nco.xml 4.2.0 4.2.0 1995–2012 2012 19 May 2012 May 2012 NCO4.2.0 User's Guide true References netCDF This file documents NCO, a collection of utilities to manipulate and analyze netCDF files. Copyright ©right; 1995–2012 Charlie Zender This is the first edition of the NCO User's 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; NCO User's Guide A suite of netCDF operators Edition 4.2.0, for <acronym><acronymword>NCO</acronymword></acronym>Version 4.2.0 May 2012 by Charlie Zender Department of Earth System Science University of California, Irvine Copyright ©right; 1995–2012 Charlie Zender. This is the first edition of the NCO User's 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 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; Top Foreword (dir) (dir) Note to readers of the NCO User's Guide in Info format: The ./nco.pdfNCO User's 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/packages/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's Guide illustrates NCO use with examples from the field of climate modeling and analysis. The NCO homepage is http://nco.sf.net, and there is a mirror at http://dust.ess.uci.edu/nco. This documentation is for NCO version 4.2.0. It was last updated 19 May 2012. Corrections, additions, and rewrites of this documentation are very welcome. Enjoy,&linebreak; Charlie Zender Foreword Foreword Summary Summary Introduction Introduction Strategies Strategies Common features Common features Operator Reference Manual Operator Reference Manual Contributing Contributing CCSM Example CCSM Example mybibnode mybibnode General Index General Index Foreword Summary Top Top forewordCharlie ZenderNCO 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's 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 sourceNCO 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 2004–2008 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; Summary Introduction Foreword Top operatorssummaryThis 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. Introduction Strategies Summary Top introduction Availability Availability Compatability Compatability Symbolic Links Symbolic Links Libraries Libraries netCDF2/3/4 and HDF4/5 Support netCDF2/3/4 and HDF4/5 Support Help Requests and Bug Reports Help Requests and Bug Reports Availability Compatability Introduction Introduction

NCO availabilitysource codeThe 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. documentationWWW documentationon-line documentationHTML&tex;infoInfoUser's GuideNCO User's GuideThe documentation for NCO is called the NCO User's Guide. The User's Guide is available in Postscript, HTML, DVI, &tex;info, and Info formats. These formats are included in the source distribution in the files 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. publicationspresentationsA 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 fxm ZeM06 and fxm Zen07. These contain copyright restrictions which limit their redistribution, but they are freely available in preprint form from the NCO. NCO homepageIf 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's Guide is also available online through the World Wide Web at URL http://nco.sf.net/nco.html. netCDFTo build and use NCO, you must have netCDF installed. The netCDF homepage is http://www.unidata.ucar.edu/packages/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.

Compatability Symbolic Links Availability Introduction

OSIBMNECSGIHPDECPGICrayDigitalSunIntelComeauCompaqMacintoshMicrosoftWindowsPathScaleQLogiccompatabilityportabilityinstallationNCO has been successfully ported and tested and is known to work on the following 32- and 64-bit platforms: IBM AIX 4.x, 5.x, FreeBSD 4.x, GNU/Linux 2.x, LinuxPPC, LinuxAlpha, LinuxARM, LinuxSparc64, SGI IRIX 5.x and 6.x, MacOS X 10.x, NEC Super-UX 10.x, DEC OSF, Sun SunOS 4.1.x, Solaris 2.x, Cray UNICOS 8.x–10.x, and MS Windows95 and all later versions. If you port the code to a new operating system, please send me a note and any patches you required. UNIXUnidataUDUnitsThe 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 C compilers including those from CCc++cccomocxxgcciccpgccpgCCpathccpathCCxlCxlcGNU (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), NEC (cc), PathScale (QLogic) (pathcc -std=c99), PGI (pgcc -c9x), SGI (cc -c99), and Sun (cc). C++ISOlibncoNCO (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), NEC (c++), PathScale (Qlogic) (pathCC), PGI (pgCC), SGI (CC -LANG:std), and Sun (CC -LANG:std). MakefileSee nco/bld/Makefile and nco/src/nco_c++/Makefile.old for more details and exact settings. ANSIC89printfUntil 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. C99These 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. 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 languageAs of August 1999, NCO built entirely in C by default. This allowed NCO to compile on any machine with an ANSI C compiler. C99C89restrictIn 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=''.. GSLncap2In 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 (see GSL special functions). gammaIn 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 “true gamma”). automagicNCO 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. Frankenstein?) could resurrect it. If you would like to volunteer to maintain nco_fortran.F please contact me. Windows Operating System Windows Operating System

Windows Operating System Compatability Compatability WindowsMicrosoftXP (Microsoft operating system)NT (Microsoft operating system)Vista (Microsoft operating system) NCO has been successfully ported and tested on most Microsoft Windows operating systems including: 95/98/NT/2000/XP/Vista. The switches necessary to accomplish this are included in the standard distribution of NCO. Using the freely available Cygwin (formerly gnu-win32) development environment The Cygwin package is available from&linebreak; http://sourceware.redhat.com/cygwin&linebreak; gccg++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 tokensCygwingnu-win32WIN32GNUmakefileMakefilef90Set 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. UNIXgetuidgethostname<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>). ftpsftprcpscpSSHremote filesFortunately, these UNIX-y calls are only invoked by the single NCO subroutine which is responsible for retrieving files stored on remote systems (see 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—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. Symbolic Links Libraries Compatability Introduction

symbolic linksNCO 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, ncea 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 ncea, ncra, or ncrcat. Logically, these are three different operators that happen to share the same executable. Cygwinsynonympseudonym--pseudonymFor 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 ncecat # ncra --pseudonym='ncecat' 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: Cygwin executable (and link) names have an '.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='/'.

Libraries netCDF2/3/4 and HDF4/5 Support Symbolic Links Introduction

librariesLD_LIBRARY_PATHdynamic linkingstatic linkingLike all executables, the NCO operators can be built using dynamic linking. performanceoperator speedspeedexecution timeThis 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: /usr/local/bin/ncea: fatal: libsunmath.so.1: can't open file: errno=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 ncea`., 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 Support Help Requests and Bug Reports Libraries Introduction

netCDF2netCDF3netCDF 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 eager to reap the performance advantages of the newer 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 conversion of NCO 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 HDFHierarchical Data FormatMike FolkHowever, the ability to compile NCO with only netCDF2 calls is worth maintaining because HDF version 4 The Hierarchical Data Format, or HDF, is another self-describing data format similar to, but more elaborate than, netCDF. (available from http://hdf.ncsa.uiuc.eduHDF) supports only the netCDF2 library calls (see http://hdf.ncsa.uiuc.edu/UG41r3_html/SDS_SD.fm12.html#47784). Note that there are multiple versions of HDF. Currently HDF version 4.x supports netCDF2 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. Does MF stands for Mike Folk? Perhaps. In any case, the MFHDF library only supports netCDF2 calls. Thus I will try to keep this capability in NCO as long as it is not too much trouble.. NETCDF2_ONLYThe 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. The NCO 1.2.x branch will be maintained with bugfixes only (no new features) until HDF begins to fully support the netCDF3 interface (which is employed by NCO 2.x). If, at compilation time, NETCDF2_ONLY is defined, then NCO version 1.2.x will not use any netCDF3 calls and, if linked properly, the resulting NCO operators will work with HDF4 files. MakefileThe Makefile supplied with NCO 1.2.x is written to simplify building in this HDF capability. When NCO is built with make HDF4=Y, the Makefile sets all required preprocessor flags and library links to build with the HDF4 libraries (which are assumed to reside under /usr/local/hdf4, edit the Makefile to suit your installation). UnidataNCSAnetCDF4HDF5HDF 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. In 2004, Unidata and NCSA 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 Selecting Output File Format 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. 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_UBYTENC_USHORTNC_UINTNC_INT64NC_UINT64The main netCDF4 features that NCO currently supports are the new atomic data types, Lempel-Ziv compression (deflation), and chunking. 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. deflationNCO 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. chunkingNCO 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. HDF5-4-3netCDF4-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 Selecting Output File Format for more details. RPMDebianAs of 2010, netCDF4 is still relatively new software. Problems with netCDF4 and HDF libraries are still being fixed. Binary NCO distributions shipped as RPMs use the netCDF4 library, while debs use the netCDF3 library, because of upstream requirements. NETCDF4_ROOTOne 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.

Help Requests and Bug Reports netCDF2/3/4 and HDF4/5 Support Introduction

reporting bugsbugs, reportingcore dumphelpfeatures, requestingWe generally receive three categories of mail from users: help requests, bug reports, and feature requests. Notes saying the equivalent of "Hey, NCO continues to work great and it saves me more time everyday than it took to write this note" 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—they preserve a record of the questions and answers so that others can learn from our exchange. Also, since NCO is 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, check first to see if that feature is already on the file:./TODOTODO list. If it is, why not implement that feature yourself and send us the patch? If the feature is not yet on the list, then send a note to the http://sourceforge.net/projects/nco/forums/forum/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-DThat 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). Post your help request to the http://sourceforge.net/projects/nco/forums/forum/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. -r--revision--version--vrs-D debug-level--debug-level debug-level--dbg_lvl debug-leveldebug-leveldbg_lvlRun 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 publically accessible location, of the file(s). Post the full bug report to the http://sf.net/bugs/?group_id=3331NCO Project buglist. installationautoconfnco.configure.${GNU_TRP}.foonco.config.log.${GNU_TRP}.foonco.make.${GNU_TRP}.fooconfig.guessconfigure.egBuild failures count as bugs. Our limited machine access means we cannot fix all build failures. The information we need to diagnose, and often fix, build failures are the three files output by GNU build tools, nco.config.log.${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 "GNU architecture triplet", the chip-vendor-OS string returned by config.guess. Please send us your improvements to the examples supplied in configure.eg. regressions archiveThe 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.

Strategies Common features Introduction Top Philosophy Philosophy Climate Model Paradigm Climate Model Paradigm Temporary Output Files Temporary Output Files Appending Variables Appending Variables Simple Arithmetic and Interpolation Simple Arithmetic and Interpolation Averaging vs. Concatenating Averaging vs. Concatenating Large Numbers of Files Large Numbers of Files Large Datasets Large Datasets Memory Requirements Memory Requirements Performance Performance Philosophy Climate Model Paradigm Strategies Strategies

philosophyclimate 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. NCLIDLPerlYorickHigher 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 switchesAnother 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.

Climate Model Paradigm Temporary Output Files Philosophy Strategies

climate modelNCARGCM NCO was developed at NCAR to aid analysis and manipulation of datasets produced by General Circulation Models (GCMs). Datasets produced by GCMs share many features with all 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.

Temporary Output Files Appending Variables Climate Model Paradigm Strategies

data safetyerror tolerancesafeguardstemporary output filesNCO operators are designed to be reasonably fault tolerant, so that if there is a system failure or the user aborts the operation (e.g., with C-c), then no data are lost. The user-specified output-file is only created upon successful completion of the operation The ncrename operator is an exception to this rule. See 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. Note the construction of a temporary output file uses more disk space than just overwriting existing files “in place” (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). performanceoperator speedspeedexecution timeAlso, note this feature increases the execution time of the operator by approximately the time it takes to copy the output-file. Finally, note this feature allows the output-file to be the same as the input-file without any danger of “overlap”. -A-O--apn--append--ovr--overwriteoverwriting filesappending variablesappending to filesOther 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.

Appending Variables Simple Arithmetic and Interpolation Temporary Output Files Strategies

Adding variables from one file to another is often desirable. concatenationappending variablesmerging filespasting variablesThis 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 dimensionIn 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—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, See ncks netCDF Kitchen Sink. union of two filesdisjoint filesThe 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.

Simple Arithmetic and Interpolation Averaging vs. Concatenating Appending Variables Strategies

Users comfortable with NCO semantics may find it easier to perform some simple mathematical operations in NCO rather than higher level languages. ncbo (see ncbo netCDF Binary Operator) does file addition, subtraction, multiplication, division, and broadcasting. ncflint (see ncflint netCDF File Interpolator) does file addition, subtraction, multiplication and interpolation. Sequences of these commands can accomplish simple but powerful operations from the command line.

Averaging vs. Concatenating Large Numbers of Files Simple Arithmetic and Interpolation Strategies

symbolic linksThe most frequently used operators of NCO are probably the averagers and concatenators. Because there are so many permutations of 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, ncea, ncwa, ncrcat, and ncecat. These operators do share many capabilities Currently ncea 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, but merely have different inner loops., but each has its unique specialty. Two of these operators, ncrcat and ncecat, are for concatenating hyperslabs across files. The other two operators, ncra and ncea, are for averaging hyperslabs across files The third averaging operator, ncwa, is the most sophisticated averager in NCO. However, ncwa is in a different class than ncra and ncea because it can only operate on a single file per invocation (as opposed to multiple files). On that single file, however, ncwa provides a richer set of averaging options—including weighting, masking, and broadcasting.. First, let's describe the concatenators, then the averagers. Concatenation Concatenation Averaging Averaging Interpolating Interpolating

Concatenation Averaging Averaging vs. Concatenating Averaging vs. Concatenating ncecatncrcat Joining independent files together along a 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'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. See 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. ensembleclimate modelFor 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 the individual realizations together 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. See ncecat netCDF Ensemble Concatenator, for a complete description of ncecat. ncpdqncpdq 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. Averaging Interpolating Concatenation Averaging vs. Concatenating nceancrancwa The differences between the averagers ncra and ncea are analogous to the differences between the concatenators. ncra is designed for averaging record variables from at least one file, while ncea 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. ncea 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 ncea can average both fixed-length and record variables. To conserve system memory, use ncra rather than ncea where possible (e.g., if each input-file is one record long). The file output from ncea will have the same dimensions (meaning dimension names as well as sizes) as the input hyperslabs (see ncea netCDF Ensemble Averager, for a complete description of ncea). 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 (see ncra netCDF Record Averager, for a complete description of ncra). Interpolating Averaging Averaging vs. Concatenating 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 (see 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. Large Numbers of Files Large Datasets Averaging vs. Concatenating Strategies

files, numerous input-n loop Occasionally one desires to digest (i.e., concatenate or average) hundreds or thousands of input files. automagicNASA EOSDISUnfortunately, data archives (e.g., NASA EOSDIS) may not name netCDF files in a format understood by the -n loop switch (see 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 (see 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 from OS to OS and from shell to shell. 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. standard inputstdinThe 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 (see History Attribute) does not record the name of any input files since the names were not passed on the command line. This makes determining the data provenance at a later date difficult. nco_input_file_numbernco_input_file_listglobal attributesattributes, globalTo remedy this situation, multi-file 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 (see 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. globbingshellextended regular expressionsregular expressionspattern matchingxargsUNIXA 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 can not 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 (see Subsetting Variables). 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 pipesThe single quotes protect the only sensitive parts of the extended regular expression (the grep argument), and allow shell interpolation (the ${CASEID} variable substitution) to proceed unhindered on the rest of the command. xargs uses the UNIX pipe feature to append the suitably filtered input file list to the end of the ncecat command options. output fileinput files-o fl_outThe -o foo.nc switch ensures that the input files supplied by xargs are not confused with the output file name. xargs does, unfortunately, have its own limit (usually about 20,000 characters) on the size of command lines it can pass. Give xargs the -x switch to ensure it dies if it reaches this internal limit. When this occurs, use either the stdin method above, or the symbolic link presented next. symbolic linksEven when its internal limits have not been reached, the xargs technique may not be sophisticated enough to handle all situations. A full scripting language like Perl can handle any level of complexity of filtering input filenames, and any number of filenames. The technique of last resort is to write a script that creates symbolic links between the irregular input filenames and a set of regular, arithmetic filenames that the -n loop switch understands. PerlFor example, the following Perl script a monotonically enumerated symbolic link to up to one million .nc files in a directory. If there are 999,999 netCDF files present, the links are named 000001.nc to 999999.nc: -n loop # Create enumerated symbolic links /bin/ls | grep \.nc | perl -e \ '$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. historyOne drawback to this method is that the history attribute (see 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 Datasets Memory Requirements Large Numbers of Files Strategies

large datasetsLFSLarge 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 ncea and ncecat. swap spaceIf 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's Large File Support (LFS) capabilities (see Large File Support). The next section will increase your familiarity with NCO'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. serverUNICOSCrayIf your local machine has problems working with large files, try running NCO from a more powerful machine, such as a network server. Certain machine architectures, e.g., Cray UNICOS, have special commands which allow one to increase the amount of interactive memory. ilimitOn Cray systems, try to increase the available memory with the ilimit command. GNU/Linuxulimitcore dumpIf you get a memory-related core dump (e.g., Error exit (core dumped)) on a GNU/Linux system, try increasing the process-available memory with ulimit. speedThe 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-leveldebug-leveldbg_lvldebuggingWhen the debug-level is nonzero, 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., ncea -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.

Memory Requirements Performance Large Datasets Strategies

memory requirementsmemory availableRAMswap spacepeak memory usage Many people use NCO on gargantuan files which dwarf the memory available (free RAM plus swap space) even on today'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! threadsOpenMPshared memory machinesThis section describes the memory NCO requires during operation. The required memory is based on the underlying algorithms. The description below is the memory usage per thread. Users with shared memory machines may use the threaded NCO operators (see OpenMP Threading). The peak and sustained memory usage will scale accordingly, i.e., by the number of threads. Memory consumption patterns of all operators are similar, with the exception of ncap2. Single and Multi-file Operators Single and Multi-file Operators Memory for ncap2 Memory for ncap2

Single and Multi-file Operators Memory for ncap2 Memory Requirements Memory Requirements multi-file operatorsThe multi-file operators currently comprise the record operators, ncra and ncrcat, and the ensemble operators, ncea 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, wherease 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 . ncecat requires . ncra requires $MS\; =\; 2FR\; +\; VR$. ncea requires $MS\; =\; 2FT\; +\; VT$. ncbo requires (both input variables and the output variable). ncflint requires (both input variables and the output variable). ncpdq requires (one input variable and the output variable). ncwa requires (see below). Note that only variables that are processed, e.g., averaged, concatenated, or differenced, contribute to $MS$. Variables which do not appear in the output file (see Subsetting Variables) are never read and contribute nothing to the memory requirements. ncwa consumes between two and seven times the memory of a 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. When invoked, the weighting and masking features contribute up to three-sevenths and two-sevenths of these requirements apiece. If weights and masks are not specified (i.e., no -w or -a options) then ncwa requirements drop to (one input variable, one tally array, and the output variable). OpenMPthreadsThe above memory requirements must be multiplied by the number of threads thr_nbr (see OpenMP Threading). -t thr_nbrIf this causes problems then reduce (with -t thr_nbr) the number of threads. Memory for ncap2 Single and Multi-file Operators Memory Requirements ncap2binary operationsunary operationsmemory leaksleft hand castingncap2 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 under most conditions. An exception to this is when left hand casting (see 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, . 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 existance. 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. Performance Memory Requirements Strategies

papersoverviewAn overview of NCO capabilities as of about 2006 is in Zender, C. S. (2008), “Analysis of Self-describing Gridded Geoscience Data with netCDF Operators (NCO)”, 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_ems.pdf. scalingperformanceNCO performance and scaling for arithmetic operations is described in 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. This paper is also available at http://dust.ess.uci.edu/ppr/ppr_ZeM07_ijhpca.pdf. It is helpful to be aware of the aspects of NCO design that can limit its performance: bufferingNo data buffering is performed during nc_get_var and nc_put_var operations. performanceoperator speedspeedexecution timeHyperslabs too large too hold in core memory will suffer substantial performance penalties because of this. monotonic coordinatesSince 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_formatFORTRAN_formatsignednessscale_formatadd_offsetC_format, FORTRAN_format, signedness, scale_format and add_offset attributes are ignored by ncks when printing variables to screen. YorickIn 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.

Common features Operator Reference Manual Strategies Top 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 switchesCommand line switches for a given feature are consistent across all operators wherever possible. If no “key switches” are listed for a feature, then that particular feature is automatic and cannot be controlled by the user. Internationalization Internationalization Metadata Optimization Metadata Optimization OpenMP Threading OpenMP Threading Command Line Options Command Line Options Specifying Input Files Specifying Input Files Specifying Output Files Specifying Output Files Remote storage Remote storage Retaining Retrieved Files Retaining Retrieved Files Selecting Output File Format Selecting Output File Format Large File Support Large File Support Subsetting Variables Subsetting Variables Subsetting Coordinate Variables Subsetting Coordinate Variables C and Fortran Index Conventions C and Fortran Index Conventions Hyperslabs Hyperslabs Stride Stride Multislabs Multislabs Wrapped Coordinates Wrapped Coordinates Auxiliary Coordinates Auxiliary Coordinates UDUnits Support UDUnits Support Rebasing Time Coordinate Rebasing Time Coordinate Missing Values Missing Values Chunking Chunking Deflation Deflation MD5 digests MD5 digests Buffer sizes Buffer sizes Packed data Packed data Operation Types Operation Types Type Conversion Type Conversion Batch Mode Batch Mode History Attribute History Attribute File List Attributes File List Attributes CF Conventions CF Conventions ARM Conventions ARM Conventions Operator Version Operator Version Internationalization Metadata Optimization Common features Common features

InternationalizationI18N Availability: All operators&linebreak; L10NNCO support for internationalization of textual input and output (e.g., Warning messages) is nascent. We hope to produce foreign language string catalogues in 2004.

Metadata Optimization OpenMP Threading Internationalization Common features

metadata optimizationperformanceoperator speedspeedexecution timenc__enddef()--hdr_pad hdr_pad--header_pad hdr_pad Availability: ncatted, ncks, ncrename&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. Future metadata expansions will not incur the performance penalty of copying the entire output file unless the expansion exceeds the amount of header padding exceeded. This can be beneficial when it is known that some metadata will be added at a future date. This optimization exploits the netCDF library nc__enddef() function, which behaves differently with different versions of netCDF. It will improve speed of future metadata expansion with CLASSIC and 64bit netCDF files, but not necessarily with NETCDF4 files, i.e., those created by the netCDF interface to the HDF5 library (see Selecting Output File Format).

OpenMP Threading Command Line Options Metadata Optimization Common features

OpenMPthreadsSMPshared memory parallelismparallelismnco_openmp_thread_number--thr_nbr thr_nbr--threads thr_nbr--omp_num_threads thr_nbr-t thr_nbr Availability: ncap2, ncbo, ncea, ncecat, ncflint, ncpdq, ncra, ncrcat, ncwa&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. thr_nbrOMP_NUM_THREADSncrcatncwancap2ncpdqlarge datasetsNCO 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 $$thr_nbr <= 2 for ncrcat. This is based on the notion that the best performance that can be expected from an operator which does no arithmetic is to have one thread reading and one thread writing simultaneously. In the future (perhaps with netCDF4), we hope to demonstrate significant threading improvements with operators like ncrcat by performing multiple simultaneous writes. Compute-intensive operators (ncap2, ncwa and ncpdq) benefit most from threading. The greatest increases in throughput due to threading occur on large datasets where each thread performs millions, at least, of floating point operations. Otherwise, the system overhead of setting up threads probably outweighs the speed enhancements due to SMP parallelism. However, we have not yet demonstrated that the SMP parallelism scales well beyond four threads for these operators. Hence we restrict $$thr_nbr <= 4 for all operators. We encourage users to play with these limits (edit file nco_omp.c) and send us their feedback. debuggingdbg_lvlOnce 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. benchmarksThis information is also useful for benchmarking.

Command Line Options Specifying Input Files OpenMP Threading Common features

command line options Availability: All operators&linebreak; POSIXUNIXGNUswitchesNCO 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. In the future, however, some features of NCO may 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 optionsExtended options, also called long options, are implemented using the system-supplied getopt.h header file, if possible. BSDgetoptgetopt_longgetopt.hThis 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@iname.com. This is BSD-licensed software available from http://www.geocities.com/ResearchTriangle/Node/9405/#my_getopt.. -D debug-level--dbg_lvl debug-levelThe 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 ncks --dbg_lvl=3 in.nc # Long option, preferred form ncks --dbg_lvl 3 in.nc # Long option, alternate form The last example is preferred for two reasons. First, --dbg_lvl is more specific and less ambiguous than -D. The long option form 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. GNU implements a superset of the POSIX standard which allows any unambiguous truncation of a valid option to be used. ncks -D 3 in.nc # Short option ncks --dbg_lvl=3 in.nc # Long option, full form ncks --dbg=3 in.nc # Long option, unambiguous truncation ncks --db=3 in.nc # Long option, unambiguous truncation ncks --d=3 in.nc # Long option, 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 scripts which are re-usable employ some form of the long options for future maintainability. This manual generally uses the short option syntax. This is for historical reasons and to conserve space. The remainder of this manual specifies the full long_name of each option. Users are expected to pick the unambiguous truncation of each option name that most suits their taste.

Specifying Input Files Specifying Output Files Command Line Options Common features

input filesglobbingregular expressionswildcardsNINTAPProcessor, CCMCCM Processor-n loop--nintap loop-p input-path--pth input-path--path input-pathinput-path Availability (-n): ncea, ncecat, ncra, ncrcat&linebreak; Availability (-p): 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. UNIXThere 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 (see 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 -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 passes valid filenames which match the expansion to ncra. The third method uses the -p input-path argument to specify the directory where all the input files reside. NCO prepends input-path (e.g., /data/usrname/model) to all input-files (but 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.. multi-file operatorsfiles, multipleThis option is only available with the multi-file operators: ncra, ncrcat, ncea, 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 is able to decode lists of such filenames encoded using the -n option. The simpler (3-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, cdf, hdf, or hd5. 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 filenamesclimate modelRecently the -n option has been extended to allow convenient specification of filenames with “circular” 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–July–August, and December–January–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 “wrap” 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 to NCO. The fourth argument to -n, if present, specifies the maximum integer value of numeric_suffix. In this case the maximum value is 12, and will be formatted as 12 in the filename string. The fifth argument to -n, if 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.

Specifying Output Files Remote storage Specifying Input Files Common features

output fileinput filespositional argumentscommand 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 operatorsThis 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. xargsinput filesSecond, specification of a long list of input files may be difficult (see 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 very ugly and undesirable. Finally, many users are more comfortable specifying output files with -o fl_out near the beginning of an argument list. compilerslinkersCompilers and linkers are usually invoked this way. Users should specify fl_out using either but not both methods. If fl_out is specified twice (once with the switch and once as the last positional argument), then the positional argument takes precedence.

Remote storage Retaining Retrieved Files Specifying Output Files Common features

rcpscp.rhostsNCAR MSSMSSMass Store SystemURLftpsftpwgetremote filessynchronous file accessasynchronous file access--pth input-path--path input-path--lcl output-path--local output-path-l output-path.netrchistory 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, or NCAR's Mass Storage System (MSS), or an OPeNDAP server. Examples of each are given below, following a brief description of the particular access protocol. To access a file via an anonymous FTP server, supply the remote file's URL. FTP is an intrinsically insecure protocol because it transfers passwords in plain text format. Users should access sites using anonymous FTP when possible. Some FTP servers require a login/password combination for a valid user account. NCO allows these transactions 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. . 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 a standard remote file syntax. 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. HPSShsimsrcpmsreadnrnetTo 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. NCO will attempt to detect whether the local machine has direct (synchronous) HPSS access. In this case, NCO attempts to use the Hierarchical Storage Interface (HSI) command hsi get The hsi command must be in the user's path in one of the following directories: /usr/local/bin or /opt/hpss/bin. 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 ncks -l . /home/zender/nco/in.nc ncks -l . http://motherlode.ucar.edu:8080/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 removed from NCO. . 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-pathinput-path-l output-pathoutput-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 . in.nc # HPSS ncks -p http://motherlode.ucar.edu:8080/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. stubWhen 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 the NCAR 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 OPeNDAP

OPeNDAP Remote storage Remote storage DAPDODSHTTP protocolDODS_ROOTDistributed Oceanographic Data Systemoceanographydata access protocolOpen-source Project for a Network Data Access ProtocolOPeNDAP.serverclient-serverThe 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 NVODSNational Virtual Ocean Data Systemopen sourceDODS 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. MakefileThis 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 The minimal set of libraries required to build NCO as OPeNDAP clients are, in link order, libnc-dap.a, libdap.a, and libxml2 and libcurl.a.. in the usual locations. 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/packages/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 expressionsserver-side processingOPeNDAP 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 "OPeNDAP Capabilities of Interest to NCO Users" for incorporation in the NCO User's 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://motherlode.ucar.edu:8080/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://motherlode.ucar.edu:8080/thredds/dodsC/testdods in_4.nc % ncks -H -v one ~/foo.nc one = 1 And, of course, OPeNDAP-enabled NCO clients continue to support other, orthogonal features such as UDUnits (see UDUnits Support): % ncks -u -C -H -v wvl -d wvl,'0.4 micron','0.7 micron' \ -p http://motherlode.ucar.edu:8080/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 -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 packingunpackingAll with one command! The data in this particular input file also happen to be packed (see Methods and functions), although this is completely 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/OThis 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. Retaining Retrieved Files Selecting Output File Format Remote storage Common features

file deletionfile removalfile 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. FTPSSHmsrcpNote 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. serverFor 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. DODSserverIf your NCO is DAP-enabled (see 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 Note that NCO is never the preffered way to transport files from remote machines. For large jobs, that is best handled by FTP, SSH, or wget. It may occasionally be useful to use NCO to transfer files when your other preferred methods are not available locally.

Selecting Output File Format Large File Support Retaining Retrieved Files Common features

HDFnetCDF2netCDF3netCDF4NETCDF4_CLASSIC filesNETCDF4 filesCLASSIC files64BIT files--3-3-4--4--netcdf4--fl_fmt--file_format--64bit Availability: ncap2, ncbo, ncea, ncecat, ncflint, ncks, ncpdq, ncra, ncrcat, ncwa&linebreak; Short options: -3, -4&linebreak; Long options: --3, --4, --64bit, --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 “Availability” 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.) netCDF supports four types of files: CLASSIC, 64BIT, NETCDF4, and NETCDF4_CLASSIC, The CLASSIC format is the traditional 32-bit offset written by netCDF2 and netCDF3. As of 2005, most netCDF datasets are in CLASSIC format. The 64BIT format was added in Fall, 2004. 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 perfectly readable by applications which use only the netCDF3 API and library. NCO must be built with netCDF4 to write files in the new NETCDF4 and NETCDF4_CLASSIC formats, and to read files in the new NETCDF4 format. Users are advised to use the default CLASSIC format or the NETCDF4_CLASSIC format until netCDF4 is more widespread. Widespread support for NETCDF4 format files is not expected for a few more years, 2013–2014, say. If performance or coolness are issues, then use NETCDF4_CLASSIC instead of CLASSIC format files. As mentioned above, all operators write use the input file format for output files unless told otherwise. Toggling the long option --64bit switch (or its key-value equivalent --fl_fmt=64bit) produces the netCDF3 64-bit offset format named 64BIT. NCO must be built with netCDF 3.6 or higher to produce a 64BIT 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., HDF). Casual users are advised to use the default (netCDF3) CLASSIC format until netCDF 3.6 and netCDF 4.0 are more widespread. Conversely, operators given the -3 (or --3) switch without arguments will (attempt to) produce netCDF3 CLASSIC output, even from netCDF4 input files. These examples demonstrate converting a file from any netCDF format into any other netCDF format (subject to limits of the format): ncks --fl_fmt=classic in.nc foo_3c.nc # netCDF3 classic ncks --fl_fmt=64bit in.nc foo_364.nc # netCDF3 64bit ncks --fl_fmt=netcdf4_classic in.nc foo_4c.nc # netCDF4 classic ncks --fl_fmt=netcdf4 in.nc foo_4.nc # netCDF4 ncks -3 in.nc foo_3c.nc # netCDF3 classic ncks --3 in.nc foo_3c.nc # netCDF3 classic ncks -4 in.nc foo_4.nc # netCDF4 ncks --4 in.nc foo_4.nc # netCDF4 ncks --64 in.nc foo364.nc # netCDF3 64bit Of course since most operators support these switches, the “conversions” can be done at the output stage of arithmetic or metadata processing rather than requiring a separate step. Producing (netCDF3) CLASSIC or 64BIT files from NETCDF4_CLASSIC files will always work. However, producing netCDF3 files from NETCDF4 files will only work if the output files are not required to contain netCDF4-specific features. 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. As of October, 2005, NCO writes no netCDF4-specific data structures and so always succeeds at writing NETCDF4_CLASSIC files. There are at least three ways to discover the format of a netCDF file, i.e., whether it is a classic (32-bit offset) or newer 64-bit offset netCDF3 format, or is netCDF4 format. Each method returns the information using slightly different terminology that becomes easier to understand with practice. First, examine the end of the first line of global metadata output by ncks -M: netCDF3 classic file formatnetCDF4 classic file formatnetCDF4 file format32-bit offset file format64-bit offset file formatncks-M % ncks -M foo_3c.nc Opened file foo_3c.nc: dimensions = 19, variables = 261, global atts. = 4, id = 65536, type = NC_FORMAT_CLASSIC % ncks -M foo_364.nc Opened file foo_364.nc: dimensions = 19, variables = 261, global atts. = 4, id = 65536, type = NC_FORMAT_64BIT % ncks -M foo_4c.nc Opened file foo_4c.nc: dimensions = 19, variables = 261, global atts. = 4, id = 65536, type = NC_FORMAT_NETCDF4_CLASSIC % ncks -M foo_4.nc Opened file foo_4.nc: dimensions = 19, variables = 261, global atts. = 4, id = 65536, type = NC_FORMAT_NETCDF4 This method requires a netCDF4-enabled NCO version 3.9.0+ (i.e., from 2007 or later). Second, query the file with ncdump -k: ncdump % ncdump -k foo_3.nc classic % ncdump -k foo_364.nc 64-bit-offset % ncdump -k foo_4c.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: odoctal dump % od -An -c -N4 foo_3c.nc C D F 001 % od -An -c -N4 foo_364.nc C D F 002 % od -An -c -N4 foo_4c.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 the newer netCDF4 file format.

Large File Support Subsetting Variables Selecting Output File Format Common features

LFSLarge 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) which now support 64-bit files directly through the netCDF interface. See Selecting Output File Format for instructions on accessing the different file formats, including 64-bit files, supported by the modern netCDF interface. If you are still interesting 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 are known to support LFS. and netCDF suppport issues. The netCDF LFS FAQ at http://my.unidata.ucar.edu/content/software/netcdf/faq-lfs.html 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.

Subsetting Variables Subsetting Coordinate Variables Large File Support Common features

subsettingexclusionextraction-v var--variable var-x--exclude--xcl Availability: (ncap2), ncbo, ncea, ncecat, ncflint, ncks, ncpdq, ncra, ncrcat, ncwa&linebreak; Short options: -v, -x&linebreak; Long options: --variable, --exclude or --xcl&linebreak; Subsetting variables refers to explicitly specifying variables to be included or excluded from operator actions. Subsetting is implemented with the -v var[,&dots;] and -x options. A list of variables to extract is specified following the -v option, e.g., -v time,lat,lon. Not using the -v option is equivalent to specifying all variables. 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. Variables explicitly specified for extraction with -v var[,&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. memory requirementsRemember, if averaging or concatenating large files stresses your systems memory or disk resources, then the easiest solution is often to use the -v option to retain only the most important variables (see Memory Requirements). Due to its special capabilities, ncap2 interprets the -v switch differently (see 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 switch. extended regular expressionsregular expressionspattern matchingwildcardsgrep -EegrepncattedGNUAs of NCO 2.8.1 (August, 2003), variable name arguments of 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. For example, -v '^DST' selects all variables beginning with the string DST. Extended regular expressions are defined by the POSIX grep -E (aka egrep) command. The meta-characters used to express pattern matching operations are ^$+?.*[]{}|. If the regular expression pattern matches any part of a variable name then that variable is selected. This capability is called wildcarding, and is very useful for sub-setting large data files. POSIXregexBecause of its wide availability, NCO uses the POSIX regular expression library regex. Regular expressions of arbitary complexity may be used. Since netCDF variable names are relatively simple constructs, only a few varieties of variable wildcards are likely to be useful. For convenience, we define the most useful pattern matching operators here: . (wildcard character)$ (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, consider a file with variables Q, Q01–Q99, Q100, QAA–QZZ, Q_H2O, X_H2O, Q_CO2, X_CO2. ncks -v '.+' in.nc # All variables 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 Beware—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. globbingshellbashcshquotesOne 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).

Subsetting Coordinate Variables C and Fortran Index Conventions Subsetting Variables Common features

subsetting-C-c--no-coords--no-crd--coords--crd Availability: ncap2, ncbo, ncea, ncecat, ncflint, ncks, ncpdq, ncra, ncrcat, ncwa&linebreak; Short options: -C, -c&linebreak; Long options: --no-coords, --no-crd, --crd, --coords&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 feature can be disabled with -C, which causes NCO to not automatically add coordinates to the variables appearing in the output-file. However, using -C does not preclude the user from including some coordinates in the output files simply by explicitly selecting the coordinates with the -v option. The -c option, on the other hand, is a shorthand way of automatically specifying that all coordinate variables in the input-files should appear in the output-file. Thus -c allows the user to select all the coordinate variables without having to know their names. CF conventionsAs 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 4.0.8 (April, 2011) both -c and -C honor the CF bounds convention described in CF Conventions.

C and Fortran Index Conventions Hyperslabs Subsetting Coordinate Variables Common features

index conventionFortran index conventionC index convention-F--fortran Availability: ncbo, ncea, ncecat, ncflint, ncks, ncpdq, ncra, ncrcat, ncwa&linebreak; Short options: -F&linebreak; Long options: --fortran&linebreak; I/OThe -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. transposeHence C and Fortran data storage conventions represent mathematical transposes of eachother. record variableNote 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 dimensionConsider 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 -v three_dmn_var in.nc lat[0]=-90 lev[0]=1000 lon[0]=-180 three_dmn_var[0]=0 ... % ncks -F -v three_dmn_var in.nc lon(1)=0 lev(1)=100 lat(1)=-90 three_dmn_var(1)=0 ...

Hyperslabs Stride C and Fortran Index Conventions Common features

hyperslabdimension limitscoordinate limits-d dim,[min][,[max][,[stride]]]--dimension dim,[min][,[max][,[stride]]]--dmn dim,[min][,[max][,[stride]]] Availability: ncbo, ncea, 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'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). 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. strideCoordinate 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. For a given dimension, the specified limits must both be coordinate values (with decimal points) or dimension indices (no decimal points). The stride option, if any, must be a dimension index, not a coordinate value. See Stride, for more information on the stride option. NC_BYTENC_CHARUser-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_BYTE and NC_CHAR are computed numerically rather than lexically, so the results are unpredictable. wrapped coordinatesThe relative magnitude of min and max indicate to the operator whether to expect a wrapped coordinate (see 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).

Stride Multislabs Hyperslabs Common features

stride-d dim,[min],[max],stride--dimension dim,[min],[max],stride--dmn dim,[min],[max],stride Availability: ncbo, ncea, 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 (see 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 (see 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 ShellC 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 See 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

Multislabs Wrapped Coordinates Stride Common features

multislabmulti-hyperslabMSA-d dim,[min][,[max][,[stride]]]--dimension dim,[min][,[max][,[stride]]]--dmn dim,[min][,[max][,[stride]]] Availability: ncbo, ncea, 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; A multislab is a union of one or more hyperslabs. One defines multislabs by chaining together hyperslab commands, i.e., -d options (see Hyperslabs). Support for specifying a multi-hyperslab or multislab for any variable was first added to ncks in late 2002. The other operators received MSA capabilities in April 2008. Sometimes multi-slabbing is referred to by the acronym MSA, which stands for “Multi-Slabbing Algorithm”. Multislabs overcome some restraints that limit 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. strideUsers 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. See 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/OThis 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. A final example shows the real power of multislabs. 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.

Wrapped Coordinates Auxiliary Coordinates Multislabs Common features

wrapped coordinateslongitude-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 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 (see Hyperslabs).

Auxiliary Coordinates UDUnits Support Wrapped Coordinates Common features

-X--auxiliarystandard_namecoordinatesCF conventions-X lon_min,lon_max,lat_min,lat_max--auxiliary lon_min,lon_max,lat_min,lat_max Availability: ncbo, ncea, 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's standard_name attributes, if any, when interpreting hyperslab and multi-slab options. Also --auxiliary. This switch supports hyperslabbing cell-based grids over coordinate ranges. This works on datasets that associate coordinate variables to grid-mappings using the CF-convention (see CF Conventions) coordinates and standard_name attributes described http://cf-pcmdi.llnl.gov/documents/cf-conventions/1.5/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 range [lon_min,lon_max,lat_min,lat_max] are included in the output hyperslab. cell-based gridsA cell-based 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 (see 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's are “latitude” and “longitude”, respectively. This standard_name attribute for latitude and longitude coordinates follows the CF-convention (see 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 “latitude”, and “longitude”: % 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—multidimensional coordinates will not work. This infrastructure allows NCO to identify, interpret, and process (e.g., 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 In contrast, arguments to -d require decimal places to be recognized as coordinates not indices (see Hyperslabs). We recommend always using decimal points with -X arguments to avoid confusion.

UDUnits Support Rebasing Time Coordinate Auxiliary Coordinates Common features

UDUnitsUnidataunitsattribute, units-d dim,[min][,[max][,[stride]]]--dimension dim,[min][,[max][,[stride]]]--dmn dim,[min][,[max][,[stride]]] Availability: ncbo, ncea, 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/packages/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 unitsFirst, 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 -C -H -v wvl -d wvl,"0.4 micron","0.7 micron" in.nc wvl[0]=5e-07 meter unitsThe 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 variableIn 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'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. unitstimeThe translation and dimensional innterpretation 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 stridewhitespaceHere, the user invokes the stride (see Stride) capability to obtain every other timeslice. This is possible because the UDUnits feature is additive, not exclusive—it works in conjunction with all other hyperslabbing (see 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. shellThis 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 (see 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. One aspect that deserves mention is that UDUnits, and thus NCO, supports run-time definition of the location of the relevant UDUnits databases. With UDUnits version 1, users may specify the directory which contains the UDUnits database, udunits.dat, via the UDUNITS_PATH environment variable. With UDUnits version 2, users may specify the UDUnits database file itself, udunits2.xml, via the UDUNITS2_XML_PATH environment variable. export UDUNITS_PATH='/nonstandard/location/share/udunits' # UDUnits1 export UDUNITS2_XML_PATH='/nonstandard/location/share/udunits/udunits2.xml' # UDUnits2 This run-time flexibility can enable the full functionality of pre-built binaries on machines with libraries in different locations. Climate and Forecast Metadata ConventionCF conventionsThe http://www.unidata.ucar.edu/packages/udunitsUDUnits package documentation describes the supported formats of time dimensions. Among the metadata conventions which 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 11:00:0.0","1939-09-09 00:00:0.0" 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 non-leading dash in a limit string indicates that a UDUnits date conversion is requested. 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; So the command "ncks -v lon_cal -d lon_cal,'1964-3-1 0:00:0.0','1964-3-4 00:00:0.0' in.nc out.nc" Results in the hyperslab lon_cal=1,2,3,4 MKS unitsGodnetCDF variables should always be stored with MKS (i.e., God's) units, so that application programs may assume MKS dimensions apply to all input variables. The UDUnits feature is intended to alleviate some of the NCO user's 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'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).

Rebasing Time Coordinate Missing Values UDUnits Support Common features

Availability: ncra, ncrcat Short options: None&linebreak; Time re-basing is performed automatically when UDUnits is installed. Time rebasing is invoked when numerous files share a common time-based record coordinate, and the record coordinate is in the same time units in each file, but the date offset differs among files. 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 0–23 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" // Find 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 the output time coordinate is based on the 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 ;

Missing Values Chunking Rebasing Time Coordinate Common features

missing valuesdata, missingaveraging datamissing_value_FillValue Availability: ncap2, ncbo, ncea, ncflint, ncpdq, ncra, 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 operatorsThe 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'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='-DNCO_MSS_VAL_SNG=missing_value'. . ncrenamencattedIt 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 (see ncrename netCDF Renamer) to rename all missing_value attributes to _FillValue: ncrename -a .missing_value,_FillValue inout.nc Alternatively, one may use ncatted (see 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 languageIf 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). ncattedYou may use the NCO operator ncatted to change the _FillValue attribute and all data whose data is _FillValue to a new value (see ncatted netCDF Attribute Editor). Identifying missing data during arithmetic operations. performanceoperator speedspeedexecution timearithmetic operatorsWhen 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. nceancrancwancboncflintNCO averagers (ncra, ncea, 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 “the right thing”. 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'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.

Chunking Deflation Missing Values Common features

--cnk_dmn--cnk_map--cnk_plc--cnk_scl--chunk_dimension--chunk_map--chunk_policy--chunk_scalarchunking Availability: ncap2, ncbo, ncea, ncecat, ncflint, ncks, ncpdq, ncra, ncrcat, ncwa&linebreak; Short options: none&linebreak; Long options: --cnk_dmn dmn_nm,cnk_sz, --chunk_dimension dmn_nm,cnk_sz&linebreak;, --cnk_map cnk_map, --chunk_map cnk_map,&linebreak; --cnk_plc cnk_plc, --chunk_policy cnk_plc,&linebreak; --cnk_scl cnk_sz, --chunk_scalar cnk_sz&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 in detail http://www.hdfgroup.org/HDF5/doc/H5.user/Chunking.htmlhere. chunking policychunking mapchunksizeThe NCO chunking implementation is designed to be flexible. Users control three aspects of the chunking implementation. These are known as the chunking policy, chunking map, and chunksize. The first two are high-level mechanisms that apply to an entire file, while the third allows per-dimension specification of parameters. hyperslabncpdqpackingThe implementation is a hybrid of the ncpdq packing policies (see ncpdq netCDF Permute Dimensions Quickly), and the hyperslab specifications (see Hyperslabs). Each aspect is intended to have a sensible default, so that most users will only need to set one switch to obtain sensible chunking. Power users can tune the three switches in tandem to obtain optimal performance. The user specifies the desired chunking policy with the -P switch (or its long option equivalents, --cnk_plc and --chunk_policy) and its cnk_plc argument. Five chunking policies are currently implemented:&linebreak; allg2dg3dxplcnk_allcnk_g2dcnk_g3dcnk_xplplc_allplc_g2dplc_g3dplc_xpl Chunk All Variables [default] Definition: Chunk all variables possible&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 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 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; Unchunking Definition: Unchunk all variables&linebreak; Alternate invocation: ncunchunk&linebreak; cnk_plc key values: uck, cnk_uck, plc_uck, 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 mapcnk_map-M cnk_map--cnk_map cnk_map--map cnk_mapThe 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. Four chunking maps are currently implemented:&linebreak; dmnsclprdrd1cnk_dmncnk_sclcnk_prdcnk_rd1map_dmnmap_sclmap_prdmap_rd1 Chunksize Equals Dimension Size [default] Definition: Chunksize defaults to dimension size. Explicitly specify chunksizes for particular dimensions with --cnk_dmn option.&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 option.&linebreak; cnk_map key values: xpl, cnk_xpl, map_xpl&linebreak; Mnemonic: EXPLicitly specified dimensions&linebreak; Chunksize Product Equals Scalar Size Specified Definition: The product of the chunksizes for each variable (approximately) equals the size specified with the --cnk_scl option. For a variable of rank R (i.e., with R non-degenerate dimensions), the chunksize in each non-degenerate dimension is the Rth root of cnk_scl.&linebreak; cnk_map key values: prd, cnk_prd, map_prd&linebreak; Mnemonic: PRoDuct&linebreak; It is possible to combine the above chunking map algorithms with user-specified per-dimension (but 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,cnk_sz, which are the dimension name and its chunksize, respectively. The --cnk_dmn option may be used as many times as necessary. # 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 # 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 /data/zender/dstmch90/dstmch90_clm.nc ~/foo.nc ncks -O -4 -D 4 --cnk_dmn lat,64 --cnk_dmn lon,128 /data/zender/dstmch90/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 /data/zender/dstmch90/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 /data/zender/dstmch90/dstmch90_clm_0112.nc ~/foo.nc record dimensionIt is appropriate to conclude by informing users about an aspect of chunking that may not be expected: Record dimensions are always chunked with a chunksize of one. Hence all variables that contain the record dimension are also stored as chunked (since data must be stored with chunking either in all dimensions, or in no dimensions). Unless otherwise specified by the user, the other (fixed, non-record) dimensions of such 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.

Deflation MD5 digests Chunking Common features

-L--deflate--dfl_lvlLempel-Ziv deflationcompressiondeflation Availability: ncap2, ncbo, ncea, ncecat, ncflint, ncks, ncpdq, ncra, ncrcat, 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 Lempel-Ziv deflation. The Lempel-Ziv algorithm is a lossless data compression technique. Activate this 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). 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 a few percent 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) ncksncks prints deflation parameters, if any, to screen (see ncks netCDF Kitchen Sink).

MD5 digests Buffer sizes Deflation Common features

--md5_digestintegritysecuritydigesthashMD5 digest Availability: ncecat, ncks, ncrcat&linebreak; Short options: &linebreak; Long options: --md5_digest&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 the multifile concantenators (ncecat and ncrcat) and ncks. Activate it with the --md5_digest long option. The behavior and verbosity of the MD5 digest is operator-dependent. When activating MD5 digests with ncks it is assumed that the user simply wishes to see the digest of every variable and this is done when the debugging level exceeds one. This incurs the minor overhead of performing the hash algorithm for each variable read. MD5 digests may be activated in both the one- and two-filename argument forms of ncks, which are used for printing and sub-setting, respectively. The MD5 digests are shown as a 32-character hexadecimal string in which each two characters represent one byte of the 16-byte digest: > ncks -O -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 -O -D 2 -C -d lev,0 --md5 -v md5_a,md5_abc ~/nco/data/in.nc ... cks: 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 “a” and “abc” are widely known. The second example shows that the hyperslab of variable md5_abc (= “abc”) consisting of only its first letter (= “a”) has the same hash as the variable md5_a (“a”). This illustrates that MD5 digests acts 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 “production mode” 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 will be returned if the digests of the variable read from the source file and from the output file do not agree. These rules are evolving and as NCO pays more attention to data integrity. We welcome feedback and suggestions from users.

Buffer sizes Packed data MD5 digests Common features

--bfr_sz_hntBuffer sizesFile buffersstat() system callI/O block sizeSystem calls Availability: All operators&linebreak; Short options: &linebreak; Long options: --bfr_sz_hnt, --buffer_size_hint&linebreak; As of NCO version 4.1.1 (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. Otherwise, netCDF uses twice the system pagesize. 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 p... ncks: INFO nc__open() will request file buffer size = 2097152 bytes ncks: INFO nc__open() opened file with buffer size = 2097152 bytes ...

Packed data Operation Types Buffer sizes Common features

packingunpackingadd_offsetscale_factormissing_value_FillValuepack(x)unpack(x) Availability: ncap2, ncbo, ncea, ncflint, ncpdq, ncra, ncwa&linebreak; Short options: None&linebreak; The phrase packed data refers to data which are stored in the standard netCDF3 packing format which employs a lossy algorithm. 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. Packing The standard netCDF packing algorithm is lossy, and produces data with the same dynamic range as the original but which requires no more than half the space to store. 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. 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 representible. 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 $ndrv\; =\; 256\; -\; 2\; =\; 254$, or type NC_INT where where $ndrv\; =\; 4294967295\; -\; 2\; =\; 4294967293$. One useful feature of (lossy) netCDF packing algorithm is that additional, loss-less packing algorithms perform well on top of it. 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. upk = scale_factor*pck + add_offset = (max-min)*pck/ndrv + 0.5*(min+max)&linebreak; 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'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. 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 (see ncpdq netCDF Permute Dimensions Quickly). Packing and unpacking individual variables may be done with ncpdq and the ncap2 pack() and unpack() functions (see Methods and functions).

Operation Types Type Conversion Packed data Common features

operation typesavgsqravgavgsqrminmaxrmssdnrmsttlsqrtaveragemeantotalminimummaximumroot-mean-squarestandard deviationvariance-y op_typ--operation op_typ--op_typ op_typ Availability: ncap2, ncra, ncea, 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 Maximium value min Minimium value rms Root-mean-square (normalized by N) rmssdn Root-mean square (normalized by N-1) sqrt Square root of the mean ttl Sum of values coordinate variableNCO 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, but not on coorniate 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. See 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 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 (see Help Requests and Bug Reports). EXAMPLES 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 minimium 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 dimensionAlso, 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 maximium surface pressure at every point. ncea -y max -v prs_sfc jan.nc feb.nc march.nc out.nc 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 var 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 (see 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 appropriately weighted (with -w gw) spatial mean values are written to the output file. This example includes the use of a weighted variable specified with -w gw. 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. The initial output file is then overwritten with the gridpoint deviations from the spatial mean. Finally the root-mean-square of the appropriately weighted spatial deviations is taken. The ncap2 solution to the spatially-weighted standard deviation problem is ncap2 -s 'prs_sfc_sdn=(prs_sfc*gw-prs_sfc*gw.avg($lat,$lon)).rmssdn($lat,$lon)' \ in.nc out.nc Be sure to multiply the variable by the weight 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.

Type Conversion Batch Mode Operation Types Common features

type conversion Availability: ncap2, ncbo, ncea, ncra, ncwa&linebreak; Short options: None&linebreak; promotiondemotionautomatic type conversionmanual type conversionType conversion (often called promotion or demotion) refers to the casting of one fundamental data type to another, e.g., converting NC_SHORT (two bytes) to NC_DOUBLE (eight bytes). 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 Automatic type conversion Manual type conversion Manual type conversion

Automatic type conversion Manual type conversion Type Conversion Type Conversion As a general rule, automatic type conversions should be avoided for at least two reasons. First, type conversions are expensive since they require creating (temporary) buffers and casting each element of a variable from the type it was stored at to some other type. Second, the dataset's creator probably 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 were made. Thus NCO tries to avoid performing automatic type conversions when performing arithmetic. C languageFortranAutomatic type conversion during arithmetic in the languages C and Fortran is performed only when necessary. All operands in an operation are converted to the most precise type before the operation takes place. However, following this parsimonious conversion rule dogmatically results in numerous headaches. For example, the average of the two NC_SHORTs 17000s and 17000s results in garbage since the intermediate value which holds their sum is also of type NC_SHORT and thus cannot represent values greater than 32,767 $32767\; =\; 2^15-1$ . 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 most pure. Thus, unlike C and Fortran, but like many other higher level interpreted languages, NCO arithmetic operators will perform automatic type conversion when all the following conditions are met Operators began performing type conversions before arithmetic in NCO version 1.2, August, 2000. Previous versions never performed unnecessary type conversion for arithmetic.: The operator is ncea, ncra, or ncwa. ncbo is not yet included in this list because subtraction did not benefit from type conversion. This will change in the future The arithmetic operation could benefit from type conversion. Operations that could benefit (e.g., from larger representable sums) include averaging, summation, or any "hard" arithmetic. 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 type converted because there is no type of higher precision to convert to. Type NC_FLOAT is not type converted because, in our judgement, the performance penalty of always doing so would outweigh the (extremely rare) potential benefits. 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. floorType conversions use the floor function to convert floating point number to integers. Type conversions do not attempt to round floating point numbers to the nearest integer. Thus the average of 1s and 2s is computed in double precisions arithmetic as $($1.0d + 1.5d)/2) = 1.5d. This result is converted to NC_SHORT and stored on disk as $$floor(1.5d) = 1s C languageThe actual type conversions are handled by intrinsic C-language type conversion, so the floor() function is not explicitly called, though the results would be the same if it were.. Thus no "rounding up" is performed. The type conversion rules of C can be stated as follows: If n is an integer then any floating point value x satisfying will have the value n when converted to an integer. Manual type conversion Automatic type conversion Type Conversion ncap2ncap2 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 See ncap2 netCDF Arithmetic Processor, for more details. Batch Mode History Attribute Type Conversion Common features

batch modeoverwriting filesappending to filesforce overwriteforce 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.

History Attribute File List Attributes Batch Mode Common features

historytimestampglobal attributesattributes, 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 foo.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 and many other netCDF operators adhere 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 "history", the first one found will be used. history attribute ncattedTo avoid information overkill, all operators have an optional switch (-h, --hst, or --history) to override automatically appending the history attribute (see ncatted netCDF Attribute Editor). Note that the -h switch also turns off writing the nco_input_file_list attribute for multi-file operators (see File List Attributes).

File List Attributes CF Conventions History Attribute Common features

nco_input_file_listnco_input_file_numberstdinglobal attributesattributes, global-H--fl_lst_in--file_list Availability: ncea, ncecat, ncra, ncrcat&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 (see 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 exact command by which the file was created. NCO solves this dilemma by archiving input file list attributes. When the input file list to a multi-file operator is specified via stdin, the operator, by default, attaches two global attributes to any file 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 “chain of evidence” 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 (see History Attribute). Hence both switches allows space-conscious users to avoid storing what may amount to many thousands of filenames in a metadata attribute.

CF Conventions ARM Conventions File List Attributes Common features

CF conventionsCCSM conventionsUDUnitsOROareadatesecdategwhyaihyamhybihybmlat_bndslon_bndsmsk_* Availability: ncbo, ncea, ncecat, ncflint, ncpdq, ncra, ncwa&linebreak; Short options: None&linebreak; NCO recognizes the Climate and Forecast (CF) metadata conventions, and applies special rules to such data. NCO also handles older NCAR model datasets, such as CCM and early CCSM datasets, with its CF rules even though the earlier data may not contain an explicit Conventions attribute (e.g., CF-1.0). We refer to all such data collectively as CF data. Skip this section if you never work with CF data. The CF netCDF conventions are described http://cf-pcmdi.llnl.gov/documents/cf-conventions/1.5/cf-conventions.html#coordinate-systemhere. Most CF netCDF conventions are transparent to NCO The exception is appending/altering the attributes x_op, y_op, z_op, and t_op for variables which have been averaged across space and time dimensions. This feature is scheduled for future inclusion in NCO. . There are no known pitfalls associated with using any NCO operator on files adhering to these conventions The CF conventions recommend time be stored in the format time since base_time, e.g., the units attribute of time might be days since 1992-10-8 15:15:42.5 -6:00. A problem with this format occurs when using ncrcat to concatenate multiple files together, each with a different base_time. That is, any time values from files following the first file to be concatenated should be corrected to the base_time offset specified in the units attribute of time from the first file. The analogous problem has been fixed in ARM files (see ARM Conventions) and could be fixed for CF files if there is sufficient lobbying. . However, to facilitate maximum user friendliness, NCO applies special rules to certain variables in CF files. The special functions are not required by the CF netCDF conventions, yet experience shows that they simplify data analysis. 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 propertiesFurthermore, the size and rank-preserving arithmetic operators try not to operate on certain grid properties. These operators are ncap2, ncbo, ncea, 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_*. These variables represent the Gaussian weights, the orography field, 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. The handling of msk_* is exceptional in that any variable name beginning with the string msk_ is considered to be a “mask” and is thus preserved (not operated on arithmetically). 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. boundsbounds conventionAs of NCO version 4.0.8 (April, 2011), NCO supports the CF bounds convention for cell boundaries described http://cf-pcmdi.llnl.gov/documents/cf-conventions/1.5/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. coordinatescoordinates conventioncoordinate variableauxiliary coordinatessubsetting-C-c--no-coords--no-crd--coords--crdAs of NCO version 3.9.6 (January, 2009), NCO supports the CF coordinates convention described http://cf-pcmdi.llnl.gov/documents/cf-conventions/1.5/cf-conventions.html#coordinate-systemhere. This convention allows variables to specify additional coordinates (including multidimensional coordinates) in a space-separated string attribute named coordinates. NCO attaches any such coordinates to the extraction list along with variable and its usual (one-dimensional) coordinates, if any. These auxiliary coordinates are subject to the user-specified overrides described in Subsetting Coordinate Variables.

ARM Conventions Operator Version CF Conventions Common features

ARM conventionstime_offsetbase_timetime 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 just blindly concatenated. Therefore ncrcat has been specially programmed to be able to chain together consecutive ARM input-files and produce and an output-file which contains the correct time information. 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. historyglobal attributesattributes, globalA short message is added to the history global attribute whenever these ARM-specific procedures are executed.

Operator Version ARM Conventions Common features

versionRCS-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'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.

Operator Reference Manual Contributing Common features Top This chapter presents reference pages for each of the operators individually. The operators are presented in alphabetical order. command line switchesAll valid command line switches are included in the syntax statement. Recall that descriptions of many of these command line switches are provided only in Common 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 ncap2 netCDF Arithmetic Processor ncatted netCDF Attribute Editor ncatted netCDF Attribute Editor ncbo netCDF Binary Operator ncbo netCDF Binary Operator ncea netCDF Ensemble Averager ncea netCDF Ensemble Averager ncecat netCDF Ensemble Concatenator ncecat netCDF Ensemble Concatenator ncflint netCDF File Interpolator ncflint netCDF File Interpolator ncks netCDF Kitchen Sink ncks netCDF Kitchen Sink ncpdq netCDF Permute Dimensions Quickly ncpdq netCDF Permute Dimensions Quickly ncra netCDF Record Averager ncra netCDF Record Averager ncrcat netCDF Record Concatenator ncrcat netCDF Record Concatenator ncrename netCDF Renamer ncrename netCDF Renamer ncwa netCDF Weighted Averager ncwa netCDF Weighted Averager ncap2 netCDF Arithmetic Processor ncatted netCDF Attribute Editor Operator Reference Manual Operator Reference Manual

parserlexerarithmetic processorncapncap2 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] [-6] [http://nco.sf.net/nco.html#-A-A] [-C] [-c] [-D dbg] [-F] [-f] [-L dfl_lvl] [-l path] [-O] [-o output-file] [-p path] [-R] [-r] [-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. This documentation refers to ncap2, which has a superset of the ncap functionality. Eventually ncap will be deprecated in favor ncap2. ncap2 may be renamed ncap in 2012.. script file--script-file--fl_spt--script--sptThe 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 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 fieldsSee Missing Values, for treatment of missing values. The results of the algebraic manipulations are called derived fields. Unlike the other operators, ncap2 does not accept a list of variables to be operated on as an argument to -v (see Subsetting Variables). Rather, the -v switch takes no arguments and indicates that ncap2 should output only user-defined variables. ncap2 neither accepts nor understands the -x switch. appending variablesNB: As of 20120515, ncap2 is unable to append to files that already contain the appended dimensions. Syntax of ncap2 statements Syntax of ncap2 statements Expressions Expressions Dimensions Dimensions Left hand casting Left hand casting Arrays and hyperslabs Arrays and hyperslabs Attributes Attributes Number literals Number literals if statement if statement print statement print statement Missing values ncap2 Missing values ncap2 Methods and functions Methods and functions RAM variables RAM variables Where statement Where statement Loops Loops Include files Include files Sort methods Sort methods Irregular grids Irregular grids Bilinear interpolation Bilinear interpolation GSL special functions GSL special functions GSL interpolation GSL interpolation GSL least-squares fitting GSL least-squares fitting GSL statistics GSL statistics GSL random number generation GSL random number generation Examples ncap2 Examples ncap2 Intrinsic mathematical methods Intrinsic mathematical methods Operators precedence and associativity Operators precedence and associativity ID Quoting ID Quoting Defining new variables in terms of existing variables is a powerful feature of ncap2. derived fieldsDerived 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 (see Left hand casting). The user must manually define the proper metadata for variables defined using left hand casting.

Syntax of ncap2 statements Expressions ncap2 netCDF Arithmetic Processor ncap2 netCDF Arithmetic Processor statementsyntaxMastering 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 languageThe syntax of statements is most like the computer language C. The following characteristics of C are preserved: Array syntaxarray syntax[] (array delimiters) Arrays elements are placed within [] characters; Array indexingarray indexing Arrays are 0-based; Array storagearray storage Last dimension is most rapidly varying; Assignment statementsassignment statementsemi-colon; (end of statement) A semi-colon ; indicates the end of an assignment statement. Commentscomments/*...*/ (comment)// (comment) Multi-line comments are enclosed within /* */ characters. Single line comments are preceded by // characters. Nestingincluding filesnesting#include Files may be nested in scripts using #include script. Note that 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 syntaxattribute syntax@ (attribute) The at-sign @ is used to delineate an attribute name from a variable name. Expressions Dimensions Syntax of ncap2 statements ncap2 netCDF Arithmetic Processor expressions Expressions are the fundamental building block of ncap2. Expressions are composed of variables, numbers, literals, and attributes. C languageThe following C operators are “overloaded” 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 'op' 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 “^” 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. 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 from the 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 OperatorsUnlike 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: see Operators precedence and associativity Examples: tm1= time>2 && time <7; // tm1 = 0, 0, 1, 1, 1, 1, 0, 0, 0, 0 ; type double; tm2= time==3 || time>=6; // tm2 = 0, 0, 1, 0, 0, 1, 1, 1, 1, 1 ; type double tm3= int(!tm1); // tm3= 1, 1, 0, 0, 0, 0, 1, 1, 1, 1 ; type int tm4= tm1 && tm2; // tm4= 0, 0, 1, 0, 0, 1, 0, 0, 0, 0 ; type double; tm5= !tm4; // tm5= 1, 1, 0, 1, 1, 0, 1, 1, 1, 1 ; type double; Regular Assign Operator &linebreak; var1 '=' exp1 &linebreak; If var1 doesn't already exist in Output then var1 is written to Output with the values and dimensions from expr1. If var1 already exists in Output, then the only requirement on expr1 is that the number of elements must match the number already on disk. The type of expr1 is converted if necessary to the disk type. Other Assign Operators +=,-=,*=./= &linebreak; var1 'ass_op' exp1 &linebreak; if exp1 is a variable and it doesn'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 Operatorexp1 ? 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); 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; RDM2=100,100,100,100,126,126,100,100,100, 100; // type double RDM2= RDM << 90s ; RDM3= 1, 9, 36, 84, 90, 90, 84, 36, 9, 1; // type int Dimensions Left hand casting Expressions ncap2 netCDF Arithmetic Processor dimensions in ncap2defdim()Dimensions are defined in Output using the defdim() function. defdim("cnt",10); 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 the dimension size to be used in an arithmetic expression: 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; 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,$1); ID Quoting &linebreak; If the dimension name contains non-regular characters use ID quoting. See see ID Quoting defdim("a--list.A",10); A1['$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) Left hand casting Arrays and hyperslabs Dimensions ncap2 netCDF Arithmetic Processor hybrid coordinate systemleft hand castingLHSThe 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. 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. Arrays and hyperslabs Attributes Left hand casting ncap2 netCDF Arithmetic Processor arrayarray functionarraysfindgen-equivalentindgen-equivalentGenerating a regularly spaced one-dimensional array with ncap2 is simple with the array() function. The syntax is output=array(val_srt,val_ncr,$dmn_nm); // One-dimensional output output=array(val_srt,val_ncr,var_tpl); // Multi-dimensional output h where the arguments are the starting value val_srt, incremental value val_ncr, and, for one-dimensional output, the single dimension $dmn_nm, or, for multi-dimensional output, a template variable var_tpl, i.e., a variable with the same shape as the desired output. Once the associated dimensions have been defined, the start and increment arguments may be supplied as values, mathmatical expressions, or variables: var_out=array(1,1,$time); // 1,2,3,4,5,6,7,8,9,10 var_out=array(1+2-2,one,$time); // 1,2,3,4,5,6,7,8,9,10 var_out=array(1,2,three_dmn_rec_var); // 1,3,5,...155,157,159 hyperslabsHyperslabs in ncap2 are more limited than hyperslabs with the other NCO operators. ncap2 does not understand the shell command-line syntax used to specify multi-slabs, wrapped co-ordinates, negative stride or coordinate value limits. However with a bit of syntactic magic they are all are possible. ncap2 accepts (in fact, it requires) N-hyperslab arguments for a variable of rank N: var1(arg1,arg2 ... argN); where each hyperslab argument is of the form start:end:stride and the arguments for different dimensions are separated by commas. If "start" is omitted, it defaults to 0. If "end" is omitted, it defaults to dimension size minus one. If "stride" is omitted, it defaults to 1. If a single value is present then it is assumed that that dimension collapses to a single value (i.e., a cross-section). The number of hyperslab arguments MUST equal the variable's rank. Hyperslabs on the Right Hand Side of an assign&linebreak; A simple 1D example: ($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,$lon]=0.0; prs(:,0,:,:)=th0; prs(:,1,:,:)=th1 Reverse method &linebreak; reverse()Use the reverse() method to reverse a dimension's elements in a variable with at least one dimension. This is equivalent to a negative stride, e.g., th_rv=th(1 ,:,:).reverse($lon); // {12,11,10,9 }, {16,15,14,13} od_rv=od.reverse($time); // {38,36,34,32,30,28,26,24,22,20} Permute method &linebreak; permute()Use the permute() method to swap the dimensions of a variable. The number and names of dimension arguments must match the dimensions in the variable. If the first dimension in the variable is of record type then this must remain the first dimension. If you want to change the record dimension then consider using ncpdq. Consider the variable: float three_dmn_var(lat,lev,lon); three_dmn_var_prm=three_dmn_var.permute($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; Attributes Number literals Arrays and hyperslabs ncap2 netCDF Arithmetic Processor attributesncap2Attributes are referred to by var_nm@att_nm &linebreak; All the following are valid statements: global@text="Test Attributes"; /* Assign a global variable attribute */ a1[$time]=time*20; a1@long_name="Kelvin"; a1@min=a1.min(); a1@max=a1.max(); a1@min++; --a1@max; q a1(0)=a1@min; a1($time.size-1)=a1@max; A value list can be used on the RHS of an assign... value list a1@trip1={1,2,3} ; a1@triplet={a1@min,(a1@min+a1@max)/2,a1@max}; The netCDF specification allows all attribute types to have a size greater than one. The maximum is defined by NC_MAX_ATTRS. The following is an ncdump of 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. ; The size() method can be used with attributes. For example, to save an attribute text string in a variable, defdim("sng_len", a1@long_name.size()); sng_arr[$sng_len]=a1@long_name; // sng_arr now contains "Kelvin" Attributes defined in a script are stored in memory and are written to Output after script completion. To stop the attribute being written use the ram_delete() method or use a bogus variable name. Attribute Propagation & Inheritance attribute propagationattribute inheritance • Attribute propagation occurs in a regular assign statement. The variable being defined on the LHS gets copies of the attributes from the leftermost variable on the RHS Attribute Inheritance: The LHS variable "inherits" attributes from an Input variable with the same name It is possible to have a regular assign statement for which both propagation and inheritance occur. prs_mdp[time,lat,lon,lev]=P0*hyam+hybm*PS; // prs_mdp gets attributes from PO th_min=1.0 + 2*three_dmn_var_dbl.min($time); // th_min gets attributes from three_dmn_var_dbl If the attribute name contains non-regular characters use ID quoting. See see ID Quoting 'b..m1@c--lost'=23; Number literals if statement Attributes ncap2 netCDF Arithmetic Processor number literals ncap2The table below lists the postfix character(s) to add to a number literal for type cohesion. To use the new netCDF4 types NCO must be compiled/linked to the netCDF4 library and the output file must be HDF5. n1[$time]=1UL; // n1 will now by type NC_UINT n2[$lon]=4b; // n2 will be of type NC_BYTE n3[$lat]=5ull; // n3 will be of type NC_UINT64 n3@a1=6.0d; // attribute will be type NC_DOUBLE n3@a2=-666L; // attribute will be type NC_INT A floating point number without a postfix will default to NC_DOUBLE. An integer without a postfix will default to type NC_INT. There is no postfix for characters, use a quoted string instead. n4[$rlev]=0.1 // n4 will be of type NC_DOUBLE n5[$lon_grd]=2.0 // n5 will be of type NC_DOUBLE n6[$gds_crd]=2e3; // n6 will be of type NC_DOUBLE n7[$gds_crd]=2e3f; // n7 will be of type NC_FLOAT n6@a1=41; // attribute will be type NC_INT n6@a2=-21; // attribute will be type NC_INT n6@units="kelvin" // attribute will be type NC_CHAR 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 if statement print statement Number literals ncap2 netCDF Arithmetic Processor 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 (&&) 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; /* [nbr] Averaging 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; /* [nbr] Averaging 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() ); print statement Missing values ncap2 if statement ncap2 netCDF Arithmetic Processor print() ncap2 print(variable_name/attribute name/string, format string); The print function takes a variable name or attribute name or a quoted string and prints the contents in a in a similar fashion to ncks -H. There is also an optional C-language style format string argument. Currently the print function cannot print RAM variables or expressions such as 'print(var_msk*3+4)'. To print an expression, first evaluate it as a non-RAM variable (so it will be saved and can be printed), and then print the variable. examples print(lon); lon[0]=0 lon[1]=90 lon[2]=180 lon[3]=270 print(lon_2D_rrg,"%3.2f,"); 0.00,0.00,180.00,0.00,180.00,0.00,180.00,0.00, 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 Missing values ncap2 Methods and functions print statement ncap2 netCDF Arithmetic Processor missing values ncap2Missing values operate slightly differently in ncap2 Consider the expression where op is any of the following operators (excluding '=') 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 'percolate' 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; The following methods are used to edit the missing value associated with a variable. They only work on variables in Output. set_miss(expr)set_miss() Takes one argument the missing value. Sets or overwrites the existing missing value. The argument given is converted if necessary to the variable type change_miss(expr)change_miss() Changes the missing value elements of the variable to the new missing value (nb an expensive function). 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() Deletes the missing value associated with a variable. 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,$lon]=1.0; new.set_miss(-997.0); // Extract only elements divisible by 3 where (three_dmn_var_dbl%3 == 0) new=three_dmn_var_dbl; elsewhere new=new.get_miss(); Methods and functions RAM variables Missing values ncap2 ncap2 netCDF Arithmetic Processor The convention within this document is that methods can be used as functions. However, functions are not and cannot be used as methods. Methods can be daisy changed together and their syntax is cleaner than functions. Method names are reserved words and CANNOT be used as variable names. The command ncap2 -f shows the complete list of methods available on your build. n2=sin(theta) n2=theta.sin() n2=sin(theta)^2 + cos(theta)^2 n2=theta.sin().pow(2) + theta.cos()^2 This statement chains methods together to convert three_dmn_var_sht to type double, average it, then convert this back to type short: three_avg=three_dmn_var_sht.double().avg().short(); Aggregate Methods &linebreak; These methods mirror the averaging types available in ncwa. The arguments to the methods are the dimensions to average over. Specifying no dimensions is equivalent to specifying all dimensions i.e., averaging over all dimensions. A masking variable and a weighting variable can be manually created and applied as needed. avg()avg() Mean value sqravg()sqravg() Square of the mean avgsqr() Mean of sum of squares max()max() Maximum value min()min() Minimum value rms() Root-mean-square (normalize by N) rmssdn()rmssdn() Root-mean square (normalize by N-1) ttl() or total()ttl() Sum of values // Average a variable over time four_time_avg=four_dmn_rec_var($time); Packing Methods &linebreak; For more information see see Packed data and see 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. 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) Utility Methods &linebreak; These functions are used to manipulate missing values and RAM variables. see 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 change_miss(expr) Changes the missing value elements of the variable to the new missing value (n.b. 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. 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 see ncpdq netCDF Permute Dimensions Quickly reverse(dim args) Reverses the dimension ordering of elements in a variable. permute(dim args) Re-shapes variables by re-ordering the dimensions. All the dims 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 &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 (see Type Conversion). You can only use the new netCDF4 types 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 Intrinsic Mathematical Methods &linebreak; The list of mathematical methods is system dependant. For the full list see Intrinsic mathematical methods All the mathematical methods take a single operand ,with the exception of atan2 and pow which take two. If the operand type is less than float then the result will be of type float. If the operand is type double then the result will be 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 RAM variables Where statement Methods and functions ncap2 netCDF Arithmetic Processor 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 a 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 Where statement Loops RAM variables ncap2 netCDF Arithmetic Processor where()A where() combines the definition and application of a mask all in one go and can lead to succinct code. The full syntax of a where() statement is as follows: // Single assign (the 'elsewhere' block 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., 'var=expr'. 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'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 not re-assigned. For these reasons, do not explicitly reference missing values in the masking condition, e.g., where(foo=foo.get_missing()) foo=1; will not work as expected. Example: 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; 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). 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(); Loops Include files Where statement ncap2 netCDF Arithmetic Processor 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"); Include files Sort methods Loops ncap2 netCDF Arithmetic Processor includeThe syntax of an include-file is: #include "script.nco" The script filename is searched relative to the run 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 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 Sort methods Irregular grids Include files ncap2 netCDF Arithmetic Processor sortasortdsortremapunmapinvert_mapIn ncap2 there are multiple ways to sort data. Beginning with NCO 4.1.0 (February, 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 inverse map of the input map srt_map. This gives the user access to both the forward and inverse sorting maps which can be useful in special situations. 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's 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's 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 Irregular grids Bilinear interpolation Sort methods ncap2 netCDF Arithmetic Processor irregular gridsrectangular gridsnon-rectangular gridsnon-standard gridsmask 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: rectangularity, regularity, and fxm. 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. Wrapped coordinates (see Wrapped Coordinates), such as longitude, are independent of these grid properties (regularity, rectangularity). wrapped coordinatesThe 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 "staggered grids" 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 For WRF files creating regional masks looks like mask_var = (XLAT >= lat_min && XLAT <= lat_max) && (XLONG >= lon_min && > XLONG <= lon_max); In practice with WRF it's a bit more complicated because you must use the global metadata to determine the grid staggering and offsets to translate XLAT and XLONG into real latitudes and longitudes and missing points. The WRF grid documentation should describe this. A few notes: Irregular regions are the union of arrays lat/lon_min/max's. The mask procedure is identical for all R. Bilinear interpolation GSL special functions Irregular grids ncap2 netCDF Arithmetic Processor 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 to find 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 montonic (increasing or decreasing). grid_in_y Y input values values. Must be montonic (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 ; GSL special functions GSL interpolation Bilinear interpolation ncap2 netCDF Arithmetic Processor GSLAs 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 functiongsl_sf_gammaConsider 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 functiongsl_sf_bessel_JnConsider 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 integralsConsider 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 polynomialgsl_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; 35 5 60 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) GSL interpolation GSL least-squares fitting GSL special functions ncap2 netCDF Arithmetic Processor GSLAs 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 'missing value' because 0.0 isn'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; Unimplemented &linebreak; gsl_spline_deriv()&linebreak; gsl_spline_deriv2()&linebreak; gsl_spline_integ()&linebreak; GSL least-squares fitting GSL statistics GSL interpolation ncap2 netCDF Arithmetic Processor Least Squares fitting is a method of calculating a straight line through a set of experimental data points in the XY plane. The data maybe 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; &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 'n' 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 '&' prefix indicates that these are call-by-reference variables. If any of the output variables don'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; &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 'n' 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; &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 &linebreak; &linebreak; GSL statistics GSL random number generation GSL least-squares fitting ncap2 netCDF Arithmetic Processor 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. Some examples 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); GSL random number generation Examples ncap2 GSL statistics ncap2 netCDF Arithmetic Processor 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 maximium 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'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 minimium value returned by the rng algorithm. gsl_rng_max() the maximium 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 maximium 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 maximium 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 maximium 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 Examples ncap2 Intrinsic mathematical methods GSL random number generation ncap2 netCDF Arithmetic Processor 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 together 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. growing dimensionsdimensions, growingncap2 may be used to “grow” 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 flagsImagine 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 maskThis 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 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 $[$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 covarianceSometimes 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. globbingshellquotesextended regular expressionsregular expressionsncap2 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 (see Subsetting Variables). 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 # Fails ($time = '') ncap2 -s "uavg=u.avg($time)" in.nc foo.nc # Fails ($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 (see CCSM Example). In such cases it may be necessary to use the shell backslash character \ to protect the ncap2 meta-character. appending datatime-averagingncksncwancradegenerate dimension-bWhether a degenerate record dimension is desirable or undesirable depends on the application. Often a degenerate time dimension is useful, e.g., for concatentating, but 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. -CEqually important is the -C argument (see 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. Intrinsic mathematical methods Operators precedence and associativity Examples ncap2 ncap2 netCDF Arithmetic Processor ncap2 supports the standard mathematical functions supplied with most operating systems. additionsubtractionmultiplicationdivisionexponentiationpowermodulus+ (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: absacoshacosasinhasinatanhatanceilcoshcoserfcerfexpfloorgammalnlog10lognearbyintpowrintroundsinhsinsqrttanhtantruncmathematical functionsnearest integer function (inexact)nearest integer function (exact)rounding functionstruncation functionabsolute valuearccosine functionarcsine functionarctangent functionceiling functioncomplementary error functioncosine functionerror functionexponentiation functionfloor functiongamma functionhyperbolic arccosine functionhyperbolic arcsine functionhyperbolic arctangent functionhyperbolic cosine functionhyperbolic sine functionhyperbolic tangentlogarithm, base 10logarithm, naturalpower functionsine functionsquare root function abs(x) Absolute value Absolute value of x. Example: $abs(-1)\; =\; 1$ acos(x) Arc-cosine Arc-cosine of x where x is specified in radians. Example: $acos(1.0)\; =\; 0.0$ acosh(x) Hyperbolic arc-cosine Hyperbolic arc-cosine of x where x is specified in radians. Example: $acosh(1.0)\; =\; 0.0$ asin(x) Arc-sine Arc-sine of x where x is specified in radians. Example: $asin(1.0)\; =\; 1.57079632679489661922$ asinh(x) Hyperbolic arc-sine Hyperbolic arc-sine of x where x is specified in radians. Example: $asinh(1.0)\; =\; 0.88137358702$ atan(x) Arc-tangent Arc-tangent of x where x is specified in radians between $-pi/2$ and $pi/2$. Example: $atan(1.0)\; =\; 0.78539816339744830961$ 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 $pi/2$. Example: $atanh(3.14159265358979323844)\; =\; 1.0$ ceil(x) Ceil Ceiling of x. Smallest integral value not less than argument. Example: $ceil(0.1)\; =\; 1.0$ cos(x) Cosine Cosine of x where x is specified in radians. Example: $cos(0.0)\; =\; 1.0$ cosh(x) Hyperbolic cosine Hyperbolic cosine of x where x is specified in radians. Example: $cosh(0.0)\; =\; 1.0$ erf(x) Error function Error function of x where x is specified between $-1$ and $1$. Example: $erf(1.0)\; =\; 0.842701$ erfc(x) Complementary error function Complementary error function of x where x is specified between $-1$ and $1$. Example: $erfc(1.0)\; =\; 0.15729920705$ exp(x) Exponential Exponential of x, $e^x$. Example: $exp(1.0)\; =\; 2.71828182845904523536$ floor(x) Floor Floor of x. Largest integral value not greater than argument. Example: $floor(1.9)\; =\; 1$ gamma(x) Gamma function Gamma function of x, $Gamma(x)$. The well-known and loved continuous factorial function. Example: $gamma(0.5)\; =\; sqrt(pi)$ gamma_inc_P(x) Incomplete Gamma function Incomplete Gamma function of parameter a and variable x, $gamma\_inc\_P(a,x)$. One of the four incomplete gamma functions. Example: $gamma\_inc\_P(1,1)\; =\; 1-1/e$ ln(x) Natural Logarithm Natural logarithm of x, $ln(x)$. Example: $ln(2.71828182845904523536)\; =\; 1.0$ log(x) Natural Logarithm Exact synonym for ln(x). log10(x) Base 10 Logarithm Base 10 logarithm of x, $log10(x)$. Example: $log(10.0)\; =\; 1.0$ nearbyint(x) Round inexactly Nearest integer to x is returned in floating point format. inexact conversionNo exceptions are raised for inexact conversions. Example: $nearbyint(0.1)\; =\; 0.0$ pow(x,y) Power promotionautomatic type conversionValue 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 (see Type Conversion) to type NC_FLOAT before evaluation. Example: $pow(2,3)\; =\; 8$ rint(x) Round exactly Nearest integer to x is returned in floating point format. Exceptions are raised for inexact conversions. Example: $rint(0.1)\; =\; 0$ 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: $round(0.5)\; =\; 1.0$ sin(x) Sine Sine of x where x is specified in radians. Example: $sin(1.57079632679489661922)\; =\; 1.0$ sinh(x) Hyperbolic sine Hyperbolic sine of x where x is specified in radians. Example: $sinh(1.0)\; =\; 1.1752$ sqrt(x) Square Root Square Root of x, $sqrt(x)$. Example: $sqrt(4.0)\; =\; 2.0$ tan(x) Tangent Tangent of x where x is specified in radians. Example: $tan(0.78539816339744830961)\; =\; 1.0$ tanh(x) Hyperbolic tangent Hyperbolic tangent of x where x is specified in radians. Example: $tanh(1.0)\; =\; 0.761594155956$ 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: $trunc(0.5)\; =\; 0.0$ 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 Cfloatprecisionquadruple precisionsingle precisiondouble precisionlong doubleNC_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_tblUse the -f (or fnc_tbl or prn_fnc_tbl) switch to print a complete list of functions supported on your platform. LinuxLinux supports more of these intrinsic functions than other OSs. Operators precedence and associativity ID Quoting Intrinsic mathematical methods ncap2 netCDF Arithmetic Processor 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. 18 62 20 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 ID Quoting Operators precedence and associativity ncap2 netCDF Arithmetic Processor ID QuotingIn this section when I refer to a name I mean a variable name, attribute name or a dimension name The allowed characters in a valid netCDF name vary from release to release. (See end section). If you want to use metacharacters in a name or use a method name as a variable name then the name has to 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. Any 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; 'avg' , '10_+10','set_miss' '+-90field' , '–test'=10.0d&linebreak; &linebreak; Quote a attribute: &linebreak; 'three@10', 'set_mss@+10', '666@hell', 't1@+units'="kelvin" &linebreak; &linebreak; Quote a dimension: &linebreak; '$10', '$t1–', '$–odd', c1['$10','$t1–']=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 a name string is valid * CDL syntax, eg, all the 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. */ ncatted netCDF Attribute Editor ncbo netCDF Binary Operator ncap2 netCDF Arithmetic Processor Operator Reference Manual

attributesattribute namesediting attributesncatted SYNTAX ncatted [-a att_dsc] [-a &dots;] [-D dbg] [-h] [--hdr_pad nbr] [-l path] [-O] [-o output-file] [-p path] [-R] [-r] input-file [[output-file]] DESCRIPTION ncattgetncatted 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 (see Filters for ncks). history-hBecause repeated use of ncatted can considerably increase the size of the history global attribute (see History Attribute), the -h switch is provided to override automatically appending the command to the history global attribute in the output-file. missing valuesdata, missing_FillValueWhen 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 together, e.g., by ncrcat, without losing any information. See Missing Values, for more information. The key to mastering ncatted is understanding the meaning of the structure that describes the attribute modification, att_dsc specified by the required option -a or --attribute. Each att_dsc contains five elements, which makes using ncatted somewhat complicated, but powerful. The att_dsc argument structure contains five arguments in the following order:&linebreak; att_dsc = att_nm, var_nm, mode, att_type, att_val&linebreak; att_nm Attribute name. Example: units var_nm Variable name. extended regular expressionsregular expressionspattern matchingwildcardsRegular expressions (see Subsetting Variables) are accepted and will select any matching variable names. Example: pressure, '^H2O'. 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 you want to edit. This meaning of this should be clear to all users of the ncatted operator. 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 attributesattributes, globalThe 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 variable in a file. A value of var_nm of “global” indicates that att_nm refers to a global attribute, rather than a particular variable's attribute. This is the method ncatted supports for editing global attributes. If var_nm is left blank, on the other hand, 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. Finally, as mentioned above, var_nm may be specified as a regular expression. The value of mode is a single character abbreviation (a, c, d, m, or o) standing for one of five editing modes:&linebreak; attributes, appendingattributes, creatingattributes, deletingattributes, modifyingattributes, editingattributes, overwriting a Append. Append value att_val to current var_nm attribute att_nm value att_val, if any. If var_nm does not have an attribute att_nm, there is no effect. 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. 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. 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. 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 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. Value(s) specified in att_val will be stored as netCDF intrinsic type NC_STRING. The specification of att_type is optional (and is ignored) in Delete mode. 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. PerlASCIINCO 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 \\. See 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 charactersASCIIAnalogous to printf, other special characters are also allowed by ncatted if they are "protected" 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, $, |, &, @, %, {, and }) will not be filtered and will be included in the attribute. stringsNUL-terminationNULC language0 (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. 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 "temperature": ncatted -a long_name,T,o,c,temperature in.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 attributesattributes, globalDelete 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. unitsModify all existing units attributes to "meter second-1": ncatted -a units,,m,c,"meter second-1" in.nc unitsAdd 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 Overwrite the quanta attribute of variable energy to an array of four integers. ncatted -O -a quanta,energy,o,s,"010,101,111,121" in.nc extended regular expressionsregular expressionspattern matchingwildcardsAs of NCO 3.9.6 (January, 2009), variable names arguments to ncatted may contain extended regular expressions. Create isotope attributes for all variables containing H2O in their names. ncatted -O -a isotope,'^H2O*',c,s,"18" in.nc See Subsetting Variables for more details. 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.

ncbo netCDF Binary Operator ncea netCDF Ensemble Averager ncatted netCDF Attribute Editor Operator Reference Manual

ncboncdiffncaddncsubncsubtractncmultncmultiplyncdividebinary operationsadditionsubtractionmultiplicationadding datasubtracting datamultiplying datadividing data SYNTAX ncbo [-3] [-4] [-6] [-A] [-C] [-c] [--cnk_dmn nm,sz] [--cnk_map map] [--cnk_plc plc] [--cnk_scl sz] [-D dbg] [-d dim,[min][,[max][,[stride]]] [-F] [-h] [-L dfl_lvl] [-l path] [-O] [-o file_3] [-p path] [-R] [-r] [-t thr_nbr] [-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). See 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). addsubtractmultiplydivide+-*/-y op_typ--operation op_typ--op_typ op_typalternate invocationsThe 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 charactersA 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 shellPlace 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=/. globbingshellquotesFor 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. aliasln -ssymbolic linksNormally, 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'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. 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 variablesVariables in file_2 are broadcast to conform to the corresponding variable in file_1 if necessary, but the reverse is not true. Broadcasting a variable means creating data in non-existing dimensions from the data in existing dimensions. For example, a two dimensional variable in file_2 can be subtracted from a four, three, or two (but not one or zero) dimensional variable (of the same name) in file_1. anomaliesThis functionality allows the user to compute anomalies from the mean. Note that variables in file_1 are not broadcast to conform to the dimensions in file_2. In the future, we will broadcast variables in file_1, if necessary to conform to their counterparts in file_2. rankThus, 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. Furthermore, 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 were 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-bThis 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 but 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 occuring, use ncwa to remove the time dimension from file_2. See the example below. coordinate variableNC_BYTENC_CHARncbo never operates on coordinate variables or variables of type NC_CHAR or NC_BYTE. This ensures that coordinates like (e.g., latitude and longitude) are physically meaningful in the output file, file_3. This behavior is hardcoded. CF conventionsncbo 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 (see 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. 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 -op_typ=sub 86_0112.nc 85_0112.nc 86m85_0112.nc ncdiff 86_0112.nc 85_0112.nc 86m85_0112.nc 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. 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's data from the five-year mean: ncbo 8501_8912.nc 8589.nc t_anm_8501_8912.nc Now use ncks to group the five January anomalies together 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 ShellBourne ShellC 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

ncea netCDF Ensemble Averager ncecat netCDF Ensemble Concatenator ncbo netCDF Binary Operator Operator Reference Manual

averaging dataensemble averagencea SYNTAX ncea [-3] [-4] [-6] [-A] [-C] [-c] [--cnk_dmn nm,sz] [--cnk_map map] [--cnk_plc plc] [--cnk_scl sz] [-D dbg] [-d dim,[min][,[max][,[stride]]] [-F] [-h] [-L dfl_lvl] [-l path] [-n loop] [-O] [-o output-file] [-p path] [-R] [-r] [-t thr_nbr] [-v var[,&dots;]] [-X ...] [-x] [-y op_typ] [input-files] [output-file] DESCRIPTION ncea performs gridpoint averages of variables across an arbitrary number (an ensemble) of input-files, with each file receiving an equal weight in the average. ensemblencea averages entire files, and weights each file evenly. This is distinct from ncra, which only averages over the record dimension (e.g., time), and weights each record in the record dimension evenly, Variables in the output-file are the same size as the variable in each of the input-files, and all input-files must be the same size. record dimensionhyperslabThe only exception is that ncea allows files to differ in the record dimension size if the requested record hyperslab (see Hyperslabs) resolves to the same size for all files. ncea 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. IPCCAR4CMIPThis simplifies analysis of unequal length timeseries from simulation ensembles (e.g., the CMIP IPCC AR4 archive). operation typesncea always averages coordinate variables regardless of the arithmetic operation type performed on the non-coordinate variables. (see Operation Types). record dimensionAll dimensions, including the record dimension, are treated identically and preserved in the output-file. See Averaging vs. Concatenating, for a description of the distinctions between the various averagers and concatenators. multi-file operatorsstandard inputstdinAs a multi-file operator, ncea will read the list of input-files from stdin if they are not specified as positional arguments on the command line (see Large Numbers of Files). The file is the logical unit of organization for the results of many scientific studies. Often one wishes to generate a file which is the gridpoint average of many separate files. 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 properties are the mean of all the input files, then ncea is the operator to use. ncea only allows coordinate variables to be processed by the linear average, minimum, and maximum operations. ncea will return the linear average of coordinates unless extrema are explicitly requested. Other requested operations (e.g., square-root, RMS) are applied only to non-coordinate variables. In these cases the linear average of the coordinate variable will be returned. 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 the ensemble average (mean) seasonal cycle. Here the numeric filename suffix denotes the experiment number (not the month): ncea 85_01.nc 85_02.nc 85_03.nc 85_04.nc 85_05.nc 85.nc ncea 85_0[1-5].nc 85.nc ncea -n 5,2,1 85_01.nc 85.nc These three commands produce identical answers. See 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., ncea -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.

ncecat netCDF Ensemble Concatenator ncflint netCDF File Interpolator ncea netCDF Ensemble Averager Operator Reference Manual

concatenationensemble concatenationncecat SYNTAX ncecat [-3] [-4] [-6] [-A] [-C] [-c] [--cnk_dmn nm,sz] [--cnk_map map] [--cnk_plc plc] [--cnk_scl sz] [-D dbg] [-d dim,[min][,[max][,[stride]]] [-F] [-h] [-L dfl_lvl] [-l path] [-M] [--md5_digest] [-n loop] [-O] [-o output-file] [-p path] [-R] [-r] [-t thr_nbr] [-u ulm_nm] [-v var[,&dots;]] [-X ...] [-x] [input-files] [output-file] DESCRIPTION ncecat concatenates an arbitrary number of input files into a single output file. The input-files are stored consecutively as records in output-file. Each variable (except coordinate variables) in each input file becomes one record in 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 "gaps" in the output file). A new record dimension is the glue which binds the input file data together. The new record dimension name is, by default, “record”. unlimited dimensionrecord dimension-u ulm_nm--ulm_nm ulm_nm--rcd_nm ulm_nmIts name can be specified 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 dimensionhyperslabThe only exception is that ncecat allows files to differ in the record dimension size if the requested record hyperslab (see Hyperslabs) resolves to the same size for all files. IPCCThis allows easier gluing/averaging of unequal length timeseries from simulation ensembles (e.g., the IPCC AR4 archive). Thus, the output-file size is the sum of the sizes of the extracted variables in the input files. See Averaging vs. Concatenating, for a description of the distinctions between the various averagers and concatenators. multi-file operatorsstandard inputstdinAs 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 (see Large Numbers of Files). -M--glb_mtd_sprmetadata, globalTurn off global metadata copying. By default all NCO operators copy the global metadata of the first input file into output-file. This helps preserve the provenance of the output data. However, the use of metadata is burgeoning and is not uncommon to encounter 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, --glb_mtd_spr and --global_metadata_suppress). climate modelConsider 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 the individual realizations together 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. ncpdqpackingunpackingadd_offsetscale_factorConcatenating 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. 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. See 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. One often prefers that the (new) record dimension have a more descriptive, context-based name than simply “record”. This is easily accomplished with the -u ulm_nm switch: 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. record dimensionConsider 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 procedure 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.

ncflint netCDF File Interpolator ncks netCDF Kitchen Sink ncecat netCDF Ensemble Concatenator Operator Reference Manual

interpolationadding datamultiplying dataadditionncflint SYNTAX ncflint [-3] [-4] [-6] [-A] [-C] [-c] [--cnk_dmn nm,sz] [--cnk_map map] [--cnk_plc plc] [--cnk_scl sz] [-D dbg] [-d dim,[min][,[max][,[stride]]] [-F] [-h] [-i var,val3] [-L dfl_lvl] [-l path] [-O] [-o file_3] [-p path] [-R] [-r] [-t thr_nbr] [-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 $$val3 = wgt1*val1 + wgt2*val2 . Here at least wgt1, and, optionally, wgt2, are specified on the command line with the -w (or --weight or --wgt_var) switch. -w wgt1[,wgt2]--weight wgt1[,wgt2]--wgt_var wgt1[,wgt2]If only wgt1 is specified then wgt2 is automatically computed as $$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 the values in a given file by a constant. The second method of using ncflint is to specify the interpolation option with -i (or with the --ntp or --interpolate long options). This is really 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 the results together to produce the output values. It makes sense to use this when the weights are known a priori. arrival valueAnother 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: $$val3 = wgt1*val1 + wgt2*val2 . Unique determination of the weights requires imposing the additional constraint of normalization on the weights: $$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 dimensionThus 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_BYTE. This behavior is hardcoded. missing values_FillValueDepending 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*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., $$val3 = val2. This behavior would appeal to those who use ncflint to estimate data using the closest available data. When a point is not bracketed by valid data on both sides, it is better to return the known datum than no datum at all. The current implementation uses the first approach, Option one. If you have strong opinions on this matter, let us know, since we are willing to implement the other approaches as options if there is enough interest. EXAMPLES Although it has other uses, the interpolation feature was designed to interpolate file_3 to a time between existing files. Consider input files 85.nc and 87.nc containing variables describing the state of a physical system at times $$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 operationThis is an example of a null operation, so tst.nc should be identical (within machine precision) to 85.nc. 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 variablesHowever this is not a good idea in practice because ncflint does not broadcast (see 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. unitsRescale 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 Sink ncpdq netCDF Permute Dimensions Quickly ncflint netCDF File Interpolator Operator Reference Manual

kitchen sinkprinting files contentsprinting variablesncks SYNTAX ncks [-3] [-4] [-6] [-A] [-a] [-B] [-b binary-file] [-C] [-c] [--cnk_dmn nm,sz] [--cnk_map map] [--cnk_plc plc] [--cnk_scl sz] [-D dbg] [-d dim,[min][,[max][,[stride]]] [--fix_rec_dmn] [-F] [-H] [-h] [--hdr_pad nbr] [-L dfl_lvl] [-l path] [-M] [-m] [--mk_rec_dmn dim] [--md5_digest] [-O] [-o output-file] [-P] [-p path] [-Q] [-q] [-R] [-r] [-s format] [-u] [-v var[,&dots;]] [-X ...] [-x] input-file [[output-file]] DESCRIPTION ncextrncks combines selected features of ncdump, ncextr, and the nccut and ncpaste specifications into one versatile utility. ncks extracts a subset of the data from input-file and prints it as ASCII text to stdout, writes it in flat binary format to binary-file, and writes (or pastes) it in netCDF format to output-file. ncks will print netCDF data in ASCII format to stdout, like ncdump, but with these differences: ncks prints data in a tabular format 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. Option -s (or long options --sng_fmt and --string) lets the user format the data using C-style format strings. Options -a, -F , -H, -M, -m, -P, -Q, -q, -s, and -u (and their long option counterparts) control the formatted appearance of the data. global attributesattributes, globalncks 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—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 datamerging filesncks 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 attributesattributes, globalWhen 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 (but 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 variables, respectively. The following list provides a short summary of the features unique to ncks. Features common to many operators are described in Common features. alphabetizationsort alphabetically-a--abc--alphabetize -a Do not alphabetize extracted fields. By default, the specified output variables are extracted, printed, and written to disk in alphabetical order. This tends to make long output lists easier to search for particular variables. Specifying -a results in the variables being extracted, printed, and written to disk in the order in which they were saved in the input file. Thus -a retains the original ordering of the variables. Also --abc and --alphabetize. binary format-B--bnr--binary -B file Activate native machine binary output writing to the default binary file, ncks.bnr. pThe -B switch is redundant when the -b file option is specified, and native binary output will be directed to the binary file file. Also --bnr and --binary. Writing packed variables in binary format is not supported. -b--fl_bnr -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. stride -d dim,[min][,[max][,[stride]]] Add stride argument to hyperslabber. For a complete description of the stride argument, See Stride. record dimensionfixed dimension--fix_rec_dmn--no_rec_dmn --fix_rec_dmn Change all record dimensions in the input file into fixed dimensions in the output file. Also --no_rec_dmn. record dimensionfixed dimension--mk_rec_dmn dim --mk_rec_dmn dim Change existing dimension dim to a record dimension in the output file. See ncecat netCDF Ensemble Concatenator and ncpdq netCDF Permute Dimensions Quickly for other methods of changing variable dimensionality, including the record dimension. -H--data--hieronymus -H Print data to screen. Also activated using --print or --prn. By default ncks prints all metadata and data to screen if no netCDF output file is specified. Use -H to print data to screen if a netCDF output is specified, or to restrict printing to data (no metadata) when no netCDF output is specified. 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 -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 -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 -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 -M--Mtd--Metadatametadata, global -M Print to screen the global metadata describing the file. This includes file summary information and global 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). 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 -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 global attributes, respectively. ncdump-m--mtd--metadatametadata -m Print variable metadata to screen (similar to ncdump -h). This displays all metadata pertaining to each variable, one variable at a time. chunkingcompressiondeflationThis 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. -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. -Q -Q Toggle printing of dimension indices and coordinate values when printing arrays. Each variable's name appears flush left in the output. This helps locate specific variables in lists with many variables and different dimensions. -q--quietquiet -q 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 (see Retaining Retrieved Files), ncks automatically sets -q. This allows ncks to retrieve remote files without automatically trying to print them. Also --quiet. -s--string--sng_fmtprintf()C language -s format String format for text output. Accepts C language escape sequences and printf() formats. Also --string and --sng_fmt. -u--units -u Toggle the printing of a variable's units attribute, if any, with its values. Also --units. Filters for <command>ncks</command> Filters for ncks

Filters for ncks ncks netCDF Kitchen Sink ncks netCDF Kitchen Sink UNIXncattgetnclist.bashrcfiltersaliasshellBash shellCsh shellSh shellbashWe 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 -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 nclist 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 -m -v z is too verbose so a robust grep and cut filter is desirable, such as ncks -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 -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 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. # NB: Untested on Csh, Ksh, Sh, Zsh! Send us feedback! # Bash shell (/bin/bash) users place these in .bashrc function nclist { ncks -m ${1} | grep -E ': type' | cut -f 1 -d ' ' | sed 's/://' | sort ; } function ncattget { ncks -M -m ${3} | grep -E -i "^${2} attribute [0-9]+: ${1}" | cut -f 11- -d ' ' | sort ; } # Csh shell (/bin/csh) users place these in .cshrc nclist() { ncks -m ${1} | grep -E ': type' | cut -f 1 -d ' ' | sed 's/://' | sort ; } ncattget() { ncks -M -m ${3} | grep -E -i "^${2} attribute [0-9]+: ${1}" | cut -f 11- -d ' ' | sort ; } # Sh shell (/bin/sh) users place these in .profile nclist() { ncks -m ${1} | grep -E ': type' | cut -f 1 -d ' ' | sed 's/://' | sort ; } ncattget() { ncks -M -m ${3} | grep -E -i "^${2} attribute [0-9]+: ${1}" | cut -f 11- -d ' ' | sort ; } 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. historyFirst, the history global attribute (see History Attribute) will contain the command used to create out.nc. alphabetize outputsort alphabetically-aSecond, 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). global attributesattributes, globalsubsettingexclusionextraction-v var--variable var-x--exclude--xclCopy 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 (see Subsetting Variables). 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--appendappending to filesWithout any variables to extract, the append (-A) command (see Appending Variables) has only to extract and copy (i.e., append) global attributes to the output file. 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 -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 -s '%f\n' -C -v three_dmn_var in.nc 0.000000 1.000000 ... 23.000000 % ncks -s '%+16.10f\n' -C -v three_dmn_var in.nc +0.0000000000 +1.0000000000 ... +23.0000000000 % ncks -s '%f, ' -C -v three_dmn_var in.nc 0.000000, 1.000000, ..., 23.000000, The second and third options are useful when pasting data into text files like reports or papers. See ncatted netCDF Attribute Editor, for more details on string formatting and special characters. 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 dumpUsing the %s format code on strings which are not NUL-terminated (and thus not technically strings) is likely to result in a core dump. subsettingexclusionextractionCreate 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 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. Print (with -H) the hyperslabs to the screen for good measure. 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 band < 0.5 in coordinate band; and cross-section closest to 1000. 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 See Hyperslabs. ncks -H -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 coordinatesAssume the domain of the monotonically increasing longitude coordinate lon is 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 See Wrapped Coordinates. ncpdq netCDF Permute Dimensions Quickly ncra netCDF Record Averager ncks netCDF Kitchen Sink Operator Reference Manual

ncpdqncpackncunpackreshape variablespermute dimensionsreverse dimensionsre-order dimensionsre-dimensionpackingunpacking SYNTAX ncpdq [-3] [-4] [-6] [-A] [-a [-]dim[,&dots;]] [-C] [-c] [--cnk_dmn nm,sz] [--cnk_map map] [--cnk_plc plc] [--cnk_scl sz] [-D dbg] [-d dim,[min][,[max][,[stride]]] [-F] [-h] [-L dfl_lvl] [-l path] [-M pck_map] [-O] [-o output-file] [-P pck_plc] [-p path] [-R] [-r] [-t thr_nbr] [-U] [-v var[,&dots;]] [-X ...] [-x] input-file [output-file] DESCRIPTION ncpdq performs one of two distinct functions, packing or dimension permutation, but not both, when invoked. ncpdq is optimized to perform these actions in a parallel fashion with a minimum of time and memory. The pdq may stand for “Permute Dimensions Quickly”, “Pack Data Quietly”, “Pillory Dan Quayle”, or other silly uses. add_offsetscale_factorncap2packing policy 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_plcThe 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. 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--upk--unpackFinally, the ncpdq -U switch (or its long option equivalents, --upk and --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. aliasln -ssymbolic linksNote that ncpdq synonyms are subject to the same constraints and recommendations discussed in the secion on ncbo synonyms (see ncbo netCDF Binary Operator). That is, symbolic links must exist from the synonym to ncpdq, or else the user must define an alias. packing mappck_map-M pck_map--pck_map pck_map--map pck_mapThe 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. Five packing maps are currently implemented:&linebreak; hgh_shthgh_bytflt_shtflt_bytnxt_lsrNC_DOUBLENC_FLOATNC_INTNC_SHORTNC_CHARNC_BYTE 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_INT,NC_SHORT,NC_CHAR,NC_BYTE]&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_INT,NC_SHORT,NC_CHAR,NC_BYTE]&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_INT] to NC_SHORT&linebreak; Types copied instead of packed: [NC_SHORT,NC_CHAR,NC_BYTE]&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_INT,NC_SHORT] to NC_BYTE&linebreak; Types copied instead of packed: [NC_CHAR,NC_BYTE]&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 to NC_INT. Pack [NC_FLOAT,NC_INT] to NC_SHORT. Pack NC_SHORT to NC_BYTE.&linebreak; Types copied instead of packed: [NC_CHAR,NC_BYTE]&linebreak; pck_map key values: nxt_lsr, pck_map_nxt_lsr&linebreak; 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%. _FillValue_FillValueNULThe netCDF packing algorithm (see Methods and functions) is lossy—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. 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 dimensionUsers may specify any permutation of dimensions, including permutations which change the record dimension identity. The record dimension is re-ordered like any other dimension. concatenationrecord dimensionThis unique ncpdq capability makes it possible to concatenate files along any dimension. See Concatenation for a detailed example. record variableThe record dimension is always the most slowly varying dimension in a record variable (see 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's dimensions in the dimension re-order list in their original order, and prefix each dimension name with the negative sign. transposeSecond, it may useful to transpose a variable's storage order, e.g., from C to Fortran data storage order (see C and Fortran Index Conventions). To do this, include all the variable's dimensions in the dimension re-order list in reversed order. Explicit examples of these two techniques appear below. 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-vMany 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 (see Appending Variables). 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 dimensionsSuppose 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 -C -v lat in.nc lat[0]=-90 lat[1]=90 % ncks -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 optionsquotesQuotes 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 reverse dimensionsTo 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 -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 -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 dataTo 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 -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 -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 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 dimensionNow 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. 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.

ncra netCDF Record Averager ncrcat netCDF Record Concatenator ncpdq netCDF Permute Dimensions Quickly Operator Reference Manual

averaging datarecord averagerecord dimensionrunning averagencra SYNTAX ncra [-3] [-4] [-6] [-A] [-C] [-c] [--cnk_dmn nm,sz] [--cnk_map map] [--cnk_plc plc] [--cnk_scl sz] [-D dbg] [-d dim,[min][,[max][,[stride]]] [-F] [-h] [-L dfl_lvl] [-l path] [-n loop] [-O] [-o output-file] [-p path] [-R] [-r] [-t thr_nbr] [-v var[,&dots;]] [-X ...] [-x] [-y op_typ] [input-files] [output-file] DESCRIPTION ncra averages record variables across an arbitrary number of input-files. degenerate dimensionrecord dimensionThe record dimension is, by default, retained as a degenerate (size 1) dimension in the output variables. See Averaging vs. Concatenating, for a description of the distinctions between the various averagers and concatenators. multi-file operatorsstandard inputstdinAs 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 (see 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). hyperslabHyperslabs of the record dimension which include more than one file work correctly. stridencra supports the stride argument to the -d hyperslab option (see Hyperslabs) for the record dimension only, stride is not supported for non-record dimensions. 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. operation typesncra always averages coordinate variables regardless of the arithmetic operation type performed on the non-coordinate variables. (see Operation Types). EXAMPLES Average files 85.nc, 86.nc, &dots; 89.nc along the record dimension, and store the results in 8589.nc: globbingNINTAPProcessorCCM 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. See Specifying Input Files, for an explanation of the distinctions between these methods. FortranAssume 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. strideThe 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 See 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–June, 1989: ncra -d time,6.,54. ??.nc 8506_8906.nc

ncrcat netCDF Record Concatenator ncrename netCDF Renamer ncra netCDF Record Averager Operator Reference Manual

concatenationrecord concatenationncrcat SYNTAX ncrcat [-3] [-4] [-6] [-A] [-C] [-c] [--cnk_dmn nm,sz] [--cnk_map map] [--cnk_plc plc] [--cnk_scl sz] [-D dbg] [-d dim,[min][,[max][,[stride]]] [-F] [-h] [-L dfl_lvl] [-l path] [--md5_digest] [-n loop] [-O] [-o output-file] [-p path] [-R] [-r] [-t thr_nbr] [-v var[,&dots;]] [-X ...] [-x] [input-files] [output-file] DESCRIPTION ncrcat concatenates record variables across an arbitrary number of input-files. record dimensionThe final record dimension is by default the sum of the lengths of the record dimensions in the input files. See Averaging vs. Concatenating, for a description of the distinctions between the various averagers and concatenators. multi-file operatorsstandard inputstdinAs 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 (see 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). hyperslabHyperslabs along the record dimension that span more than one file are handled correctly. stridencra supports the stride argument to the -d hyperslab option for the record dimension only, stride is not supported for non-record dimensions. ncpdqpackingunpackingadd_offsetscale_factorConcatenating 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 conventionsncrcat applies special rules to ARM convention time fields (e.g., time_offset). See ARM Conventions for a complete description. EXAMPLES Concatenate files 85.nc, 86.nc, &dots; 89.nc along the record dimension, and store the results in 8589.nc: globbingNINTAPProcessorCCM 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. See Specifying Input Files, for an explanation of the distinctions between these methods. FortranAssume 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–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 See 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–June, 1989: ncrcat -d time,6.,54. ??.nc 8506_8906.nc

ncrename netCDF Renamer ncwa netCDF Weighted Averager ncrcat netCDF Record Concatenator Operator Reference Manual

renaming variablesrenaming dimensionsrenaming attributesvariable namesdimension namesattribute namesncrename SYNTAX ncrename [-a old_name,new_name] [-a &dots;] [-D dbg] [-d old_name,new_name] [-d &dots;] [-h] [--hdr_pad nbr] [-l path] [-O] [-o output-file] [-p path] [-R] [-r] [-v old_name,new_name] [-v &dots;] input-file [[output-file]] DESCRIPTION .ncrename renames dimensions, variables, and attributes in a netCDF file. 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 “dot”) character .. The validity of old_name is not checked prior to the renaming. Thus, if old_name is specified without the the . prefix and is not present in input-file, 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). The OPTIONS and EXAMPLES show how to select specific variables whose attributes are to be renamed. data safetysafeguardstemporary output filesncrename is the exception to the normal rules 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 will change the names of 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. performanceoperator speedspeedexecution timeAccording to the netCDF User's Guide, renaming properties in netCDF files does not incur the penalty of recopying the entire file when the new_name is shorter than the old_name. 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 attributesattributes, globalGlobal 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@old_att_name. The @ symbol delimits the variable and attribute names. If the attribute is uniquely named (no other variables contain the attribute) then the old_var_name@old_att_name syntax is redundant. The var_name@att_name syntax is accepted, but 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. -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. 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 coordinate variablesncrename does not automatically attach dimensions to variables of the same name. 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 global attributesattributes, global_FillValuemissing_valueCreate 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 @ 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.

ncwa netCDF Weighted Averager ncrename netCDF Renamer Operator Reference Manual

averaging dataweighted averagemasked averagebroadcasting variablesncwa SYNTAX ncwa [-3] [-4] [-6] [-A] [-a dim[,&dots;]] [-B mask_cond] [-b] [-C] [-c] [--cnk_dmn nm,sz] [--cnk_map map] [--cnk_plc plc] [--cnk_scl sz] [-D dbg] [-d dim,[min][,[max][,[stride]]] [-F] [-h] [-I] [-L dfl_lvl] [-l path] [-M mask_val] [-m mask_var] [-N] [-O] [-o output-file] [-p path] [-R] [-r] [-T mask_comp] [-t thr_nbr] [-v var[,&dots;]] [-w weight] [-X ...] [-x] [-y op_typ] input-file [output-file] DESCRIPTION ncwa averages variables in a single file over arbitrary dimensions, with options to specify weights, masks, and normalization. See Averaging vs. Concatenating, for a description of the distinctions between the various averagers 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 dimensionAveraged dimensions are, by default, eliminated as dimensions. Their corresponding coordinates, if any, are output as scalars. 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 operatorshyperslab-d dim,[min][,[max]]As with all arithmetic operators, the operation may be restricted to an arbitrary hypserslab by employing the -d option (see Hyperslabs). ncwa also handles values matching the variable'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. -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_condmask_var and weight, if specified, are broadcast to conform to the variables being averaged. rankThe 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-IOption -I overrides this default behavior and causes ncwa not to weight or mask coordinate variables The default behavior of (-I) changed on 1998/12/01—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.. averageoperation typesAs explained in See 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 (see Operation Types). Mask condition Mask condition Normalization and Integration Normalization and Integration

Mask condition Normalization and Integration ncwa netCDF Weighted Averager ncwa netCDF Weighted Averager mask conditiontruth condition --op_rlt mask_comp--mask_comparator mask_comp--msk_cmp_typ mask_comp--msk_cnd_sng mask_cond--mask_condition mask_cond-B mask_condThe 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. comparatorHere 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 $==$, $!=$, $>$, , $>=$, . The mask comparator defaults to eq (equality). --mask-value mask_val--mask_value mask_val--msk_val mask_valThe mask_val argument to -M (or --mask-value, or --msk_val) is the right hand side of the mask condition. Thus for the i'th element of the hyperslab to be averaged, the mask condition is $mask(i)$ mask_comp mask_val. Normalization and Integration Mask condition ncwa netCDF Weighted Averager normalization-Nnumeratorintegrationdot productncwa 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 (see 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 $$lev_wgt = [10,2,1]. dot productThe vertical integral of lev, weighted by lev_wgt, is the dot product of lev and lev_wgt. That this is is 3000.0 can be seen by inspection and verified with the integration command ncwa -N -a lev -v lev -w lev_wgt in.nc foo.nc;ncks foo.nc EXAMPLES Given file 85_0112.nc: netcdf 85_0112 { dimensions: lat = 64 ; lev = 18 ; lon = 128 ; time = UNLIMITED ; // (12 currently) variables: float lat(lat) ; float lev(lev) ; float lon(lon) ; float time(time) ; float scalar_var ; float three_dmn_var(lat, lev, lon) ; float two_dmn_var(lat, lev) ; float mask(lat, lon) ; float gw(lat) ; } Average all variables in in.nc over all dimensions and store results in out.nc: ncwa in.nc out.nc All variables in in.nc are reduced to scalars in out.nc since ncwa averages over all dimensions unless otherwise specified (with -a). Store the zonal (longitudinal) mean of in.nc in out.nc: ncwa -a lon in.nc out1.nc ncwa -a lon -b in.nc out2.nc degenerate dimensionThe first command turns lon into a scalar and the second retains lon as a degenerate dimension in all variables. % ncks -C -H -v lon out1.nc lon = 135 % ncks -C -H -v lon out2.nc lon[0] = 135 In either case the tally is simply the size of lon, i.e., 180 for the 85_0112.nc file described by the sample header above. gwGaussian weightsclimate modelCompute the meridional (latitudinal) mean, with values weighted by the corresponding element of gw gw stands for Gaussian weight in many climate models.: ncwa -w gw -a lat in.nc out.nc Here the tally is simply the size of lat, or 64. The sum of the Gaussian weights is 2.0. Compute the area mean over the tropical Pacific: ncwa -w gw -a lat,lon -d lat,-20.,20. -d lon,120.,270. in.nc out.nc Here the tally is 64 times 128 = 8192. OROclimate modelCompute the area-mean over the globe using only points for which ORO < 0.5 ORO stands for Orography in some climate models and in those models $$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. 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 = 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 conditiontruth conditionMask 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. Contributing CCSM Example Operator Reference Manual Top contributingWe welcome contributions from anyone. The project homepage at https://sf.net/projects/nco contains more information on how to contribute. PayPalFinancial 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 chocolateHappy 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'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! Contributors Contributors Proposals for Institutional Funding Proposals for Institutional Funding Contributors Proposals for Institutional Funding Contributing Contributing

contributorsThe primary contributors to NCO development have been: Charlie Zender Charlie Zender Concept, design and implementation of operators from 1995-2000. Since then autotools, bug-squashing, chunking, documentation, packing, 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, and I/O. Multislabbing algorithm. Variable wildcarding. Various hacks. ncap2 language. Rorik Peterson Rorik Peterson Original autotool build support. Long command-line options. Original UDUnits support. Debianization. Numerous bug-fixes. Daniel Wang Daniel Wang Script Workflow Analysis for MultiProcessing (SWAMP). RPM support. Harry Mangalam Harry Mangalam Benchmarking. OPeNDAP configuration. 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 Mark FlannerKeith LindsayMartin DixMike PageMartin SchmidtMichael SchulzRemik Ziemlinski Martin Dix, Mark Flanner, Keith Lindsay, Mike Page, Martin Schmidt, Michael Schulz, Remik Ziemlinski Excellent bug reports and feature requests. Markus Liebig Markus Liebig Proof-read the ncap documentation Daniel BaumannBarry deFreeseFrancesco LovergineMatej VelaLuk Claes Daniel Baumann, Barry deFreese, Francesco Lovergine, Matej Vela, Luk Claes Debian packaging Patrice DumasEd HillOrion Powlawski Patrice Dumas, Ed Hill, Orion Poplawski RedHat packaging George ShapavalovPatrick Kursawe George Shapavalov, Patrick Kursawe Gentoo packaging Filipe Fernandes Filipe Fernandes OpenSuse packaging Alexander HansenTakeshi Enomoto Takeshi Enomoto, Alexander Hansen Mac OS packaging Eric Blake Eric Blake Autoconf/M4 help Gavin BurrisKyle Wilcox Gavin Burris, Kyle Wilcox RHEL and CentOS build scripts and bug reports. Please let me know if your name was omitted! Proposals for Institutional Funding Proposals for Institutional Funding

Proposals for Institutional Funding Contributors Contributing

fundingproposalsNSFserver-side processingDistributed Data Reduction & AnalysisScientific Data OperatorsDDRAServer-Side Distributed Data Reduction & AnalysisSSDDRACCSMIPCCNSFSDOSEIIIOptIPuterNSF has funded a http://nco.sf.net#prp_seiproject to improve Distributed Data Reduction & Analysis (DDRA) by evolving NCO into a suite of Scientific Data Operators called SDO. parallelismThe two main components of this project are NCO parallelism (OpenMP, MPI) and Server-Side DDRA (SSDDRA) implemented through extensions to OPeNDAP and netCDF4. This project will dramatically reduce bandwidth usage for NCO DDRA. NASANRAHDFWith this first NCO proposal funded, the content of the next NCO proposal is clear. We are interested in obtaining NASA support for HDF-specific enhancements to NCO. We plan to submit a proposal to the next suitable NASA NRA or NSF opportunity. We are considering a lot of 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.

CCSM Example mybibnode Contributing Top CCSM This chapter illustrates how to use NCO to process and analyze the results of a CCSM climate simulation. ************************************************************************ Task 0: Finding input files ************************************************************************ 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 ************************************************************************ The first 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 -M prints global metadata: ncks -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 -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 -M -p http://motherlode.ucar.edu:8080/thredds/dodsC/testdods in.nc ncrcat -O -C -D 3 -p http://motherlode.ucar.edu:8080/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 -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 (but 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 mybibnode General Index CCSM Example Top [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] 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] 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] 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] 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] 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] 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] 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] 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'l Symposium on Cluster Computing and the Grid (IEEE CCGRID '08), pp. 449-458, Lyon, France, May 2008. [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. General Index mybibnode Top cp