input data dimensions into this order:

Time,depth,nVertInterfaces,nVertLevels,nVertLevelsP1,nZBGCTracers,nBioLayersP1,nAlgaeIceLayers,nDisIronIceLayers,nIceLayers,maxEdges,MaxEdges2,nCategories,R3,ONE,TWO,FOUR,nEdges,nCells

, and invokes renormalization. An MPAS dataset that contains any other dimensions will fail to regrid until/unless those dimensions are added to the ncremap dimension permutation option. &linebreak; &linebreak; MWF-mode:&linebreak; Make-Weight-Files (MWF) MWF-mode As mentioned above in other options, ncremap includes an MWF-mode (for &textldquo;Make All Weight Files&textrdquo;) that generates and names, with one command and in a self-consistent manner, all combinations of (for instance, E3SM or CESM) global atmosphere<->ocean maps with both ERWG and Tempest. MWF-mode automates the laborious and error-prone process of generating numerous map-files with various switches. Its chief use occurs when developing and testing new global grid-pairs for the E3SM atmosphere and ocean components. Invoke MWF-mode with a number of specialized options to control the naming of the output map-files: ncremap -P mwf -s grd_ocn -g grd_atm --nm_src=ocn_nm \ --nm_dst=atm_nm --dt_sng=date where grd_ocn is the "global" ocean grid, grd_atm, is the global atmosphere grid, nm_src sets the shortened name for the source (ocean) grid as it will appear in the output map-files, nm_dst sets, similarly, the shortend named for the destination (atmosphere) grid, and dt_sng sets the date-stamp in the output map-file name map_${nm_src}_to_${nm_dst}_${alg_typ}.${dt_sng}.nc. Setting nm_src, nm_dst, and dt_sng, is optional though highly recommended. For example, ncremap -P mwf -s ocean.RRS.30-10km_scrip_150722.nc \ -g t62_SCRIP.20150901.nc --nm_src=oRRS30to10 --nm_dst=T62 \ --dt_sng=20180901 produces the 10 ERWG map-files: map_oRRS30to10_to_T62_aave.20180901.nc map_oRRS30to10_to_T62_blin.20180901.nc map_oRRS30to10_to_T62_ndtos.20180901.nc map_oRRS30to10_to_T62_nstod.20180901.nc map_oRRS30to10_to_T62_patc.20180901.nc map_T62_to_oRRS30to10_aave.20180901.nc map_T62_to_oRRS30to10_blin.20180901.nc map_T62_to_oRRS30to10_ndtos.20180901.nc map_T62_to_oRRS30to10_nstod.20180901.nc map_T62_to_oRRS30to10_patc.20180901.nc The ordering of source and destination grids is immaterial for ERWG maps since MWF-mode produces all map combinations. However, as described above in the TempestRemap section, the Tempest overlap-mesh generator must be called with the smaller grid preceding the larger grid. For this reason, always invoke MWF-mode with the smaller grid (i.e., the ocean) as the source, otherwise some Tempest map-file will fail to generate. The six optimized SE<->FV Tempest maps described above in the TempestRemap section will be generated when the destination grid has a .g suffix which ncremap interprets as indicating an Exodus-format SE grid (NB: this assumption is an implementation convenience that can be modified if necessary). For example, ncremap -P mwf -s ocean.RRS.30-10km_scrip_150722.nc -g ne30.g \ --nm_src=oRRS30to10 --nm_dst=ne30np4 --dt_sng=20180901 produces the 6 TempestRemap map-files: map_oRRS30to10_to_ne30np4_monotr.20180901.nc map_oRRS30to10_to_ne30np4_highorder.20180901.nc map_oRRS30to10_to_ne30np4_mono.20180901.nc map_ne30np4_to_oRRS30to10_mono.20180901.nc map_ne30np4_to_oRRS30to10_highorder.20180901.nc map_ne30np4_to_oRRS30to10_intbilin.20180901.nc MWF-mode takes significant time to complete (~20 minutes on my MacBookPro) for the above grids. To accelerate this, consider installing the MPI-enabled instead of the serial version of ERWG. Then use the --wgt_cmd option to tell ncremap the MPI configuration to invoke ERWG with, for example: ncremap -P mwf --wgt_cmd='mpirun -np 12 ESMF_RegridWeightGen' \ -s ocean.RRS.30-10km_scrip_150722.nc -g t62_SCRIP.20150901.nc \ --nm_src=oRRS30to10 --nm_dst=T62 --dt_sng=20180901 Background and distributed node parallelism (as described above in the the Parallelism section) of MWF-mode are possible though not yet implemented. Please let us know if this feature is desired. &linebreak; &linebreak; RRG-mode:&linebreak; Regridding ReGional Data (RRG) RRG-mode rrg_bb_wesn rrg_dat_glb rrg_grd_glb rrg_grd_rgn rrg_rnm_sng bb_wesn dat_glb grd_glb grd_rgn rnm_sng --rrg_bb_wesn=bb_wesn --rrg_dat_glb=dat_glb --rrg_grd_glb=grd_glb --rrg_grd_rgn=grd_rgn --rrg_rnm_sng=rnm_sng EAM and CAM-SE will produce regional output if requested to with the finclNlonlat namelist parameter. Output for a single region can be higher temporal resolution than the host global simulation. This facilitates detailed yet economical regional process studies. Regional output files are in a special format that we call RRG (for &textldquo;regional regridding&textrdquo;). An RRG file may contain any number of rectangular regions. However, ncremap can process only one region per invocation (change the argument to the --rnm_sng option, described below, in each invocation). The coordinates and variables for one region do not interfere with other (possibly overlapping) regions because all variables and dimensions are named with a per-region suffix string, e.g., lat_128e_to_134e_9s_to_16s. ncremap can easily regrid RRG output from an atmospheric FV-dycore because ncremap can infer (as discussed above) the regional grid from any rectangular FV data file. Regridding regional SE data, however, is more complex because SE gridcells are essentially weights without vertices and SE weight-generators are not yet flexible enough to output regional weights. To summarize, regridding RRG data leads to three SE-specific difficulties (#1&textndash;3 below) and two difficulties (#4&textndash;5) shared with FV RRG files: RRG files contain only regional gridcell center locations, not weights Global SE grids have well-defined weights not vertices for each gridpoint Grid generation software (ESMF and TempestRemap) only create global not regional SE grid files Non-standard variable names and dimension names Regional files can contain multiple regions ncremap&textrsquo;s RRG mode resolves these issues to allow trouble-free regridding of SE RRG files. The user must provide two additional input arguments, --dat_glb=dat_glb (or synonynms --rrg_dat_glb, --data_global, or --global_data) and --grd_glb=grd_glb (or synonyms --rrg_grd_glb, --grid_global, or global_grid) that point to a global SE dataset and grid, respectively, of the same resolution as the model that generated the RRG datasets. Hence a typical RRG regridding invocation is: ncremap --dat_glb=dat_glb.nc --grd_glb=grd_glb.nc -g grd_rgn.nc \ dat_rgn.nc dat_rgr.nc Here grd_rgn.nc is a regional destination grid-file, dat_rgn.nc is the RRG file to regrid, and dat_rgr.nc is the regridded output. Typically grd_rgn.nc is a uniform rectangular grid covering the same region as the RRG file. Generate this as described in the last example in the section that describes Manual Grid-file Generation with the -G option. grd_glb.nc is the standard dual-grid grid-file for the SE resolution, e.g., ne30np4_pentagons.091226.nc. ncremap regrids the global data file dat_glb.nc to the global dual-grid in order to produce a intermediate global file annotated with gridcell vertices. Then it hyperslabs the lat/lon coordinates (and vertices) from the regional domain to use with regridding the RRG file. A grd_glb.nc file with only one 2D field suffices (and is fastest) for producing the information needed by the RRG procedure. One can prepare an optimal dat_glb.nc file by subsetting any 2D variable from any full global SE output dataset with, e.g., ncks -v FSNT in.nc dat_glb.nc. ncremap RRG mode supports two additional options to override internal parameters. First, the per-region suffix string may be set with --rnm_sng=rnm_sng (or synonyms --rrg_rnm_sng or --rename_string). RRG mode will, by default, regrid the first region it finds in an RRG file. Explicitly set the desired region with rnm_sng for files with multiple regions, e.g., --rnm_sng=_128e_to_134e_9s_to_16s. Second, the regional bounding-box may be explicitly set with --bb_wesn=lon_wst,lon_est,lat_sth,lat_nrt. The normal parsing of the bounding-box string from the suffix string may fail in (as yet undiscovered) corner cases, and the --bb_wesn option provides a workaround should that occur. The bounding-box string must include the entire RRG region (not a subset thereof), specified in WESN order. The two override options may be used independently or together, as in: ncremap --rnm_sng='_128e_to_134e_9s_to_16s' --bb_wesn='128,134,-16,-9' \ --dat_glb=dat_glb.nc --grd_glb=grd_glb.nc -g grd_rgn.nc \ dat_rgn.nc dat_rgr.nc RRG-mode supports most normal ncremap options, including input and output methods and regridding algorithms. However, RRG-mode is not widely used and, as of 20240529, has not been parallelized like the rest of ncremap. &linebreak; <a name="alm"></a>  <a name="cice"></a>  <a name="clm"></a>  <a name="ctsm"></a>  <a name="elm"></a>  <a name="mpasseaice"></a>  <a name="sgs"></a>  <a name="sgs_frc"></a>  <a name="sgs_msk"></a>  <a name="sub-grid"></a>  <a name="sub-gridscale"></a>  <a name="subgrid"></a>  SGS-mode:&linebreak; Sub-gridscale (SGS) data SGS-mode sgs_frc sgs_msk sgs_nrm --sgs_frc=sgs_frc --sgs_msk=sgs_msk --sgs_nrm=sgs_nrm ncremap has a sub-gridscale (SGS) mode that performs the special pre-processing and weighting necessary to to conserve fields that represent fractional spatial portions of a gridcell, and/or fractional temporal periods of the analysis. Spatial fields output by most geophysical models are intensive, and so by default the regridder attempts to conserve the integral of the area times the field value such that the integral is equal on source and destination grids. However some models (like ELM, CLM, CICE, and MPAS-Seaice) output gridcell values intended to apply to only a fraction sgs_frc (for &textldquo;sub-gridscale fraction&textrdquo;) of the gridcell. The sub-gridscale (SGS) fraction usually changes spatially with the distribution of land and ocean, and spatiotemporally with the distribution of sea ice and possibly vegetation. For concreteness consider a sub-grid field that represents the land fraction. Land fraction is less than one in gridcells that resolve coastlines or islands. ELM and CLM happily output temperature values valid only for a small (i.e.,

sgs_frc << 1) island within the larger gridcell. Model architecture dictates this behavior and savvy researchers expect it. The goal of the NCO weight-application algorithm is to treat SGS fields as seamlessly as possible so that those less familiar with sub-gridscale models can easily regrid them correctly. Fortunately, models like ELM and CLM that run on the same horizontal grid as the overlying atmosphere can use the same mapping-file as the atmosphere, so long as the SGS weight-application procedure is invoked. Not invoking an SGS-aware weight application algorithm is equivalent to assuming

sgs_frc = 1 everywhere. Regridding sub-grid values correctly versus incorrectly (e.g., with and without SGS-mode) alters global-mean answers for land-based quantities by about 1% for horizontal grid resolutions of about one degree. The resulting biases are in intricately shaped regions (coasts, lakes, sea-ice floes) and so are easy to overlook. To invoke SGS mode and correctly regrid sub-gridscale data, specify the names of the fractional area sgs_frc and, if applicable, the mask variable sgs_msk (strictly, this is only necessary if these names differ from their respective defaults landfrac and landmask). Trouble will ensue if sgs_frc is a percentage or an absolute area rather than a fractional area (between zero and one). ncremap must know the normalization factor sgs_nrm by which sgs_frc must be divided (not multiplied) to obtain a true, normalized fraction. Datasets (such as those from CICE) that store sgs_frc in percent should specify the option --sgs_nrm=100 to instruct ncremap to normalize the sub-grid area appropriately before regridding. ncremap will re-derive sgs_msk based on the regridded values of sgs_frc:

sgs_msk = 1 is assigned to destination gridcells with

sgs_frc > 0.0, and all others

sgs_msk = 0. As of NCO version 4.6.8 (released June, 2017), invoking any of the options --sgs_frc, --sgs_msk, or --sgs_nrm, automatically triggers SGS-mode, so that also invoking -P sgs is redundant though legal. As of NCO version 4.9.0 (released December, 2019), the values of the sgs_frc and sgs_msk variables should be explicitly specified. In previous versions they defaulted to landfrac and landmask, respectively, when -P sgs was selected. This behavior still exists but will likely be deprecated in a future version. The area and sgs_frc fields in the regridded file will be in units of sterradians and fraction, respectively. However, ncremap offers custom options to reproduce the idiosyncratic data and metadata format of two particular models, ELM and CICE. When invoked with -P elm (or -P clm), a final step converts the output area from sterradians to square kilometers. When invoked with -P cice, the final step converts the output area from sterradians to square meters, and the output sgs_frc from a fraction to a percent.

# ELM/CLM: output "area" in [sr]
ncremap --sgs_frc=landfrac --sgs_msk=landmask in.nc out.nc
ncremap -P sgs in.nc out.nc
# ELM/CLM pedantic format: output "area" in [km2]
ncremap -P elm in.nc out.nc # Same as -P clm, alm, ctsm

# CICE: output "area" in [sr]
ncremap --sgs_frc=aice --sgs_msk=tmask --sgs_nrm=100 in.nc out.nc
# CICE pedantic format: output "area" in [m2], "aice" in [%]
ncremap -P cice in.nc out.nc

# MPAS-Seaice: both commands are equivalent
ncremap -P mpasseaice in.nc out.nc
ncremap --sgs_frc=timeMonthly_avg_iceAreaCell in.nc out.nc

<a name="sgs_frc_fl"></a>  sgs_frc_fl It is sometimes convenient to store the sgs_frc field in an external file from the field(s) to be regridded. For example, CMIP-style timeseries are often written with only one variable per file. NCO supports this organization by accepting sgs_frc arguments in the form of a filename followed by a slash and then a variable name:

ncremap --sgs_frc=sgs_landfrac_ne30.nc/landfrac -m map.nc in.nc out.nc

This feature is most useful for datasets whose sgs_frc field is time-invariant, as is usually the case for land models. This is because a single sgs_frc location (e.g., r05.nc/landfrac) can be used for all files of the same resolution. Time-varying sgs_frc fields (e.g., for sea-ice models) change with the same frequency as the simulation output. Thus fields associated with time-varying sgs_frc must be regridded &textldquo;timestep-by-timestep&textrdquo;, i.e., with a separate ncremap invocation for each snapshot of sgs_frc. Of course, ncrcat can later concatenate these separate regriddings can be recombined back into into a single, regridded timeseries. Files regridded using explicitly specified SGS options will differ slightly from those regridded using the -P elm or -P cice options. The former will have an area field in sterradians, the generic units used internally by the regridder. The latter produces model-specific area fields in square kilometers (for ELM) or square meters (for CICE), as expected in the raw output from these two models. To convert from angular to areal values, NCO assumes a spherical Earth with radius 6,371,220 m or 6,371,229 m, for ELM and CICE, respectively. The ouput sgs_frc field is expressed as a decimal fraction in all cases except for -P cice which stores the fraction in percent. Thus the generic SGS and model-specific convenience options produce equivalent results, and the latter is intended to be indistinguishable (in terms of metadata and units) to raw model output. This makes it more interoperable with many existing analysis scripts. <a name="par_typ"></a>  <a name="par_typ_ncremap"></a>  -p par_typ par_typ --par_typ --par_md --parallel_type --parallel_mode --parallel -p par_typ (--par_typ, --par_md, --parallel_type, --parallel_mode, --parallel) Specifies the desired file-level parallelism mode, either Background, MPI, or Serial. File-level parallelism accelerates throughput when regridding multiple files in one ncremap invocation, and has no effect when only one file is to be regridded. Note that the ncclimo and ncremap semantics for selecting file-level parallelism are identical, though their defaults differ (Background mode for ncclimo and Serial mode for ncremap). Select the desired mode with the argument to --par_typ=par_typ. Explicitly select Background mode with par_typ values of bck, background, or Background. The values mpi or MPI select MPI mode, and the srl, serial, Serial, nil, or none will select Serial mode (which disables file-level parallelism, though still allows intra-file OpenMP parallelism). The default file-level parallelism for ncremap is Serial mode (i.e., no file-level parallelism), in which ncremap processes one input file at a time. Background and MPI modes implement true file-level parallelism. Typically both these parallel modes scale well with sufficent memory unless and until I/O contention becomes the bottleneck. In Background mode ncremap issues all commands to regrid the input file list as UNIX background processes on the local node. Nodes with mutiple cores and sufficient RAM take advantage of this to simultaneously regrid multiple files. In MPI mode ncremap issues commands to regrid the input file list in round-robin fashion to all available compute nodes. Prior to NCO version 4.9.0 (released December, 2019), Background and MPI parallelism modes both regridded all the input files at one time and there was no way to limit the number of files being simultaneously regridded. Subsequent versions allow finer grained parallelism by introducing the ability to limit the number of discrete workflow elements or &textldquo;jobs&textrdquo; (i.e., file regriddings) to perform simultaneously within an ncremap invocation or &textldquo;workflow&textrdquo;. As of NCO version 4.9.0 (released December, 2019), the --job_nbr=job_nbr option specifies the maximum number of files to regrid simultaneously on all nodes being harnessed by the workflow. Thus job_nbr is an additional parameter to fine-tune file level parallelism (it has no effect in Serial mode). Please see the ncremap job_nbr documentation for more details. <a name="pdq_opt"></a>  <a name="prm_opt"></a>  <a name="prm"></a>  <a name="pdq"></a>  <a name="permute"></a>  pdq_opt --pdq_opt --pdq --prm_opt --prm --permute --pdq_opt pdq_opt (--pdq, --prm_opt, --prm, --permute) Specifies the dimension permutation option used by ncpdq prior to regridding. Synonyms include --pdq, --prm, --prm_opt, and --permute. Files to be regridded must have their horizontal spatial dimension(s) in the last (most-rapidly-varying) position. Most data files store variables with dimensions arranged in this order, and ncremap internally sets the permutation option for datasets known (via the --prc_typ option) to require permutation. Use --permute=pdq_opt to override the internally preset defaults. This is useful when regridding files that contain new dimensions that ncremap has not encountered before. For example, if a development version of an MPAS model inserts a new dimension new_dim after the horizontal spatial dimension nCells in some variables, that would prevent the regridder from working because the horizontal dimension(s) must be the last dimension(s). The workaround is to instruct ncremap what the permutation option to ncpdq should be in order to place the horizontal spatial dimension(s) at the end of all variables:

ncremap --permute=Time,new_dim,nCells  --map=map.nc in.nc out.nc
ncremap --permute=time,new_dim,lat,lon --map=map.nc in.nc out.nc

The API for this option changed in NCO version 5.0.4 (released December, 2021). Prior to this, the option argument needed to include the entire option string to be passed to ncpdq including the -a, e.g., --permute='-a time,new_dim,lat,lon'. Now ncremap supplies the implicit -a internally so the user does not need to know the ncpdq syntax. <a name="no_permute"></a>  permute ncpdq SLURM CWL --no_permute --no_permute (--no_permute, --no_prm, --no_pdq, --no_ncpdq) Introduced in NCO version 5.0.0 (released June, 2021), this switch (which takes no argument) causes the regridder to skip the default permutation of dimensions before regridding (notably MPAS) datasets known to store data with non-horizontal most-rapidly varying dimensions. ncremap normally ensures that input fields are stored in the shape expected by regridder weights (horizontal dimensions last) by permuting the dimensions with ncpdq. However, permutation consumes time and generates an extra intermediate file. Avoid this time penalty by using the --no_permute flag if the input fields are known to already have trailing horizontal dimensions. <a name="preserve"></a>  --preserve=prs_stt prs_stt --preserve --prs_stt --preserve_statistic --preserve=prs_stt (--preserve, --prs_stt, --preserve_statistic) This is a simple, intuitive option to specify how weight application should treat destination gridcells that are not completely overlapped by source gridcells with valid values. Destination gridcells that are completely overlapped by valid source values are unaffected. The two statistics that can be preserved for incompletely overlapped gridcells are the local mean and/or the global integral of the source values. Hence the valid values for this option are integral (and, as of NCO version 5.0.2, released in September, 2021, its synonyms global and global_integral) and mean (or its synonyms local, local_mean, gridcell, gridcell_mean, natural_values). NCO version 5.1.5, released in March, 2023, fixed a longstanding problem with the implmentation of this option, which had essentially been broken since its inception. The option finally works as documented. Specifying --preserve=integral sets the destination gridcell equal to the sum of the valid source values times their source weights. This sum is not renormalized by the (valid) fractional area covered. This is exactly equivalent to setting --rnr=off, i.e., no renormalization (see Regridding). If the weights were generated by a conservative algorithm then the output will be conservative, and will conserve the global integral of the input field in all cases. This is often desired for regridding quantities that should be conserved, e.g., fluxes, and is the default weight application method in ncremap (except in MPAS-mode). Specifying --preserve=mean sets the destination gridcell equal to the mean of the valid source values times their source weights. This is exactly equivalent to setting --rnr=0.0, i.e., renormalizing the integral value by the (valid) fractional area covered (see Regridding). This is often desired for regridding state variables, e.g., temperature, though it is not the default behavior and must be explicitly requested (except in MPAS-mode). These two types of preserved statistics, integral and mean, produce identical output in all gridcells where there are no missing data, i.e., where valid data completely tile the gridcell. By extension, these two statistics produce identical global means if valid data completely tile the sphere. <a name="rgr_opt"></a>  -R rgr_opt rgr_opt --rgr_opt --regrid_options -R rgr_opt (--rgr_opt, --regrid_options) ncremap passes rgr_opt directly through to the regridder. This is useful to customize output grids and metadata. One use is to rename output variables and dimensions from the defaults provided by or derived from input data. The default value is --rgr lat_nm_out=lat --rgr lon_nm_out=lon, i.e., by default ncremap always names latitude and longitude &textldquo;lat&textrdquo; and &textldquo;lon&textrdquo;, respectively, regardless of their input names. Users might use this option to set different canonical axes names, e.g., --rgr lat_nm_out=y --rgr lon_nm_out=x. <a name="rnr_thr"></a>  -r rnr_thr rnr_thr --rnr_thr --thr_rnr --rnr --renormalize --renormalization_threshold -r rnr_thr (--rnr_thr, --thr_rnr, --rnr, --renormalize, --renormalization_threshold) Use this option to request renormalized (see Regridding) weight-application and to specify the weight threshold, if any. For example, -r 0.9 tells the regridder to renormalize with a weight threshold of 90%, so that all destination gridcells with at least 90% of their area contributed by valid source gridcells will be contain valid (not missing) values that are the area-weighted mean of the valid source values. If the weights are conservative, then the output gridcells on the destination grid will preserve the mean of the input gridcells. Specifying -r 0.9 and --rnr_thr=0.9 are equivalent. Renormalization can be explicitly turned-off by setting rnr_thr to either of the values off, or none. The --preserve=prs_stt option performs the same task as this option except it does not allow setting an arbitrary threshold fraction. <a name="rgn_dst"></a>  <a name="rgn_src"></a>  rgn_dst --rgn_dst --dst_rgn --regional_destination --rgn_dst (--rgn_dst, --dst_rgn, --regional_destination) rgn_src --rgn_src --src_rgn --regional_source --rgn_src (--rgn_src, --src_rgn, --regional_source) Use these flags which take no argument to indicate that a user-supplied (i.e., with -s grd_src or -g grd_dst) grid is regional. The ERWG weight-generator (at least all versions before 8.0) needs to be told whether the source, destination, or both grids are regional or global in order to optimize weight production. ncremap supplies this information to the regridder for grids it automatically infers from data files. However, the regridder needs to be explicitly told if user-supplied (i.e., with either -s grd_src or -g grd_dst) grids are regional because the regridder does not examine supplied grids before calling ERWG which assumes, unless told otherwise, that grids are global in extent. The sole effect of these flags is to add the arguments --src_regional and/or --dst_regional to ERWG calls. Supplying regional grids without invoking these flags may dramatically increase the map-file size and time to compute. According to E3SM MPAS documentation, ERWG &textldquo;considers a mesh to be regional when the mesh is not a full sphere (including if it is planar and does not cover the full sphere). In other words, all MPAS-O and MPAS-LI grids are regional&textrdquo; to ERWG. <a name="grd_src"></a>  -s grd_src grd_src --grd_src --grid_source --source_grid --src_grd -s grd_src (--grd_src, --grid_source, --source_grid, --src_grd) Specifies the source gridfile. NCO will use ERWG or TempestRemap weight-generator to combine this with a destination gridfile (either inferred from dst_fl, or generated by supplying a -G grd_sng option) to generate remapping weights. grd_src is not modified, and may have read-only permissions. One appropriate circumstance to specify grd_src is when the input-file(s) do not contain sufficient information for NCO to infer an accurate or complete source grid. (Unfortunately many dataset producers do not record information like cell edges/vertices in their datasets. This is problematic for non-rectangular grids.) NCO assumes that grd_src, when supplied, applies to every input-file. Thus NCO will call the weight generator only once, and will use that map_fl to regrid every input-file. Although ncremap usually uses the contents of a pre-existing grd_src to create mapping weights, there are some situations where ncremap creates the file specified by grd_src (i.e., treats it as a location for storing output). When a source grid is inferred or created from other user-specified input, ncremap will store it in the location specified by grd_src. This allows users to, for example, name the grid on which an input dataset is stored when that grid is not known a priori. This functionality is only available for SCRIP-format grids. <a name="skl_fl"></a>  --skl_fl=skl_fl skl_fl --skl_fl --skl --skeleton --skeleton_file skeleton --skl_fl=skl_fl (--skl_fl, --skl, --skl_fl) Normally ncremap only creates a SCRIP-format gridfile named grd_dst when it receives the grd_sng option. The --skl option instructs ncremap to also produce a &textldquo;skeleton&textrdquo; file based on the grd_sng argument. A skeleton file is a bare-bones datafile on the specified grid. It contains the complete latitude/longitude grid and an area field. Skeleton files are useful for validating that the grid-creation instructions in grd_sng perform as expected. <a name="stdin"></a>  SLURM PBS --stdin --inp_std --std_flg --redirect --standard_input --stdin (--stdin, --inp_std, --std_flg, --redirect, --standard_input) This switch (which takes no argument) explicitly indicates that input file lists are provided via stdin, i.e., standard input. In interactive environments, ncremap can automatically (i.e., without any switch) detect whether input is provided via stdin. This switch is never required for jobs run in an interactive shell. However, non-interactive batch jobs (such as those submitted to the SLURM and PBS schedulers) make it impossible to unambiguously determine whether input has been provided via stdin. Specifically, the --stdin switch must be used with ncremap in non-interactive batch jobs on PBS when the input files are piped to stdin, and on SLURM when the input files are redirected from a file to stdin Until version 5.0.4 (December, 2021) the --stdin was also supported by ncclimo, and used for the same reasons as it still is for ncclimo. At that time, the --split switch superceded the --stdin switch in ncclimo, where it is now deprecated.. Using --stdin in any other context (e.g., interactive shells) is optional. In some other non-interactive environments (e.g., crontab, nohup, Azure CI, CWL), ncremap may mistakenly expect input to be provided on stdin simply because the environment is using stdin for other purposes. In such cases users may disable checking stdin by explicitly invoking the --no_stdin flag (described next), which works for both ncclimo and ncremap. <a name="no_stdin"></a>  Chrysalis Common Workflow Language Azure CI SLURM CWL --no_stdin --no_inp_std --no_standard_input --no_redirect --no_stdin (--no_stdin, --no_inp_std, --no_redirect, --no_standard_input) First introduced in NCO version 4.8.0 (released May, 2019), this switch (which takes no argument) disables checking standard input (aka stdin) for input files. This is useful because ncclimo and ncremap may mistakenly expect input to be provided on stdin in environments that use stdin for other purposes. Some non-interactive environments (e.g., crontab, nohup, Azure CI, CWL), may use standard input for their own purposes, and thus confuse NCO into thinking that you provided the input files names via the stdin mechanism. In such cases users may disable the automatic checks for standard input by explicitly invoking the --no_stdin flag. This switch is usually not required for jobs in an interactive shell. Interactive SLURM shells can also commandeer stdin, as is the case on the DOE machine named Chrysalis. This behavior appears to vary depending on the SLURM implementation. srun -N 1 -n 1 ncremap --no_stdin -m map.nc in.nc out.nc <a name="tmp_drc"></a>  -T tmp_drc tmp_drc --tmp_drc --drc_tmp --tmp_dir --dir_tmp --tmp -T tmp_drc (--tmp_drc, --drc_tmp, --tmp_dir, --dir_tmp, --tmp_drc) Specifies the directory in which to place intermediate output files. Depending on how it is invoked, ncremap may generate a few or many intermediate files (grids and maps) that it will, by default, remove upon successful completion. These files can be large, so the option to set tmp_drc is offered to ensure their location is convenient to the system. If the user does not specify tmp_drc, then ncremap uses the value of $TMPDIR, if any, or else /tmp if it exists, or else it uses the current working director ($PWD). <a name="thr_nbr"></a>  <a name="thr_nbr_ncremap"></a>  -t thr_nbr thr_nbr --thr --thr_nbr --thread_number --threads -t thr_nbr (--thr_nbr, --thr, --thread_number, --threads) Specifies the number of threads used per regridding process (OpenMP Threading). ncremap can use OpenMP shared-memory techniques to simultaneosly regrid multiple variables within a single file. This shared memory parallelism is quite efficient because it uses a single copy of the regridding weights in physical memory to regrid multiple variable simultaneously. Even so, simultaneously regridding multiple variables, especially at high resolution, may be memory-limited, meaning that the insufficient RAM can often limit the number of variables that the system can simultaneously regrid. By convention all variables to be regridded share the same regridding weights stored in a map-file, so that only one copy of the weights needs to be in memory, just as in Serial mode. However, the per-thread (i.e., per-variable) OpenMP memory demands are considerable, with the memory required to regrid variables amounting to no less than about 5&textndash;7 times (for type NC_FLOAT) and 2.5&textndash;3.5 times (for type NC_DOUBLE) the size of the uncompressed variable, respectively. Memory requirements are so high because the regridder performs all arithmetic in double precision to retain the highest accuracy, and must allocate separate buffers to hold the input and output (regridded) variable, a tally array to count the number of missing values and an array to sum the of the weights contributing to each output gridcell (the last two arrays are only necessary for variables with a _FillValue attribute). The input, output, and weight-sum arrays are always double precision, and the tally array is composed of four-byte integers. Given the high memory demands, one strategy to optimize thr_nbr for repetitious workflows is to increase it to keep doubling it (1, 2, 4, &dots;) until throughput stops improving. With sufficient RAM, the NCO regridder scales well up to 8&textndash;16 threads. <a name="upk_inp"></a>  -U --unpack --upk --upk_inp -U (--unpack, --upk, --upk_inp) This switch (which takes no argument) causes ncremap to unpack (see Packed data) input data before regridding it. This switch causes unpacking at the regridding stage that occurs after map generation. Hence this switch does not benefit grid inferral. Grid inferral examines only the coordinate variables in a dataset. If coordinates are packed (a terrible practice) in a file from which a grid will be inferred, users should first manually unpack the file (this option will not help). Fortunately, coordinate variables are usually not packed, even in files with other packed data. Many institutions (like NASA) pack datasets to conserve space before distributing them. This option allows one to regrid input data without having to manually unpack it first. Beware that NASA uses at least three different and incompatible versions of packing in its L2 datasets. The unpacking algorithm employed by this option is the default netCDF algorithm, which is appropriate for MOD04 and is inappropriate for MOD08 and MOD13. See Packed data for more details and workarounds. <a name="ugrid_fl"></a>  --ugrid_fl=ugrid_fl ugrid_fl --ugrid_fl --ugrid --ugrid_file infer --ugrid_fl=ugrid_fl (--ugrid_fl, --ugrid, --ugrid_fl) Normally ncremap only infers a gridfile named grd_dst in SCRIP-format. The ugrid_fl option instructs ncremap to infer both a SCRIP-format gridfile named grd_dst and a UGRID-format gridfile named ugrid_fl. This is an experimental feature and the UGRID file is only expected to be valid for global rectangular grids. <a name="unq_sfx"></a>  -u unq_sfx unq_sfx noclean --unq_sfx --unique_suffix --suffix -u unq_sfx (--unq_sfx, --unique_suffix, --suffix) Specifies the suffix used to label intermediate (internal) files generated by the regridding workflow. Unique names are required to avoid interference among parallel invocations of ncremap. The default unq_sfx generated internally by ncremap is .pidPID where PID is the process ID. Applications can provide their own more or less informative suffixes using the --unq_sfx=unq_sfx option. The suffix should be unique so that no two simultaneously executing instances of ncremap can generate the same file. For example, when the ncclimo climatology script issues a dozen ncremap commands to regrid all twelve months simultaneously, it uses --unq_sfx=mth_idx to encode the climatological month index in the unique suffix. Note that the controlling process PID is insufficient to disambiguate all the similar temporary files when the input file list is divided into multiple concurrent jobs (controlled by the --job_nbr=job_nbr option). Those files have their user-provided or internally generated unq_sfx extended by fl_idx, their position in the input file list, so that their full suffix is .pidPID.fl_idx. Finally, a special value of unq_sfx is available to aid developers: if unq_sfx is noclean then ncremap retains (not removes) all intermediate files after completion. <a name="var_lst"></a>  -v var_lst var_lst --var_lst --var --vars --variables --variable_list -v var_lst (--var_lst, --var, --vars, --variables, --variable_list) The -v option causes ncremap to regrid only the variables in var_lst. It behaves like subsetting (Subsetting Files) in the rest of NCO. <a name="rgr_var"></a>  -V rgr_var rgr_var --rgr_var --var_rgr --var_cf --cf_var --cf_variable -V var_rgr (--var_rgr, --rgr_var, --var_cf, --cf_var, cf_variable) The -V option tells ncremap to use the same grid as var_rgr in the input file. If var_rgr adheres to the CF coordinates convention described http://cfconventions.org/cf-conventions/cf-conventions.html#coordinate-systemhere, then ncclimo will infer the grid as represented by those coordinate variables. This option simplifies inferring grids when the grid coordinate names are unknown, since ncclimo will follow the CF convention to learn the identity of the grid coordinates. Until NCO version 4.6.0 (May, 2016), ncremap would not follow CF conventions to identify coordinate variables. Instead, ncremap used an internal database of &textldquo;usual suspects&textrdquo; to identify latitude and longitude coordinate variables. Now, if var_rgr is CF-compliant, then ncremap will automatically identify the horizontal spatial dimensions. If var_rgr is supplied but is not CF-compliant, then ncremap will still attempt to identify horizontal spatial dimensions using its internal database of &textldquo;likely names&textrdquo;. If both these automated methods fail, manually supply ncremap with the names of the horizontal spatial dimensions

# Method used to obtain horizontal spatial coordinates:
ncremap -V var_rgr -d dst.nc -O ~/rgr in.nc # CF coordinates convention
ncremap -d dst.nc -O ~/rgr in.nc # Internal database
ncremap -R "--rgr lat_nm=xq --rgr lon_nm=zj" -d dst.nc -O ~/rgr in.nc # Manual

<a name="vrb_lvl"></a>  --vrb=vrb_lvl vrb_lvl --vrb_lvl --vrb --verbosity --verbosity_level --vrb=vrb_lvl (--vrb_lvl, --vrb, --verbosity, --verbosity_level) Specifies a verbosity level similar to the rest of NCO. If

vrb_lvl = 0, ncremap prints nothing except potentially serious warnings. If

vrb_lvl = 1, ncremap prints the basic filenames involved in the remapping. If

vrb_lvl = 2, ncremap prints helpful comments about the code path taken. If

vrb_lvl > 2, ncremap prints even more detailed information. Note that vrb_lvl is distinct from dbg_lvl which is passed to the regridder (ncks) for additional diagnostics. <a name="vrt_crd"></a>  <a name="plev_nm"></a>  <a name="vrt_nm"></a>  <a name="vertical_coordinate_name"></a>  --vrt_crd --plev_nm --vertical_coordinate_name --vrt_nm=vrt_fl vrt_crd Vertical coordinate MPAS --vrt_nm=vrt_nm (--vrt_nm, --plev_nm, --vrt_crd, --vertical_coordinate_name) The --vrt_nm=vrt_nm option instructs ncremap to use vrt_nm, instead of the default plev, as the vertical coordinate name for pure pressure grids. This option first appeared in NCO version 4.8.0, released in May, 2019. Note that the vertical coordinate may be specified in millibars in some important reanalyses like ERA5, whereas many models express the vertical coordinate in Pascals. The user must ensure that the vertical coordinate in the template vertical grid-file is in the same units (e.g., mb or Pa) as the vertical coordinate in the file to be vertically interpolated. <a name="vrt"></a>  <a name="vrt_fl"></a>  <a name="vrt_grd_out"></a>  <a name="vrt_out"></a>  vrt --vrt --vrt_fl_out --vrt_grd_out --vrt_out=vrt_fl vrt_fl Vertical coordinate MPAS --vrt_out=vrt_fl (--vrt_out, --vrt_fl, --vrt, --vrt_grd_out) The --vrt_out=vrt_fl option instructs ncremap to vertically interpolate the input file to the vertical coordinate grid contained in the file vrt_fl. This option first appeared in NCO version 4.8.0, released in May, 2019. The vertical gridfile vrt_fl must specify a vertical gridtype that ncremap understands, currently either pure-pressure or hybrid-coordinate pressure. We plan to add pure-sigma coordinates in the future. Besides the vertical grid-type, the main assumptions, constraints, and priorities for future development of vertical regridding are: When originally released, the vertical interpolation feature required that the input datasets have netCDF (and thus C-based) dimension-ordering all other dimensions, a single vertical dimension, then one or two horizontal dimensions, if any so that the horizontal dimension(s) vary most rapidly. NCO version 5.1.4, released in January, 2023, eliminated this constraint. Now the regridder automatically determines whether the vertical or the horizontal dimensions vary most rapidly, and adjusts its algorithms accordingly. The vertical interpolation algorithm defaults to linear in log(pressure) for pure pressure and for hybrid sigma-pressure coordinates. This assumption is more natural for gases (like the atmosphere) than for condensed media (like oceans or Earth&textrsquo;s interior). To instead interpolate linearly in the vertical coordinate, use the ntp_mth=lin options (as of NCO 4.9.0). NCO version 5.1.4, released in January, 2023, supports interpolation of data structured with a geometric depth/height grid (such as ocean data usually uses). Data on a depth/height grid defaults to linear interpolation. Vertical interpolation and horizontal regridding may be invoked simultaneously (as of NCO 4.9.0) by the user simply by supplying both a (horizontal) map-file and a vertical grid-file to ncremap. When this occurs, ncremap internally performs the vertical interpolation prior to the horizontal regridding. Exploiting this feature can have some unintended consequences. For example, horizontal regridding requires that the horizontal spatial dimension vary most rapidly, whereas vertical interpolation makes no such assumption. When the regridder needs to permute the dimension ordering of the input dataset in order to perform horizontal regridding, this step actually precedes the vertical regridding. This order of operations is problematic and we are working to address the issues in future updates. The default extrapolation method uses nearest neighbor except for temperature and geopotential (those extrapolation methods are described below). These defaults are well-suited to extrapolate valid initial conditions from data on older vertical grids. Note that the default approximation used for geopotential is inaccurate in cold regions. As of July 2019 and NCO version 4.8.1, one may instead set points outside the input domain to missing-values with the --vrt_xtr=mss_val option. More extrapolation options, exposed to user-access as of November 2022 in NCO version 5.1.1, are: linear extrapolation (--vrt_xtr=linear), setting to 0.0 (--vrt_xtr=zero). Linear extrapolation does exactly what you think: Values outside the input domain are linearly extrapolated from the nearest two values inside the input domain. Zero extrapolation sets values outside the extrapoloation domain to 0.0. Supporting other methods, or improving the existing special-case approximations for temperature or geopotential, will remain low priority until we are lobbied with compelling use-cases for other algorithms. Missing values may not always be treated correctly Eliminating this constraint was not originally a priority because atmospheric datasets often contain no missing data. However, now that vertical regridding can be applied to ocean data with a depth coordinate, we need to verify that using missing values to indicate bathymetry works as expected. Time-varying vertical grids are only allowed for hybrid grids (not pure pressure grids), and these must store the time dimension as a record dimension. This constraint applies to the vertical grid only, not to the other fields in the dataset. Hence this does not preclude interpolating timeseries to/from time-invariant vertical grids. For example, time-varying hybrid grid data such as temperature may be interpolated to timeseries on a time-invariant pressure grid. Eliminating this constraint will not be a priority unless/until an important use-case is identified. Variable names for input and output vertical grids must match E3SM/CESM, ECMWF, MPAS, and NCEP implementations. These names include hyai, hyam, hybi, hybm, ilev, lev, P0, and PS (for E3SM/CESM hybrid grids), lev, lev_2, and lnsp (for ECMWF hybrid grids only), depth, timeMonthly_avg_zMid (for MPAS depth grids), and plev and level (for pure-pressure grids with NCEP and ERA5 conventions, respectively). The infrastructure to provide alternate names for any of these input/output variables names is straightforward, and is heavily used for horizontal spatial regridding. Allowing this functionality will not be a priority until we are presented with a compelling use-case. <a name="vrt_prs_mk"></a>  <a name="vrt_prs"></a>  <a name="vrt_ncep"></a>  The simplest vertical grid-type, a pure-pressure grid, contains the horizontally uniform vertical pressure levels in a one-dimensional coordinate array named (by default) plev. The plev dimension may have any number of levels and the values must monotonically increase or decrease. A 17-level NCEP pressure grid, for example, is easy to create: # Construct monotonically decreasing 17-level NCEP pressure grid ncap2 -O -v -s 'defdim("plev",17);plev[$plev]={100000,92500,85000, \ 70000,60000,50000,40000,30000,25000,20000,15000,10000,7000,5000, \ 3000,2000,1000};' vrt_prs_ncep_L17.nc ncremap will search the supplied vertical grid file for the coordinate named plev, or, for any coordinate name specified by with the plev_nm_in option to the regridder, e.g., --plev_nm_in=z. <a name="vrt_hyb_mk"></a>  <a name="vrt_hyb"></a>  Hybrid-coordinate grids are a hybrid between a sigma-coordinate grid (where each pressure level is a fixed fraction of a spatiotemporally varying surface pressure) and a pure-pressure grid that is spatially invariant (as described above). The so-called hybrid A and B coefficients specify the fractional weight of the pure-pressure and sigma-grids, respectively, at each level. P0 PS hyai hyam hybi hybm formula_terms attribute The hybrid gridfile must specify A and B coefficients for both layer midpoints and interfaces with these standard (as employed by CESM and E3SM) names and dimensions: hyai(ilev), hybi(ilev), hyam(lev), and hybm(lev). The reference pressure and surface pressure must be named P0 and PS, respectively. The pressures at all midpoints and interfaces are then defined as prs_mdp[time,lev, lat,lon]=hyam*P0+hybm*PS # Midlayer prs_ntf[time,ilev,lat,lon]=hyai*P0+hybi*PS # Interface The scalar reference pressure P0 is typically 100000 Pa (or 1000 hPa) while the surface pressure PS is a (possibly time-varying) array with one or two spatial dimensions, and its values are in the same dimensional units (e.g., Pa or hPa) as P0. It is often useful to create a vertical grid file from existing model or reanalysis output. We call vertical grid files &textldquo;skinny&textrdquo; if they contain only the vertical information. Skinny grid-files are easy to create with ncks, e.g., ncks -C -v hyai,hyam,hybi,hybm,P0 in_L128.nc vrt_hyb_L128.nc Such files are extremely small and portable, and represent all the hybrid files created by the model because the vertical grid parameters are time-invariant. A &textldquo;fat&textrdquo; vertical grid file would also include the time-varying grid information, i.e., the surface pressure field. Fat grid-files are also easy to create with ncks, e.g., ncks -C -v hyai,hyam,hybi,hybm,P0,PS in_L128.nc vrt_hyb_L128.nc The full (layer-midpoint) and half (layer-interface) pressure fields prs_mdp and prs_ntf, respectively, can be reconstructed from any fat grid-file with an ncap2 command: ncap2 -s 'prs_mdp[time,lat,lon,lev]=P0*hyam+PS*hybm' \ -s 'prs_ntf[time,lat,lon,ilev]=P0*hyai+PS*hybi' in.nc out.nc Hybrid-coordinate grids define a pure-sigma or pure-pressure grid when either their A or B coefficients are zero, respectively. For example, the following creates the hybrid-coordinate representation of a pure-pressure grid with midpoints every 100 hPa from 100 hPa to 1000 hPa: ncap2 -O -v -s 'defdim("ilev",11);defdim("lev",10);P0=100000.0; \ hyai=array(0.05,0.1,$ilev);hyam=array(0.1,0.1,$lev); \ hybi=0.0*hyai;hybm=0.0*hyam;' vrt_hyb_L10.nc NCO currently has no other means of representing pure sigma vertical grids (as opposed to pure pressure grids). <a name="vrt_hyb_ifs"></a>  As of July 2019 and NCO version 4.8.1, NCO supports regridding ECMWF datasets in IFS hybrid vertical coordinate format to CESM/E3SM-format hybrid vertical grids. Unfortunately there was a regression and this functionality was broken between about 2023&textndash;2024 (the workaround is to use older NCO versions like 4.9.0). NCO once agains supports this functionality as of October 2024 (NCO version 5.2.9), though now the user must employ the --ps_nm=lnsp option shown below. The native IFS hybrid datasets that we have seen store pressure coordinates in terms of a slightly different formula that employs the log of surface pressure (lnsp) instead of surface pressure PS, that redefines hyai and hyam to be pure-pressure offsets (rather than coefficients), and that omits P0: prs_mdp[time,lev, lat,lon]=hyam+hybm*exp(lnsp) # Midlayer prs_ntf[time,lev_2,lat,lon]=hyai+hybi*exp(lnsp) # Interface Note that ECMWF also alters the names of the vertical half-layer coordinate and employs distinct dimensions (nhym and nhyi) for the hybrid variables hyai(nhyi), hybi(nhyi), hyam(nhym), and hybm(nhym). ECMWF uses the vertical coordinates lev and lev_2 for full-layer (i.e., midlayer) and half-layer (i.e., interface) for all other variables. To invoke ncremap on a hybrid coordinate dataset in IFS format, one must specify that the surface pressure variable is named lnsp. No modifications to the IFS dataset are necessary. The vertical grid file should be in CESM/E3SM format. zender@spectral:~$ ncks -m -C -v lnsp,hyai,hyam,hybi,hybm,lev,lev_2 ifs.nc netcdf ecmwf_ifs_f640L137 { dimensions: lev = 137 ; lev_2 = 1 ; nhyi = 138 ; nhym = 137 ; variables: double hyai(nhyi) ; hyai:long_name = "hybrid A coefficient at layer interfaces" ; hyai:units = "Pa" ; double hyam(nhym) ; hyam:long_name = "hybrid A coefficient at layer midpoints" ; hyam:units = "Pa" ; double hybi(nhyi) ; hybi:long_name = "hybrid B coefficient at layer interfaces" ; hybi:units = "1" ; double hybm(nhym) ; hybm:long_name = "hybrid B coefficient at layer midpoints" ; hybm:units = "1" ; double lev(lev) ; lev:standard_name = "hybrid_sigma_pressure" ; lev:long_name = "hybrid level at layer midpoints" ; lev:formula = "hyam hybm (mlev=hyam+hybm*aps)" ; lev:formula_terms = "ap: hyam b: hybm ps: aps" ; lev:units = "level" ; lev:positive = "down" ; double lev_2(lev_2) ; lev_2:standard_name = "hybrid_sigma_pressure" ; lev_2:long_name = "hybrid level at layer midpoints" ; lev_2:formula = "hyam hybm (mlev=hyam+hybm*aps)" ; lev_2:formula_terms = "ap: hyam b: hybm ps: aps" ; lev_2:units = "level" ; lev_2:positive = "down" ; float lnsp(time,lev_2,lat,lon) ; lnsp:long_name = "Logarithm of surface pressure" ; lnsp:param = "25.3.0" ; } // group / zender@spectral:~$ ncks -m vrt_grd.nc netcdf vrt_hyb_L72 { dimensions: ilev = 73 ; lev = 72 ; variables: double P0 ; P0:long_name = "reference pressure" ; P0:units = "Pa" ; double hyai(ilev) ; hyai:long_name = "hybrid A coefficient at layer interfaces" ; double hyam(lev) ; hyam:long_name = "hybrid A coefficient at layer midpoints" ; double hybi(ilev) ; hybi:long_name = "hybrid B coefficient at layer interfaces" ; double hybm(lev) ; hybm:long_name = "hybrid B coefficient at layer midpoints" ; } // group / zender@spectral:~$ ncremap --ps_nm=lnsp --vrt_grd=vrt_grd.nc ifs.nc out.nc zender@spectral:~$ The IFS file can be horizontally regridded in the same invocation. ncremap automagically handles all of the other details. Currently ncremap can only interpolate data from (not to) an IFS-format hybrid vertical grid data file. To interpolate to an IFS-format hybrid vertical grid, one must place the destination vertical grid into a CESM/E3SM-format hybrid vertical grid file (see above) that includes a PS surface pressure field (not lnsp log-surface pressure) for the destination grid. The lev and ilev coordinates of a hybrid grid are defined by the hybrid coefficients and reference pressure, and are by convention stored in millibars (not Pascals) as follows: ilev[ilev]=P0*(hyai+hybi)/100.0; lev[lev]=P0*(hyam+hybm)/100.0; A vertical hybrid grid file vrt_fl must contain at least hyai, hybi, hyam, hybm(lev) and P0; PS, lev, and ilev are optional. (Exceptions for ECMWF grids are noted above). All hybrid-coordinate data files must contain PS. Interpolating a pure-pressure coordinate data file to hybrid coordinates requires, therefore, that the hybrid-coordinate vrt_fl must contain PS and/or the input data file must contain PS. If both contain PS then the PS from the vrt_fl takes precedence and will be used to construct the hybrid grid and then copied without to the output file. In all cases lev and ilev are optional in input hybrid-coordinate data files and vertical grid-files. They are diagnosed from the other parameters using the above definitions. The minimal requirements&textmdash;a plev coordinate for a pure-pressure grid or five parameters for a hybrid grid&textmdash;allow vertical gridfiles to be much smaller than horizontal gridfiles such as SCRIP files. Moreover, data files from ESMs or analyses (NCEP, MERRA2, ERA5) are also valid gridfiles. The flexibility in gridfile structure makes it easy to intercompare data from the same or different sources. ncremap supports vertical interpolation between all combinations of pure-pressure and hybrid-pressure grids. The input and output (aka source and destination) pressure grids may monotonically increase or decrease independently of eachother (i.e., one may increase and the other may decrease). When an output pressure level is outside the input pressure range for that column, then all variables must be extrapolated (not interpolated) to that/those level(s). By default ncremap sets all extrapolated values to the nearest valid value. Temperature and geopotential height are exceptions to this rule. Temperature variables (those named T or ta, anyway) are extrapolated upwards towards space using the nearest neighbor assumption, and downwards beneath the surface assuming a moist adiabatic lapse rate of 6.5 degrees centigrade per 100 millibars. Geopotential variables (those named Z3 or zg, anyway) are extrapolated upwards and downwards using the hypsometric equation

Z_2-Z_1=(R_d*T_v/g_0)*ln(p_1/p_2)=(R_d*T_v/g_0)*(ln(p_1)-ln(p_2))

with constant global mean virtual temperature

T = 288K. This assumption leads to unrealistic values where T differs significantly from the global mean surface temperature. Using the local T itself would be a much better approximation, yet would require a time-consuming implementation. Please let us know if accurate surface geopotential extrapolation in cold regions is important to you. Interpolation to and from hybrid coordinate grids works on both midpoint and interface fields (i.e., on variables with lev or ilev dimensions), while interpolation to and from pure-pressure grids applies to fields with, or places output of fields on, a plev dimension. All other fields pass through the interpolation procedure unscathed. Input can be rectangular (aka RLL), curvilinear, or unstructured. <a name="ps_nm"></a>  <a name="ps"></a>  --ps_nm=ps_nm ps_nm --ps_nm PS surface pressure --ps_nm=ps_nm (--ps_nm, --ps_name, --vrt_ps, --ps) It is sometimes convenient to store the ps_nm field in an external file from the field(s) to be regridded. For example, CMIP-style timeseries are often written with only one variable per file. NCO supports this organization by accepting ps_nm arguments in the form of a filename followed by a slash and then a variable name:

ncremap --vrt_in=vrt.nc --ps_nm=ps in.nc out.nc # Use ps not PS
ncremap --vrt_in=vrt.nc --ps_nm=/external/file.nc/ps in.nc out.nc

This same functionality (of optionally embedding a filename into the variable name) is also implemented for the sgs_frc variable. <a name="ps_rtn"></a>  <a name="rtn_sfc_prs"></a>  <a name="retain_surface_pressure"></a>  --ps_rtn ps_nm --ps_rtn --retain_surface_pressure --rtn_sfc_prs surface pressure, retaining --ps_rtn (--ps_rtn, --rtn_sfc_prs, --retain_surface_pressure) As of NCO version 5.1.5 (March, 2023), ncremap includes a --ps_rtn switch (with long-option equivalents --rtn_sfc_prs and --retain_surface_pressure) to facilitate &textldquo;round-trip&textrdquo; vertical interpolation such as hybrid-to-pressure followed by pressure-to-hybrid interpolation. By default ncremap excludes the surface pressure field named ps_nm from the output after hybrid-to-pressure interpolation. The --ps_rtn switch (which takes no argument) instructs the regridder to retain the surface pressure field after hybrid-to-pressure interpolation. The surface pressure field is then available for subsequent interpolation back to a hybrid vertical coordinate:

ncremap --ps_rtn --ps_nm=ps --vrt_out=ncep.nc in.nc out_ncep.nc
ncremap --ps_rtn -v T,Q,U,PS --vrt_out=ncep.nc in.nc out_ncep.nc
ncremap --vrt_out=hybrid.nc out_ncep.nc out_hybrid.nc

<a name="vrt_ntp"></a>  --vrt_ntp=vrt_ntp vrt_ntp --vrt_ntp --ntp_mth --interpolation_type --interpolation_method --vrt_ntp=vrt_ntp (--vrt_ntp, --ntp_mth, --interpolation_type, --interpolation_method) Specifies the interpolation method for destination points within the vertical range of the input data during vertical interpolation. Valid values and their synonyms are lin (synonyms linear and lnr), and log (synonyms logarithmic and lgr). Default is

vrt_ntp = log. The vertical interpolation algorithm defaults to linear in log(pressure). Logarithmic interpolation is more natural for gases like the atmosphere, because it is compressible, than for condensed media like oceans or Earth&textrsquo;s interior, which are incompressible. To instead interpolate linearly in the vertical coordinate, use the ntp_mth=lin option. NCO supports this feature as of version 4.9.0 (December, 2019). <a name="vrt_xtr"></a>  --vrt_xtr=vrt_xtr vrt_xtr --vrt_xtr --xtr_mth --extrapolation_type --extrapolation_method --vrt_xtr=vrt_xtr (--vrt_xtr, --xtr_mth, --extrapolation_type, --extrapolation_method) Specifies the extrapolation method for destination points outside the vertical range of the input data during vertical interpolation. Valid values and their synonyms are linear (synonyms lnr and lin), mss_val (synonyms msv and missing_value), nrs_ngh (synonyms nn and nearest_neighbor), and zero (synonym nil). Default is

vrt_xtr = nrs_ngh. NCO supports this feature as of version 4.8.1 (July, 2019). <a name="wgt_opt"></a>  -W wgt_opt wgt_opt --wgt_opt --weight_options --tps_opt --tempest_options --esmf_opt --esmf_options -W wgt_opt (--wgt_opt, --weight_options, --esmf_opt, --esmf_options, --tps_opt, --tempest_options) ncremap passes wgt_opt directly through to the weight-generator (currently ERWG or TempestRemap&textrsquo;s GenerateOfflineMap) (and not to GenerateOverlapMesh). The user-specified contents of wgt_opt, if any, supercede the default contents for the weight-generator. The default option for ERWG is --ignore_unmapped). ncremap 4.7.7 and later additionally set the ERWG --ignore_degenerate option, though if the run-time ERWG reports its version is 7.0 (March, 2018) or later. This is done to preserve backwards compatibility since, ERWG 7.1.0r and later require --ignore_degenerate to successfully regrid some datasets (e.g., CICE) that previous ERWG versions handle fine. Users of earlier versions of ncremap that call ESMF 7.1.0r and later can explicitly pass the base ERWG options with ncremap&textrsquo;s --esmf_opt option: # Use when NCO <= 4.7.6 and ERWG >= 7.1.0r ncremap --esmf_opt='--ignore_unmapped --ignore_degenerate' ... The ERWG and TempestRemap documentation shows all available options. For example, to cause ERWG to output to a netCDF4 file, pass -W "--netcdf4" to ncremap. By default, ncremap runs GenerateOfflineMap without any options. To cause GenerateOfflineMap to use a _FillValue of

-1

, pass -W '--fillvalue -1.0' to ncremap. Other common options include enforcing monotonicity (which is not the default in TempestRemap) constraints. To guarantee monotonicity in regridding from Finite Volume FV to FV maps (e.g., MPAS-to-rectangular), pass -W '-in_np 1' to ncremap. To guarantee monotonicity in regridding from Finite Element FE to FV maps, pass -W '--mono'. Common sets of specialized options recommended for TempestRemap are collected into six boutique algorithms invokable with --alg_typ as described above. <a name="wgt_cmd"></a>  -w wgt_cmd wgt_cmd --wgt_cmd --wgt_gnr --weight_command --weight_generator -w wgt_cmd (--wgt_cmd, --weight_command, --wgt_gnr, --weight_generator) Specifies a (possibly extended) command to use to run the weight-generator when a map-file is not provided. This command overrides the default executable executable for the weight generator, which is ESMF_RegridWeightGen for ESMF and GenerateOfflineMap for TempestRemap. (There is currently no way to override GenerateOverlapMesh for TempestRemap). The wgt_cmd must accept the same arguments as the default command. Examples include mpirun -np 24 ESMF_RegridWeightGen, mpirun-openmpi-mp -np 16 ESMF_RegridWeightGen, and other ways of exploiting parallelism that are system-dependent. Specifying wgt_cmd and supplying (with -m) a map-file is not permitted (since the weight-generator would not be used). <a name="xcl_var"></a>  --xcl_var --xcl --exclude --exclude_variables --xcl_var (--xcl_var, --xcl, --exclude, --exclude_variables) This flag (which takes no argument) changes var_lst, as set by the --var_lst option, from an extraction list to an exclusion list so that variables in var_lst will not be processed, and variables not in var_lst will be processed. Thus the option -v var_lst must also be present for this flag to take effect. Variables explicitly specified for exclusion by --xcl --vars=var_lst[,&dots;] need not be present in the input file. <a name="xtn_lst"></a>  -v xtn_lst xtn_lst --xtn_lst --xtn_var --var_xtn --extensive --extensive_variables -x xtn_lst (--xtn_lst, --xtn_var, --var_xtn, --extensive, --extensive_variables) The -x option causes ncremap to treat the variables in xtn_lst as extensive, meaning that their value depends on the gridcell boundaries. Support for extensive variables during regridding is nascent. Currently variables marked as extensive are summed, not regridded. We are interested in &textldquo;real-world&textrdquo; situations that require regridding extensive variables, please contact us if you have one. Limitations to ncremap ncremap has two significant limitations to be aware of. First, for two-dimensional input grids the fields to be regridded must have latitude and longitude, or, in the case of curvilinear data, the two equivalent horizontal dimensions, as the final two dimensions in in_fl. Fields with other dimension orders (e.g., lat,lev,lon) will not regrid properly. To circumvent this limitation one can employ ncpdq (ncpdq netCDF Permute Dimensions Quickly) to permute the dimensions before (and un-permute them after) regridding. ncremap utilizes this method internally for some common input grids. For example,

# AIRS Level2 vertical profiles
ncpdq -a StdPressureLev,GeoTrack,GeoXTrack AIRS_L2.hdf AIRS_L2_ncpdq.nc
ncremap -i AIRS_L2_ncpdq.nc -d dst_1x1.nc -O ~/rgr
# MPAS-O fields
ncpdq -a Time,nVertLevels,maxEdges,MaxEdges2,nEdges,nCells mpas.nc mpas_ncpdq.nc
ncremap -R "--rgr col_nm=nCells" -i mpas_ncpdq.nc -m mpas120_to_t62.nc -O ~/rgr

The previous two examples occur so frequently that ncremap has been specially equipped to handle AIRS and MPAS files. As of NCO version 4.5.5 (February, 2016), the following ncremap commands with the -P prc_typ option automagically perform all required permutation and renaming necessary:

# AIRS Level2 vertical profiles
ncremap -P airs -i AIRS_L2.nc -d dst_1x1.nc -O ~/rgr
# MPAS-O/I fields
ncremap -P mpas -i mpas.nc -m mpas120_to_t62.nc -O ~/rgr

The machinery to handle permutations and special options for other datafiles is relatively easy to extend with new prc_typ options. If you work with common datasets that could benefit from their own pre-processing options, contact us and we will try to implement them. The second limitation is that to perform regridding, ncremap must read weights from an on-disk mapfile, and cannot yet compute weights itself and use them directly from RAM. This makes ncremap an &textldquo;offline regridder&textrdquo; and unnecessarily slow compared to an &textldquo;integrated regridder&textrdquo; that computes weights and immediately applies them in RAM without any disk-access. In practice, the difference is most noticeable when the weights are easily computable &textldquo;on the fly&textrdquo;, e.g., rectangular-to-rectangular mappings. Otherwise the weight-generation takes much more time than the weight-application, at which ncremap is quite fast. As of NCO version 4.9.0, released in December, 2019, regridder supports generation of intersection grids and overlap weights for all finite volume grid combinations. However these weights are first stored in an offline mapfile, are not usable otherwise. One side-effect of ncremap being an offline regridder is that, when necessary, it can generate files to store intermediate versions of grids, maps, and data. These files are named, by default, ncremap_tmp_att.nc${unq_sfx}, ncremap_tmp_d2f.nc${unq_sfx}, ncremap_tmp_grd_dst.nc${unq_sfx}, ncremap_tmp_grd_src.nc${unq_sfx}, ncremap_tmp_gnr_out.nc${unq_sfx}, ncremap_tmp_map_*.nc${unq_sfx}, ncremap_tmp_msh_ovr_*.nc${unq_sfx}, and ncremap_tmp_pdq.nc${unq_sfx}. They are placed in drc_out with the output file(s). In general, no intermediate grid or map files are generated when the map-file is provided. Intermediate files are always generated when the -P prm_typ option is invoked. By default these files are automatically removed upon successful completion of the script, unless ncremap was invoked by --unq_sfx=noclean to explitly override this &textldquo;self-cleaning&textrdquo; behavior. Nevertheless, early or unexpected termination of ncremap will almost always leave behind a collection of these intermediate files. Should intermediate files proliferate and/or annoy you, locate and/or remove all such files under the current directory with

find . -name 'ncremap_tmp*'
rm `find . -name 'ncremap_tmp*'`

<a name="xmp_ncremap"></a>  EXAMPLES Regrid input file in.nc to the spatial grid in file dst.nc and write the output to out.nc:

ncremap -d dst.nc in.nc out.nc
ncremap -d dst.nc -i in.nc -o out.nc
ncremap -d dst.nc -O regrid in.nc out.nc
ncremap -d dst.nc in.nc regrid/out.nc
ncremap -d dst.nc -O regrid in.nc # output named in.nc

NCO infers the destination spatial grid from dst.nc by reading its coordinate variables and CF attributes. In the first example, ncremap places the output in out.nc. In the second and third examples, the output file is regrid/out.nc. In the fourth example, ncremap places the output in the specified output directory. Since no output filename is provided, the output file will be named regrid/in.nc. Generate a mapfile with ncremap and store it for later re-use. A pre-computed mapfile (supplied with -m map_fl) eliminates time-consuming weight-generation, and thus considerably reduces wallclock time:

ncremap -m map.nc in.nc out.nc
ncremap -m map.nc -I drc_in -O regrid

As of NCO version 4.7.2 (January, 2018), ncremap supports &textldquo;canonical&textrdquo; argument ordering of command line arguments most frequently desired for one-off regridding, where a single input and output filename are supplied as command-line positional arguments without switches, pipes, or redirection:

ncremap -m map.nc in.nc out.nc # Requires 4.7.2+
ncremap -m map.nc -i in.nc -o out.nc
ncremap -m map.nc -o out.nc in.nc
ncremap -m map.nc -O out_dir in1.nc in2.nc
ncremap -m map.nc -o out.nc < in.nc
ls in.nc | ncremap -m map.nc -o out.nc

These are all equivalent methods, but the canonical ordering shown in the first example only works in NCO version 4.7.2 and later. ncremap annotates the gridfiles and mapfiles that it creates with helpful metadata containing the full provenance of the command. Consequently, ncremap is a sensible tool for generating mapfiles for later use. To generate a mapfile with the specified (non-default) name map.nc, and then regrid a single file,

ncremap -d dst.nc -m map.nc in.nc out.nc

To test the remapping workflow, regrid only one or a few variables instead of the entire file:

ncremap -v T,Q,FSNT -m map.nc in.nc out.nc

Regridding generally scales linearly with the size of data to be regridded, so eliminating unnecessary variables produces a snappier response. Regrid multiple input files with a single mapfile map.nc and write the output to the regrid directory:

ncremap -m map.nc -I drc_in -O regrid
ls drc_in/*.nc | ncremap -m map.nc -O regrid

The three ways NCO obtains the destination spatial grid are, in decreasing order of precedence, from map_fl (specified with -m), from grd_dst (specified with -g), and (inferred) from dst_fl (specified with -d). In the first example all likely data files from drc_in are regridded using the same specified mapfile, map_fl = map.nc. Each output file is written to drc_out = regrid with the same name as the corresponding input file. The second example obtains the input file list from standard input, and uses the mapfile and output directory as before. If multiple input files are on the same grid, yet the mapfile does not exist in advance, one can still regrid all input files without incurring the time-penalty of generating multiple mapfiles. To do so, provide the (known-in-advance) source gridfile or toggle the -M switch:

ncremap -M -I drc_in -d dst.nc -O regrid
ls drc_in/*.nc | ncremap -M -d dst.nc -O regrid
ncremap -I drc_in -s grd_src.nc -d dst.nc -O regrid
ls drc_in/*.nc | ncremap -s grd_src.nc -d dst.nc -O regrid
ncremap -I drc_in -s grd_src.nc -g grd_dst.nc -O regrid
ls drc_in/*.nc | ncremap -s grd_src.nc -g grd_dst.nc -O regrid

The first two examples explicitly toggle the multi-map-generation switch (with -M), so that ncremap refrains from generating multiple mapfiles. In this case the source grid is inferred from the first input file, the destination grid is inferred from dst.nc, and ncremap uses ERWG to generate a single mapfile and uses that to regrid every input file. The next four examples are variants on this theme. In these cases, the user provides (with -s grd_src.nc) the source gridfile, which will be used directly instead of being inferred. Any of these styles works well when each input file is known in advance to be on the same grid, e.g., model data for successive time periods in a simulation. The most powerful, time-consuming (yet simultaneously time-saving!) feature of ncremap is its ability to regrid multiple input files on unique grids. Both input and output can be on any CRUD grid.

ncremap -I drc_in -d dst.nc -O regrid
ls drc_in/*.nc | ncremap -d dst.nc -O regrid
ncremap -I drc_in -g grd_dst.nc -O regrid
ls drc_in/*.nc | ncremap -g grd_dst.nc -O regrid

There is no pre-supplied map_fl or grd_src in these examples, so ncremap first infers the output grid from dst.nc (first two examples), or directly uses the supplied gridfile grd_dst (second two examples), and calls ERWG to generate a new mapfile for each input file, whose grid it infers. This is necessary when each input file is on a unique grid, e.g., swath-like data from satellite observations or models with time-varying grids. These examples require remarkably little input, since ncremap automates most of the work. Finally, ncremap uses the parallelization options -p par_typ and -j job_nbr to help manage high-volume workflow. On a single node such as a local workstation, use Background mode to regrid multiple files in parallel

ls drc_in/*.nc | ncremap -p bck -d dst.nc -O regrid
ls drc_in/*.nc | ncremap -p bck -j 4 -d dst.nc -O regrid

Both examples will eventually regrid all input files. The first example regrids two at a time because two is the default batch size ncremap employs. The second example regrids files in batches of four at a time. Increasing job_nbr will increase throughput so long as the node is not I/O-limited. Multi-node clusters can exploit inter-node parallelism in MPI-mode:

qsub -I -A CLI115 -V -l nodes=4 -l walltime=03:00:00 -N ncremap
ls drc_in/*.nc | ncremap -p mpi -j 4 -d dst.nc -O regrid

This example shows a typical request for four compute nodes. After receiving the login prompt from the interactive master node, execute the ncremap command with -p mpi. ncremap will send regridding jobs in round-robin fashion to all available compute nodes until all jobs finish. It does this by internally prepending an MPI execution command, like mpirun -H node_name -npernode 1 -n 1, to the usual regridding command. MPI-mode typically has excellent scaling because most nodes have independent access to hard storage. This is the easiest way to speed your cumbersome job by factors of ten or more. As mentioned above under Limitations, parallelism is currently only supported when all regridding uses the same map-file. <a name="ncrename"></a>

ncrename netCDF Renamerncwa netCDF Weighted Averagerncremap netCDF RemapperReference Manual

ncrename netCDF Renamer renaming variables renaming groups renaming dimensions renaming attributes variable names dimension names attribute names group names ncrename SYNTAX

ncrename [-a old_name,new_name] [-a &dots;] [-D dbg] 
[-d old_name,new_name] [-d &dots;] [-g old_name,new_name] [-g &dots;] 
[--glb ...] [-H] [-h] [--hdf] [--hdr_pad nbr] [--hpss] 
[-l path] [-O] [-o output-file] [-p path] [-R] [-r] 
[-v old_name,new_name] [-v &dots;]
input-file [[output-file]]

DESCRIPTION . ncrename renames netCDF dimensions, variables, attributes, and groups. Each object that has a name in the list of old names is renamed using the corresponding name in the list of new names. All the new names must be unique. Every old name must exist in the input file, unless the old name is preceded by the period (or &textldquo;dot&textrdquo;) character .. The validity of old_name is not checked prior to the renaming. Thus, if old_name is specified without the . prefix that indicates the presence of old_name is optional, and old_name is not present in input-file, then ncrename will abort. The new_name should never be prefixed by a . (or else the period will be included as part of the new name). As of NCO version 4.4.6 (released October, 2014), the old_name and new_name arguments may include (or be, for groups) partial or full group paths. The OPTIONS and EXAMPLES show how to select specific variables whose attributes are to be renamed. <a name="bug_nc4_rename"></a>  Caveat lector: Unforunately from 2007&textndash;present (August, 2023) the netCDF library (versions 4.0.0&textndash;4.9.3) contains bugs or limitations that sometimes prevent NCO from correctly renaming coordinate variables, dimensions, and groups in netCDF4 files. (To our knowledge the netCDF library calls for renaming always work well on netCDF3 files so one workaround to many netCDF4 issues is convert to netCDF3, rename, then convert back). To understand the renaming limitations associated with particular netCDF versions, read the ncrename documentation below in its entirety. Although ncrename supports full pathnames for both old_name and new_name, this is really &textldquo;window dressing&textrdquo;. The full-path to new_name must be identical to the full-path to old_name in all classes of objects (attributes, variables, dimensions, or groups). In other words, ncrename can change only the local names of objects, it cannot change the location of the object in the group hierarchy within the file. Hence using a full-path in new_name is redundant. The object name is the terminal path component of new_name and this object must already exist in the group specified by the old_name path. data safety safeguards temporary output files ncrename is an exception to the normal NCO rule that the user will be interactively prompted before an existing file is changed, and that a temporary copy of an output file is constructed during the operation. If only input-file is specified, then ncrename changes object names in the input-file in place without prompting and without creating a temporary copy of input-file. This is because the renaming operation is considered reversible if the user makes a mistake. The new_name can easily be changed back to old_name by using ncrename one more time. Note that renaming a dimension to the name of a dependent variable can be used to invert the relationship between an independent coordinate variable and a dependent variable. In this case, the named dependent variable must be one-dimensional and should have no missing values. Such a variable will become a coordinate variable. performance operator speed speed execution time According to the netCDF User Guide, renaming objects in netCDF files does not incur the penalty of recopying the entire file when the new_name is shorter than the old_name. Thus ncrename may run much faster (at least on netCDF3 files) if judicious use of header padding (Metadata Optimization) was made when producing the input-file. Similarly, using the --hdr_pad option with ncrename helps ensure that future metadata changes to output-file occur as swifly as possible. OPTIONS -a old_name,new_name Attribute renaming. The old and new names of the attribute are specified with -a (or --attribute) by the associated old_name and new_name values. global attribute global attributes attributes, global Global attributes are treated no differently than variable attributes. This option may be specified more than once. As mentioned above, all occurrences of the attribute of a given name will be renamed unless the . form is used, with one exception. To change the attribute name for a particular variable, specify the old_name in the format old_var_name&arobase;old_att_name. The &arobase; symbol delimits the variable from the attribute name. If the attribute is uniquely named (no other variables contain the attribute) then the old_var_name&arobase;old_att_name syntax is redundant. The old_var_name variable names global and group have special significance. They indicate that old_att_nm should only be renamed where it occurs as a global (i.e., root group) metadata attribute (for global), or (for group) as any group attribute, and not where it occurs as a variable attribute. The var_name&arobase;att_name syntax is accepted, though not required, for the new_name. -d old_name,new_name Dimension renaming. The old and new names of the dimension are specified with -d (or --dmn, --dimension) by the associated old_name and new_name values. This option may be specified more than once. -g old_name,new_name Group renaming. The old and new names of the group are specified with -g (or --grp, --group) by the associated old_name and new_name values. This option may be specified more than once. This functionality is only available in NCO version 4.3.7 (October, 2013) or later, and only when built on netCDF library version 4.3.1-rc1 (August, 2013) or later. -v old_name,new_name Variable renaming. The old and new names of the variable are specified with -v (or --variable) by the associated old_name and new_name values. This option may be specified more than once.

<a name="xmp_ncrename"></a>  EXAMPLES Rename the variable p to pressure and t to temperature in netCDF in.nc. In this case p must exist in the input file (or ncrename will abort), but the presence of t is optional:

ncrename -v p,pressure -v .t,temperature in.nc

Rename the attribute long_name to largo_nombre in the variable u, and no other variables in netCDF in.nc. ncrename -a u@long_name,largo_nombre in.nc Rename the group g8 to g20 in netCDF4 file in_grp.nc:

ncrename -g g8,g20 in_grp.nc

Rename the variable /g1/lon to longitude in netCDF4 in_grp.nc:

ncrename -v /g1/lon,longitude in_grp.nc
ncrename -v /g1/lon,/g1/longitude in_grp.nc # Alternate

<a name="ncrename_crd"></a>  coordinate variables ncrename does not automatically attach dimensions to variables of the same name. This is done to make renaming an easy way to change whether a variable is a coordinate. If you want to rename a coordinate variable so that it remains a coordinate variable, you must separately rename both the dimension and the variable:

ncrename -d lon,longitude -v lon,longitude in.nc

Unfortunately, the netCDF4 library had a longstanding bug (all versions until 4.3.1-rc5 released in December, 2013) that crashed NCO when performing this operation. Simultaneously renaming variables and dimensions in netCDF4 files with earlier versions of netCDF is impossible; it must instead be done in two separate ncrename invocations (e.g., first rename the variable, then rename the dimension) to avoid triggering the libary bug. A related bug causes unintended side-effects with ncrename also built with all versions of the netCDF4 library until 4.3.1-rc5 released in December, 2013): This bug caused renaming either a dimension or its associated coordinate variable (not both, which would fail as above) in a netCDF4 file to inadvertently rename both:

# Demonstrate bug in netCDF4/HDF5 library prior to netCDF-4.3.1-rc5
ncks -O -h -m -M -4 -v lat_T42 ~/nco/data/in.nc ~/foo.nc
ncrename -O -v lat_T42,lat ~/foo.nc ~/foo2.nc # Also renames dimension
ncrename -O -d lat_T42,lat ~/foo.nc ~/foo2.nc # Also renames variable

To avoid this faulty behavior, either build NCO with netCDF version 4.3.1-rc5 or later, or convert the file to netCDF3 first, then rename as intended, then convert back. Unforunately while this bug and the related coordinate renaming bug were fixed in 4.3.1-rc5 (released in December, 2013), a new and related bug was discovered in October 2014. Another netCDF4 bug that causes unintended side-effects with ncrename affects (at least) versions 4.3.1&textndash;4.3.2 and all snapshots of the netCDF4 library until January, 2015. This bug (fixed in 4.3.3 in February, 2015) corrupts values or renamed netCDF4 coordinate variables (i.e., variables with underlying dimensions of the same name) and other (non-coordinate) variables that include an underlying dimension that was renamed. In other words, renaming coordinate variables and dimensions succeeds yet it corrupts the values contained by the affected array variables. This bug corrupts affected variables by replacing their values with the default _FillValue for that variable&textrsquo;s type:

# Demonstrate bug in netCDF4 libraries prior to version 4.3.3
ncks -O -4 -C -M -v lat ~/nco/data/in.nc ~/bug.nc
ncrename -O -v lat,tal ~/bug.nc ~/foo.nc # Broken until netCDF-4.3.3
ncrename -O -d lat,tal ~/bug.nc ~/foo.nc # Broken until netCDF-4.3.3
ncrename -O -d lat,tal -v lat,tal ~/bug.nc ~/foo.nc # Broken too
ncks ~/foo.nc

To avoid this faulty behavior, either build NCO with netCDF version 4.3.3 or later, or convert the file to netCDF3 first, then rename as intended, then convert back. This bug does not affect renaming of groups or of attributes. Yet another netCDF4 bug that causes unintended side-effects with ncrename affects only snapshots from January&textndash;February, 2015, and released version 4.3.3 (February, 2015). It was fixed in (and was the reason for releasing) netCDF version 4.3.3.1 (March, 2015). This bug causes renamed attributes of coordinate variables in netCDF4 to files to disappear: # Demonstrate bug in netCDF4 library version 4.3.3 ncrename -O -h -a /g1/lon@units,new_units ~/nco/data/in_grp.nc ~/foo.nc ncks -v /g1/lon ~/foo.nc # Shows units and new_units are both gone Clearly, renaming coordinates in netCDF4 files is non-trivial. The penultimate chapter in this saga is a netCDF4 bug discovered in September, 2015, and present in versions 4.3.3.1 (and possibly earlier versions too) and later. As of this writing (February, 2018), this bug is still present in netCDF4 version 4.6.0.1-development. This bug causes ncrename to create corrupted output files when attempting to rename two or more dimensions simultaneously. The workaround is to rename the dimensions sequentially, in two separate ncrename calls. # Demonstrate bug in netCDF4 library versions 4.3.3.1--4.6.1+ ncrename -O -d lev,z -d lat,y -d lon,x ~/nco/data/in_grp.nc ~/foo.nc # Completes but file is unreadable ncks -v one ~/foo.nc # File is unreadable (multiple dimensions with same ID?) A new netCDF4 renaming bug was discovered in March, 2017. It is present in versions 4.4.1&textndash;4.6.0 (and possibly earlier versions). This bug was fixed in netCDF4 version 4.6.1 (Yay Ed!). This bug caused ncrename to fail to rename a variable when the result would become a coordinate. # Demonstrate bug in netCDF4 library versions 4.4.1--4.6.0 ncrename -O -v non_coord,coord ~/nco/data/in_grp.nc ~/foo.nc # Fails (HDF error) The fix is to upgrade to netCDF version 4.6.1. The workaround is to convert to netCDF3, then rename, then convert back to netCDF4. A potentially new netCDF4 bug was discovered in November, 2017 and is now fixed. It is present in versions 4.4.1.1&textndash;4.6.0 (and possibly earlier versions too). This bug causes ncrename to fail to rename a variable when the result would become a coordinate. Oddly this issue shows that simultaneously renaming a dimension and coordinate can succeed (in contrast to a bug described above), and that separating that into two steps can fail. # Demonstrate bug in netCDF4 library versions 4.4.1--4.6.0 # 20171107: https://github.com/Unidata/netcdf-c/issues/597 # Create test dataset ncks -O -C -v lon ~/nco/data/in_grp.nc ~/in_grp.nc ncks -O -x -g g1,g2 ~/in_grp.nc ~/in_grp.nc # Rename dimension then variable ncrename -d lon,longitude ~/in_grp.nc # works ncrename -v lon,longitude ~/in_grp.nc # borken "HDF error" # Rename variable then dimension ncrename -v lon,longitude ~/in_grp.nc # works ncrename -d lon,longitude ~/in_grp.nc # borken "nc4_reform_coord_var: Assertion `dim_datasetid > 0' failed." # Oddly renaming both simultaneously works: ncrename -d lon,longitude -v lon,longitude ~/in_grp.nc # works The fix is to upgrade to netCDF version 4.6.1. The workaround is to convert to netCDF3, then rename, then convert back to netCDF4. A new netCDF3 bug was discovered in April, 2018 and is now fixed. It is present in netCDF versions 4.4.1&textndash;4.6.0 (and possibly earlier versions too). This bug caused ncrename to fail to rename many coordinates and dimensions simultaneously. This bug affects netCDF3 64BIT_OFFSET files and possibly other formats as well. As such it is the first and so far only bug we have identified that affects netCDF3 files. cp /glade/scratch/gus/GFDL/exp/CM3_test/pp/0001/0001.land_month_crop.AllD.nc ~/correa_in.nc ncrename -O -d grid_xt,lon -d grid_yt,lat -v grid_xt,lon -v grid_yt,lat \ -v grid_xt_bnds,lon_bnds -v grid_yt_bnds,lat_bnds ~/correa_in.nc ~/correa_out.nc The fix is to upgrade to netCDF version 4.6.1. global attributes group attributes attributes, global _FillValue missing_value Create netCDF out.nc identical to in.nc except the attribute _FillValue is changed to missing_value, the attribute units is changed to CGS_units (but only in those variables which possess it), the attribute hieght is changed to height in the variable tpt, and in the variable prs_sfc, if it exists. ncrename -a _FillValue,missing_value -a .units,CGS_units \ -a tpt@hieght,height -a prs_sfc@.hieght,height in.nc out.nc The presence and absence of the . and &arobase; features cause this command to execute successfully only if a number of conditions are met. All variables must have a _FillValue attribute and _FillValue must also be a global attribute. The units attribute, on the other hand, will be renamed to CGS_units wherever it is found but need not be present in the file at all (either as a global or a variable attribute). The variable tpt must contain the hieght attribute. The variable prs_sfc need not exist, and need not contain the hieght attribute. Rename the global or group attribute Convention to Conventions ncrename -a Convention,Conventions in.nc # Variable and group atts. ncrename -a .Convention,Conventions in.nc # Variable and group atts. ncrename -a @Convention,Conventions in.nc # Group atts. only ncrename -a @.Convention,Conventions in.nc # Group atts. only ncrename -a global@Convention,Conventions in.nc # Group atts. only ncrename -a .global@.Convention,Conventions in.nc # Group atts. only ncrename -a global@Convention,Conventions in.nc # Global atts. only ncrename -a .global@.Convention,Conventions in.nc # Global atts. only The examples without the &arobase; character attempt to change the attribute name in both Global or Group and variable attributes. The examples with the &arobase; character attempt to change only global and group Convention attributes, and leave unchanged any Convention attributes attached directly to variables. Attributes prefixed with a period (.Convention) need not be present. Attributes not prefixed with a period (Convention) must be present. Variables prefixed with a period (. or .global) need not be present. Variables not prefixed with a period (global) must be present. <a name="ncwa"></a>

ncwa netCDF Weighted Averagerncrename netCDF RenamerReference Manual

ncwa netCDF Weighted Averager averaging data weights weighted average masked average broadcasting variables ncwa SYNTAX

ncwa [-3] [-4] [-5] [-6] [-7] [-A] [-a dim[,&dots;]]
[-B mask_cond] [-b] [-C] [-c] [--cmp cmp_sng]
[--cnk_byt sz_byt] [--cnk_csh sz_byt] [--cnk_dmn nm,sz_lmn]
[--cnk_map map] [--cnk_min sz_byt] [--cnk_plc plc] [--cnk_scl sz_lmn]
[-D dbg] [-d dim,[min][,[max][,[stride]]] [-F] [--fl_fmt fl_fmt]
[-G gpe_dsc] [-g grp[,&dots;]] [--glb ...] [-H] [-h] [--hdr_pad nbr] [--hpss] [-I]
[-L dfl_lvl] [-l path] [-M mask_val] [-m mask_var] [-N] 
[--no_cll_msr] [--no_cll_mth] [--no_frm_trm] [--no_tmp_fl] 
[-O] [-o output-file] [-p path] [--qnt ...] [--qnt_alg alg_nm]
[-R] [-r] [--ram_all] [--rth_dbl|flt] [-T mask_comp] [-t thr_nbr]
[--unn] [-v var[,&dots;]] [-w weight] [-X ...] [-x] [-y op_typ]
input-file [output-file]

DESCRIPTION ncwa performs statistics (including, but not limited to, averages) on variables in a single file over arbitrary dimensions, with options to specify weights, masks, and normalization. Statistics vs Concatenation, for a description of the distinctions between the various statistics tools and concatenators. The default behavior of ncwa is to arithmetically average every numerical variable over all dimensions and to produce a scalar result for each. degenerate dimension Averaged dimensions are, by default, eliminated as dimensions. Their corresponding coordinates, if any, are output as scalar variables. The -b switch (and its long option equivalents --rdd and --retain-degenerate-dimensions) causes ncwa to retain averaged dimensions as degenerate (size 1) dimensions. This maintains the association between a dimension (or coordinate) and variables after averaging and simplifies, for instance, later concatenation along the degenerate dimension. To average variables over only a subset of their dimensions, specify these dimensions in a comma-separated list following -a, e.g., -a time,lat,lon. arithmetic operators hyperslab -d dim,[min][,[max]] As with all arithmetic operators, the operation may be restricted to an arbitrary hyperslab by employing the -d option (Hyperslabs). ncwa also handles values matching the variable&textrsquo;s _FillValue attribute correctly. Moreover, ncwa understands how to manipulate user-specified weights, masks, and normalization options. With these options, ncwa can compute sophisticated averages (and integrals) from the command line. <a name="-w"></a>  <a name="wgt"></a>  -w weight --weight weight --wgt_var weight -m mask_var --mask-variable mask_var --mask_variable mask_var --msk_nm mask_var --msk_var mask_var -B mask_cond --msk_cnd mask_cond --mask_condition mask_cond mask_var and weight, if specified, are broadcast to conform to the variables being averaged. rank The rank of variables is reduced by the number of dimensions which they are averaged over. Thus arrays which are one dimensional in the input-file and are averaged by ncwa appear in the output-file as scalars. This allows the user to infer which dimensions may have been averaged. Note that that it is impossible for ncwa to make make a weight or mask_var of rank W conform to a var of rank V if W > V. This situation often arises when coordinate variables (which, by definition, are one dimensional) are weighted and averaged. ncwa assumes you know this is impossible and so ncwa does not attempt to broadcast weight or mask_var to conform to var in this case, nor does ncwa print a warning message telling you this, because it is so common. Specifying dbg > 2 does cause ncwa to emit warnings in these situations, however. Non-coordinate variables are always masked and weighted if specified. Coordinate variables, however, may be treated specially. By default, an averaged coordinate variable, e.g., latitude, appears in output-file averaged the same way as any other variable containing an averaged dimension. In other words, by default ncwa weights and masks coordinate variables like all other variables. This design decision was intended to be helpful but for some applications it may be preferable not to weight or mask coordinate variables just like all other variables. Consider the following arguments to ncwa: -a latitude -w lat_wgt -d latitude,0.,90. where lat_wgt is a weight in the latitude dimension. Since, by default ncwa weights coordinate variables, the value of latitude in the output-file depends on the weights in lat_wgt and is not likely to be 45.0, the midpoint latitude of the hyperslab. coordinate variable -I Option -I overrides this default behavior and causes ncwa not to weight or mask coordinate variables The default behavior of (-I) changed on 19981201&textmdash;before this date the default was not to weight or mask coordinate variables.. In the above case, this causes the value of latitude in the output-file to be 45.0, an appealing result. Thus, -I specifies simple arithmetic averages for the coordinate variables. In the case of latitude, -I specifies that you prefer to archive the arithmetic mean latitude of the averaged hyperslabs rather than the area-weighted mean latitude. If lat_wgt contains Gaussian weights then the value of latitude in the output-file will be the area-weighted centroid of the hyperslab. For the example given, this is about 30 degrees.. average operation types As explained in Operation Types, ncwa always averages coordinate variables regardless of the arithmetic operation type performed on the non-coordinate variables. This is independent of the setting of the -I option. The mathematical definition of operations involving rank reduction is given above (Operation Types). * Mask condition::

* Normalization and Integration::

<a name="mask"></a>  <a name="msk"></a>  Mask conditionNormalization and Integrationncwa netCDF Weighted Averagerncwa netCDF Weighted Averager Mask condition mask condition truth condition Each $\xxx_{\idx}$ also has an associated masking weight~$\mskflg_{\idx}$ whose value is~0 or~1 (false or true). The value of~$\mskflg_{\idx}$ is always~1 unless a @var{mask\_var} is specified (with @samp{-m}). As noted above, @var{mask\_var} is broadcast, if possible, to conform to the variable being averaged. In this case, the value of~$\mskflg_{\idx}$ depends on the @dfn{mask condition} also known as the @dfn{truth condition}. As expected, $\mskflg_{\idx} = 1$ when the mask condition is @dfn{true} and $\mskflg_{\idx} = 0$ otherwise. --op_rlt mask_comp --mask_comparator mask_comp --msk_cmp_typ mask_comp --msk_cnd_sng mask_cond --mask_condition mask_cond -B mask_cond The mask condition has the syntax

mask_var

mask_comp

mask_val. The preferred method to specify the mask condition is in one string with the -B or --mask_condition switches. The older method is to use the three switches -m, -T, and -M to specify the mask_var, mask_comp, and mask_val, respectively. The three switches -m, -T, and -M are maintained for backward compatibility and may be deprecated in the future. It is safest to write scripts using --mask_condition.. The mask_condition string is automatically parsed into its three constituents mask_var, mask_comp, and mask_val. comparator Here mask_var is the name of the masking variable (specified with -m, --mask-variable, --mask_variable, --msk_nm, or --msk_var). The truth mask_comp argument (specified with -T, --mask_comparator, --msk_cmp_typ, or --op_rlt may be any one of the six arithmetic comparators: eq, ne, gt, lt, ge, le. These are the Fortran-style character abbreviations for the logical comparisons $=$, $\neq$, $>$, $<$, $\ge$, $\le$. @clear flg These are the Fortran-style character abbreviations for the logical comparisons

==

!=

>

<

>=

<=

. The mask comparator defaults to eq (equality). --mask-value mask_val --mask_value mask_val --msk_val mask_val The mask_val argument to -M (or --mask-value, or --msk_val) is the right hand side of the mask condition. Thus for the i&textrsquo;th element of the hyperslab to be averaged, the mask condition is {\it mask$_{\idx}$ mask\_comp mask\_val}. @clear flg

mask(i)

mask_comp mask_val. Each~$\xxx_{\idx}$ is also associated with an additional weight~$\wgt_{\idx}$ whose value may be user-specified. The value of~$\wgt_{\idx}$ is identically~1 unless the user specifies a weighting variable @var{weight} (with @samp{-w}, @samp{--weight}, or @samp{--wgt\_var}). In this case, the value of~$\wgt_{\idx}$ is determined by the @var{weight} variable in the @var{input-file}. As noted above, @var{weight} is broadcast, if possible, to conform to the variable being averaged. $\tllnbr$ is the number of input elements $\xxx_{\idx}$ which actually contribute to output element $\xxx_{\jjj}$. $\tllnbr$ is also known as the @dfn{tally} and is defined as $$ \tllnbr = \sum_{\idx=1}^{\idx=\lmnnbr} \mssflg_{\idx} \mskflg_{\idx} $$ $\tllnbr$ is identical to the denominator of the generic averaging expression except for the omission of the weight $\wgt_{\idx}$. Thus $\tllnbr = \lmnnbr$ whenever no input points are missing values or are masked. Whether an element contributes to the output, and thus increments $\tllnbr$ by one, has more to do with the above two criteria (missing value and masking) than with the numeric value of the element per se. For example, $\xxx_{\idx}=0.0$ does contribute to $\xxx_{\jjj}$ (assuming the @code{_FillValue} attribute is not~0.0 and location $\idx$ is not masked). The value $\xxx_{\idx}=0.0$ will not change the numerator of the generic averaging expression, but it will change the denominator (unless its weight $\wgt_{\idx}=0.0$ as well). <a name="nrm"></a>  <a name="ntg"></a>  Normalization and IntegrationMask conditionncwa netCDF Weighted Averager Normalization and Integration normalization -N numerator integration dot product ncwa has one switch which controls the normalization of the averages appearing in the output-file. Short option -N (or long options --nmr or --numerator) prevents ncwa from dividing the weighted sum of the variable (the numerator in the averaging expression) by the weighted sum of the weights (the denominator in the averaging expression). Thus -N tells ncwa to return just the numerator of the arithmetic expression defining the operation (Operation Types). With this normalization option, ncwa can integrate variables. Averages are first computed as sums, and then normalized to obtain the average. The original sum (i.e., the numerator of the expression in Operation Types) is output if default normalization is turned off (with -N). This sum is the integral (not the average) over the specified (with -a, or all, if none are specified) dimensions. The weighting variable, if specified (with -w), plays the role of the differential increment and thus permits more sophisticated integrals (i.e., weighted sums) to be output. For example, consider the variable lev where

lev = [100,500,1000] weighted by the weight lev_wgt where

lev_wgt = [10,2,1]. dot product The vertical integral of lev, weighted by lev_wgt, is the dot product of lev and lev_wgt. That this is is 3000.0 can be seen by inspection and verified with the integration command

ncwa -N -a lev -v lev -w lev_wgt in.nc foo.nc;ncks foo.nc

fxm TODO nco702 As explained in @xref{Operation Types}, @command{ncwa} @emph{always averages} coordinate variables regardless of the arithmetic operation type performed on the non-coordinate variables. The single exception is shown in the above example. The @samp{-N} switch turns off normalization so variables which are to be averaged, including coordinate variables, are not normalized. This is equivalent to summation or integration. @c NB: these masking features are deprecated The second normalization option tells @command{ncwa} to multiply the weighted average the variable (given by the averaging expression) by the @w{tally, @var{M}}. Thus this option is similar to integration---multiplying the mean value of a quantity by the number of gridpoints to which it applies. The third normalization option is equivalent to specifying the first two options simultaneously. In other words this option causes @command{ncwa} to return @w{@var{M} times} the numerator of the generic averaging expression. With these normalization options, @command{ncwa} can compute sophisticated averages (and integrals) from the command line. <a name="xmp_ncwa"></a>  EXAMPLES Given file 85_0112.nc: netcdf 85_0112 { dimensions: lat = 64 ; lev = 18 ; lon = 128 ; time = UNLIMITED ; // (12 currently) variables: float lat(lat) ; float lev(lev) ; float lon(lon) ; float time(time) ; float scalar_var ; float three_dmn_var(lat, lev, lon) ; float two_dmn_var(lat, lev) ; float mask(lat, lon) ; float gw(lat) ; } Average all variables in in.nc over all dimensions and store results in out.nc:

ncwa in.nc out.nc

All variables in in.nc are reduced to scalars in out.nc since ncwa averages over all dimensions unless otherwise specified (with -a). Store the zonal (longitudinal) mean of in.nc in out.nc:

ncwa -a lon in.nc out1.nc
ncwa -a lon -b in.nc out2.nc

degenerate dimension The first command turns lon into a scalar and the second retains lon as a degenerate dimension in all variables.

% ncks --trd -C -H -v lon out1.nc
lon = 135
% ncks --trd -C -H -v lon out2.nc
lon[0] = 135

In either case the tally is simply the size of lon, i.e., 180 for the 85_0112.nc file described by the sample header above. gw Gaussian weights climate model Compute the meridional (latitudinal) mean, with values weighted by the corresponding element of gw gw stands for Gaussian weight in many climate models.:

ncwa -w gw -a lat in.nc out.nc

Here the tally is simply the size of lat, or 64. The sum of the Gaussian weights is 2.0. Compute the area mean over the tropical Pacific:

ncwa -w gw -a lat,lon -d lat,-20.,20. -d lon,120.,270. in.nc out.nc

Here the tally is $64 \times 128 = 8192$. @clear flg 64 times 128 = 8192. ORO climate model Compute the area-mean over the globe using only points for which $ORO < 0.5$ @clear flg ORO < 0.5 ORO stands for Orography in some climate models and in those models

ORO < 0.5 selects ocean gridpoints.:

ncwa -B 'ORO < 0.5'      -w gw -a lat,lon in.nc out.nc
ncwa -m ORO -M 0.5 -T lt -w gw -a lat,lon in.nc out.nc

It is considerably simpler to specify the complete mask_cond with the single string argument to -B than with the three separate switches -m, -T, and -M Unfortunately the -B and --mask_condition options are unsupported on Windows (with the MVS compiler), which lacks a free, standard parser and lexer.. If in doubt, enclose the mask_cond within quotes since some of the comparators have special meanings to the shell. Assuming 70% of the gridpoints are maritime, then here the tally is $0.70 \times 8192 \approx 5734$. @clear flg 0.70 times 8192 = 5734. Compute the global annual mean over the maritime tropical Pacific:

ncwa -B 'ORO < 0.5'      -w gw -a lat,lon,time \
  -d lat,-20.0,20.0 -d lon,120.0,270.0 in.nc out.nc
ncwa -m ORO -M 0.5 -T lt -w gw -a lat,lon,time \
  -d lat,-20.0,20.0 -d lon,120.0,270.0 in.nc out.nc

Further examples will use the one-switch specification of mask_cond. Determine the total area of the maritime tropical Pacific, assuming the variable area contains the area of each gridcell

ncwa -N -v area -B 'ORO < 0.5' -a lat,lon \
  -d lat,-20.0,20.0 -d lon,120.0,270.0 in.nc out.nc

Weighting area (e.g., by gw) is not appropriate because area is already area-weighted by definition. Thus the -N switch, or, equivalently, the -y ttl switch, correctly integrate the cell areas into a total regional area. mask condition truth condition Mask a file to contain _FillValue everywhere except where

thr_min <= msk_var <= thr_max: # Set masking variable and its scalar thresholds export msk_var='three_dmn_var_dbl' # Masking variable export thr_max='20' # Maximum allowed value export thr_min='10' # Minimum allowed value ncecat -O in.nc out.nc # Wrap out.nc in degenerate "record" dimension ncwa -O -a record -B "${msk_var} <= ${thr_max}" out.nc out.nc ncecat -O out.nc out.nc # Wrap out.nc in degenerate "record" dimension ncwa -O -a record -B "${msk_var} >= ${thr_min}" out.nc out.nc After the first use of ncwa, out.nc contains _FillValue where ${msk_var} >= ${thr_max}. The process is then repeated on the remaining data to filter out points where ${msk_var} <= ${thr_min}. The resulting out.nc contains valid data only where

thr_min <= msk_var <= thr_max. <a name="ctr"></a>

ContributingQuick StartReference ManualTop Contributing contributing We welcome contributions from anyone. The project homepage at https://sf.net/projects/nco contains more information on how to contribute. PayPal Financial contributions to NCO development may be made through https://www.paypal.com/xclick/business=zender%40uci.edu&item_name=NCO+development&item_number=nco_dnt_dvl&no_note=1&tax=0&currency_code=USDPayPal. NCO has been shared for over 10 years yet only two users have contributed any money to the developers chocolate Happy users have sent me a few gifts, though. This includes a box of imported chocolate. Mmm. Appreciation and gifts are definitely better than money. Naturally, I&textrsquo;m too lazy to split and send gifts to the other developers. However, unlike some NCO developers, I have a steady "real job". My intent is to split monetary donations among the active developers and to send them their shares via PayPal.. So you could be the third! <a name="dvl"></a>  <a name="cnt"></a>  <a name="ppl"></a>  * Contributors::

* Citation::

* Proposals for Institutional Funding::

ContributorsCitationContributingContributing

Contributors NCO would not exist without the dedicated efforts of the remarkable software engineers who conceive, develop, and maintain netCDF, UDUnits, and OPeNDAP. Russ Rew John Caron Glenn Davis Steve Emmerson Ward Fisher James Gallagher Ed Hartnett Dennis Heimbigner Since 1995 NCO has received support from nearly the entire staff of all these projects, including Russ Rew, John Caron, Glenn Davis, Steve Emmerson, Ward Fisher, James Gallagher, Ed Hartnett, and Dennis Heimbigner. In addition to their roles in maintaining the software stack on which NCO perches, Yertl-like, some of these gentlemen have advised or contributed to NCO specifically. That support is acknowledged separately below. contributors The primary contributors to NCO development have been: Charlie Zender Charlie Zender All concept, design and implementation from 1995&textndash;2000. Since then autotools, bug-squashing, CDL, chunking, documentation, anchoring, recursion, GPE, packing, regridding, CDL/XML backends, compression, NCO library redesign, ncap2 features, ncbo, ncpdq, SMP threading and MPI parallelization, netCDF4 integration, external funding, project management, science research, releases. Henry Butowsky Henry Butowsky Non-linear operations and min(), max(), total() support in ncra and ncwa. Type conversion for arithmetic. Migration to netCDF3 API. ncap2 parser, lexer, GSL-support, and I/O. Multislabbing algorithm. Variable wildcarding. JSON backend. Numerous hacks. ncap2 language. Rorik Peterson Rorik Peterson Original autotools build support. Long command-line options. Original UDUnits support. Debianization. Numerous bug-fixes. Joe Hamman Suizer Joe Hamman Suizer Python bindings (PyNCO). Milan Klower Rostislav Kouznetsov Milan Klower, Rostislav Kouznetsov Quantization by rounding Daniel Wang Daniel Wang Script Workflow Analysis for MultiProcessing (SWAMP). RPM support. Harry Mangalam Harry Mangalam Benchmarking. OPeNDAP configuration. Pedro Vicente Pedro Vicente Windows Visual Studio support. netCDF4 groups. CMake build-engine. Jerome Mao Jerome Mao Multi-argument parsing. Joseph O&textrsquo;Rourke Joseph O&textrsquo;Rourke Routines from his book &textldquo;Computational Geometry in C&textrdquo;. Russ Rew Russ Rew Advice on NCO structural algorithms. Brian Mays Brian Mays Original packaging for Debian GNU/Linux, nroff man pages. George Shapovalov George Shapovalov Packaging for Gentoo GNU/Linux. Bill Kocik Bill Kocik Memory management. Len Makin Len Makin NEC SX architecture support. Jim Edwards Jim Edwards AIX architecture support. Juliana Rew Juliana Rew Compatibility with large PIDs. Karen Schuchardt Karen Schuchardt Auxiliary coordinate support. Gayathri Venkitachalam Gayathri Venkitachalam MPI implementation. Scott Capps Scott Capps Large work-load testing Xylar Asay-Davis Sterling Baldwin Tony Bartoletti Dave Blodgett Peter Campbell Peter Caldwell Philip Cameron-Smith Martin Dix Mark Flanner Ryan Forsyth Chris Golaz Barron Henderson Ben Hillman Aleksandar Jelenak Markus Liebig Keith Lindsay Daniel Macks, Stu Muller Daniel Neumann Mike Page Martin Schmidt Lori Sentman Michael Schulz Rich Signell Bob Simons Gary Strand Qi Tang Mark Taylor Matthew Thompson Adrian Tompkins Paul Ullrich Andrew Wittenberg George White Min Xu Remik Ziemlinski Jill Zhang Xylar Asay-Davis, Sterling Baldwin, Tony Bartoletti, Dave Blodgett, Peter Caldwell, Philip Cameron-Smith, Peter Campbell, Martin Dix, Mark Flanner, Ryan Forsyth, Chris Golaz, Barron Henderson, Ben Hillman, Aleksandar Jelenak, Markus Liebig, Keith Lindsay, Daniel Macks, Daniel Neumann, Mike Page, Martin Schmidt, Michael Schulz, Lori Sentman, Rich Signell, Bob Simons, Gary Strand, Mark Taylor, Matthew Thompson, Qi Tang, Adrian Tompkins, Paul Ullrich, George White, Andrew Wittenberg, Min Xu, Remik Ziemlinski, Jill Zhang Excellent bug reports and feature requests. Xylar Asay-Davis Filipe Fernandes Isuru Fernando Craig MacLachlan Hugo Oliveira Rich Signell Kyle Wilcox Klaus Zimmermann Filipe Fernandes, Isuru Fernando, Craig MacLachlan, Hugo Oliveira, Rich Signell, Kyle Wilcox, Klaus Zimmermann Anaconda packaging Xylar Asay-Davis Daniel Baumann Nick Bower Luk Claes Barry deFreese Francesco Lovergine Bas Couwenberg Matej Vela Xylar Asay-Davis, Daniel Baumann, Nick Bower, Luk Claebs, Bas Couwenberg, Barry deFreese, Francesco Lovergine, Matej Vela Cygwin packaging Marco Atzeri Marco Atzeri Debian packaging Patrice Dumas Ed Hill Orion Poplawski Patrice Dumas, Ed Hill, Orion Poplawski Gentoo packaging Filipe Fernandes Filipe Fernandes OpenSuse packaging Takeshi Enomoto Alexander Hansen Ian Lancaster Alejandro Soto Takeshi Enomoto, Alexander Hansen, Ian Lancaster, Alejandro Soto Mac OS packaging Eric Blake Eric Blake PyNCO Anaconda and Pip packaging, bug fixes Tim Heap Tim Heap RedHat packaging George Shapavalov Patrick Kursawe Manfred Schwarb George Shapavalov, Patrick Kursawe, Manfred Schwarb Autoconf/M4 help Gavin Burris Kyle Wilcox Gavin Burris, Kyle Wilcox RHEL and CentOS build scripts and bug reports. Andrea Cimatoribus Andrea Cimatoribus NCO Spiral Logo Sha Feng Walter Hannah Martin Otte Etienne Tourigny Sha Feng, Walter Hannah, Martin Otte, Etienne Tourigny Miscellaneous bug reports and fixes Wenshan Wang Wenshan Wang CMIP5 and MODIS processing documentation, reference card Thomas Hornigold Ian McHugh Todd Mitchell Emily Wilbur Thomas Hornigold Ian McHugh Todd Mitchell Emily Wilbur Acknowledgement via financial donations

Please let me know if your name was omitted! <a name="ctt"></a>

CitationProposals for Institutional FundingContributorsContributing

Citation citation The recommended citations for NCO software are Zender, C. S. (2008), Analysis of Self-describing Gridded Geoscience Data with netCDF Operators (NCO), Environ. Modell. Softw., 23(10), 1338-1342, doi:10.1016/j.envsoft.2008.03.004. Zender, C. S. and H. J. Mangalam (2007), Scaling Properties of Common Statistical Operators for Gridded Datasets, Int. J. High Perform. Comput. Appl., 21(4), 485-498, doi:10.1177/1094342007083802. Zender, C. S. (2016), Bit Grooming: Statistically accurate precision-preserving quantization with compression, evaluated in the netCDF Operators (NCO, v4.4.8+), Geosci. Model Dev., 9, 3199-3211, doi:10.5194/gmd-9-3199-2016. Zender, C. S. (Year), netCDF Operator (NCO) User Guide, http://nco.sf.net/nco.pdf. Use the first when referring to overall design, purpose, and optimization of NCO, the second for the speed and throughput of NCO, the third for compressions, and the fourth for specific features and/or the User Guide itself, or in a non-academic setting. A complete list of NCO publications and presentations is at http://nco.sf.net#pub. This list links to the full papers and seminars themselves. <a name="prp"></a>  <a name="prp_sei"></a>  <a name="fnd"></a>  * Proposals for Institutional Funding::

Proposals for Institutional FundingCitationContributing

Proposals for Institutional Funding funding proposals NSF server-side processing Distributed Data Reduction & Analysis Scientific Data Operators DDRA Server-Side Distributed Data Reduction & Analysis SSDDRA CCSM IPCC NSF SDO SEIII OptIPuter parallelism From 2004&textndash;2007, NSF funded a http://nco.sf.net#prp_seiproject to improve Distributed Data Reduction & Analysis (DDRA) by evolving NCO parallelism (OpenMP, MPI) and Server-Side DDRA (SSDDRA) implemented through extensions to OPeNDAP and netCDF4. The SSDDRA features were implemented in SWAMP, the PhD Thesis of Daniel Wang. SWAMP dramatically reduced bandwidth usage for NCO between client and server. NASA NRA HDF With this first NCO proposal funded, the content of the next NCO proposal became clear. We had long been interested in obtaining NASA support for HDF-specific enhancements to NCO. From 2012&textndash;2015 the NASA ACCESS program funded us to implement support support netCDF4 group functionality. Thus NCO will grow and evade bit-rot for the foreseeable future. We are considering other interesting ideas for still more proposals. Please contact us if you wish to be involved with any future NCO-related proposals. Comments on the proposals and letters of support are also very welcome. <a name="quicksrt"></a>

Quick StartCMIP5 ExampleContributingTop Quick Start Quick Start Simple examples in Bash shell scripts showing how to average data with different file structures. Here we include monthly, seasonal and annual average with daily or monthly data in either one file or multiple files. * Daily data in one file::

* Monthly data in one file::

* One time point one file::

* Multiple files with multiple time points::

Daily data in one fileMonthly data in one fileQuick StartQuick Start

Daily data in one file daily data Suppose we have daily data from Jan 1st, 1990 to Dec. 31, 2005 in the file of in.nc with the record dimension as time. Monthly average: monthly average average time-averaging for yyyy in {1990..2005}; do # Loop over years for moy in {1..12}; do # Loop over months mm=$( printf "%02d" ${moy} ) # Change to 2-digit format # Average specific month yyyy-mm ncra -O -d time,"${yyyy}-${mm}-01","${yyyy}-${mm}-31" \ in.nc in_${yyyy}${mm}.nc done done # Concatenate monthly files together ncrcat -O in_??????.nc out.nc Annual average: annual average from daily data average time-averaging for yyyy in {1990..2005}; do # Loop over years ncra -O -d time,"${yyyy}-01-01","${yyyy}-12-31" in.nc in_${yyyy}.nc done # Concatenate annual files together ncrcat -O in_????.nc out.nc The switch means to overwrite the pre-existing files (Batch Mode). The option is to specify the range of hyperslabs (Hyperslabs). There are detailed instructions on ncra (ncra netCDF Record Averager and ncrcat (ncrcat netCDF Record Concatenator). NCO supports UDUnits so that we can use readable dates as time dimension (UDUnits Support).

Monthly data in one fileOne time point one fileDaily data in one fileQuick Start

Monthly data in one file monthly data Inside the input file in.nc, the record dimension time is from Jan 1990 to Dec 2005. Seasonal average (e.g., DJF): seasonal average average time-averaging

ncra -O --mro -d time,"1990-12-01",,12,3 in.nc out.nc

Annual average: annual average from monthly data average time-averaging

ncra -O --mro -d time,,,12,12 in.nc out.nc

Here we use the subcycle feature (i.e., the number after the fourth comma: 3 in the seasonal example and the second 12 in the annual example) to retrieve groups of records separated by regular intervals (Subcycle). The option switches ncra to produce a Multi-Record Output instead of a single-record output. For example, assume snd is a 3D array with dimensions time * latitude * longitude and time includes every month from Jan. 1990 to Dec. 2005, 192 months in total, or 16 years. Consider the following two command lines:

ncra --mro -v snd -d time,"1990-12-01",,12,3 in.nc out_mro.nc
ncra -v snd -d time,"1990-12-01",,12,3 in.nc out_sro.nc

In the first output file, out_mro.nc, snd is still a 3D array with dimensions time * latitude * longitude, but the length of time now is 16, meaning 16 winters. In the second output file, out_sro.nc, the length of time is only 1, which contains the average of all 16 winters. When using -d dim,min[,max] to specify the hyperslabs, you can leave it blank if you want to include the minimum or the maximum of the data, like we did above.

One time point one fileMultiple files with multiple time pointsMonthly data in one fileQuick Start

One time point one file daily data monthly data average time-averaging This means if you have daily data of 30 days, there will be 30 data files. Or if you have monthly data of 12 months, there will be 12 data files. Dealing with this kind of files, you need to specify the file names in shell scripts and pass them to NCO operators. For example, your daily data files may look like snd_19900101.nc, snd_19900102.nc, snd_19900103.nc ... If you want to know the monthly average of Jan 1990, you can write like,

ncra -O snd_199001??.nc out.nc

You might want to use loop if you need the average of each month. for moy in {1..12}; do # Loop over months mm=$( printf "%02d" ${moy} ) # Change to 2-digit format ncra -O snd_????${mm}??.nc out_${mm}.nc done

Multiple files with multiple time pointsOne time point one fileQuick Start

Multiple files with multiple time points daily data monthly data Similar as the last one, it&textrsquo;s more about shell scripts. Suppose you have daily data with one month of them in one data file. The monthly average is simply to apply ncra on the specific data file. And for seasonal averages, you can specify the three months by shell scripts. <a name="cmip5"></a>  <a name="godad"></a>

CMIP5 ExampleParallelQuick StartTop CMIP5 Example CMIP5 GODAD This @uref{http://nco.sf.net/xmp_cesm.html,Wonderful CMIP5 Documentation} shows complete processing of the @acronym{CMIP5} dataset. The fifth phase of the Coupled Model Intercomparison Project (http://cmip-pcmdi.llnl.gov/cmip5/index.html?submenuheader=0CMIP5) provides a multi-model framework for comparing the mechanisms and responses of climate models from around the world. However, it is a tremendous workload to retrieve a single climate statistic from all these models, each of which includes several ensemble members. Not only that, it is too often a tedious process that impedes new research and hypothesis testing. Our NASA ACCESS 2011 project simplified and accelerated this process. Traditional geoscience data analysis requires users to work with numerous flat (data in one level or namespace) files. In that paradigm instruments or models produce, and then repositories archive and distribute, and then researchers request and analyze, collections of flat files. NCO works well with that paradigm, yet it also embodies the necessary algorithms to transition geoscience data analysis from relying solely on traditional (or &textldquo;flat&textrdquo;) datasets to allowing newer hierarchical (or &textldquo;nested&textrdquo;) datasets. Hierarchical datasets support and enable combining all datastreams that meet user-specified criteria into a single or small number of files that hold all the science-relevant data. NCO (and no other software to our knowledge) exploits this capability now. Data and metadata may be aggregated into and analyzed in hierarchical structures. We call the resulting data storage, distribution, and analysis paradigm Group-Oriented Data Analysis and Distribution (GODAD). GODAD lets the scientific question organize the data, not the ad hoc granularity of all relevant datasets. This chapter illustrates GODAD techniques applied to analysis of the CMIP5 dataset. To begin, we document below a prototypical example of CMIP5 analysis and evaluation using traditional NCO commands on netCDF3-format model and HDF-EOS format observational (NASA MODIS satellite instrument) datasets. These examples complement the NCO User Guide by detailing in-depth data analysis in a frequently encountered &textldquo;real world&textrdquo; context. Graphical representations of the results (NCL scripts available upon request) are provided to illustrate physical meaning of the analysis. In 2013 we added scripts which make use of new @acronym{NCO} features that combine all the loops in the analysis into single commands by exploiting @acronym{NCO}'s new group aggregation and arithmetic features. Since NCO can process hierarchical datasets, i.e., datasets stored with netCDF4 groups, we present sample scripts illustrating group-based processing as well. * Combine Files::

* Global Distribution of Long-term Average::

* Annual Average over Regions::

* Monthly Cycle::

* Regrid MODIS Data::

* Add Coordinates to MODIS Data::

* Permute MODIS Coordinates::

Combine FilesGlobal Distribution of Long-term AverageCMIP5 ExampleCMIP5 Example

Combine Files file combination Sometimes, the data of one ensemble member will be stored in several files to reduce single file size. It is more convenient to concatenate these files into a single timeseries, and the following script illustrates how. Key steps include: Obtain number and names (or partial names) of files in a directory Concatenate files along the record dimension (usually time) using ncrcat (ncrcat netCDF Record Concatenator). xmp/cmb_fl.sh CMIP5 model data downloaded from the Earth System Grid Federation (http://pcmdi9.llnl.gov/esgf-web-fe/ESGF) does not contain group features yet. Therefore users must aggregate flat files into hierarchical ones themselves. The following script shows how. Each dataset becomes a group in the output file. There can be several levels of groups. In this example, we employ two experiments (&textldquo;scenarios&textrdquo;) as the top-level. The second-level comprises different models (e.g., CCSM4, CESM1-BGC). Many models are run multiple times with slight perturbed initial conditions to produce an ensemble of realizations. These ensemble members comprise the third level of the hierarchy. The script selects two variables, snc and snd (snow cover and snow depth). aggregation group aggregation groups, creating xmp/cmb_fl_grp.sh

Global Distribution of Long-term AverageAnnual Average over RegionsCombine FilesCMIP5 Example

Global Distribution of Long-term Average spatial distribution long-term average average time-averaging Figurefgr:glb xmp/fgr13.5in Global Distribution of Long-term Average. This section illustrates how to calculate the global distribution of long-term average (fgr:glb) with either flat files or http://nco.sourceforge.net/nco.html#index-groupsgroup file. Key steps include: Average ensemble members of each model using nces (nces netCDF Ensemble Statistics) Average the record dimension using ncra (ncra netCDF Record Averager) Store results of each model as a distinct group in a single output file using ncecat (ncrcat netCDF Record Concatenator) with the option The first example shows how to process flat files. xmp/glb_avg.sh With the use of group, the above script will be shortened to ONE LINE. groups, averaging

# Data from cmb_fl_grp.sh
# ensemble averaging
nces -O --nsm_grp --nsm_sfx='_avg' \
sn_LImon_all-mdl_all-xpt_all-nsm_200001-200512.nc \
  sn_LImon_all-mdl_all-xpt_nsm-avg.nc

The input file, sn_LImon_all-mdl_all-xpt_all-nsm_200001-200512.nc, produced by cmb_fl_grp.sh, includes all the ensemble members as groups. The option --nsm_grp denotes that we are using http://nco.sf.net/nco.html#nsm_grpgroup ensembles mode of nces, instead of http://nco.sf.net/nco.html#nsm_flfile ensembles mode, --nsm_fl. The option --nsm_sfx='_avg' instructs nces to store the output as a new child group /[model]/[model name]_avg/var; otherwise, the output will be stored directly in the parent group /[model]/var. In the final output file, sn_LImon_all-mdl_all-xpt_nsm-avg_tm-avg.nc, sub-groups with a suffix of &textlsquo;avg&textrsquo; are the long-term averages of each model. One thing to notice is that for now, ensembles with only one ensemble member will be left untouched.

Annual Average over RegionsMonthly CycleGlobal Distribution of Long-term AverageCMIP5 Example

Annual Average over Regions annual average average time-averaging area-averaging dimension order anomalies standard deviation renaming variables attributes, editing attributes, modifying attributes, overwriting regression nco script file variables, appending Figurefgr:anl xmp/fgr24in Annual Average over Regions. This section illustrates how to calculate the annual average over specific regions (fgr:anl). Key steps include: Spatial average using ncap2 (ncap2 netCDF Arithmetic Processor) and ncwa (ncwa netCDF Weighted Averager); Change dimension order using ncpdq (ncpdq netCDF Permute Dimensions Quickly); Annual average using ncra (ncra netCDF Record Averager); Anomaly from long-term average using ncbo (ncbo netCDF Binary Operator); Standard deviation using ncbo (ncbo netCDF Binary Operator) and nces (nces netCDF Ensemble Statistics); Rename variables using ncrename (ncrename netCDF Renamer); Edit attributions using ncatted (ncatted netCDF Attribute Editor); Linear regression using ncap2 (ncap2 netCDF Arithmetic Processor); Use ncap2 (ncap2 netCDF Arithmetic Processor) with nco script file (i.e., .nco file); Move variables around using ncks (ncks netCDF Kitchen Sink). Flat files example xmp/ann_avg.sh gsl_rgr.nco xmp/gsl_rgr.nco With the group feature, all the loops over experiments, models and ensemble members can be omitted. As we are working on implementing group feature in all NCO operators, some functions (e.g., regression and standard deviation over ensemble members) may have to wait until the new versions. group, spatial averaging group, temporal averaging group, anomaly group, standard deviation group, aggregation group, dimension permutation xmp/ann_avg_grp.sh

Monthly CycleRegrid MODIS DataAnnual Average over RegionsCMIP5 Example

Monthly Cycle monthly average average time-averaging anomalies geographical weight weighted average Figurefgr:mon xmp/fgr34in Monthly Cycle. This script illustrates how to calculate the monthly anomaly from the annual average (fgr:mon). In order to keep only the monthly cycle, we will subtract the annual average of each year from the monthly data, instead of subtracting the long-term average. This is a little more complicated in coding since we need to loop over years. Flat files example xmp/mcc.sh Using group feature and http://nco.sourceforge.net/nco.html#Hyperslabshyperslabs of ncbo, the script will be shortened. xmp/mcc_grp.sh

Regrid MODIS DataAdd Coordinates to MODIS DataMonthly CycleCMIP5 Example

Regrid MODIS Data regrid MODIS bilinear interpolation interpolation renaming variables renaming attributes renaming dimensions attributes, editing attributes, modifying attributes, overwriting In order to compare the results between MODIS and CMIP5 models, one usually regrids one or both datasets so that the spatial resolutions match. Here, the script illustrates how to regrid MODIS data. Key steps include: Regrid using bilinear interpolation (Bilinear interpolation) Rename variables, dimensions and attributions using ncrename (ncrename netCDF Renamer). Main Script xmp/rgr.sh bi_interp.nco xmp/bi_interp.nco

Add Coordinates to MODIS DataPermute MODIS CoordinatesRegrid MODIS DataCMIP5 Example

Add Coordinates to MODIS Data MODIS coordinates Main Script xmp/add_crd.sh crd.nco xmp/crd.nco

Permute MODIS CoordinatesAdd Coordinates to MODIS DataCMIP5 Example

Permute MODIS Coordinates coordinates, modifying MODIS orders latitude data from 90°N to -90°N, and longitude from -180°E to 180°E. However, CMIP5 orders latitude from -90°N to 90°N, and longitude from 0°E to 360°E. This script changes the MODIS coordinates to follow the CMIP5 convention. xmp/pmt_crd.sh @node Hierarchical Data Files, Parallel, CMIP5 Example, Top section Hierarchical Data Files @cindex hierarchical data @cindex groups Hierarchical Data Files support arbitrarily nested groups. The following @acronym{NCO} operators now can work recursively through all groups: @itemize @bullet @item @command{ncbo} (@pxref{ncbo netCDF Binary Operator}) @item @command{ncecat} (@pxref{ncecat netCDF Ensemble Concatenator}) @item @command{ncks} (@pxref{ncks netCDF Kitchen Sink}) @item @command{ncpdq} (@pxref{ncpdq netCDF Permute Dimensions Quickly}) @item @command{ncwa} (@pxref{ncwa netCDF Weighted Averager}) @end itemize Here is an example showing: @enumerate @item How to create a hierarchical data file from multiple files using @command{ncecat} (@pxref{ncecat netCDF Ensemble Concatenator}) or @command{ncks} (@pxref{ncks netCDF Kitchen Sink}); @item Hyperslabs using @command{ncks} (@pxref{ncks netCDF Kitchen Sink}); @item Spatial average and time average using @command{ncwa} (@pxref{ncwa netCDF Weighted Averager}); @item Anomaly from average using @command{ncbo} (@pxref{ncbo netCDF Binary Operator}). @end enumerate @example @verbatiminclude xmp/grp.sh @end example <a name="parallel"></a>  <a name="prl"></a>  <a name="swift"></a>  <a name="swf"></a>  <a name="Parallel"></a>  <a name="Swift"></a>

ParallelCCSM ExampleCMIP5 ExampleTop Parallel Parallel Swift parallel This section will describe NCO scripting strategies. Many techniques can be used to exploit script-level parallelism, including GNU Parallel and Swift. ls *historical*.nc | parallel ncks -O -d time,"1950-01-01","2000-01-01" {} 50y/{} <a name="ccsm"></a>  CCSM ExamplemybibnodeParallelTop CCSM Example CCSM This chapter illustrates how to use NCO to process and analyze the results of a CCSM climate simulation. ************************************************************************ Task 0: Finding input files x************************************************************************ The CCSM model outputs files to a local directory like: /ptmp/zender/archive/T42x1_40 Each component model has its own subdirectory, e.g., /ptmp/zender/archive/T42x1_40/atm /ptmp/zender/archive/T42x1_40/cpl /ptmp/zender/archive/T42x1_40/ice /ptmp/zender/archive/T42x1_40/lnd /ptmp/zender/archive/T42x1_40/ocn within which model output is tagged with the particular model name /ptmp/zender/archive/T42x1_40/atm/T42x1_40.cam2.h0.0001-01.nc /ptmp/zender/archive/T42x1_40/atm/T42x1_40.cam2.h0.0001-02.nc /ptmp/zender/archive/T42x1_40/atm/T42x1_40.cam2.h0.0001-03.nc ... /ptmp/zender/archive/T42x1_40/atm/T42x1_40.cam2.h0.0001-12.nc /ptmp/zender/archive/T42x1_40/atm/T42x1_40.cam2.h0.0002-01.nc /ptmp/zender/archive/T42x1_40/atm/T42x1_40.cam2.h0.0002-02.nc ... or /ptmp/zender/archive/T42x1_40/lnd/T42x1_40.clm2.h0.0001-01.nc /ptmp/zender/archive/T42x1_40/lnd/T42x1_40.clm2.h0.0001-02.nc /ptmp/zender/archive/T42x1_40/lnd/T42x1_40.clm2.h0.0001-03.nc ... ************************************************************************ Task 1: Regional processing ************************************************************************ A common task in data processing is often creating seasonal cycles. Imagine a 100-year simulation with its 1200 monthly mean files. Our goal is to create a single file containing 12 months of data. Each month in the output file is the mean of 100 input files. Normally, we store the "reduced" data in a smaller, local directory. caseid='T42x1_40' #drc_in="${DATA}/archive/${caseid}/atm" drc_in="${DATA}/${caseid}" drc_out="${DATA}/${caseid}" mkdir -p ${drc_out} cd ${drc_out} Method 1: Assume all data in directory applies for mth in {1..12}; do mm=`printf "%02d" $mth` ncra -O -D 1 -o ${drc_out}/${caseid}_clm${mm}.nc \ ${drc_in}/${caseid}.cam2.h0.*-${mm}.nc done # end loop over mth Method 2: Use shell 'globbing' to construct input filenames for mth in {1..12}; do mm=`printf "%02d" $mth` ncra -O -D 1 -o ${drc_out}/${caseid}_clm${mm}.nc \ ${drc_in}/${caseid}.cam2.h0.00??-${mm}.nc \ ${drc_in}/${caseid}.cam2.h0.0100-${mm}.nc done # end loop over mth Method 3: Construct input filename list explicitly for mth in {1..12}; do mm=`printf "%02d" $mth` fl_lst_in='' for yr in {1..100}; do yyyy=`printf "%04d" $yr` fl_in=${caseid}.cam2.h0.${yyyy}-${mm}.nc fl_lst_in="${fl_lst_in} ${caseid}.cam2.h0.${yyyy}-${mm}.nc" done # end loop over yr ncra -O -D 1 -o ${drc_out}/${caseid}_clm${mm}.nc -p ${drc_in} \ ${fl_lst_in} done # end loop over mth Make sure the output file averages correct input files! ncks --trd -M prints global metadata: ncks --trd -M ${drc_out}/${caseid}_clm01.nc The input files ncra used to create the climatological monthly mean will appear in the global attribute named 'history'. Use ncrcat to aggregate the climatological monthly means ncrcat -O -D 1 \ ${drc_out}/${caseid}_clm??.nc ${drc_out}/${caseid}_clm_0112.nc Finally, create climatological means for reference. The climatological time-mean: ncra -O -D 1 \ ${drc_out}/${caseid}_clm_0112.nc ${drc_out}/${caseid}_clm.nc The climatological zonal-mean: ncwa -O -D 1 -a lon \ ${drc_out}/${caseid}_clm.nc ${drc_out}/${caseid}_clm_x.nc The climatological time- and spatial-mean: ncwa -O -D 1 -a lon,lat,time -w gw \ ${drc_out}/${caseid}_clm.nc ${drc_out}/${caseid}_clm_xyt.nc This file contains only scalars, e.g., "global mean temperature", used for summarizing global results of a climate experiment. Climatological monthly anomalies = Annual Cycle: Subtract climatological mean from climatological monthly means. Result is annual cycle, i.e., climate-mean has been removed. ncbo -O -D 1 -o ${drc_out}/${caseid}_clm_0112_anm.nc \ ${drc_out}/${caseid}_clm_0112.nc ${drc_out}/${caseid}_clm_xyt.nc ************************************************************************ Task 2: Correcting monthly averages ************************************************************************ The previous step appoximates all months as being equal, so, e.g., February weighs slightly too much in the climatological mean. This approximation can be removed by weighting months appropriately. We must add the number of days per month to the monthly mean files. First, create a shell variable dpm: unset dpm # Days per month declare -a dpm dpm=(0 31 28.25 31 30 31 30 31 31 30 31 30 31) # Allows 1-based indexing Method 1: Create dpm directly in climatological monthly means for mth in {1..12}; do mm=`printf "%02d" ${mth}` ncap2 -O -s "dpm=0.0*date+${dpm[${mth}]}" \ ${drc_out}/${caseid}_clm${mm}.nc ${drc_out}/${caseid}_clm${mm}.nc done # end loop over mth Method 2: Create dpm by aggregating small files for mth in {1..12}; do mm=`printf "%02d" ${mth}` ncap2 -O -v -s "dpm=${dpm[${mth}]}" ~/nco/data/in.nc \ ${drc_out}/foo_${mm}.nc done # end loop over mth ncecat -O -D 1 -p ${drc_out} -n 12,2,2 foo_${mm}.nc foo.nc ncrename -O -D 1 -d record,time ${drc_out}/foo.nc ncatted -O -h \ -a long_name,dpm,o,c,"Days per month" \ -a units,dpm,o,c,"days" \ ${drc_out}/${caseid}_clm_0112.nc ncks -A -v dpm ${drc_out}/foo.nc ${drc_out}/${caseid}_clm_0112.nc Method 3: Create small netCDF file using ncgen cat > foo.cdl << 'EOF' netcdf foo { dimensions: time=unlimited; variables: float dpm(time); dpm:long_name="Days per month"; dpm:units="days"; data: dpm=31,28.25,31,30,31,30,31,31,30,31,30,31; } EOF ncgen -b -o foo.nc foo.cdl ncks -A -v dpm ${drc_out}/foo.nc ${drc_out}/${caseid}_clm_0112.nc Another way to get correct monthly weighting is to average daily output files, if available. ************************************************************************ Task 3: Regional processing ************************************************************************ Let's say you are interested in examining the California region. Hyperslab your dataset to isolate the appropriate latitude/longitudes. ncks -O -D 1 -d lat,30.0,37.0 -d lon,240.0,270.0 \ ${drc_out}/${caseid}_clm_0112.nc \ ${drc_out}/${caseid}_clm_0112_Cal.nc The dataset is now much smaller! To examine particular metrics. ************************************************************************ Task 4: Accessing data stored remotely ************************************************************************ OPeNDAP server examples: UCI DAP servers: ncks --trd -M -p http://dust.ess.uci.edu/cgi-bin/dods/nph-dods/dodsdata in.nc ncrcat -O -C -D 3 \ -p http://dust.ess.uci.edu/cgi-bin/dods/nph-dods/dodsdata \ -l /tmp in.nc in.nc ~/foo.nc Unidata DAP servers: ncks --trd -M -p http://thredds-test.ucar.edu/thredds/dodsC/testdods in.nc ncrcat -O -C -D 3 \ -p http://thredds-test.ucar.edu/thredds/dodsC/testdods \ -l /tmp in.nc in.nc ~/foo.nc NOAA DAP servers: ncwa -O -C -a lat,lon,time -d lon,-10.,10. -d lat,-10.,10. -l /tmp -p \ http://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets/ncep.reanalysis.dailyavgs/surface \ pres.sfc.1969.nc ~/foo.nc LLNL PCMDI IPCC OPeNDAP Data Portal: ncks --trd -M -p http://username:password@esgcet.llnl.gov/cgi-bin/dap-cgi.py/ipcc4/sresa1b/ncar_ccsm3_0 pcmdi.ipcc4.ncar_ccsm3_0.sresa1b.run1.atm.mo.xml Earth System Grid (ESG): http://www.earthsystemgrid.org caseid='b30.025.ES01' CCSM3.0 1% increasing CO2 run, T42_gx1v3, 200 years starting in year 400 Atmospheric post-processed data, monthly averages, e.g., /data/zender/tmp/b30.025.ES01.cam2.h0.TREFHT.0400-01_cat_0449-12.nc /data/zender/tmp/b30.025.ES01.cam2.h0.TREFHT.0400-01_cat_0599-12.nc ESG supports password-protected FTP access by registered users NCO uses the .netrc file, if present, for password-protected FTP access Syntax for accessing single file is, e.g., ncks -O -D 3 \ -p ftp://climate.llnl.gov/sresa1b/atm/mo/tas/ncar_ccsm3_0/run1 \ -l /tmp tas_A1.SRESA1B_1.CCSM.atmm.2000-01_cat_2099-12.nc ~/foo.nc # Average surface air temperature tas for SRESA1B scenario # This loop is illustrative and will not work until NCO correctly # translates '*' to FTP 'mget' all remote files for var in 'tas'; do for scn in 'sresa1b'; do for mdl in 'cccma_cgcm3_1 cccma_cgcm3_1_t63 cnrm_cm3 csiro_mk3_0 \ gfdl_cm2_0 gfdl_cm2_1 giss_aom giss_model_e_h giss_model_e_r \ iap_fgoals1_0_g inmcm3_0 ipsl_cm4 miroc3_2_hires miroc3_2_medres \ miub_echo_g mpi_echam5 mri_cgcm2_3_2a ncar_ccsm3_0 ncar_pcm1 \ ukmo_hadcm3 ukmo_hadgem1'; do for run in '1'; do ncks -R -O -D 3 -p ftp://climate.llnl.gov/${scn}/atm/mo/${var}/${mdl}/run${run} -l ${DATA}/${scn}/atm/mo/${var}/${mdl}/run${run} '*' ${scn}_${mdl}_${run}_${var}_${yyyymm}_${yyyymm}.nc done # end loop over run done # end loop over mdl done # end loop over scn done # end loop over var cd sresa1b/atm/mo/tas/ukmo_hadcm3/run1/ ncks -H -m -v lat,lon,lat_bnds,lon_bnds -M tas_A1.nc | m bds -x 096 -y 073 -m 33 -o ${DATA}/data/dst_3.75x2.5.nc # ukmo_hadcm3 ncview ${DATA}/data/dst_3.75x2.5.nc # msk_rgn is California mask on ukmo_hadcm3 grid # area is correct area weight on ukmo_hadcm3 grid ncks -A -v area,msk_rgn ${DATA}/data/dst_3.75x2.5.nc \ ${DATA}/sresa1b/atm/mo/tas/ukmo_hadcm3/run1/area_msk_ukmo_hadcm3.nc Template for standardized data: ${scn}_${mdl}_${run}_${var}_${yyyymm}_${yyyymm}.nc e.g., raw data ${DATA}/sresa1b/atm/mo/tas/ukmo_hadcm3/run1/tas_A1.nc becomes standardized data Level 0: raw from IPCC site--no changes except for name Make symbolic link name match raw data Template: ${scn}_${mdl}_${run}_${var}_${yyyymm}_${yyyymm}.nc ln -s -f tas_A1.nc sresa1b_ukmo_hadcm3_run1_tas_200101_209911.nc area_msk_ukmo_hadcm3.nc Level I: Add all variables (not standardized in time) to file containing msk_rgn and area Template: ${scn}_${mdl}_${run}_${yyyymm}_${yyyymm}.nc /bin/cp area_msk_ukmo_hadcm3.nc sresa1b_ukmo_hadcm3_run1_200101_209911.nc ncks -A -v tas sresa1b_ukmo_hadcm3_run1_tas_200101_209911.nc \ sresa1b_ukmo_hadcm3_run1_200101_209911.nc ncks -A -v pr sresa1b_ukmo_hadcm3_run1_pr_200101_209911.nc \ sresa1b_ukmo_hadcm3_run1_200101_209911.nc If already have file then: mv sresa1b_ukmo_hadcm3_run1_200101_209911.nc foo.nc /bin/cp area_msk_ukmo_hadcm3.nc sresa1b_ukmo_hadcm3_run1_200101_209911.nc ncks -A -v tas,pr foo.nc sresa1b_ukmo_hadcm3_run1_200101_209911.nc Level II: Correct # years, months Template: ${scn}_${mdl}_${run}_${var}_${yyyymm}_${yyyymm}.nc ncks -d time,....... file1.nc file2.nc ncrcat file2.nc file3.nc sresa1b_ukmo_hadcm3_run1_200001_209912.nc Level III: Many derived products from level II, e.g., A. Global mean timeseries ncwa -w area -a lat,lon \ sresa1b_ukmo_hadcm3_run1_200001_209912.nc \ sresa1b_ukmo_hadcm3_run1_200001_209912_xy.nc B. Califoria average timeseries ncwa -m msk_rgn -w area -a lat,lon \ sresa1b_ukmo_hadcm3_run1_200001_209912.nc \ sresa1b_ukmo_hadcm3_run1_200001_209912_xy_Cal.nc <a name="bibliography"></a>  <a name="bib"></a>  mybibnodeGeneral IndexCCSM ExampleTop References • ZeM07[ZeM07] Zender, C. S., and H. J. Mangalam (2007), Scaling Properties of Common Statistical Operators for Gridded Datasets, Int. J. High Perform. Comput. Appl., 21(4), 485-498, doi:10.1177/1094342007083802. • Zen08[Zen08] Zender, C. S. (2008), Analysis of Self-describing Gridded Geoscience Data with netCDF Operators (NCO), Environ. Modell. Softw., 23(10), 1338-1342, doi:10.1016/j.envsoft.2008.03.004. • WZJ07[WZJ07] Wang, D. L., C. S. Zender, and S. F. Jenks (2007), DAP-enabled Server-side Data Reduction and Analysis, Proceedings of the 23rd AMS Conference on Interactive Information and Processing Systems (IIPS) for Meteorology, Oceanography, and Hydrology, Paper 3B.2, January 14-18, San Antonio, TX. American Meteorological Society, AMS Press, Boston, MA. • ZMW06[ZMW06] Zender, C. S., H. Mangalam, and D. L. Wang (2006), Improving Scaling Properties of Common Statistical Operators for Gridded Geoscience Datasets, Eos Trans. AGU, 87(52), Fall Meet. Suppl., Abstract IN53B-0827. • ZeW07[ZeW07] Zender, C. S., and D. L. Wang (2007), High performance distributed data reduction and analysis with the netCDF Operators (NCO), Proceedings of the 23rd AMS Conference on Interactive Information and Processing Systems (IIPS) for Meteorology, Oceanography, and Hydrology, Paper 3B.4, January 14-18, San Antonio, TX. American Meteorological Society, AMS Press, Boston, MA. • WZJ06[WZJ06] Wang, D. L., C. S. Zender, and S. F. Jenks (2006), Server-side netCDF Data Reduction and Analysis, Eos Trans. AGU, 87(52), Fall Meet. Suppl., Abstract IN53B-0826. • WZJ073[WZJ073] Wang, D. L., C. S. Zender, and S. F. Jenks (2007), Server-side parallel data reduction and analysis, in Advances in Grid and Pervasive Computing, Second International Conference, GPC 2007, Paris, France, May 2-4, 2007, Proceedings. IEEE Lecture Notes in Computer Science, vol. 4459, edited by C. Cerin and K.-C. Li, pp. 744-750, Springer-Verlag, Berlin/Heidelberg, doi:10.1007/978-3-540-72360-8_67. • WZJ074[WZJ074] Wang, D. L., C. S. Zender and S. F. Jenks (2007), A System for Scripted Data Analysis at Remote Data Centers, Eos Trans. AGU, 88(52), Fall Meet. Suppl., Abstract IN11B-0469. • WZJ081[WZJ081] Wang, D. L., C. S. Zender and S. F. Jenks (2008), Cluster Workflow Execution of Retargeted Data Analysis Scripts, Proceedings of the 8th IEEE Int&textrsquo;l Symposium on Cluster Computing and the Grid (IEEE CCGRID &textrsquo;08), pp. 449-458, Lyon, France, May 2008. • WZJ091[WZJ091] Wang, D. L., C. S. Zender, and S. F. Jenks (2009), Efficient Clustered Server-side Data Analysis Workflows using SWAMP, Earth Sci. Inform., 2(3), 141-155, doi:10.1007/s12145-009-0021-z. • PFT88[PFT88] Press, Flannery, Teukolsky, and Vetterling (1988), Numerical Recipes in C, Cambridge Univ. Press, New York, NY. <a name="index"></a>  <a name="idx"></a>  General IndexmybibnodeTop General Index