NCO 4.4.9 User Guide

This file documents NCO, a collection of utilities to manipulate and analyze netCDF files.

Copyright © 1995–2015 Charlie Zender

This is the first edition of the NCO User Guide,
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

The original author of this software, Charlie Zender, wants to improve it with the help of your suggestions, improvements, bug-reports, and patches.
Charlie Zender <surname at uci dot edu> (yes, my surname is zender)
3200 Croul Hall
Department of Earth System Science
University of California, Irvine
Irvine, CA 92697-3100

Table of Contents

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NCO User Guide

Note to readers of the NCO User Guide in HTML format: The NCO User Guide in PDF format (also on SourceForge) contains the complete NCO documentation.
This HTML documentation is equivalent except it refers you to the printed (i.e., DVI, PostScript, and PDF) documentation for description of complex mathematical expressions. Also, images appear only in the PDF document due to SourceForge limitations.

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 ( Each NCO operator (e.g., ncks) takes netCDF input file(s), performs an operation (e.g., averaging, hyperslabbing, or renaming), and outputs a processed netCDF file. Although most users of netCDF data are involved in scientific research, these data formats, and thus NCO, are generic and are equally useful in fields from agriculture to zoology. The NCO User Guide illustrates NCO use with examples from the field of climate modeling and analysis. The NCO homepage is, and there is a mirror at

This documentation is for NCO version 4.4.9. It was last updated 10 March 2015. Corrections, additions, and rewrites of this documentation are very welcome.

Charlie Zender

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NCO is the result of software needs that arose while I worked on projects funded by NCAR, NASA, and ARM. Thinking they might prove useful as tools or templates to others, it is my pleasure to provide them freely to the scientific community. Many users (most of whom I have never met) have encouraged the development of NCO. Thanks espcially to Jan Polcher, Keith Lindsay, Arlindo da Silva, John Sheldon, and William Weibel for stimulating suggestions and correspondence. Your encouragment motivated me to complete the NCO User Guide. So if you like NCO, send me a note! I should mention that NCO is not connected to or officially endorsed by Unidata, ACD, ASP, CGD, or Nike.

Charlie Zender
May 1997
Boulder, Colorado

Major feature improvements entitle me to write another Foreword. In the last five years a lot of work has been done to refine NCO. NCO is now an open source project and appears to be much healthier for it. The list of illustrious institutions that do not endorse NCO continues to grow, and now includes UCI.

Charlie Zender
October 2000
Irvine, California

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.

Charlie Zender
January 2003
Irvine, California

NCO was generously supported from 2004–2008 by US National Science Foundation (NSF) grant IIS-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.

Charlie Zender
March 2008
Grenoble, France

The end of the NSF SEI grant in August, 2008 curtailed NCO development. Fortunately we could justify supporting Henry Butowsky on other research grants until May, 2010 while he developed the key ncap2 features used in our climate research. And recently the NASA ACCESS program commenced funding us to support netCDF4 group functionality. Thus NCO will grow and evade bit-rot for the foreseeable future.

I continue to receive with gratitude the thanks of NCO users at nearly every scientific meeting I attend. People introduce themselves, shake my hand and extol NCO, often effusively, while I grin in stupid embarassment. These exchanges lighten me like anti-gravity. Sometimes I daydream how many hours NCO has turned from grunt work to productive research for researchers world-wide, or from research into early happy-hours. It’s a cool feeling.

Charlie Zender
April, 2012
Irvine, California

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This manual describes NCO, which stands for netCDF Operators. NCO is a suite of programs known as operators. Each operator is a standalone, command line program executed at the shell-level like, e.g., ls or mkdir. The operators take netCDF files (including HDF5 files constructed using the netCDF API) as input, perform an operation (e.g., averaging or hyperslabbing), and produce a netCDF file as output. The operators are primarily designed to aid manipulation and analysis of data. The examples in this documentation are typical applications of the operators for processing climate model output. This stems from their origin, though the operators are as general as netCDF itself.

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1 Introduction

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1.1 Availability

The complete NCO source distribution is currently distributed as a compressed tarfile from and from 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’.

The documentation for NCO is called the NCO User Guide. The User Guide is available in PDF, Postscript, HTML, DVI, TeXinfo, and Info formats. These formats are included in the source distribution in the files nco.pdf,, nco.html, nco.dvi, nco.texi, and*, respectively. All the documentation descends from a single source file, nco.texi 1. 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.

A complete list of papers and publications on/about NCO is available on the NCO homepage. Most of these are freely available. The primary refereed publications are ZeM06 and Zen08. These contain copyright restrictions which limit their redistribution, but they are freely available in preprint form from the NCO.

If you want to quickly see what the latest improvements in NCO are (without downloading the entire source distribution), visit the NCO homepage at The HTML version of the User Guide is also available online through the World Wide Web at URL To build and use NCO, you must have netCDF installed. The netCDF homepage is

New NCO releases are announced on the netCDF list and on the nco-announce mailing list

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1.2 How to Use This Guide

Detailed instructions about how to download the newest version, and how to complie source code, as well as a FAQ and descriptions of Known Problems etc. are on our homepage (

There are twelve operators in the current version (4.4.9). The function of each is explained in Reference Manual. Many of the tasks that NCO can accomplish are described during the explanation of common NCO Features (see Shared features). More specific use examples for each operator can be seen by visiting the operator-specific examples in the Reference Manual. These can be found directly by prepending the operator name with the xmp_ tag, e.g., Also, users can type the operator name on the shell command line to see all the available options, or type, e.g., ‘man ncks’ to see a help man-page.

NCO is a command-line language. You may either use an operator after the prompt (e.g., ‘$’ here), like,

$ operator [options] input [output]

or write all commands lines into a shell script, as in the CMIP5 Example (see CMIP5 Example).

If you are new to NCO, the Quick Start (see Quick Start) shows simple examples about how to use NCO on different kinds of data files. More detailed “real-world” examples are in the CMIP5 Example. The Index is presents multiple keyword entries for the same subject. If these resources do not help enough, please see Help Requests and Bug Reports.

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1.3 Operating systems compatible with NCO

In its time on Earth, NCO has been successfully ported and tested on so many 32- and 64-bit platforms that if we did not write them down here we would forget their names: IBM AIX 4.x, 5.x, FreeBSD 4.x, GNU/Linux 2.x, LinuxPPC, LinuxAlpha, LinuxARM, LinuxSparc64, LinuxAMD64, SGI IRIX 5.x and 6.x, MacOS X 10.x, DEC OSF, NEC Super-UX 10.x, Sun SunOS 4.1.x, Solaris 2.x, Cray UNICOS 8.x–10.x, and Microsoft Windows (95, 98, NT, 2000, XP, Vista, 7, 8, 10). If you port the code to a new operating system, please send me a note and any patches you required.

The major prerequisite for installing NCO on a particular platform is the successful, prior installation of the netCDF library (and, as of 2003, the UDUnits library). Unidata has shown a commitment to maintaining netCDF and UDUnits on all popular UNIX platforms, and is moving towards full support for the Microsoft Windows operating system (OS). Given this, the only difficulty in implementing NCO on a particular platform is standardization of various C-language API system calls. NCO code is tested for ANSI compliance by compiling with C99 compilers including those from GNU (‘gcc -std=c99 -pedantic -D_BSD_SOURCE -D_POSIX_SOURCE’ -Wall) 2, Comeau Computing (‘como --c99’), Cray (‘cc’), HP/Compaq/DEC (‘cc’), IBM (‘xlc -c -qlanglvl=extc99’), Intel (‘icc -std=c99’), LLVM (‘clang’), NEC (‘cc’), PathScale (QLogic) (‘pathcc -std=c99’), PGI (‘pgcc -c9x’), SGI (‘cc -c99’), and Sun (‘cc’). NCO (all commands and the libnco library) and the C++ interface to netCDF (called libnco_c++) comply with the ISO C++ standards as implemented by Comeau Computing (‘como’), Cray (‘CC’), GNU (‘g++ -Wall’), HP/Compaq/DEC (‘cxx’), IBM (‘xlC’), Intel (‘icc’), Microsoft (‘MVS’), NEC (‘c++’), PathScale (Qlogic) (‘pathCC’), PGI (‘pgCC’), SGI (‘CC -LANG:std’), and Sun (‘CC -LANG:std’). See nco/bld/Makefile and nco/src/nco_c++/Makefile.old for more details and exact settings.

Until recently (and not even yet), ANSI-compliant has meant compliance with the 1989 ISO C-standard, usually called C89 (with minor revisions made in 1994 and 1995). C89 lacks variable-size arrays, restricted pointers, some useful printf formats, and many mathematical special functions. These are valuable features of C99, the 1999 ISO C-standard. NCO is C99-compliant where possible and C89-compliant where necessary. Certain branches in the code are required to satisfy the native SGI and SunOS C compilers, which are strictly ANSI C89 compliant, and cannot benefit from C99 features. However, C99 features are fully supported by modern AIX, GNU, Intel, NEC, Solaris, and UNICOS compilers. NCO requires a C99-compliant compiler as of NCO version 2.9.8, released in August, 2004.

The most time-intensive portion of NCO execution is spent in arithmetic operations, e.g., multiplication, averaging, subtraction. These operations were performed in Fortran by default until August, 1999. This was a design decision based on the relative speed of Fortran-based object code vs. 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. As of August 1999, NCO built entirely in C by default. This allowed NCO to compile on any machine with an ANSI C compiler. In August 2004, the first C99 feature, the restrict type qualifier, entered NCO in version 2.9.8. C compilers can obtain better performance with C99 restricted pointers since they inform the compiler when it may make Fortran-like assumptions regarding pointer contents alteration. Subsequently, NCO requires a C99 compiler to build correctly 3.

In January 2009, NCO version 3.9.6 was the first to link to the GNU Scientific Library (GSL). GSL must be version 1.4 or later. NCO, in particular ncap2, uses the GSL special function library to evaluate geoscience-relevant mathematics such as Bessel functions, Legendre polynomials, and incomplete gamma functions (see GSL special functions).

In June 2005, NCO version 3.0.1 began to take advantage of C99 mathematical special functions. These include the standarized gamma function (called tgamma() for “true gamma”). NCO automagically takes advantage of some GNU Compiler Collection (GCC) extensions to ANSI C.

As of July 2000 and NCO version 1.2, NCO no longer performs arithmetic operations in Fortran. We decided to sacrifice executable speed for code maintainability. Since no objective statistics were ever performed to quantify the difference in speed between the Fortran and C code, the performance penalty incurred by this decision is unknown. Supporting Fortran involves maintaining two sets of routines for every arithmetic operation. The USE_FORTRAN_ARITHMETIC flag is still retained in the Makefile. The file containing the Fortran code, nco_fortran.F, has been deprecated but a volunteer (Dr. Frankenstein?) could resurrect it. If you would like to volunteer to maintain nco_fortran.F please contact me.

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1.3.1 Compiling NCO for Microsoft Windows OS

NCO has been successfully ported and tested on most Microsoft Windows operating systems including: XP SP2/Vista/7. Support is provided for compiling either native Windows executables, using the Microsoft Visual Studio 2010 Compiler, or with Cygwin, the UNIX-emulating compatibility layer with the GNU toolchain. The switches necessary to accomplish both are included in the standard distribution of NCO.

Using Microsoft Visual Studio (MVS), one must build NCO with the C++ compiler since MVS does not support C99. Qt, a convenient integrated development environment, was used to convert the project files to MVS format. The Qt files themselves are distributed in the nco/qt directory.

Using the freely available Cygwin (formerly gnu-win32) development environment 4, the compilation process is very similar to installing NCO on a UNIX system. Set the PVM_ARCH preprocessor token to WIN32. Note that defining WIN32 has the side effect of disabling Internet features of NCO (see below). NCO should now build like it does on UNIX.

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>). Fortunately, 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.

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1.4 Symbolic Links

NCO relies on a common set of underlying algorithms. To minimize duplication of source code, multiple operators sometimes share the same underlying source. This is accomplished by symbolic links from a single underlying executable program to one or more invoked executable names. For example, nces and ncrcat are symbolically linked to the ncra executable. The ncra executable behaves slightly differently based on its invocation name (i.e., ‘argv[0]’), which can be nces, ncra, or ncrcat. Logically, these are three different operators that happen to share the same executable.

For historical reasons, and to be more user friendly, multiple synonyms (or pseudonyms) may refer to the same operator invoked with different switches. For example, ncdiff is the same as ncbo and ncpack is the same as ncpdq. We implement the symbolic links and synonyms by the executing the following UNIX commands in the directory where the NCO executables are installed.

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

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

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1.5 Libraries

Like all executables, the NCO operators can be built using dynamic linking. This reduces the size of the executable and can result in significant performance enhancements on multiuser systems. Unfortunately, if your library search path (usually the LD_LIBRARY_PATH environment variable) is not set correctly, or if the system libraries have been moved, renamed, or deleted since NCO was installed, it is possible NCO operators will fail with a message that they cannot find a dynamically loaded (aka shared object or ‘.so’) library. This will produce a distinctive error message, such as ‘ /usr/local/bin/nces: fatal: 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 5, 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.

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1.6 netCDF2/3/4 and HDF4/5 Support

netCDF version 2 was released in 1993. NCO (specifically ncks) began soon after this in 1994. netCDF 3.0 was released in 1996, and we were not exactly eager to convert all code to the newer, less tested netCDF implementation. One netCDF3 interface call (nc_inq_libvers) was added to NCO in January, 1998, to aid in maintainance and debugging. In March, 2001, the final NCO conversion to netCDF3 was completed (coincidentally on the same day netCDF 3.5 was released). NCO versions 2.0 and higher are built with the -DNO_NETCDF_2 flag to ensure no netCDF2 interface calls are used.

However, the ability to compile NCO with only netCDF2 calls is worth maintaining because HDF version 4, aka HDF4 or simply HDF, 6 (available from HDF) supports only the netCDF2 library calls (see There are two versions of HDF. Currently HDF version 4.x supports the full netCDF2 API and thus NCO version 1.2.x. If NCO version 1.2.x (or earlier) is built with only netCDF2 calls then all NCO operators should work with HDF4 files as well as netCDF files 7. The preprocessor token NETCDF2_ONLY exists in NCO version 1.2.x to eliminate all netCDF3 calls. Only versions of NCO numbered 1.2.x and earlier have this capability.

HDF version 5 became available in 1999, but did not support netCDF (or, for that matter, Fortran) as of December 1999. By early 2001, HDF5 did support Fortran90. Thanks to an NSF-funded “harmonization” partnership, HDF began to fully support the netCDF3 read interface (which is employed by NCO 2.x and later). In 2004, Unidata and THG began a project to implement the HDF5 features necessary to support the netCDF API. NCO version 3.0.3 added support for reading/writing netCDF4-formatted HDF5 files in October, 2005. See File Formats and Conversion for more details.

HDF support for netCDF was completed with HDF5 version version 1.8 in 2007. The netCDF front-end that uses this HDF5 back-end was completed and released soon after as netCDF version 4. Download it from the netCDF4 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.

NCO supports many netCDF4 features including atomic data types, Lempel-Ziv compression (deflation), chunking, and groups. The new atomic data types are NC_UBYTE, NC_USHORT, NC_UINT, NC_INT64, and NC_UINT64. Eight-byte integer support is an especially useful improvement from netCDF3. All NCO operators support these types, e.g., ncks copies and prints them, ncra averages them, and ncap2 processes algebraic scripts with them. ncks prints compression information, if any, to screen.

NCO version 3.9.1 (June, 2007) added support for netCDF4 Lempel-Ziv deflation. Lempel-Ziv deflation is a lossless compression technique. See Deflation for more details.

NCO version 3.9.9 (June, 2009) added support for netCDF4 chunking in ncks and ncecat. NCO version 4.0.4 (September, 2010) completed support for netCDF4 chunking in the remaining operators. See Chunking for more details.

NCO version 4.2.2 (October, 2012) added support for netCDF4 groups in ncks and ncecat. Group support for these operators was complete (e.g., regular expressions to select groups and Group Path Editing) as of NCO version 4.2.6 (March, 2013). See Group Path Editing for more details. Group support for all other operators was finished in the NCO version 4.3.x series completed in December, 2013.

Support for netCDF4 in the first arithmetic operator, ncbo, was introduced in NCO version 4.3.0 (March, 2013). NCO version 4.3.1 (May, 2013) completed this support and introduced the first example of automatic group broadcasting. See ncbo netCDF Binary Operator for more details.

netCDF4-enabled NCO handles netCDF3 files without change. In addition, it automagically handles netCDF4 (HDF5) files: If you feed NCO netCDF3 files, it produces netCDF3 output. If you feed NCO netCDF4 files, it produces netCDF4 output. Use the handy-dandy ‘-4’ switch to request netCDF4 output from netCDF3 input, i.e., to convert netCDF3 to netCDF4. See File Formats and Conversion for more details.

When linked to a netCDF library that was built with HDF4 support 8, NCO automatically supports reading HDF4 files and writing them as netCDF3/netCDF4/HDF5 files. NCO can only write through the netCDF API, which can only write netCDF3/netCDF4/HDF5 files. So NCO can read HDF4 files, perform manipulations and calculations, and then it must write the results in netCDF format.

NCO support for HDF4 has been quite functional since December, 2013. For best results install NCO versions 4.4.0 or later on top of netCDF versions 4.3.1 or later. Getting to this point has been an iterative effort where Unidata improved netCDF library capabilities in response to our requests. NCO versions 4.3.6 and earlier do not explicitly support HDF4, yet should work with HDF4 if compiled with a version of netCDF (4.3.2 or later?) that does not unexpectedly die when probing HDF4 files with standard netCDF calls. NCO versions 4.3.7–4.3.9 (October–December, 2013) use a special flag to workaround netCDF HDF4 issues. The user must tell these versions of NCO that an input file is HDF4 format by using the ‘--hdf4’ switch.

When compiled with netCDF version 4.3.1 (20140116) or later, NCO versions 4.4.0 (January, 2014) and later more gracefully handle HDF4 files. In particular, the ‘--hdf4’ switch is obsolete. Current versions of NCO use netCDF to determine automatically whether the underlying file is HDF4, and then take appropriate precautions to avoid netCDF4 API calls that fail when applied to HDF4 files (e.g., nc_inq_var_chunking(), nc_inq_var_deflate()). When compiled with netCDF version 4.3.2 (20140423) or earlier, NCO will report that chunking and deflation properties of HDF4 files as HDF4_UNKNOWN, because determining those properties was impossible. When compiled with netCDF version 4.3.3-rc2 (20140925) or later, NCO versions 4.4.6 (October, 2014) and later fully support chunking and deflation features of HDF4 files. The ‘--hdf4’ switch is supported (for backwards compatibility) yet redundant (i.e., does no harm) with current versions of NCO and netCDF.

Converting HDF4 files to netCDF: Since NCO reads HDF4 files natively, it is now easy to convert HDF4 files to netCDF files directly, e.g.,

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

The most efficient and accurate way to convert HDF4 data to netCDF format is to convert to netCDF4 using NCO as above. Many HDF4 producers (NASA!) love to use netCDF4 types, e.g., unsigned bytes, so this procedure is the most typical. Conversion of HDF4 to netCDF4 as above suffices when the data will only be processed by NCO and other netCDF4-aware tools.

However, many tools are not fully netCDF4-aware, and so conversion to netCDF3 may be desirable. Obtaining any netCDF file from an HDF4 is easy:

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

As of NCO version 4.4.0 (January, 2014), these commands work even when the HDF4 file contains netCDF4 atomic types (e.g., unsigned bytes, 64-bit integers) because NCO can autoconvert everything to atomic types supported by netCDF3 9.

As of NCO version 4.4.4 (May, 2014) both ncl_convert2nc and NCO have built-in, automatic workarounds to handle element names that contain characters that are legal in HDF though are illegal in netCDF. For example, slashes and leading special characters are are legal in HDF and illegal in netCDF element (i.e., group, variable, dimension, and attribute) names. NCO converts these forbidden characters to underscores, and retains the original names of variables in automatically produced attributes named hdf_name 10.

Finally, in February 2014, we learned that the HDF group has a project called H4CF (described here) whose goal is to make HDF4 files accessible to CF tools and conventions. Their project includes a tool named h4tonccf that converts HDF4 files to netCDF3 or netCDF4 files. We are not yet sure what advantages or features h4tonccf has that are not in NCO, though we suspect both methods have their own advantages. Corrections welcome.

As of 2012, netCDF4 is relatively stable software. Problems with netCDF4 and HDF libraries have mainly been fixed. Binary NCO distributions shipped as RPMs and as debs have used the netCDF4 library since 2010 and 2011, respectively.

One must often build NCO from source to obtain netCDF4 support. Typically, one specifies the root of the netCDF4 installation directory. Do this with the NETCDF4_ROOT variable. Then use your preferred NCO build mechanism, e.g.,

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

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

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1.7 Help Requests and Bug Reports

We 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 register your name and email address with 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 TODO 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 NCO 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). That is, describe what you are trying to do, and include the complete commands (run with ‘-D 5’), error messages, and version of NCO (with ‘-r’). Post your help request to the NCO 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. Run the operator with ‘-r’ and then run the command with ‘-D 5’ to increase the verbosity of the debugging output. It is very important that your report contain the exact error messages and compile-time environment. Include a copy of your sample input file, or place one on a publically accessible location, of the file(s). Post the full bug report to the NCO Project buglist.

Build failures count as bugs. Our limited machine access means we cannot fix all build failures. The information we need to diagnose, and often fix, build failures are the three files output by GNU build tools, nco.config.log.${GNU_TRP}.foo, nco.configure.${GNU_TRP}.foo, and nco.make.${GNU_TRP}.foo. The file 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 The regressions archive at 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.

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2 Operator Strategies

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2.1 Philosophy

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. Higher level interpretive languages (e.g., IDL, Yorick, Matlab, NCL, Perl, Python), and lower level compiled languages (e.g., C, Fortran) can always perform any task performed by NCO, but often with more overhead. NCO, on the other hand, is limited to a much smaller set of arithmetic and metadata operations than these full blown languages.

Another goal has been to implement enough command line switches so that frequently used sequences of these operators can be executed from a shell script or batch file. Finally, NCO was written to consume the absolute minimum amount of system memory required to perform a given job. The arithmetic operators are extremely efficient; their exact memory usage is detailed in Memory Requirements.

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2.2 Climate Model Paradigm

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

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2.3 Temporary Output Files

NCO operators are designed to be reasonably fault tolerant, so that a system failure or user-abort of the operation (e.g., with C-c) does not cause loss of data. The user-specified output-file is only created upon successful completion of the operation 11. This is accomplished by performing all operations in a temporary copy of output-file. The name of the temporary output file is constructed by appending .pid<process ID>.<operator name>.tmp to the user-specified output-file name. When the operator completes its task with no fatal errors, the temporary output file is moved to the user-specified output-file. This imbues the process with fault-tolerance since fatal error (e.g., disk space fills up) affect only the temporary output file, leaving the final output file not created if it did not already exist. Note the construction of a temporary output file uses more disk space than just overwriting existing files “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). Also, note this feature increases the execution time of the operator by approximately the time it takes to copy the output-file 12. Finally, note this fault-tolerant feature allows the output-file to be the same as the input-file without any danger of “overlap”.

Over time many “power users” have requested a way to turn-off the fault-tolerance safety feature of automatically creating a temporary file. Often these users build and execute production data analysis scripts that are repeated frequently on large datasets. Obviating an extra file write can then conserve significant disk space and time. For this purpose NCO has, since version 4.2.1 in August, 2012, made configurable the controls over temporary file creation. The ‘--wrt_tmp_fl’ and equivalent ‘--write_tmp_fl’ switches ensure NCO writes output to an intermediate temporary file. This is and has always been the default behavior so there is currently no need to specify these switches. However, the default may change some day, especially since writing to RAM disks (see RAM disks) may some day become the default. The ‘--no_tmp_fl’ switch causes NCO to write directly to the final output file instead of to an intermediate temporary file. “Power users” may wish to invoke this switch to increase performance (i.e., reduce wallclock time) when manipulating large files. When eschewing temporary files, users may forsake the ability to have the same name for both output-file and input-file since, as described above, the temporary file prevented overlap issues. However, if the user creates the output file in RAM (see RAM disks) then it is still possible to have the same name for both output-file and input-file.

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

There is no reason to expect the fourth example to work. The behavior of overwriting a file while reading from the same file is undefined, much as is the shell command ‘cat foo > foo’. Although it may “work” in some cases, it is unreliable. One way around this is to use ‘--create_ram’ so that the output file is not written to disk until the input file is closed, See RAM disks. However, as of 20130328, the behavior of the ‘--create_ram’ and ‘--open_ram’ examples has not been thoroughly tested.

The NCO authors have seen compelling use cases for utilizing the RAM switches, though not (yet) for combining them with ‘--no_tmp_fl’. NCO implements both options because they are largely independent of eachother. It is up to “power users” to discover which best fit their needs. We welcome accounts of your experiences posted to the forums.

Other safeguards exist to protect the user from inadvertently overwriting data. If the output-file specified for a command is a pre-existing file, then the operator will prompt the user whether to overwrite (erase) the existing output-file, attempt to append to it, or abort the operation. However, in processing large amounts of data, too many interactive questions slows productivity. Therefore NCO also implements two ways to override its own safety features, the ‘-O’ and ‘-A’ switches. Specifying ‘-O’ tells the operator to overwrite any existing output-file without prompting the user interactively. Specifying ‘-A’ tells the operator to attempt to append to any existing output-file without prompting the user interactively. These switches are useful in batch environments because they suppress interactive keyboard input.

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2.4 Appending Variables

Adding variables from one file to another is often desirable. This is referred to as appending, although some prefer the terminology merging 13 or pasting. Appending is often confused with what NCO calls concatenation. In NCO, concatenation refers to splicing a variable along the record dimension. The length along the record dimension of the output is the sum of the lengths of the input files. Appending, on the other hand, refers to copying a variable from one file to another file which may or may not already contain the variable 14. 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.

The simplest way to create the union of two files is

ncks -A

This puts the contents of into The ‘-A’ is optional. On output, 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.

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2.5 Simple Arithmetic and Interpolation

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

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2.6 Statistics vs. Concatenation

The most frequently used operators of NCO are probably the statisticians (i.e., tools that do statistics) and concatenators. Because there are so many types of statistics like averaging (e.g., across files, within a file, over the record dimension, over other dimensions, with or without weights and masks) and of concatenating (across files, along the record dimension, along other dimensions), there are currently no fewer than five operators which tackle these two purposes: ncra, nces, ncwa, ncrcat, and ncecat. These operators do share many capabilities 15, though each has its unique specialty. Two of these operators, ncrcat and ncecat, concatenate hyperslabs across files. The other two operators, ncra and nces, compute statistics across (and/or within) files 16. First, let’s describe the concatenators, then the statistics tools.

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2.6.1 Concatenators ncrcat and ncecat

Joining together independent files along a common record dimension is called concatenation. ncrcat is designed for concatenating record variables, while ncecat is designed for concatenating fixed length variables. Consider five files,,, … each containing a year’s worth of data. Say you wish to create from them a single file, 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 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. For example, the files may not be conceptually sequential, but rather members of the same group, or ensemble. Members of an ensemble may have no reason to contain a record dimension. ncecat will create a new record dimension (named record by default) with which to glue together the individual files into the single ensemble file. If ncecat is used on files which contain an existing record dimension, that record dimension is converted to a fixed-length dimension of the same name and a new record dimension (named record) is created. Consider five realizations,,, … of 1985 predictions from the same climate model. Then ncecat 85?.nc glues together the individual realizations into the single file, 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.

ncpdq makes it possible to concatenate files along any dimension, not just the record dimension. First, use ncpdq to convert the dimension to be concatenated (i.e., extended with data from other files) into the record dimension. Second, use ncrcat to concatenate these files. Finally, if desirable, use ncpdq to revert to the original dimensionality. As a concrete example, say that files,, … 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
ncrcat foo_??.nc       # Concatenate along x
ncpdq -a time,x # 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.

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2.6.2 Averagers nces, ncra, and ncwa

The differences between the averagers ncra and nces are analogous to the differences between the concatenators. ncra is designed for averaging record variables from at least one file, while nces is designed for averaging fixed length variables from multiple files. ncra performs a simple arithmetic average over the record dimension of all the input files, with each record having an equal weight in the average. nces performs a simple arithmetic average of all the input files, with each file having an equal weight in the average. Note that ncra cannot average fixed-length variables, but nces can average both fixed-length and record variables. To conserve system memory, use ncra rather than nces where possible (e.g., if each input-file is one record long). The file output from nces will have the same dimensions (meaning dimension names as well as sizes) as the input hyperslabs (see nces netCDF Ensemble Statistics, for a complete description of nces). The file output from ncra will have the same dimensions as the input hyperslabs except for the record dimension, which will have a size of 1 (see ncra netCDF Record Averager, for a complete description of ncra).

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2.6.3 Interpolator 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.

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2.7 Large Numbers of Files

Occasionally one desires to digest (i.e., concatenate or average) hundreds or thousands of input files. Unfortunately, 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.,,, … 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 17. 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.

The recommended method for sending very large numbers (hundreds or more, typically) of input filenames to the multi-file operators is to pass the filenames with the UNIX standard input feature, aka stdin:

# Pipe large numbers of filenames to stdin
/bin/ls | grep ${CASEID}_'......'.nc | ncecat -o

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. To 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.

A second option is to use the UNIX xargs command. This simple example selects as input to xargs all the filenames in the current directory that match a given pattern. For illustration, consider a user trying to average millions of files which each have a six character filename. If the shell buffer cannot hold the results of the corresponding globbing operator, ??????.nc, then the filename globbing technique will fail. Instead we express the filename pattern as an extended regular expression, ......\.nc (see Subsetting Files). We use grep to filter the directory listing for this pattern and to pipe the results to xargs which, in turn, passes the matching filenames to an NCO multi-file operator, e.g., ncecat.

# Use xargs to transfer filenames on the command line
/bin/ls | grep ${CASEID}_'......'.nc | xargs -x ncecat -o

The single quotes protect the only sensitive parts of the extended regular expression (the grep argument), and allow shell interpolation (the ${CASEID} variable substitution) to proceed unhindered on the rest of the command. xargs uses the UNIX pipe feature to append the suitably filtered input file list to the end of the ncecat command options. The -o 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.

Even 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. For example, the following Perl script creates a monotonically enumerated symbolic link to up to one million .nc files in a directory. If there are 999,999 netCDF files present, the links are named to

# Create enumerated symbolic links
/bin/ls | grep \.nc | perl -e \
'$idx=1;while(<STDIN>){chop;symlink $_,sprintf("",$idx++);}'
ncecat -n 999999,6,1
# 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. One 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.

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2.8 Large Datasets

Large datasets are those files that are comparable in size to the amount of random access memory (RAM) in your computer. Many users of NCO work with files larger than 100 MB. Files this large not only push the current edge of storage technology, they present special problems for programs which attempt to access the entire file at once, such as nces and ncecat. If you work with a 300 MB files on a machine with only 32 MB of memory then you will need large amounts of swap space (virtual memory on disk) and NCO will work slowly, or even fail. There is no easy solution for this. The best strategy is to work on a machine with sufficient amounts of memory and swap space. Since about 2004, many users have begun to produce or analyze files exceeding 2 GB in size. These users should familiarize themselves with NCO’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.

If your local machine has problems working with large files, try running NCO from a more powerful machine, such as a network server. If you get a memory-related core dump (e.g., ‘Error exit (core dumped)’) on a GNU/Linux system, or the operation ends before the entire output file is written, try increasing the process-available memory with ulimit:

ulimit -f unlimited

This may solve constraints on clusters where sufficient hardware resources exist yet where system administrators felt it wise to prevent any individual user from consuming too much of resource. Certain machine architectures, e.g., Cray UNICOS, have special commands which allow one to increase the amount of interactive memory. On Cray systems, try to increase the available memory with the ilimit command.

The speed of the NCO operators also depends on file size. When processing large files the operators may appear to hang, or do nothing, for large periods of time. In order to see what the operator is actually doing, it is useful to activate a more verbose output mode. This is accomplished by supplying a number greater than 0 to the ‘-D debug-level’ (or ‘--debug-level’, or ‘--dbg_lvl’) switch. When 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., nces -D 2 A full description of how to estimate the actual amount of memory the multi-file NCO operators consume is given in Memory Requirements.

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2.9 Memory Requirements

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!

This 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.

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2.9.1 Single and Multi-file Operators

The multi-file operators currently comprise the record operators, ncra and ncrcat, and the ensemble operators, nces and ncecat. The record operators require much less memory than the ensemble operators. This is because the record operators operate on one single record (i.e., time-slice) at a time, whereas the ensemble operators retrieve the entire variable into memory. Let MS be the peak sustained memory demand of an operator, FT be the memory required to store the entire contents of all the variables to be processed in an input file, FR be the memory required to store the entire contents of a single record of each of the variables to be processed in an input file, VR be the memory required to store a single record of the largest record variable to be processed in an input file, VT be the memory required to store the largest variable to be processed in an input file, VI be the memory required to store the largest variable which is not processed, but is copied from the initial file to the output file. All operators require MI = VI during the initial copying of variables from the first input file to the output file. This is the initial (and transient) memory demand. The sustained memory demand is that memory required by the operators during the processing (i.e., averaging, concatenation) phase which lasts until all the input files have been processed. The operators have the following memory requirements: ncrcat requires MS <= VR. ncecat requires MS <= VT. ncra requires MS = 2FR + VR. nces requires MS = 2FT + VT. ncbo requires MS <= 3VT (both input variables and the output variable). ncflint requires MS <= 3VT (both input variables and the output variable). ncpdq requires MS <= 2VT (one input variable and the output variable). ncwa requires MS <= 8VT (see below). Note that only variables that are processed, e.g., averaged, concatenated, or differenced, contribute to MS. Variables which do not appear in the output file (see Subsetting Files) are never read and contribute nothing to the memory requirements.

Further note that some operators perform internal type-promotion on some variables prior to arithmetic (see Type Conversion). For example, ncra and nces both promote integer types to double-precision floating point prior to arithmetic, then perform the arithmetic, then demote back to the original integer type after arithmetic. This preserves the on-disk storage type while obtaining the accuracy advantages of floating point arithmetic. Since version 4.3.6 (released in September, 2013), NCO also by default converts single-precision floating point to double-precision prior to arithmetic, which incurs the same RAM penalty. Hence, the sustained memory required for integer variables and single-precision floats are two or four-times their on-disk, uncompressed, unpacked sizes if they meet the rules for automatic internal promotion. Put another way, disabling auto-promotion of single-precision variables (with ‘--flt’) considerably reduces the RAM footprint of arithmetic operators.

The ‘--open_ram’ switch (and switches that invoke it like ‘--ram_all’ and ‘--diskless_all’) incurs a RAM penalty. These switches cause each input file to be copied to RAM upon opening. Hence any operator invoking these switches utilizes an additional FT of RAM (i.e., MS += FT). See RAM disks for further details.

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 MS <= 3VT (one input variable, one tally array, and the output variable).

The above memory requirements must be multiplied by the number of threads thr_nbr (see OpenMP Threading). If this causes problems then reduce (with ‘-t thr_nbr’) the number of threads.

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2.9.2 Memory for ncap2

ncap2 has unique memory requirements due its ability to process arbitrarily long scripts of any complexity. All scripts acceptable to ncap2 are ultimately processed as a sequence of binary or unary operations. ncap2 requires MS <= 2VT under most conditions. An exception to this is when left hand casting (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, MS <= 2VC.

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.

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2.10 Performance

An 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

NCO 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

It is helpful to be aware of the aspects of NCO design that can limit its performance:

  1. No data buffering is performed during nc_get_var and nc_put_var operations. Hyperslabs too large too hold in core memory will suffer substantial performance penalties because of this.
  2. Since coordinate variables are assumed to be monotonic, the search for bracketing the user-specified limits should employ a quicker algorithm, like bisection, than the two-sided incremental search currently implemented.
  3. C_format, FORTRAN_format, signedness, scale_format and add_offset attributes are ignored by ncks when printing variables to screen.
  4. In the late 1990s it was discovered that some random access operations on large files on certain architectures (e.g., UNICOS) were much slower with NCO than with similar operations performed using languages that bypass the netCDF interface (e.g., Yorick). This may have been a penalty of unnecessary byte-swapping in the netCDF interface. It is unclear whether such problems exist in present day (2007) netCDF/NCO environments, where unnecessary byte-swapping has been reduced or eliminated.

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3 Shared Features

Many features have been implemented in more than one operator and are described here for brevity. The description of each feature is preceded by a box listing the operators for which the feature is implemented. Command line switches 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.

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3.1 Internationalization

Availability: All operators

NCO support for internationalization of textual input and output (e.g., Warning messages) is nascent. We introduced the first foreign language string catalogues (French and Spanish) in 2004, yet did not activate these in distributions because the catalogues were nearly empty. We seek volunteers to populate our templates with translations for their favorite languages.

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3.2 Metadata Optimization

Availability: All operators
Short options: None
Long options: ‘--hdr_pad’, ‘--header_pad

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 netCDF3 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. The operators which benefit most from judicious use of header padding are ncatted and ncrename, since they only alter metadata.

This optimization exploits the netCDF library nc__enddef() function, which behaves differently with different versions of netCDF. It will improve speed of future metadata expansion with CLASSIC and 64bit netCDF files, though not necessarily with NETCDF4 files, i.e., those created by the netCDF interface to the HDF5 library (see File Formats and Conversion).

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3.3 OpenMP Threading

Availability: ncap2, ncbo, nces, ncecat, ncflint, ncpdq, ncra, ncrcat, ncwa
Short options: ‘-t
Long options: ‘--thr_nbr’, ‘--threads’, ‘--omp_num_threads

NCO supports shared memory parallelism (SMP) when compiled with an OpenMP-enabled compiler. Threads requests and allocations occur in two stages. First, users may request a specific number of threads thr_nbr with the ‘-t’ switch (or its long option equivalents, ‘--thr_nbr’, ‘--threads’, and ‘--omp_num_threads’). If not user-specified, OpenMP obtains thr_nbr from the OMP_NUM_THREADS environment variable, if present, or from the OS, if not.

Caveat: Unfortunately, threading does not improve NCO throughput (i.e., wallclock time) because nearly all NCO operations are I/O-bound. This means that NCO spends negligible time doing anything compared to reading and writing. We have seen some and can imagine other use cases where ncwa, ncpdq, and ncap2 (with long scripts) will complete faster due to threading. The main benefits of threading so far have been to isolate the serial from parallel portions of code. This parallelism is now exploited by OpenMP but then runs into the I/O bottleneck during output. The bottleneck will be ameliorated for large files by the use of MPI-enabled calls in the netCDF4 library when the underlying filesystem is parallel (e.g., PVFS or JFS). Implementation of the parallel output calls in NCO is not a goal of our current funding and would require new volunteers or funding.

NCO may modify thr_nbr according to its own internal settings before it requests any threads from the system. Certain operators contain hard-code limits to the number of threads they request. We base these limits on our experience and common sense, and to reduce potentially wasteful system usage by inexperienced users. For example, ncrcat is extremely I/O-intensive so we restrict thr_nbr <= 2 for ncrcat. This is based on the notion that the best performance that can be expected from an operator which does no arithmetic is to have one thread reading and one thread writing simultaneously. In the future (perhaps with netCDF4), we hope to demonstrate significant threading improvements with operators like ncrcat by performing multiple simultaneous writes.

Compute-intensive operators (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 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.

Once the initial thr_nbr has been modified for any operator-specific limits, NCO requests the system to allocate a team of thr_nbr threads for the body of the code. The operating system then decides how many threads to allocate based on this request. Users may keep track of this information by running the operator with dbg_lvl > 0.

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

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3.4 Command Line Options

Availability: All operators

NCO achieves flexibility by using command line options. These options are implemented in all traditional UNIX commands as single letter switches, e.g., ‘ls -l’. For many years NCO used only single letter option names. In late 2002, we implemented GNU/POSIX extended or long option names for all options. This was done in a backward compatible way such that the full functionality of NCO is still available through the familiar single letter options. 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.

Extended options, also called long options, are implemented using the system-supplied getopt.h header file, if possible. This provides the getopt_long function to NCO 18.

The syntax of short options (single letter options) is -key value (dash-key-space-value). Here, key is the single letter option name, e.g., ‘-D 2’.

The syntax of long options (multi-letter options) is --long_name value (dash-dash-key-space-value), e.g., ‘--dbg_lvl 2’ or --long_name=value (dash-dash-key-equal-value), e.g., ‘--dbg_lvl=2’. Thus the following are all valid for the ‘-D’ (short version) or ‘--dbg_lvl’ (long version) command line option.

ncks -D 3        # Short option
ncks --dbg_lvl=3 # Long option, preferred form
ncks --dbg_lvl 3 # 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        # Short option
ncks --dbg_lvl=3 # Long option, full form
ncks --dbg=3     # Long option, unambiguous truncation
ncks --db=3      # Long option, unambiguous truncation
ncks --d=3       # 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 in examples. This is for historical reasons and to conserve space in printed output. Users are expected to pick the unambiguous truncation of each option name that most suits their taste.

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3.5 Specifying Input Files

Availability (-n): nces, ncecat, ncra, ncrcat
Availability (-p): All operators
Short options: ‘-n’, ‘-p
Long options: ‘--nintap’, ‘--pth’, ‘--path

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. There are four different ways of specifying input files to NCO: explicitly typing each, using UNIX shell wildcards, and using the NCO-n’ and ‘-p’ switches (or their long option equivalents, ‘--nintap’ or ‘--pth’ and ‘--path’, respectively). Techniques to augment these methods to specify arbitrary numbers (e.g., thousands) and patterns of filenames are discussed separately (see Large Numbers of Files).

To illustrate these methods, consider the simple problem of using ncra to average five input files,,, …, and store the results in Here are the four methods in order. They produce identical answers.

ncra 8[56789].nc
ncra -p input-path
ncra -n 5,2,1

The first method (explicitly specifying all filenames) works by brute force. The second method relies on the operating system shell to glob (expand) the regular expression 8[56789].nc. The shell then passes the valid filenames (those which match the regular expansion) to ncra. In this case ncra never knows that a regular expression was used, because the shell intercepts and expands and matches the regular expression before ncra is actually invoked. The third method uses the ‘-p input-path’ argument to specify the directory where all the input files reside. NCO prepends input-path (e.g., /data/username/model) to all input-files (though not to output-file). Thus, using ‘-p’, the path to any number of input files need only be specified once. Note input-path need not end with ‘/’; the ‘/’ is automatically generated if necessary.

The last method passes (with ‘-n’) syntax concisely describing the entire set of filenames 19. This option is only available with the multi-file operators: ncra, ncrcat, nces, and ncecat. By definition, multi-file operators are able to process an arbitrary number of input-files. This option is very useful for abbreviating lists of filenames representable as alphanumeric_prefix+numeric_suffix+.+filetype where alphanumeric_prefix is a string of arbitrary length and composition, numeric_suffix is a fixed width field of digits, and filetype is a standard filetype indicator. For example, in the file, we have alphanumeric_prefix = ccm3_h, numeric_suffix = 0001, and filetype = nc.

NCO decodes lists of such filenames encoded using the ‘-n’ syntax. The simpler (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 tells NCO to construct five (5) filenames identical to the template 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.

Recently 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
ncra -n 3,2,1,12
ncra -n 3,2,1,12,1

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. The fourth argument to ‘-n’, when present, specifies the maximum integer value of numeric_suffix. In the example the maximum value is 12, and will be formatted as 12 in the filename string. The fifth argument to ‘-n’, when present, specifies the minimum integer value of numeric_suffix. The default minimum filename suffix is 1, which is formatted as 01 in this case. Thus the second and third examples have the same effect, that is, they automatically generate, in order, the filenames,, and as input to NCO.

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3.6 Specifying Output Files

Availability: All operators
Short options: ‘-o
Long options: ‘--fl_out’, ‘--output

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. This is particularly useful with multi-file operators for three reasons. Multi-file operators may be invoked with hundreds (or more) filenames. Visual or automatic location of fl_out in such a list is difficult when the only syntactic distinction between input and output files is their position. Second, 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 heinous and undesirable. Finally, many users are more comfortable specifying output files with ‘-o fl_out’ near the beginning of an argument list. Compilers and linkers are usually invoked this way.

Users should specify fl_out using either (not both) method. If fl_out is specified twice (once with the switch and once as the last positional argument), then the positional argument takes precedence.

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3.7 Accessing Remote Files

Availability: All operators
Short options: ‘-p’, ‘-l
Long options: ‘--pth’, ‘--path’, ‘--lcl’, ‘--local

All NCO operators can retrieve files from remote sites as well as from the local file system. A remote site can be an anonymous FTP server, a machine on which the user has rcp, scp, or sftp privileges, NCAR’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, simply supply the remote file’s URL. Anonymous FTP usually requires no further credentials, e.g., no .netrc file is necessary. FTP is an intrinsically insecure protocol because it transfers passwords in plain text format. Users should access sites using anonymous FTP, or better yet, secure FTP (SFTP, see below) when possible. Some FTP servers require a login/password combination for a valid user account. NCO allows transactions that require additional credentials so long as the required information is stored in the .netrc file. Usually this information is the remote machine name, login, and password, in plain text, separated by those very keywords, e.g.,

machine login zender password bushlied

Eschew using valuable passwords for FTP transactions, since .netrc passwords are potentially exposed to eavesdropping software 20.

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


Note the second colon following the top-level-domain, tld. This syntax is a hybrid between an FTP URL and 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.

To access a file on a High Performance Storage System (HPSS) (such as that at NCAR, ECMWF, LANL, DKRZ, LLNL) specify the full HPSS pathname of the remote file. 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 21.

The following examples show how one might analyze files stored on remote systems.

ncks -l .
ncks -l . s
ncks -l .
ncks -l . /ZENDER/nco/
ncks -l . /home/zender/nco/
ncks -l . 

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 The third example works if you have rcp or scp access to the machine The fourth and fifth examples work on NCAR computers with local access to the HPSS hsi get command 22. 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:

ncks -p -l .
ncks -p s -l .
ncks -p -l .
ncks -p /ZENDER/nco -l .
ncks -p /home/zender/nco -l . # HPSS
ncks -p \ 
     -l .

Using ‘-p’ is recommended because it clearly separates the input-path from the filename itself, sometimes called the stub. When input-path is not explicitly specified using ‘-p’, NCO internally generates an input-path from the first input filename. The automatically generated input-path is constructed by stripping the input filename of everything following the final ‘/’ character (i.e., removing the stub). The ‘-l output-path’ option tells NCO where to store the remotely retrieved file. It has no effect on locally-retrieved files, or on the output file. Often the path to a remotely retrieved file is quite different than the path on the local machine where you would like to store the file. If ‘-l’ is not specified then NCO internally generates an output-path by simply setting output-path equal to input-path stripped of any machine names. If ‘-l’ is not specified and the remote file resides on 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.

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3.7.1 OPeNDAP

The Distributed Oceanographic Data System (DODS) provides useful replacements for common data interface libraries like netCDF. The DODS versions of these libraries implement network transparent access to data via a client-server data access protocol that uses the HTTP protocol for communication. Although DODS-technology originated with oceanography data, it applyies to virtually all scientific data. In recognition of this, the data access protocol underlying DODS (which is what NCO cares about) has been renamed the Open-source Project for a Network Data Access Protocol, OPeNDAP. We use the terms DODS and OPeNDAP interchangeably, and often write OPeNDAP/DODS for now. In the future we will deprecate DODS in favor of DAP or OPeNDAP, as appropriate 23.

NCO may be DAP-enabled by linking NCO to the OPeNDAP libraries. This is described in the OPeNDAP documentation and automagically implemented in NCO build mechanisms 24. The ./configure mechanism automatically enables NCO as OPeNDAP clients if it can find the required OPeNDAP libraries 25. 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/ contains a generic script intended to help users install OPeNDAP before building NCO. The documentation at the OPeNDAP Homepage is voluminous. Check there and on the DODS mail lists. to learn more about the extensive capabilities of OPeNDAP 26.

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 ~/ -C -H -v one -l /tmp \
% ncks -H -v one ~/
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 ~/ -C -H -v one -l /tmp \
% ncks -H -v one ~/
one = 1

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

% ncks -u -C -H -v wvl -d wvl,'0.4 micron','0.7 micron' \
% 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 \ \ ~/

All with one command! The data in this particular input file also happen to be packed (see Methods and functions), although this complication is transparent to the user since NCO automatically unpacks data before attempting arithmetic.

NCO obtains remote files from the OPeNDAP server (e.g., 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. This allows the I/O to appear to NCO as if the input files were local. The local machine performs all arithmetic operations. Only the hyperslabbed output data are transferred over the network (to the local machine) for the number-crunching to begin. The advantages of this are obvious if you are examining small parts of large files stored at remote locations.

Natually there are many versions of OPeNDAP servers supplying data and bugs in the server can appear to be bugs in NCO. However, with very few exceptions 27 an NCO command that works on a local file must work across an OPeNDAP connection or else there is a bug in the server. This is because NCO does nothing special to handle files served by OPeNDAP, the whole process is (supposed to be) completely transparent to the client NCO software. Therefore it is often useful to try NCO commands on various OPeNDAP servers in order to isolate whether a problem may be due to a bug in the OPeNDAP server on a particular machine. For this purpose, one might try variations of the following commands that access files on public OPeNDAP servers:

# Strided access to HDF5 file
ncks -v Time -d Time,0,10,2
# Strided access to netCDF3 file
ncks -O -D 1 -d time,1 -d lev,0 -d lat,0,100,10 -d lon,0,100,10 -v u_velocity ~/

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

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3.8 Retaining Retrieved Files

Availability: All operators
Short options: ‘-R
Long options: ‘--rtn’, ‘--retain

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.

Note that the remote retrieval features of NCO can always be used to retrieve any file, including non-netCDF files, via SSH, anonymous FTP, or msrcp. Often this method is quicker than using a browser, or running an FTP session from a shell window yourself. For example, say you want to obtain a JPEG file from a weather server.

ncks -R -p -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.

If 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 \ \

It may occasionally be useful to use NCO to transfer files when your other preferred methods are not available locally.

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3.9 File Formats and Conversion

Availability: ncap2, nces, ncecat, ncflint, ncks, ncpdq, ncra, ncrcat, ncwa
Short options: ‘-3’, ‘-4’, ‘-6’, ‘-7
Long options: ‘--3’, ‘--4’, ‘--6’, ‘--64bit’, ‘--7’, ‘--fl_fmt’, ‘--netcdf4

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.)

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3.9.1 File Formats

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, nearly all netCDF datasets were in CLASSIC format. The 64BIT format was added in Fall, 2004. As of 2010, many netCDF datasets were in 64BIT format. As of 2013, an increasing number of netCDF datasets were in NETCDF4_CLASSIC format.

The NETCDF4 format uses HDF5 as the file storage layer. The files are (usually) created, accessed, and manipulated using the traditional netCDF3 API (with numerous extensions). The NETCDF4_CLASSIC format refers to netCDF4 files created with the NC_CLASSIC_MODEL mask. Such files use HDF5 as the back-end storage format (unlike netCDF3), though they incorporate only netCDF3 features. Hence NETCDF4_CLASSIC files are entirely readable by applications that use only the netCDF3 API (though the applications must be linked with the netCDF4 library). NCO must be built with netCDF4 to write files in the new NETCDF4 and NETCDF4_CLASSIC formats, and to read files in these formats. Datasets in the default CLASSIC or the newer 64BIT formats have maximum backwards-compatibility with older applications. NCO has deep support for NETCDF4 formats. If performance or disk-space as important as backwards compatibility, then use NETCDF4_CLASSIC instead of CLASSIC format files. As of 2014, NCO support for the NETCDF4 format is nearly complete and the most powerful and disk/RAM efficient workflows will utilize this format.

As mentioned above, all operators write use the input file format for output files unless told otherwise. Toggling the short option ‘-6’ or the long option ‘--6’ or ‘--64bit’ (or their 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., with all supported HDF5 features). Using the ‘-7’ switch (or its long option equivalent ‘--728, or setting its key-value equivalent ‘--fl_fmt=netcdf4_classic’ produces a NETCDF4_CLASSIC file (i.e., with all supported HDF5 features like compression and chunking but without groups or new atomic types). Operators given the ‘-3’ (or ‘--3’) switch without arguments will (attempt to) produce netCDF3 CLASSIC output, even from netCDF4 input files.

Note that NETCDF4 and NETCDF4_CLASSIC are the same binary format. The latter simply causes a writing application to fail if it attempts to write a NETCDF4 file that cannot be completely read by the netCDF3 library. Conversely, NETCDF4_CLASSIC indicates to a reading application that all of the file contents are readable with the netCDF3 library. NCO has supported reading/writing basic NETCDF4 and NETCDF4_CLASSIC files since October, 2005.

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3.9.2 Determining File Format

Input files often end with the generic .nc suffix that leaves (perhaps by intention) the internal file format ambiguous. There are at least three ways to discover the internal format of a netCDF-supported file. These methods determine whether it is a classic (32-bit offset) or newer 64-bit offset netCDF3 format, or is a netCDF4 format. Each method returns the information using slightly different terminology that becomes easier to understand with practice.

First, examine the first line of global metadata output by ‘ncks -M’:

% ncks -M
Summary of filetype = NC_FORMAT_CLASSIC, 0 groups ...
% ncks -M
Summary of filetype = NC_FORMAT_64BIT, 0 groups ...
% ncks -M
Summary of filetype = NC_FORMAT_NETCDF4_CLASSIC, 0 groups ...
% ncks -M
Summary of filetype = NC_FORMAT_NETCDF4, 0 groups ...

This method requires a netCDF4-enabled NCO version 3.9.0+ (i.e., from 2007 or later). As of NCO version 4.4.0 (January, 2014), ncks will also print the extended or underlying format of the input file. The extended filetype will be one of the six underlying formats that are accessible through the netCDF API. These formats are NC_FORMAT_NC3 (classic and 64-bit versions of netCDF3 formats), NC_FORMAT_NC_HDF5 (classic and extended versions of netCDF4, and “pure” HDF5 format), NC_FORMAT_NC_HDF4 (HDF4 format), NC_FORMAT_PNETCDF (PnetCDF format), NC_FORMAT_DAP2 (accessed via DAP2 protocol), and NC_FORMAT_DAP4 (accessed via DAP2 protocol). For example,

% ncks -D 2 -M hdf.hdf
Summary of hdf.hdf: filetype = NC_FORMAT_NETCDF4 (representation of \
  extended/underlying filetype NC_FORMAT_HDF4), 0 groups ...
% ncks -D 2 -M
Summary of \
  filetype = NC_FORMAT_CLASSIC (representation of extended/underlying \
  filetype NC_FORMAT_DAP2), 0 groups  
% ncks -D 2 -M
Summary of filetype = NC_FORMAT_NETCDF4 (representation of \
  extended/underlying filetype NC_FORMAT_HDF5), 0 groups  

The extended filetype determines some of the capabilities that netCDF has to alter the file.

Second, query the file with ‘ncdump -k’:

% ncdump -k
% ncdump -k
% ncdump -k
netCDF-4 classic model
% ncdump -k

This method requires a netCDF4-enabled netCDF 3.6.2+ (i.e., from 2007 or later).

The third option uses the POSIX-standard od (octal dump) command:

% od -An -c -N4
   C   D   F 001
% od -An -c -N4
   C   D   F 002
% od -An -c -N4
 211   H   D   F
% od -An -c -N4
 211   H   D   F

This option works without NCO and ncdump. Values of ‘C D F 001’ and ‘C D F 002’ indicate 32-bit (classic) and 64-bit netCDF3 formats, respectively, while values of ‘211 H D F’ indicate either of the newer netCDF4 file formats.

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3.9.3 File Conversion

Let us demonstrate converting a file from any netCDF-supported input format into any netCDF output format (subject to limits of the output format). Here the input file may be in any of these formats: netCDF3 (classic and 64bit), netCDF4 (classic and extended), HDF4, HDF5, HDF-EOS (version 2 or 5), and DAP. The switch determines the output format written in the comment: 29

ncks --fl_fmt=classic # netCDF3 classic
ncks --fl_fmt=64bit # netCDF3 64bit
ncks --fl_fmt=netcdf4_classic # netCDF4 classic
ncks --fl_fmt=netcdf4 # netCDF4 
ncks -3 # netCDF3 classic
ncks --3 # netCDF3 classic
ncks -6 # netCDF3 64bit
ncks --64 # netCDF3 64bit
ncks -4 # netCDF4 
ncks --4 # netCDF4 
ncks -7 # netCDF4 classic
ncks --7 # netCDF4 classic

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.

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3.9.4 Autoconversion

Because of the dearth of support for netCDF4 amongst tools and user communities (including the CF conventions), it is often useful to convert netCDF4 to netCDF3 for certain applications. Until NCO version 4.4.0 (January, 2014), producing netCDF3 files from netCDF4 files only worked if the input files contained no netCDF4-specific features (e.g., atomic types, multiple record dimensions, or groups). As of NCO version 4.4.0, ncks supports autoconversion of many netCDF4 features to their closest netCDF3-compatible representations. Since converting netCDF4 to netCDF3 results in loss of features, “automatic down-conversion” may be a more precise description of what we term autoconversion.

NCO employs three algorithms to downconvert netCDF4 to netCDF3:

  1. Autoconversion of atomic types: Autoconversion automatically promotes NC_UBYTE to NC_SHORT, and NC_USHORT to NC_INT. It automatically demotes the three types NC_UINT, NC_UINT64, and NC_INT64 to NC_INT. And it converts NC_STRING to NC_CHAR. All numeric conversions work for attributes and variables of any rank. Two numeric types (NC_UBYTE and NC_USHORT) are promoted to types with greater range (and greater storage). This extra range is often not used so promotion perhaps conveys the wrong impression. However, promotion never truncates values or loses data (this perhaps justifies the extra storage). Three numeric types (NC_UINT, NC_UINT64 and NC_INT64) are demoted. Since the input range is larger than the output range, demotion can result in numeric truncation and thus loss of data. In such cases, it would possible to convert the data to floating point values instead. If this feature interests you, please be the squeaky wheel and let us know.

    String conversions (to NC_CHAR) work for all attributes, but not for variables. This is because attributes are at most one-dimensional and may be of any size whereas variables require gridded dimensions that usually do not fit the ragged sizes of text strings. Hence scalar NC_STRING attributes are correctly converted to and stored as NC_CHAR attributes in the netCDF3 output file, but NC_STRING variables are not correctly converted. If this limitation annoys or enrages you, please let us know by being the squeaky wheel.

  2. Convert multiple record dimensions to fixed-size dimensions. Many netCDF4 and HDF5 datasets have multiple unlimited dimensions. Since a netCDF3 file may have at most one unlimited dimension, all but possibly one unlimited dimension from the input file must be converted to fixed-length dimensions prior to storing netCDF4 input as netCDF3 output. By invoking --fix_rec_dmn all the user ensures the output file will adhere to netCDF3 conventions and the user need not know the names of the specific record dimensions to fix. See ncks netCDF Kitchen Sink for a description of the ‘--fix_rec_dmn’ option.
  3. Flattening (removal) of groups. Many netCDF4 and HDF5 datasets have group hierarchies. Since a netCDF3 file may not have any groups, groups in the input file must be removed. This is also called “flattening” the hierarchical file. See Group Path Editing for a description of the GPE option ‘-G :’ to flatten files.

Putting the three algorithms together, one sees that the recipe to convert netCDF4 to netCDF4 becomes increasingly complex as the netCDF4 features in the input file become more elaborate:

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

Future versions of NCO may automatically invoke the record dimension fixation and group flattening when converting to netCDF3 (rather than requiring it be specified manually). If this feature would interest you, please let us know.

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3.10 Large File Support

Availability: All operators
Short options: none
Long options: none

NCO has Large File Support (LFS), meaning that NCO can write files larger than 2 GB on some 32-bit operating systems with netCDF libraries earlier than version 3.6. If desired, LFS support must be configured when both netCDF and NCO are installed. netCDF versions 3.6 and higher support 64-bit file addresses as part of the netCDF standard. We recommend that users ignore LFS support which is difficult to configure and is implemented in NCO only to support netCDF versions prior to 3.6. This obviates the need for configuring explicit LFS support in applications (such as NCO) that now support 64-bit files directly through the netCDF interface. See File Formats and Conversion for instructions on accessing the different file formats, including 64-bit files, supported by the modern netCDF interface.

If you are still interested in explicit LFS support for netCDF versions prior to 3.6, know that LFS support depends on a complex, interlocking set of operating system 30 and netCDF support issues. The netCDF LFS FAQ 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.

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3.11 Subsetting Files

Options -g grp
Availability: ncbo, nces, ncecat, ncflint, ncks, ncpdq, ncra, ncrcat, ncwa
Short options: ‘-g
Long options: ‘--grp’ and ‘--group
Options -v var and -x
Availability: (ncap2), ncbo, nces, ncecat, ncflint, ncks, ncpdq, ncra, ncrcat, ncwa
Short options: ‘-v’, ‘-x
Long options: ‘--variable’, ‘--exclude’ or ‘--xcl
Options --unn
Availability: ncbo, nces, ncecat, ncflint, ncks, ncpdq, ncra, ncrcat, ncwa
Short options:
Long options: ‘--unn’ and ‘--union
Options --grp_xtr_var_xcl
Availability: ncks
Short options:
Long options: ‘--gxvx’ and ‘--grp_xtr_var_xcl

Subsetting variables refers to explicitly specifying variables and groups to be included or excluded from operator actions. Subsetting is controlled by the ‘-v var[,…]’ and ‘-x’ options for directly specifying variables. Specifying groups, whether in addition to or instead of variables, is quite similar and is controlled by the ‘-g grp[,…]’ and ‘-x’ options. A list of variables or groups to extract is specified following the ‘-v’ and ‘-g’ options, e.g., ‘-v time,lat,lon’ or ‘-g grp1,grp2’. Both options may be specified simultaneously and NCO will extract the intersection of the lists, i.e., only variables of the specified names found in groups of the specified names. The ‘--unn’ option causes NCO to extract the union, rather than the intersection, of the specified groups and variables. Not using the ‘-v’ or ‘-g’ option is equivalent to specifying all variables or groupp, respectively. The ‘-x’ option causes the list of variables specified with ‘-v’ to be excluded rather than extracted. Thus ‘-x’ saves typing when you only want to extract fewer than half of the variables in a file.

Variables or groups explicitly specified for extraction with ‘-v var[,…]’ or ‘-g grp[,…]must be present in the input file or an error will result. Variables explicitly specified for exclusion with ‘-x -v var[,…]’ need not be present in the input file. To accord with the sophistication of the underlying hierarchy, group subsetting is controlled by a few powerful yet subtle syntactical distinctions. When learning this syntax it is helpful to keep in mind the similarity between group hierarchies and directory structures.

As of NCO 4.4.4 (June, 2014), ncks (alone) supports an option to include specified groups yet exclude specified variables. The ‘--grp_xtr_var_xcl’ switch (with long option equivalent ‘--gxvx’) extracts all contents of groups given as arguments to ‘-g grp[,…]’, except for variables given as arguments to ‘-v var[,…]’. Use this when one or a few variables in hierarchical files are not to be extracted, and all other variables are. This is useful when coercing netCDF4 files into netCDF3 files such as with converting, flattening, or dismembering files (see Flattening Groups).

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

Two properties of subsetting, recursion and anchoring, are best illustrated by reminding the user of their UNIX equivalents. The UNIX command mv src dst moves src and all its subdirectories (and all their subdirectories etc.) to dst. In other words mv is, by default, recursive. In contrast, the UNIX command cp src dst moves src, and only src, to dst, If src is a directory, not a file, then that command fails. One must explicitly request to copy directories recursively, i.e., with cp -r src dst. In NCO recursive extraction (and copying) of groups is the default (like with mv, not with cp). Recursion is turned off by appending a trailing slash to the path.

These UNIX commands also illustrate a property we call anchoring. The command mv src dst moves (recursively) the source directory src to the destination directory dst. If src begins with the slash character then the specified path is relative to the root directory, otherwise the path is relative to the current working directory. In other words, an initial slash character anchors the subsequent path to the root directory. In NCO an initial slash anchors the path at the root group. Paths that begin and end with slash characters (e.g., //, /g1/, and /g1/g2/) are both anchored and non-recursive.

Consider the following commands, all of which may be assumed to end with ‘’:

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

The first command is probably the most useful and common. It would extract these groups, if present, and all their direct ancestors and children: /g1, /g2/g1, and /g3/g1/g2. In other words, the simplest form of ‘-g grp’ grabs all groups that (and their direct ancestors and children, recursively) that have grp as a complete component of their path. A simple string match is insufficient, grp must be a complete component (i.e., group name) in the path. The option ‘-g g1’ would not extract these groups because g1 is not a complete component of the path: /g12, /fg1, and /g1g1. The second command above shows how a terminating slash character / cancels the recursive copying of groups. An argument to ‘-g’ which terminates with a slash character extracts the group and its direct ancestors, but none of its children. The third command above shows how an initial slash character / anchors the argument to the root group. The third command would not extract the group /g2/g1 because the g1 group is not at the root level, but it would extract, any group /g1 at the root level and all its children, recursively. The fourth command is the non-recursive version of the third command. The fifth command is a special case of the fourth command.

As mentioned above, both ‘-v’ and ‘-g’ options may be specified simultaneously and NCO will, by default, extract the intersection of the lists, i.e., the specified variables found in the specified groups 31. The ‘--unn’ option causes NCO to extract the union, rather than the intersection, of the specified groups and variables. Consider the following commands (which may be assumed to end with ‘’):

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

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

The first command (‘-g g1 -v v1’) extracts the variable v1 from any group named g1 or descendent g1. The second command extracts v1 from any root group named g1 and any descendent groups as well. The third and fourth commands are equivalent ways of extracting v1 only from the root group named g1 (not its descendents). The fifth and sixth commands are equivalent ways of extracting the variable v1 only from the root group named g1. Subsetting in union-mode (with ‘--unn’) causes all variables to be extracted which meet either one or both of the specifications of the variable and group specifications. Union-mode subsetting is simply the logical “OR” of intersection-mode subsetting. As discussed below, the group and variable specifications may be comma separated lists of regular expressions for added control over subsetting.

Remember, if averaging or concatenating large files stresses your systems memory or disk resources, then the easiest solution is often to subset (with ‘-g’ and/or ‘-v’) to retain only the most important variables (see Memory Requirements).

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

Overwriting and appending work as expected:

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

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 and -g switches.

Regular expressions the syntax that NCO use pattern-match object names in netCDF file against user requests. The user can select all variables beginning with the string ‘DST’ from an input file by supplying the regular expression ‘^DST’ to the ‘-v’ switch, i.e., ‘-v '^DST'’. The meta-characters used to express pattern matching operations are ‘^$+?.*[]{}|’. If the regular expression pattern matches any part of a variable name then that variable is selected. This capability is also called wildcarding, and is very useful for sub-setting large data files.

Extended regular expressions are defined by the POSIX grep -E (aka egrep) command. As of NCO 2.8.1 (August, 2003), variable name arguments to the ‘-v’ switch may contain extended regular expressions. As of NCO 3.9.6 (January, 2009), variable names arguments to ncatted may contain extended regular expressions. As of NCO 4.2.4 (November, 2012), group name arguments to the ‘-g’ switch may contain extended regular expressions.

Because of its wide availability, NCO uses the POSIX regular expression library regex. Regular expressions of arbitary complexity may be used. Since netCDF variable names are relatively simple constructs, only a few varieties of variable wildcards are likely to be useful. For convenience, we define the most useful pattern matching operators here:


Matches the beginning of a string


Matches the end of a string


Matches any single character

The most useful repetition and combination operators are


The preceding regular expression is optional and matched at most once


The preceding regular expression will be matched zero or more times


The preceding regular expression will be matched one or more times


The preceding regular expression will be joined to the following regular expression. The resulting regular expression matches any string matching either subexpression.

To illustrate the use of these operators in extracting variables and groups, consider file with groups g0g9, and subgroups s0s9, in each of those groups, and file with variables Q, Q01Q99, Q100, QAAQZZ, Q_H2O, X_H2O, Q_CO2, X_CO2.

ncks -v '.+'               # All variables (default)
ncks -v 'Q.?'              # Variables that contain Q
ncks -v '^Q.?'             # Variables that start with Q
ncks -v '^Q+.?.'           # Q, Q0--Q9, Q01--Q99, QAA--QZZ, etc.
ncks -v '^Q..'             # Q01--Q99, QAA--QZZ, etc.
ncks -v '^Q[0-9][0-9]'     # Q01--Q99, Q100
ncks -v '^Q[[:digit:]]{2}' # Q01--Q99
ncks -v 'H2O$'             # Q_H2O, X_H2O 
ncks -v 'H2O$|CO2$'        # Q_H2O, X_H2O, Q_CO2, X_CO2 
ncks -v '^Q[0-9][0-9]$'    # Q01--Q99
ncks -v '^Q[0-6][0-9]|7[0-3]' # Q01--Q73, Q100
ncks -v '(Q[0-6][0-9]|7[0-3])$' # Q01--Q73
ncks -v '^[a-z]_[a-z]{3}$' # Q_H2O, X_H2O, Q_CO2, X_CO2
ncks -g 'g.'           # 10 Groups g0-g9
ncks -g 's.'       # 100 sub-groups g0/s0, g0/s1, ... g9/s9
ncks -g 'g.' -v 'v.'   # All variables 'v.' in groups 'g.'

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.

One must be careful to protect any special characters in the regular expression specification from being interpreted (globbed) by the shell. This is accomplish by enclosing special characters within single or double quotes

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

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3.12 Subsetting Coordinate Variables

Availability: ncap2, ncbo, nces, ncecat, ncflint, ncks, ncpdq, ncra, ncrcat, ncwa
Short options: ‘-C’, ‘-c
Long options: ‘--no-coords’, ‘--no-crd’, ‘--crd’, ‘--coords

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. As of NCO version 4.4.5 (July, 2014) both ‘-c’ and ‘-C’ honor the CF ancillary_variables convention described in CF Conventions. As of NCO version 4.0.8 (April, 2011) both ‘-c’ and ‘-C’ honor the CF bounds convention described in CF Conventions. As of NCO version 3.9.6 (January, 2009) both ‘-c’ and ‘-C’ honor the CF coordinates convention described in CF Conventions.

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3.13 Group Path Editing

Options -G gpe_dsc
Availability: ncbo, ncecat, nces, ncflint, ncks, ncpdq, ncra, ncrcat, ncwa
Short options: ‘-G
Long options: ‘--gpe

Group Path Editing, or GPE, allows the user to restructure (i.e., add, remove, and rename groups) in the output file relative to the input file based on the instructions they provide. As of NCO 4.2.3 (November, 2012), all operators that accept netCDF4 files with groups accept the ‘-G’ switch, or its long-option equivalent ‘--gpe’. To master GPE one must understand the meaning of the required gpe_dsc structure/argument that specifies the transformation of input-to-output group paths.

Each gpe_dsc contains up to three elements (two are optional) in the following order:
gpe_dsc = grp_pth:lvl_nbr or grp_pth@lvl_nbr


Group Path. This (optional) component specifies the output group path that should be appended after any editing (i.e., deletion or truncation) of the input path is performed.


The number of levels to delete (from the head) or truncate (from the tail) of the input path.

If both components of the argument are present, then a single character, either the colon or at-sign (: or @), must separate them. If only grp_pth is specifed, the separator character may be omitted, e.g., ‘-G g1’. If only lvl_nbr is specifed, the separator character is still required to indicate it is a lvl_nbr arugment and not a grp_pth, e.g., ‘-G :-1’ or ‘-G @1’.

If the at-sign separator character @ is used instead of the colon separator character :, then the following lvl_nbr arugment must be positive and it will be assumed to refer to Truncation-Mode. Hence, ‘-G :-1’ is the same as ‘-G @1’. This is simply a way of making the lvl_nbr argument positive-definite.

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3.13.1 Deletion, Truncation, and Flattening of Groups

GPE has three editing modes: Delete, Truncate, and Flatten. Select one of GPE’s three editing modes by supplying a lvl_nbr that is positive, negative, or zero for Delete-, Truncate- and Flatten-mode, respectively.

In Delete-mode, lvl_nbr is a positive integer which specifies the maximum number of group path components (i.e., groups) that GPE will try to delete from the head of grp_pth. For example lvl_nbr = 3 changes the input path /g1/g2/g3/g4/g5 to the output path /g4/g5. Input paths with lvl_nbr or fewer components (groups) are completely erased and the output path commences from the root level.

In other words, GPE is tolerant of specifying too many group components to delete. It deletes as many as possible, without complaint, and then begins to flatten the file (which will fail if namespace conflicts arise).

In Truncate-mode, lvl_nbr is a negative integer which specifies the maximum number of group path components (i.e., groups) that GPE will try to truncate from the tail of grp_pth. For example lvl_nbr = -3 changes the input path /g1/g2/g3/g4/g5 to the output path /g1/g2. Input paths with lvl_nbr or fewer components (groups) are completely erased and the output path commences from the root level.

In Flatten-mode, indicated by the separator character alone or with lvl_nbr = 0, GPE removes the entire group path from the input file and constructs the output path beginning at the root level. For example -G :0 and -G : are identical and change the input path /g1/g2/g3/g4/g5 to the output path / whereas -G g1:0 and -G g1: are identical and result in the output path /g1 for all variables.

Subsequent to the alteration of the input path by the specified editing mode, if any, GPE prepends (in Delete Mode) or Appends (in Truncate-mode) any specifed grp_pth to the output path. For example -G g2 changes the input paths / and /g1 to /g2 and /g1/g2, respectively. Likewise, -G g2/g3 changes the input paths / and /g1 to /g2/g3 and /g1/g2/g3, respectively. When grp_pth and lvl_nbr are both specified, the editing actions are taken in sequence so that, e.g., -G g1/g2:2 changes the input paths / and /h1/h2/h3/h4 to /g1/g2 and /g1/g2/h3/h4, respectively. Likewise, -G g1/g2:-2 changes the input paths / and /h1/h2/h3/h4 to /g1/g2 and /h1/h2/g1/g2, respectively.

Combining GPE with subsetting (see Subsetting Files) yields powerful control over the extracted (or excluded) variables and groups and their placement in the output file as shown by the following commands. All commands below may be assumed to end with ‘’.

# Prepending paths without editing:
ncks                   # /g?/v? -> /g?/v?
ncks             -v v1 # /g?/v1 -> /g?/v1
ncks       -g g1       # /g1/v? -> /g1/v?
ncks -G o1             # /g?/v? -> /o1/g?/v?
ncks -G o1 -g g1       # /g1/v? -> /o1/g1/v?
ncks       -g g1 -v v1 # /g1/v1 -> /g1/v1
ncks -G o1       -v v1 # /g?/v1 -> /o1/g?/v1
ncks -G o1 -g g1 -v v1 # /g1/v1 -> /o1/g1/v1
ncks -G g1 -g /  -v v1 # /v1    -> /g1/v1
ncks -G g1/g2    -v v1 # /g?/v1 -> /g1/g2/g?/v1
# Delete-mode: Delete from and Prepend to path head
# Syntax: -G [ppn]:lvl_nbr = # of levels to delete
ncks -G :1    -g g1    -v v1 # /g1/v1    -> /v1
ncks -G :1    -g g1/g1 -v v1 # /g1/g1/v1 -> /g1/v1
ncks -G :2    -g g1/g1 -v v1 # /g1/g1/v1 -> /v1
ncks -G :2    -g g1    -v v1 # /g1/v1    -> /v1
ncks -G g2:1  -g g1    -v v1 # /g1/v1    -> /g2/v1
ncks -G g2:2  -g g1/g1 -v v1 # /g1/g1/v1 -> /g2/v1
ncks -G g2:1  -g /     -v v1 # /v1       -> /g2/v1
ncks -G g2:1           -v v1 # /v1       -> /g2/v1
ncks -G g2:1  -g g1/g1 -v v1 # /g1/g1/v1 -> /g2/g1/v1
# Flatten-mode: Remove all input path components
# Syntax: -G [apn]: colon without numerical argument
ncks -G :            -v v1 # /g?/v1    -> /v1
ncks -G :   -g g1    -v v1 # /g1/v1    -> /v1
ncks -G :   -g g1/g1 -v v1 # /g1/g1/v1 -> /v1
ncks -G g2:          -v v1 # /g?/v1    -> /g2/v1
ncks -G g2:                # /g?/v?    -> /g2/v?
ncks -G g2: -g g1/g1 -v v1 # /g1/g1/v1 -> /g2/v1
# Truncate-mode: Truncate from and Append to path tail
# Syntax: -G [apn]:-lvl_nbr = # of levels to truncate
# NB: -G [apn]:-lvl_nbr is equivalent to -G [apn]@lvl_nbr
ncks -G :-1   -g g1    -v v1 # /g1/v1    -> /v1
ncks -G :-1   -g g1/g2 -v v1 # /g1/g2/v1 -> /g1/v1
ncks -G :-2   -g g1/g2 -v v1 # /g1/g2/v1 -> /v1
ncks -G :-2   -g g1    -v v1 # /g1/v1    -> /v1
ncks -G g2:-1          -v v1 # /g?/v1    -> /g2/v1
ncks -G g2:-1 -g g1    -v v1 # /g1/v1    -> /g2/v1
ncks -G g1:-1 -g g1/g2 -v v1 # /g1/g2/v1 -> /g1/g1/v1

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3.13.2 Moving Groups

Until fall 2013 (netCDF version 4.3.1-pre1), netCDF contained no library function for renaming groups, and therefore ncrename cannot rename groups. However, NCO built on earlier versions of netCDF than 4.3.1 can use a GPE-based workaround mechanism to “rename” groups. The GPE mechanism actually moves (i.e., copies to a new location) groups, a more arduous procedure than simply renaming them. GPE applies to all selected groups, so, in the general case, one must move only the desired group to a new file, and then merge that new file with the original to obtain a file where the desired group has been “renamed” and all else is unchanged. Here is how to “rename” group /g4 to group /f4 with GPE instead of ncrename

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

If the original group g4 is not excised from (step two above), then the final output file would contain both g4 and a copy named f4. Thus GPE can be used to both “rename” and copy groups. The recommended way to rename groups when when netCDF version 4.3.1 is availale is to use ncrename (see ncrename netCDF Renamer).

One may wish to flatten hierarchical group files for many reasons. These include 1. Obtaining flat netCDF3 files for use with tools that do not work with netCDF4 files, 2. Splitting apart hierarchies to re-assemble into different hierarchies, and 3. Providing a subset of a hierarchical file with the simplest possible storage structure.

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

The -3 switch 32 specifies the output dataset should be in netCDF3 format, the -G : option flattens all extracted groups, and the -g cesm option extracts only the cesm group and leaves all other groups (e.g., ecmwf, giss).

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3.13.3 Dismembering Files

Let us show how to completely disaggregate (or, more memorably) dismember a hierarchical dataset. For now we take this to mean: store each group as a standalone flat dataset in netCDF3 format. This can be accomplished by looping the previous example over all groups. This script ncdismember dismembers the input file fl_in specified in the first argument and places the resulting files in the directory drc_out specified by the second argument:

cat > ~/ncdismember << 'EOF'
# Purpose: Dismember netCDF4/HDF5 hierarchical files. CF-check them.
# Place each input file group in separate netCDF3 output file
# Described in NCO User Guide at
# Requirements: NCO 4.3.x+, UNIX shell utilities awk, grep, sed
# Optional: CFchecker command

# Usage:
# ncdismember <fl_in> <drc_out> [flg_cf] [cf_vrs] [opt]
# where fl_in is input file/URL to dismember, drc_out is output directory,
# CF-compliance check is performed when optional third argument is 'cf',
# Optional fourth argument cf_vrs is CF version to check
# Optional fifth argument opt passes straight through to ncks
# chmod a+x ~/sh/ncdismember
# ncdismember ~/nco/data/ /data/zender/nco/tmp
# ncdismember ~/nco/data/ /data/zender/nco/tmp
# ncdismember /data/zender/nco/tmp
# ncdismember /data/zender/nco/tmp
# ncdismember ~/nco/data/ /data/zender/nco/tmp cf
# ncdismember ~/nco/data/ /data/zender/nco/tmp cf 1.3
# ncdismember ~/nco/data/ /data/zender/nco/tmp cf 1.5 --fix_rec_dmn=all

# Command line argument defaults
fl_in="${HOME}/nco/data/" # [sng] Input file to dismember/check
drc_out="${DATA}/nco/tmp" # [sng] Output directory
flg_cf='0' # [flg] Perform CF-compliance check
cf_vrs='1.5' # [sng] Compliance-check this CF version (e.g., '1.5')
opt='' # [flg] Additional ncks options (e.g., '--fix_rec_dmn=all')
# Use single quotes to pass multiple arguments to opt=${5}
# Otherwise arguments would be seen as ${5}, ${6}, ${7} ...

# Command line argument option parsing
if [ -n "${1}" ]; then fl_in=${1}; fi
if [ -n "${2}" ]; then drc_out=${2}; fi
if [ -n "${3}" ]; then flg_cf=${3}; fi
if [ -n "${4}" ]; then cf_vrs=${4}; fi
if [ -n "${5}" ]; then opt=${5}; fi

# Prepare output directory
echo "NCO dismembering file ${fl_in}"
fl_stb=$(basename ${fl_in})
mkdir -p ${drc_out}
cd ${drc_out}
# Obtain group list
grp_lst=`ncks --cdl -m ${fl_in} | grep '// group' | awk '{$1=$2=$3="";sub(/^  */,"",$0);print}'`
IFS=$'\n' # Change Internal-Field-Separator from <Space><Tab><Newline> to <Newline>
for grp_in in ${grp_lst} ; do
    # Replace slashes by dots for output group filenames
    grp_out=`echo ${grp_in} | sed 's/\///' | sed 's/\//./g'`
    if [ "${grp_out}" = '' ]; then grp_out='root' ; fi
    # Tell older NCO/netCDF if HDF4 with --hdf4 switch (signified by .hdf/.HDF suffix)
    hdf4=`echo ${fl_in} | awk '{if(match(tolower($1),".hdf$")) hdf4="--hdf4"; print hdf4}'`
    # Flatten to netCDF3, anchor, no history, no temporary file, padding, HDF4 flag, options
    cmd="ncks -O -3 -G : -g ${grp_in}/ -h --no_tmp_fl --hdr_pad=40 ${hdf4} ${opt} ${fl_in} ${drc_out}/${grp_out}.nc"
    # Use eval in case ${opt} contains multiple arguments separated by whitespace
    eval ${cmd}
    if [ ${flg_cf} = 'cf' ]; then
       # cfchecker needs Conventions <= 1.5
       no_bck_sls=`echo ${drc_out}/${grp_out} | sed 's/\\\ / /g'`
       ncatted -h -a Conventions,global,o,c,CF-${cf_vrs} ${no_bck_sls}.nc
    else # !flg_cf
       echo ${drc_out}/${grp_out}.nc
    fi # !flg_cf
if [ ${flg_cf} = 'cf' ]; then
    echo "CFchecker reports CF-compliance of each group in flat netCDF3 format"
    cfchecker -c ${cf_vrs} *.nc
fi # !flg_cf
chmod 755 ~/ncdismember # Make command executable
/bin/mv -f ~/ncdismember ~/sh # Store in location on $PATH, e.g., /usr/local/bin

zender@roulee:~$ ncdismember ~/nco/data/ ${DATA}/nco/tmp
NCO dismembering file /home/zender/nco/data/

A (potentially more portable) binary executable could be written to dismember all groups with a single invocation, yet dismembering without loss of information is possible now with this simple script on all platforms with UNIXy utilities. Note that all dimensions inherited by groups in the input file are correctly placed by ncdismember into the flat files. Moreover, each output file preserves the group metadata of all ancestor groups, including the global metadata from the input file. As written, the script could fail on groups that contain advanced netCDF4 features because the user requests (with the ‘-3’ switch) that output be netCDF3 classic format. However, ncks detects many format incompatibilities in advance and works around them. For example, ncks autoconverts netCDF4-only atomic-types (such as NC_STRING and NC_UBYTE) to corresponding netCDF3 atomic types (NC_CHAR and NC_SHORT) when the output format is netCDF3.

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3.13.4 Checking CF-compliance

One application of dismembering is to check the CF-compliance of each group in a file. When invoked with the optional third argumnt ‘cf’, ncdismember passes each file it generates to the freely available 33 cfchecker command.

zender@roulee:~$ ncdismember ~/nco/data/ /data/zender/nco/tmp cf
NCO dismembering file /home/zender/nco/data/
CFchecker reports CF-compliance of each group in flat netCDF3 format
WARNING: Using the default (non-CF) Udunits database 
INFO: INIT:     running CFchecker version 1.5.15
INFO: INIT:     checking compliance with convention CF-1.5
INFO: INIT:     using standard name table version: 25, last modified: 2013-07-05T05:40:30Z
INFO: INIT:     using area type table version: 2, date: 10 July 2013
INFO: 2.4:      no axis information found in dimension variables, not checking dimension order
WARNING: 3:     variable "tas1" contains neither long_name nor standard_name attribute
WARNING: 3:     variable "tas2" contains neither long_name nor standard_name attribute
INFO: 3.1:      variable "tas1" does not contain units attribute
INFO: 3.1:      variable "tas2" does not contain units attribute

By default the CF version checked is determined automatically by cfchecker. The user can override this default by supplying a supported CF version, e.g., ‘1.3’, as an optional fourth argument to ncdismember. Current valid CF options are ‘1.0’, ‘1.1’, ‘1.2’, ‘1.3’, ‘1.4’, and ‘1.5’.

Our development and testing of ncdismember is funded by our involvement in NASA’s Dataset Interoperability Working Group (DIWG), though our interest extends beyond NASA datasets. Taken together, NCO’s features (autoconversion to netCDF3 atomic types, fixing multiple record dimensions, autosensing HDF4 input, scoping rules for CF conventions) make ncdismember reliable and friendly for both dismembering hierarchical files and for CF-compliance checks. Most HDF4 and HDF5 datasets can be checked for CF-compliance with a one-line command. Example compliance checks of common NASA datasets are at Our long-term goal is to enrich the hierarchical data model with the expressivity and syntactic power of CF conventions.

NASA asked the DIWG to prepare a one-page summary of the procedure necessary to check HDF files for CF-compliance:

cat > ~/ncdismember.txt << 'EOF'
    Preparing an RPM-based OS to Test HDF & netCDF Files for CF-Compliance

By Charlie Zender, UCI & NASA Dataset Interoperability Working Group (DIWG)

Installation Summary:
1. HDF4 [with internal netCDF support _disabled_]
2. HDF5
3. netCDF [with external HDF4 support _enabled_]
4. NCO
5. numpy
6. netcdf4-python
7. python-lxml
8. CFunits-python
9. CFChecker
10. ncdismember

All 10 packages can use default installs _except_ HDF4 and netCDF.
Following instructions for Fedora Core 20 (FC20), an RPM-based Linux OS
Feedback and changes for other Linux-based OS's welcome to zender at
${H4DIR}, ${H5DIR}, ${NETCDFDIR}, ${NCODIR}, may all be different
For simplicity CZ sets them all to /usr/local

# 1. HDF4. Build in non-default manner. Turn-off its own netCDF support.
# Per
# HDF4 support not necessary though it makes ncdismember more comprehensive
wget -c
tar xvzf hdf-4.2.9.tar.gz
cd hdf-4.2.9
./configure --enable-shared --disable-netcdf --disable-fortran --prefix=${H4DIR}
make && make check && make install

# 2. HDF5. Build normally. RPM may work too. Please let me know if so.
# HDF5 is a necessary pre-requisite for netCDF4
wget -c
tar xvzf hdf5-1.8.11.tar.gz
cd hdf5-1.8.11
./configure --enable-shared --prefix=${H5DIR}
make && make check && make install

# 3. netCDF version 4.3.1 or later. Build in non-default manner with HDF4.
# Per
# Earlier versions of netCDF may fail checking some HDF4 files
wget -c
tar xvzf netcdf-4.3.2.tar.gz
cd netcdf-4.3.2
CPPFLAGS="-I${H5DIR}/include -I${H4DIR}/include" \
LDFLAGS="-L${H5DIR}/lib -L${H4DIR}/lib" \
./configure --enable-hdf4 --enable-hdf4-file-tests
make && make check && make install

# 4. NCO version 4.4.0 or later. Some RPMs available. Or install by hand.
# Later versions of NCO have much better support for ncdismember
wget .
tar xvzf nco-4.4.4.tar.gz
cd nco-4.4.4
./configure --prefix=${NCODIR}
make && make install

# 5. numpy
sudo yum install numpy -y

# 6. netcdf4-python
sudo yum install netcdf4-python -y

# 7. python-lxml
sudo yum install python-lxml -y

# 8. CFunits-python. No RPM available. Must install by hand.
wget .
tar xvzf cfunits-0.9.6.tar.gz
cd cfunits-0.9.6
sudo python install

# 9. CFChecker. No RPM available. Must install by hand.
wget . 
tar xvjf CFchecker-1.5.15.tar.bz2 
cd CFchecker
sudo python install

# 10. ncdismember. Copy script from
# Store dismembered files somewhere, e.g., ${DATA}/nco/tmp/hdf
mkdir -p ${DATA}/nco/tmp/hdf
# Many datasets work with a simpler command...
ncdismember ~/nco/data/ ${DATA}/nco/tmp/hdf cf 1.5
ncdismember ~/nco/data/ ${DATA}/nco/tmp/hdf cf 1.5
ncdismember ${DATA}/hdf/AMSR_E_L2_Rain_V10_200905312326_A.hdf \
            ${DATA}/nco/tmp/hdf cf 1.5
ncdismember ${DATA}/hdf/BUV-Nimbus04_L3zm_v01-00-2012m0203t144121.h5 \
            ${DATA}/nco/tmp/hdf cf 1.5
ncdismember ${DATA}/hdf/HIRDLS-Aura_L3ZAD_v06-00-00-c02_2005d022-2008d077.he5 ${DATA}/nco/tmp/hdf cf 1.5
# Some datasets, typically .h5, require the --fix_rec_dmn=all argument
ncdismember_${DATA}/hdf/GATMO_npp_d20100906_t1935191_e1935505_b00012_c20110707155932065809_noaa_ops.h5 ${DATA}/nco/tmp/hdf cf 1.5 --fix_rec_dmn=all
ncdismember ${DATA}/hdf/mabel_l2_20130927t201800_008_1.h5 \
            ${DATA}/nco/tmp/hdf cf 1.5 --fix_rec_dmn=all

A PDF version of these instructions is available here.

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3.14 C and Fortran Index conventions

Availability: ncbo, nces, ncecat, ncflint, ncks, ncpdq, ncra, ncrcat, ncwa
Short options: ‘-F
Long options: ‘--fortran

The ‘-F’ switch changes NCO to read and write with the Fortran index convention. By default, NCO uses C-style (0-based) indices for all I/O. In C, indices count from 0 (rather than 1), and dimensions are ordered from slowest (inner-most) to fastest (outer-most) varying. In Fortran, indices count from 1 (rather than 0), and dimensions are ordered from fastest (inner-most) to slowest (outer-most) varying. Hence C and Fortran data storage conventions represent mathematical transposes of eachother. Note that record variables contain the record dimension as the most slowly varying dimension. See ncpdq netCDF Permute Dimensions Quickly for techniques to re-order (including transpose) dimensions and to reverse data storage order.

Consider a file 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
ncra -F -d time,6,8

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

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

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3.15 Hyperslabs

Availability: ncbo, nces, ncecat, ncflint, ncks, ncpdq, ncra, ncrcat, ncwa
Short options: ‘-d dim,[min][,[max][,[stride]]]
Long options: ‘--dimension dim,[min][,[max][,[stride]]]’,
--dmn dim,[min][,[max][,[stride]]]

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).

# First and second longitudes
ncks -F -d lon,1,2
# Second and third longitudes
ncks -d lon,1,2

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

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

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

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

Coordinate values should be specified using real notation with a decimal point required in the value, whereas dimension indices are specified using integer notation without a decimal point. This convention serves only to differentiate coordinate values from dimension indices. It is independent of the type of any netCDF coordinate variables. For a given dimension, the specified limits must both be coordinate values (with decimal points) or dimension indices (no decimal points).

If values of a coordinate-variable are used to specify a range or cross-section, then the coordinate variable must be monotonic (values either increasing or decreasing). In this case, command-line values need not exactly match coordinate values for the specified dimension. Ranges are determined by seeking the first coordinate value to occur in the closed range [min,max] and including all subsequent values until one falls outside the range. The coordinate value for a cross-section is the coordinate-variable value closest to the specified value and must lie within the range or coordinate-variable values. The stride argument, if any, must be a dimension index, not a coordinate value. See Stride, for more information on the stride option.

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

As shown, we recommend using a full floating point suffix of .0 instead of simply . in order to make obvious the selection of hyperslab elements based on coordinate value rather than index.

User-specified coordinate limits are promoted to double-precision values while searching for the indices which bracket the range. Thus, hyperslabs on coordinates of type NC_CHAR are computed numerically rather than lexically, so the results are unpredictable.

The 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).

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3.16 Stride

Availability: ncbo, nces, ncecat, ncflint, ncks, ncpdq, ncra, ncrcat, ncwa
Short options: ‘-d dim,[min][,[max][,[stride]]]
Long options: ‘--dimension dim,[min][,[max][,[stride]]]’,
--dmn dim,[min][,[max][,[stride]]]

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.. 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 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, … 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:

for idx in 02 14 26 38 50; do # Bourne Shell
  ncks -d time,${idx} foo.${idx}
foreach idx (02 14 26 38 50) # C Shell
  ncks -d time,${idx} foo.${idx}
ncrcat foo.??
rm foo.??

With the stride option, ncks performs this hyperslab extraction in one operation:

ncks -d time,2,,12

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
ncrcat -F -d time,3,,12

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3.17 Record Appending

Availability: ncra, ncrcat
Short options: None
Long options: ‘--rec_apn’, ‘--record_append

As of version 4.2.6 (March, 2013), NCO allows both Multi-File, Multi-Record operators (ncra and ncrcat) to append their output directly to the end of an existing file. This feature may be used to augment a target file, rather than construct it from scratch. This helps, for example, when a timeseries is concatenated from input data that becomes available in stages rather than all at once. In such cases this switch significantly speeds writing.

Consider the use case where one wishes to preserve the contents of, and add to them new records contained in Previously the output had to be placed in a third file, (which could also safely be named, via

ncrcat -O

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

The ‘--record_append’ switch appends all records in to the end (after the last record) of

ncrcat --rec_apn

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

ncrcat --rec_apn --no_tmp_fl

This avoids creating an intermediate file, and copies only the minimal amount of data (i.e., all of Hence, it is fast. We recommend users try to understand the safety trade-offs involved.

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3.18 Subcycle

Availability: ncra, ncrcat
Short options: ‘-d dim,[min][,[max][,[stride][,[subcycle]]]]
Long options: ‘--mro’ ‘--dimension dim,[min][,[max][,[stride][,[subcycle]]]]
--dmn dim,[min][,[max][,[stride][,[subcycle]]]]

As of version 4.2.1 (August, 2012), NCO allows both Multi-File, Multi-Record operators, ncra and ncrcat, to extract and operate on multiple groups of records. These groups may be connected to physical sub-cycles of a periodic nature, e.g., months of a year, or hours of a day. Or they may be thought of as groups of a specifed duration. The feature and the terminology to describe it are new. For now, we call this the subcycle feature, sometimes abbreviated SSC 34.

The subcycle feature allows processing of groups of records separated by regular intervals of records. It is perhaps best illustrated by an extended example which describes how to solve the same problem both with and without the SSC feature.

The first task in climate data processing is often creating seasonal cycles. Suppose a 150-year climate simulation produces 150 output files, each comprising 12 records, each record a monthly mean:,, ... Our goal is to create a single file containing the summertime (June, July, and August, aka JJA) mean. Traditionally, we would first compute the climatological monthly mean for each month of summer. Each of these is a 150-year mean, i.e.,

# Step 1: Create climatological monthly files
for mth in {6..8}; do
  mm=`printf "%02d" $mth`
  ncra -O -F -d time,${mm},,12 -n 150,4,1 clm${mm}.nc
# Step 2: Average climatological monthly files into summertime mean
ncra -O clm06

So far, nothing is unusual and this task can be performed by any NCO version. The SSC feature makes obsolete the need for the shell loop used in Step 1 above.

The new SSC option aggregates more than one input record at a time before performing arithmetic operations, and, with an additional switch, allows us to archive those results in multiple record output (MRO) files. This reduces the task of producing the climatological summertime mean to one step:

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

The SSC option instructs ncra (or ncrcat) to process files in groups of three records. To better understand the meaning of each argument to the ‘-d’ hyperslab option, read it this way: “for the time dimension start with the sixth record, continue without end, repeat the process every twelfth record, and define a sub-cycle as three consecutive records”.

A separate option, ‘--mro’, instructs ncra to output its results from each sub-group, and to produce a Multi-Record Output (MRO) file rather than a Single-Record Output (SRO) file. Unless ‘--mro’ is specified, ncra collects together all the sub-groups, operates on their ensemble, and produces a single output record. The addition of ‘--mro’ to the above example causes ncra to archive all (150) annual summertime means to one file:

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

These operations generate and require no intermediate files. This contrasts to previous NCO methods, which require generating, averaging, then catenating 150 files. The ‘--mro’ option has no effect on, or rather is redundant for, ncrcat since ncrcat always outputs all selected records.

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3.19 Multislabs

Availability: ncbo, nces, ncecat, ncflint, ncks, ncpdq, ncra, ncrcat
Short options: ‘-d dim,[min][,[max][,[stride]]]
Long options: ‘--dimension dim,[min][,[max][,[stride]]]’,
--dmn dim,[min][,[max][,[stride]]]
--msa_usr_rdr’, ‘--msa_user_order

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 these capabilities in April 2008. Multi-slabbing is often referred to by the acronym MSA, which stands for “Multi-Slabbing Algorithm”. As explained below, the user may additionally request that the multislabs be returned in the user-specified order, rather than the on-disk storage order. Although MSA user-ordering has been available in all operators since 2008, most users were unaware of it since the documentation (below, and in the man pages) was not written until July 2013.

Multislabs overcome many restraints that limit simple hyperslabs. A single -d option can only specify a contiguous and/or a regularly spaced multi-dimensional data array. Multislabs are constructed from multiple -d options and may therefore have non-regularly spaced arrays. For example, suppose it is desired to operate on all longitudes from 10.0 to 20.0 and from 80.0 to 90.0 degrees. The combined range of longitudes is not selectable in a single hyperslab specfication of the form ‘-d dimension,min,max’ or ‘-d dimension,min,max,stride’ because its elements are irregularly spaced in coordinate space (and presumably in index space too). The multislab specification for obtaining these values is simply the union of the hyperslabs specifications that comprise the multislab, i.e.,

ncks -d lon,10.,20. -d lon,80.,90.
ncks -d lon,10.,15. -d lon,15.,20. -d lon,80.,90.

Any number of hyperslabs specifications may be chained together to specify the multislab. MSA creates an output dimension equal in size to the sum of the sizes of the multislabs. This can be used to extend and or pad coordinate grids.

Users may specify redundant ranges of indices in a multislab, e.g.,

ncks -d lon,0,4 -d lon,2,9,2

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. This is because NCO employs a novel multislab algorithm to minimize the number of I/O operations when retrieving irregularly spaced data from disk. The NCO multislab algorithm retrieves each element from disk once and only once. Thus users may take some shortcuts in specifying multislabs and the algorithm will obtain the intended values. Specifying redundant ranges is not encouraged, but may be useful on occasion and will not result in unintended consequences.

Suppose the Q variable contains three dimensional arrays of distinct chemical constituents in no particular order. We are interested in the NOy species in a certain geographic range. Say that NO, NO2, and N2O5 are elements 0, 1, and 5 of the species dimension of Q. The multislab specification might look something like

ncks -d species,0,1 -d species,5 -d lon,0,4 -d lon,2,9,2

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.

The MSA user-order switch ‘--msa_usr_rdr’ (or ‘--msa_user_order’, both of which shorten to ‘--msa’) requests that the multislabs be output in the user-specified order from the command-line, rather than in the input-file on-disk storage order. This allows the user to perform complex data re-ordering in one operation that would otherwise require cumbersome steps of hyperslabbing, concatenating, and permuting. Consider the recent example of a user who needed to convert datasets stored with the longitude coordinate Lon ranging from [-180,180) to datasets that follow the [0,360) convention.

% ncks -H -v Lon

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

% ncks -O -H       -v Lon -d Lon,0.,180. -d Lon,-180.,-1.0
% ncks -O -H --msa -v Lon -d Lon,0.,180. -d Lon,-180.,-1.0

The two multi-slabs are the same but they can be presented to screen, or to an output file, in either order. The second example shows how to place the western hemisphere after the eastern hemisphere, although they are stored in the opposite order in the input file.

With this background, one sees that the following commands suffice to rotate the input file by 180 degrees longitude:

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

There are other workable, valid methods to accomplish this rotation, yet none are simpler nor more efficient than utilizing MSA user-ordering. Some final comments on applying this algorithm: Be careful to specify hemispheres that do not overlap, e.g., by inadvertently specifying coordinate ranges that both include Greenwich. Some users will find using index-based rather than coordinate-based hyperslabs makes this clearer.

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3.20 Wrapped Coordinates

Availability: ncks
Short options: ‘-d dim,[min][,[max][,[stride]]]
Long options: ‘--dimension dim,[min][,[max][,[stride]]]’,
--dmn dim,[min][,[max][,[stride]]]

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

Assume the domain of the monotonically increasing longitude coordinate lon is 0 < lon < 360. ncks will extract a hyperslab which crosses the Greenwich meridian simply by specifying the westernmost longitude as min and the easternmost longitude as max. The following commands extract a hyperslab containing the Saharan desert:

ncks -d lon,340.,50.
ncks -d lon,340.,50. -d lat,10.,35.

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,, 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 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).

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3.21 Auxiliary Coordinates

Availability: ncbo, nces, ncecat, ncflint, ncks, ncpdq, ncra, ncrcat
Short options: ‘-X lon_min,lon_max,lat_min,lat_max
Long options: ‘--auxiliary lon_min,lon_max,lat_min,lat_max

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 here. 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.

A 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/
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 \ 
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/
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

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.

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.
ncra -X 0,180,-30,30

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.

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3.22 UDUnits Support

Availability: ncbo, nces, ncecat, ncflint, ncks, ncpdq, ncra, ncrcat, ncwa
Short options: ‘-d dim,[min][,[max][,[stride]]]
Long options: ‘--dimension dim,[min][,[max][,[stride]]]’,
--dmn dim,[min][,[max][,[stride]]]

There is more than one way to hyperskin a cat. The UDUnits 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. First, consider extraction of a variable containing non-record coordinates with physical dimensions stored in MKS units. In the following example, the user extracts all wavelengths in the visible portion of the spectrum in terms of the units very frequently used in visible spectroscopy, microns:

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

The hyperslab returns the correct values because the wvl variable is stored on disk with a length dimension that UDUnits recognizes in the units attribute. The automagical algorithm that implements this functionality is worth describing since understanding it helps one avoid some potential pitfalls. First, the user includes the physical units of the hyperslab dimensions she supplies, separated by a simple space from the numerical values of the hyperslab limits. She encloses each coordinate specifications in quotes so that the shell does not break the value-space-unit string into separate arguments before passing them to NCO. Double quotes ("foo") or single quotes ('foo') are equally valid for this purpose. Second, NCO recognizes that units translation is requested because each hyperslab argument contains text characters and non-initial spaces. Third, NCO determines whether the wvl is dimensioned with a coordinate variable that has a units attribute. In this case, wvl itself is a coordinate variable. The value of its units attribute is meter. Thus wvl passes this test so UDUnits conversion is attempted. If the coordinate associated with the variable does not contain a units attribute, then NCO aborts. Fourth, NCO passes the specified and desired dimension strings (microns are specified by the user, meters are required by NCO) to the UDUnits library. Fifth, the UDUnits library that these dimension are commensurate and it returns the appropriate linear scaling factors to convert from microns to meters to NCO. If the units are incommensurate (i.e., not expressible in the same fundamental MKS units), or are not listed in the UDUnits database, then NCO aborts since it cannot determine the user’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.

The 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"
time_udunits[1]=876018 hours since 1900-01-01 00:00:0.0

Here, 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" \

Note that there is no excess whitespace before or after the individual elements of the ‘-d’ argument. This is important since, as far as the shell knows, ‘-d’ takes only one command-line argument. Parsing this argument into its component dim,[min][,[max][,[stride]]] elements (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 UDUnits2 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.

# UDUnits1
export UDUNITS_PATH='/unusual/location/share/udunits'
# UDUnits2
export UDUNITS2_XML_PATH='/unusual/location/share/udunits/udunits2.xml'

This run-time flexibility can enable the full functionality of pre-built binaries on machines with libraries in different locations.

The UDUnits package documentation describes the supported formats of time dimensions. Among the metadata conventions that adhere to these formats are the Climate and Forecast (CF) Conventions and the Cooperative Ocean/Atmosphere Research Data Service (COARDS) Conventions. The following ‘-d arguments’ extract the same data using commonly encountered time dimension formats:

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

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

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

Hence, use the YYYY-MM-DD format rather than YYYYMMDD for dates.

As of version 4.0.0 (January, 2010), NCO supports some calendar attributes specified by the CF conventions.

Supported types:

"365_day"/"noleap", "360_day", "gregorian", "standard"

Unsupported types:


Unsupported types default to mixed Gregorian/Julian as defined by UDUnits.

An Example: Consider the following netCDF variable

  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" ;
  lon_cal = 1,2,3,4,5,6,7,8,9,10;

ncks -v lon_cal -d lon_cal,'1964-3-1 0:00:0.0','1964-3-4 00:00:0.0'’ results in lon_cal=1,2,3,4.

netCDF 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).

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3.23 Rebasing Time Coordinate

Availability: ncra, ncrcat Short options: None

Time rebasing is invoked when numerous files share a common record coordinate, and the record coordinate units change among input files. The rebasing is performed automatically if and only if UDUnits is installed. Usually rebasing occurs when the recoordinate is a time-based variable, and times are recorded in units of a time-since-basetime, and the basetime changes from file to file. Since the output file can have only one unit (i.e., one basetime) for the record coordinate, NCO, in such cases, chooses the units of the first input file to be the units of the output file. It is necessary to “rebase” all the input record variables to this output time unit in order for the output file to have the correct values.

For example suppose the time coordinate is in hours and each day in January is stored in its own daily file. Each daily file records the temperature variable tpt(time) with an (unadjusted) time coordinate value between 0–23 hours, and uses the units attribute to advance the base time: time:units="hours since 1990-1-1" time:units="hours since 1990-1-2"   
... time:units="hours since 1990-1-31"   
// Mean noontime temperature in January
ncra -v tpt -d time,"1990-1-1 12:00:00","1990-1-31 23:59:59",24 \

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

// Results: time is "re-based" to the time units in ""
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" \ file03    

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

As of NCO version 4.2.1 (August, 2012), NCO automatically rebases not only the record coordinate (time, here) but also any bounds associated with the record coordinate (e.g., time_bnds) (see CF Conventions).

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3.24 Multiple Record Dimensions

Availability: ncecat, ncpdq Short options: None
Long options: ‘--mrd

The netCDF3 file format allows only one record dimension, and that dimension must be the first dimension (i.e., the least rapidly varying dimension) of any variable in which it appears. This imposes certain rules on how operators must perform operations that alter the ordering of dimensions or the number of record variables. The netCDF4 file format has no such restrictions. Files and variables may have any number of record dimensions in any order. This additional flexibility of netCDF4 can only be realized by selectively abandoning the constraints that would make operations behave completely consistently between netCDF3 and netCDF4 files.

NCO chooses, by default, to impose netCDF3-based constraints on netCDF4 files. This reduces the number of unanticipated consequences and keeps the operators functioning in a familiar way. Put another way, NCO limits production of additional record dimensions so processing netCDF4 files leads to the same results as processing netCDF4 files. Users can override this default with the ‘--mrd’ (or ‘--multiple_record_dimension’) switch, which enables netCDF4 variables to accumulate additional record dimensions.

How can additional record dimensions be produced? Most commonly ncecat (in record-aggregate mode) defines a new leading record dimension. In netCDF4 files this becomes an additional record dimension unless the original record dimension is changed to a fixed dimension (as must be done in netCDF3 files). Also when ncpdq reorders dimensions it can preserve the “record” property of record variables. ncpdq tries to define as a record dimension whichever dimension ends up first in a record variable, and, in netCDF4 files, this becomes an additional record dimension unless the original record dimension is changed to a fixed dimension (as must be done in netCDF3 files). It it easier if ncpdq and ncecat do not increase the number of record dimensions in a variable so that is the default. Use ‘--mrd’ to override this.

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3.25 Missing values

Availability: ncap2, ncbo, nces, ncflint, ncpdq, ncra, ncwa
Short options: None

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. The NCO arithmetic operators attempt to handle missing data in an intelligent fashion. There are four steps in the NCO treatment of missing data:

  1. 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 35.

    It has been and remains most advisable to create both _FillValue and missing_value attributes with identical values in datasets. Many legacy datasets contain only missing_value attributes. NCO can help migrating datasets between these conventions. One may use ncrename (see ncrename netCDF Renamer) to rename all missing_value attributes to _FillValue:

    ncrename -a .missing_value,_FillValue

    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
  2. 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. If not, then NCO converts _FillValue from att_type to var_type by using the implicit conversion rules of C, or, if att_type is NC_CHAR 36, by typecasting the results of the C function strtod(_FillValue). You 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).

  3. Identifying missing data during arithmetic operations.

    When an NCO arithmetic operator processes a variable var with a _FillValue attribute, it compares each value of var to _FillValue before performing an operation. Note the _FillValue comparison imposes a performance penalty on the operator. Arithmetic processing of variables which contain the _FillValue attribute always incurs this penalty, even when none of the data are missing. Conversely, arithmetic processing of variables which do not contain the _FillValue attribute never incurs this penalty. In other words, do not attach a _FillValue attribute to a variable which does not contain missing data. This exhortation can usually be obeyed for model generated data, but it may be harder to know in advance whether all observational data will be valid or not.

  4. Treatment of any data identified as missing in arithmetic operators.

    NCO averagers (ncra, nces, ncwa) do not count any element with the value _FillValue towards the average. ncbo and ncflint define a _FillValue result when either of the input values is a _FillValue. Sometimes the _FillValue may change from file to file in a multi-file operator, e.g., ncra. NCO is written to account for this (it always compares a variable to the _FillValue assigned to that variable in the current file). Suffice it to say that, in all known cases, NCO does “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.

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3.26 Chunking

Availability: ncap2, ncbo, nces, ncecat, ncflint, ncks, ncpdq, ncra, ncrcat, ncwa
Short options: none
Long options: ‘--cnk_byt cnk_sz’, ‘--chunk_byte cnk_sz
--cnk_dmn dmn_nm,cnk_sz’, ‘--chunk_dimension dmn_nm,cnk_sz
, ‘--cnk_map cnk_map’, ‘--chunk_map cnk_map’,
--cnk_min var_sz’, ‘--chunk_min var_sz’,
--cnk_plc cnk_plc’, ‘--chunk_policy cnk_plc’,
--cnk_scl cnk_sz’, ‘--chunk_scalar cnk_sz

All netCDF4-enabled NCO operators that define variables support a plethora of chunksize options. Chunking can significantly accelerate or degrade read/write access to large datasets. Dataset chunking issues are described by THG and Unidata here, here, and here. NCO authors are working on generalized algorithms and applications of chunking strategies (stay tuned for more in 2015).

The NCO chunking implementation is designed to be flexible. Users control four aspects of the chunking implementation. These are the chunking policy, chunking map, chunksize, and minimum chunksize. The chunking policy determines which variables to chunk, and the chunking map determines how (with what exact sizes) to chunk those variables. These are high-level mechanisms that apply to an entire file and all variables and dimensions. The chunksize option allows per-dimension specification of sizes that will override the selected (or default) chunking map.

The var_sz parameter is the minimum size in bytes (not elements) of variables to chunk. This threshold is intended to restrict use of chunking to variables for which it is efficient. By default this minimum variable size for chunking is twice the system blocksize (when available) and is 8192 bytes otherwise. Users may set this to any value with the ‘--cnk_min’ switch.

The chunking implementation is similar to a hybrid of the ncpdq packing policies (see ncpdq netCDF Permute Dimensions Quickly) and hyperslab specifications (see Hyperslabs). Each aspect is intended to have a sensible default, so that many users only need to set one switch to obtain sensible chunking. Power users can tune chunking with the three switches in tandem to obtain optimal performance.

By default, NCO preserves the chunking characteristics of the input file in the output file 37. In other words, preserving chunking requires no switches or user intervention.

Users specify the desired chunking policy with the ‘-P’ switch (or its long option equivalents, ‘--cnk_plc’ and ‘--chunk_policy’) and its cnk_plc argument. As of August, 2014, six chunking policies are implemented:

Chunk All Variables

Definition: Chunk all variables possible. For obvious reasons, scalar variables cannot be chunked.
Alternate invocation: ncchunk
cnk_plc key values: ‘all’, ‘cnk_all’, ‘plc_all
Mnemonic: All

Chunk Variables with at least Two Dimensions [default]

Definition: Chunk all variables possible with at least two dimensions
Alternate invocation: none
cnk_plc key values: ‘g2d’, ‘cnk_g2d’, ‘plc_g2d
Mnemonic: Greater than or equal to 2 Dimensions

Chunk Variables with at least Three Dimensions

Definition: Chunk all variables possible with at least three dimensions
Alternate invocation: none
cnk_plc key values: ‘g3d’, ‘cnk_g3d’, ‘plc_g3d
Mnemonic: Greater than or equal to 3 Dimensions

Chunk One-Dimensional Record Variables

Definition: Chunk all 1-D record variables
Alternate invocation: none
Any specified (with ‘--cnk_dmn’) record dimension chunksizes will be applied only to 1-D record variables (and to no other variables). Other dimensions may be chunked with their own ‘--cnk_dmn’ options that will apply to all variables. cnk_plc key values: ‘r1d’, ‘cnk_r1d’, ‘plc_r1d
Mnemonic: Record 1-D variables

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
cnk_plc key values: ‘xpl’, ‘cnk_xpl’, ‘plc_xpl
Mnemonic: EXPLicitly specified dimensions

Chunk Variables that are already Chunked

Definition: Chunk only variables that are already chunked in the input file. When used in conjunction with ‘cnk_map=xst’ this option preserves and copies the chunking parameters from the input to the output file. Alternate invocation: none
cnk_plc key values: ‘xst’, ‘cnk_xst’, ‘plc_xst
Mnemonic: EXiSTing chunked variables

Chunk Variables with NCO recommendations

Definition: Chunk all variables according to NCO best practices. This is a virtual option that ensures the chunking policy is (in the subjective opinion of the authors) the best policy for typical usage. As of NCO version 4.4.8 (February, 2015), this virtual policy implements ‘map_rew’ for 3-D variables and ‘map_lfp’ for all other variables.
Alternate invocation: none
cnk_plc key values: ‘nco’, ‘cnk_nco’, ‘plc_nco
Mnemonic: NetCDFOperator


Definition: Unchunk all variables possible. The HDF5 storge layer requires that record variables (i.e., variables that contain at least one record dimension) must be chunked. Also variables that are compressed or use checksums must be chunked. Such variables cannot be unchunked.
Alternate invocation: ncunchunk
cnk_plc key values: ‘uck’, ‘cnk_uck’, ‘plc_uck’, ‘unchunk
Mnemonic: UnChunK

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.

The chunking algorithms must know the chunksizes of each dimension of each variable to be chunked. The correspondence between the input variable shape and the chunksizes is called the chunking map. The user specifies the desired chunking map with the ‘-M’ switch (or its long option equivalents, ‘--cnk_map’ and ‘--chunk_map’) and its cnk_map argument. Eight chunking maps are currently implemented:

Chunksize Equals Dimension Size [default]

Definition: Chunksize defaults to dimension size. Explicitly specify chunksizes for particular dimensions with ‘--cnk_dmn’ option.
cnk_map key values: ‘dmn’, ‘cnk_dmn’, ‘map_dmn
Mnemonic: DiMeNsion

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.
cnk_map key values: ‘rd1’, ‘cnk_rd1’, ‘map_rd1
Mnemonic: Record Dimension size 1

Chunksize Equals Scalar Size Specified

Definition: Chunksize for all dimensions is set with the ‘--cnk_scl’ option.
cnk_map key values: ‘xpl’, ‘cnk_xpl’, ‘map_xpl
Mnemonic: EXPLicitly specified dimensions

Chunksize Product Matches Scalar Size Specified

Definition: The product of the chunksizes for each variable matches (approximately equals) the size specified with the ‘--cnk_scl’ option. A dimension of size one is said to be degenerate. For a variable of rank R (i.e., with R non-degenerate dimensions), the chunksize in each non-degenerate dimension is the Rth root of cnk_scl.
cnk_map key values: ‘prd’, ‘cnk_prd’, ‘map_prd
Mnemonic: PRoDuct

Chunksize Lefter Product Matches Scalar Size Specified

Definition: The product of the chunksizes for each variable (approximately) equals the size specified with the ‘--cnk_scl’ option. This is accomplished by using dimension sizes as chunksizes for the rightmost (most rapidly varying) dimensions, and then “flexing” the chunksize of the leftmost (least rapidly varying) dimensions such that the product of all chunksizes matches the specified size. All dimensions to the left of and including the first record dimension define the left-hand side. This map was first proposed by Chris Barker.
cnk_map key values: ‘lfp’, ‘cnk_lfp’, ‘map_lfp
Mnemonic: LeFter Product

Chunksize Equals Existing Chunksize

Definition: Chunksizes are copied from the input to the output file for every variable that is chunked in the input file. Variables not chunked in the input file will be chunked with default mappings.
cnk_map key values: ‘xst’, ‘cnk_xst’, ‘map_xst
Mnemonic: EXiST

Chunksize Balances 1D and (N-1)-D Access to N-D Variable

Definition: Chunksizes are chosen so that 1-D and ((N-1))-D hyperslabs of 3-D variables (e.g., point-timeseries orn latitude/longitude surfaces of 3-D fields) both require approximately the number of chunks. Hence their access time should be balanced. Russ Rew explains the motivation and derivation for this strategy here.
cnk_map key values: ‘rew’, ‘cnk_rew’, ‘map_rew
Mnemonic: Russ REW

Chunksizes use netCDF4 defaults

Definition: Chunksizes are determined by the underlying netCDF library. All variables selected by the current chunking policy have their chunksizes determined by netCDF library defaults. The default algorithm netCDF uses to determine chunksizes has changed through the years, and thus depends on the netCDF library version. This map can be used to reset (portions of) previously chunked files to default chunking values.
cnk_map key values: ‘nc4’, ‘cnk_nc4’, ‘map_nc4
Mnemonic: NetCDF4

Chunksizes use NCO recommendations

Definition: Chunksizes are determined by the currently recommended NCO map. This is a virtual option that ensures the chunking map is (in the subjective opinion of the authors) the best map for typical usage. As of NCO version 4.4.8 (February, 2015), this virtual map calls ‘map_rew’ for 3-D variables and ‘map_lfp’ for all others.
cnk_map key values: ‘nco’, ‘cnk_nco’, ‘map_nco
Mnemonic: NetCDFOperator

It is possible to combine the above chunking map algorithms with user-specified per-dimension (though not per-variable) chunksizes that override specific chunksizes determined by the maps above. The user specifies the per-dimension chunksizes with the (equivalent) long options ‘--cnk_dmn’ or ‘--chunk_dimension’). The option takes two comma-separated arguments, dmn_nm,cnk_sz, which are the dimension name and its chunksize, respectively. The ‘--cnk_dmn’ option may be used as many times as necessary.

The default behavior of chunking depends on several factors. As mentioned above, when no chunking options are explicitly specified by the user, then NCO preserves the chunking characteristics of the input file in the output file. This is equivalent to specifying both cnk_plc and cnk_map as “existing”, i.e., ‘--cnk_plc=xst --cnk_map=xst’. If output netCDF4 files are chunked with the default behavior of the netCDF4 library.

When any chunking parameter exceptcnk_plc’ or ‘cnk_map’ is specified (such as ‘cnk_dmn’ or ‘cnk_sz_scl’), then the “existing” policy and map are retained and the output chunksizes are modified where necessary in accord with the user-specified parameter. When ‘cnk_map’ is specified and ‘cnk_plc’ is not, then NCO picks (what it thinks is) the optimal chunking policy. This has always been policy ‘map_g2d’. When ‘cnk_plc’ is specified and ‘cnk_map’ is not, then NCO picks (what it thinks is) the optimal chunking map. This has always been map ‘map_rd1’.

To start afresh and return to netCDF4 chunking defaults, select ‘cnk_map=nc4’.

# Simple chunking and unchunking
ncks -O -4 --cnk_plc=all # Chunk
ncks -O -4 --cnk_plc=unchunk # Unchunk

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

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

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

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

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

ncks --cnk_plc=r1d --cnk_dmn=time,1000.

This sets time chunks to 1000 only in 1-D record variables. Without the ‘r1d’ policy, time chunks would change in all variables.

It is appropriate to conclude by informing users about an aspect of chunking that may not be expected. Three types of variables are always chunked: Record variables, Deflated (compressed) variables, and Checksummed variables. Hence all variables that contain a record dimension are also chunked (since data must be chunked in all dimensions, not just one). Unless otherwise specified by the user, the other (fixed, non-record) dimensions of record variables are assigned default chunk sizes. The HDF5 layer does all this automatically to optimize the on-disk variable/file storage geometry of record variables. Do not be surprised to learn that files created without any explicit instructions to activate chunking nevertheless contain chunked variables.

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3.27 Compression

Availability: ncbo, ncecat, nces, ncflint, ncks, ncpdq, ncra, ncrcat, ncwa
Short options: None
Long options: ‘--ppc var1[,var2[,...]]=prc’,
--precision_preserving_compression var1[,var2[,...]]=prc’,
--quantize var1[,var2[,...]]=prc

NCO implements or accesses four different compression algorithms, the standard lossless DEFLATE algorithm and three lossy compression algorithms. All four algorithms reduce the on-disk size of a dataset while sacrificing no (lossless) or a tolerable amount (lossy) of precision. First, NCO can access the lossless DEFLATE algorithm, a combination of Lempel-Ziv encoding and Huffman coding, algorithm on any netCDF4 dataset (see Deflation). Because it is lossless, this algorithm re-inflates deflated data to their full original precision. This algorithm is accessed via the HDF5 library layer (which itself calls the zlib library also used by gzip), and is unavailable with netCDF3.

The three lossy compression algorithms are Packing (see Packed data), and two precision-preserving algorithms. Packing quantizes data of a higher precision type into a lower precision type (often NC_SHORT) that thus stores a fewer (though constant) number of bytes per value. Packed data almost unpacks into a (much) smaller dynamic range than the floating-point data can represent. The type-conversion and reduced dynamic range of the data allows packing to eliminate bits typically used to store an exponent, thus improving its packing efficiency. Packed data also can also be deflated for additional space savings.

A limitation of packing is that unpacking data stored as integers into the linear range defined by scale_factor and add_offset rapidly loses precision outside of a narrow range of floating point values. Variables packed as NC_SHORT, for example, can represent only about 64000 discrete values in the range -32768*scale_factor+add_offset to 32767*scale_factor+add_offset. The precision of packed data equals the value of scale_factor, and scale_factor is usually chosen to span the range of valid data, not to represent the intrinsic precision of the variable. In other words, the precision of packed data cannot be specified in advance because it depends on the range of values to quantize.

NCO implemented the final two lossy compression algorithms in version 4.4.8 (February, 2015). These are both Precision-Preserving Compression (PPC) algorithms and since standard terminology for precision is remarkably imprecise, so is our nomenclature. The operational definition of “significant digit” in our precision preserving algorithms is that the exact value, before rounding or quantization, is within one-half the value of the decimal place occupied by the Least Significant Digit (LSD) of the rounded value. For example, the value pi = 3.14 correctly represents the exact mathematical constant pi to three significant digits because the LSD of the rounded value (i.e., 4) is in the one-hundredths digit place, and the difference between the exact value and the rounded value is less than one-half of one one-hundredth, i.e., (3.14159265358979323844 - 3.14 = 0.00159 < 0.005).

One PPC algorithm preserves the specified total Number of Signifcant Digits (NSD) of the value. For example there is only one significant digit in the weight of most “eight-hundred pound gorillas” that you will encounter, i.e., so nsd=1. This is the most straightforward measure of precision, and thus NSD is the default PPC algorithm.

The other PPC algorithm preserves the number of Decimal Significant Digits (DSD), i.e., the number of significant digits following (positive, by convention) or preceding (negative) the decimal point. For example, “0.008” and “800” have, respectively, three and negative two digits digits following the decimal point, corresponding dsd=3 and dsd=-2.

The only justifiable NSD for a given value depends on intrinsic accuracy and error characteristics of the model or measurements, and not on the units with which the value is stored. The appropriate DSD for a given value depends on these intrinsic characteristics and, in addition, the units of storage. This is the fundamental difference between the NSD and DSD approaches. The eight-hundred pound gorilla always has nsd=1 regardless of whether the value is store in pounds or in some other unit. DSD corresponding to this weight is dsd=-2 if the value is store in pounds, dsd=4 if stored in megapounds.

Users may wish to express the precision to be preserved as either NSD or DSD. Invoke PPC with the long option ‘--ppc var=prc’, or give the same arguments to the synonyms ‘--precision_preserving_compression’, or to ‘--quantize’. Here var is the variable to quantize, and prc is the precision. The default algorithm assumes prc specifies NSD precision, e.g., ‘T=2’ means nsd=2. Prepend prc with a decimal point to specify DSD precision, e.g., ‘T=.2’ means dsd=2. NSD precision must be specified as a positive integer. DSD precision may be a positive or negative integer; and is specified as the negative base 10 logarithm of the desired precision, in accord with common usage. For example, specifying ‘T=.3’ or ‘T=.-2’ tells the DSD algorithm to store only enough bits to preserve the value of T rounded to the nearest thousandth or hundred, respectively.

Setting var to default has the special meaning of applying the associated NSD or DSD algorithm to all floating point variables except coordinate variables. Variables not affected by default include integer and non-numeric atomic types, coordinates, and variables mentioned in the coordinates or bounds attribute of any variable. NCO applies PPC to coordinate variables only if those variables are explicitly specified (i.e., not with the ‘default=prc’ mechanism. NCO applies PPC to integer-type variables only if those variables are explicitly specified (i.e., not with the ‘default=prc’, and only if the DSD algorithm is invoked with a negative prc. To prevent PPC from applying to certain non-coordinate variables (e.g., gridcell_area or gaussian_weight), explicitly specify a precision exceeding 7 (for NC_FLOAT) or 15 (for NC_DOUBLE) for those variables. Since these are the maximum representable precisions in decimal digits, NCO turns-off PPC (i.e., does nothing) when more precision is requested.

The time penalty for compressing and uncompressing data varies according to the algorithm. The Number of Significant Digit (NSD) algorithm quantizes by bitmasking, and employs no floating point math. The Decimal Significant Digit (DSD) algorithm quantizes by rounding, which does require floating point math. Hence NSD is likely faster than DSD, though the difference has not been measured. NSD creates a bitmask to alter the significand of IEEE 754 floating point data. The bitmask is one for all bits to be retained and zero or one for all bits to be ignored. The algorithm assumes that the number of binary digits (i.e., bits) necessary to represent a single base-10 digit is ln(10)/ln(2) = 3.32. The exact numbers of bits Nbit retained for single and double precision values are ceil(3.32*nsd)+1 and ceil(3.32*nsd)+2, respectively. Once these reach 23 and 53, respectively, bitmasking is completely ineffective. This occurs at nsd=6.3 and 15.4, respectively.

The DSD algorithm, by contrast, uses rounding to remove undesired precision. The rounding 38 zeroes the greatest number of significand bits consistent with the desired precision.

To demonstrate the change in IEEE representation caused by PPC rounding algorithms, consider again the case of pi, represented as an NC_FLOAT. The IEEE 754 single precision representations of the exact value (3.141592...), the value with only three significant digits treated as exact (3.140000...), and the value as stored (3.140625) after PPC-rounding with either the NSD (prc=3) or DSD (prc=2) algorithm are, respectively,

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

The string of trailing zero-bits in the rounded values facilitates byte-stream compression. Note that the NSD and DSD algorithms do not always produce results that are bit-for-bit identical, although they do in this particular case.

Reducing the preserved precision of NSD-rounding produces increasingly long strings of identical-bits amenable to compression:

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

The consumption of about 3 bits per digit of base-10 precision is evident, as is the coincidence of a quantized value that greatly exceeds the mandated precision for NSD = 2. Although the NSD algorithm generally masks some bits for all nsd <= 7 (for NC_FLOAT), compression algorithms like DEFLATE may need byte-size-or-greater (i.e., at least eight-bit) bit patterns before their algorithms can take advantage of of encoding such patterns for compression. Do not expect significantly enhanced compression from nsd > 5 (for NC_FLOAT) or nsd > 14 (for NC_DOUBLE). Clearly values stored as NC_DOUBLE (i.e., eight-bytes) are susceptible to much greater compression than NC_FLOAT for a given precision because their significands explicitly contain 53 bits rather than 23 bits.

Maintaining non-biased statistical properties during lossy compression requires special attention. The DSD algorithm uses rint(), which rounds toward the nearest even integer. Thus DSD has no systematic bias. However, the NSD algorithm uses a bitmask technique susceptible to statistical bias. Zeroing all non-significant bits is guaranteed to produce numbers quantized to the specified tolerance, i.e., half of the decimal value of the position occupied by the LSD. However, always zeroing the non-significant bits results in quantized numbers that never exceed the exact number. This would produce a negative bias in statistical quantities (e.g., the average) subsequently derived from the quantized numbers. To avoid this bias, NSD rounds non-significant bits down (to zero) or up (to one) in an alternating fashion when processing array data. In general, the first element is rounded down, the second up, and so on. This results in a mean bias quite close to zero. The only exception is that the floating point value of zero is never quantized upwards. For simplicity, NSD always rounds scalars downwards.

Although NSD or DSD are different algorithms under the hood, they both replace the (unwanted) least siginificant bits of the IEEE significand with a string of consecutive zeroes. Byte-stream compression techniques, such as the gzip DEFLATE algorithm compression available in HDF5, always compress zero-strings more efficiently than random digits. The net result is netCDF files that utilize compression can be significantly reduced in size. This feature only works when the data are compressed, either internally (by netCDF) or externally (by another user-supplied mechanism). It is most straightfoward to compress data internally using the built-in compression and decompression supported by netCDF4. For convenience, NCO automatically activates file-wide Lempel-Ziv deflation (see Deflation) level one (i.e., ‘-L 1’) when PPC is invoked on any variable in a netCDF4 output file. This makes PPC easier to use effectively, since the user need not explicitly specify deflation. Any explicitly specified deflation (including no deflation, ‘-L 0’) will override the PPC deflation default. If the output file is a netCDF3 format, NCO will emit a message suggesting internal netCDF4 or external netCDF3 compression. netCDF3 files compressed by an external utility such as gzip accrue approximately the same benefits (shrinkage) as netCDF4, although with netCDF3 the user or provider must uncompress (e.g., gunzip) the file before accessing the data. There is no benefit to rounding numbers and storing them in netCDF3 files unless such custom compression/decompression is employed. Without that, one may as well maintain the undesired precision.

The user accesses PPC through a single switch, ‘--ppc’, repeated as many times as necessary. To apply the NSD algorithm to variable u use, e.g.,

ncks -7 --ppc u=2

The output file will preserve only two significant digits of u. The options ‘-4’ or ‘-7’ ensure a netCDF4-format output (regardless of the input file format) to support internal compression. It is recommended though not required to write netCDF4 files after PPC. For clarity the ‘-4/-7’ switches are omitted in subsequent examples. NCO attaches attributes that indicate the algorithm used and degree of precision retained for each variable affected by PPC. The NSD and DSD algorithms store the attributes number_of_significant_digits and least_significant_digit 39, respectively.

It is safe to attempt PPC on input that has already been rounded. Variables can be made rounder, not sharper, i.e., variables cannot be “un-rounded”. Thus PPC attempted on an input variable with an existing PPC attribute proceeds only if the new rounding level exceeds the old, otherwise no new rounding occurs (i.e., a “no-op”), and the original PPC attribute is retained rather than replaced with the newer value of prc.

To request, say, five significant digits (nsd=5) for all fields, except, say, wind speeds which are only known to integer values (dsd=0) in the supplied units, requires ‘--ppc’ twice:

ncks -4 --ppc default=5 --ppc u,v=.0

To preserve five digits in all variables except coordinate variables and u and v, use the ‘default’ option and separately specify the exceptions:

ncks --ppc default=5 --ppc u,v=20

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

ncks --ppc p,w,z=5 --ppc q,RH=4 --ppc T,u,v=3

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

ncks --ppc Q.?=5 --ppc FS.?,FL.?=4 --ppc RH=.3

Although PPC-rounding instantly reduces data precision, on-disk storage reduction only occurs once the data are compressed.

How can one be sure the lossy data are sufficiently precise? PPC preserves all significant digits of every value. The DSD algorithm uses floating point math to round each value optimally so that it has the maximum number of zeroed bits that preserve the specified precision. The NSD algorithm uses a theoretical approach (3.2 bits per base-10 digit), tuned and tested to ensure the worst case quantization error is less than half the value of the minimum increment in the least significant digit.

Note for HTML users:
The definition of error metrics relies heavily on mathematical expressions which cannot easily be represented in HTML. See the printed manual for much more detailed and complete documentation of this subject.

All three metrics are expressed in terms of the fraction of the ten’s place occupied by the LSD. If the LSD is the hundreds digit or the thousandths digit, then the metrics are fractions of 100, or of 1/100, respectively. PPC algorithms should produce maximum absolute errors no greater than 0.5 in these units. If the LSD is the hundreds digit, then quantized versions of true values will be within fifty of the true value. It is much easier to satisfy this tolerance for a true value of 100 (only 50% accuracy required) than for 999 (5% accuracy required). Thus the minimum accuracy guaranteed for nsd=1 ranges from 5–50%. For this reason, the best and worst cast performance usually occurs for true values whose LSD value is close to one and nine, respectively. Of course most users prefer prc > 1 because accuracies increase exponentially with prc. Continuing the previous example to prc=2, quantized versions of true values from 1000–9999 will also be within 50 of the true value, i.e., have accuracies from 0.5–5%. In other words, only two significant digits are necessary to guarantee better than 5% accuracy in quantization. We recommend that dataset producers and users consider quantizing datasets with nsd=3. This guarantees accuracy of 0.05–0.5% for individual values. Statistics computed from ensembles of quantized values will, assuming the mean error Emean is small, have much better accuracy than 0.5%. This accuracy is the most that can be justified for many applications.

To demonstrate these principles we conduct error analyses on an artificial, reproducible dataset, and on an actual dataset of observational analysis values. 40 The table summarizes quantization accuracy based on the three metrics.


Number of Significant Digits.


Maximum absolute error.


Mean absolute error.


Mean error.

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

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

All results show that PPC quantization performs as expected. Absolute maximum errors Emabs < 0.5 for all prc. For 1 <= prc <= 6, quantization results in comparable maximum absolute and mean absolute errors Emabs and Emebs, respectively. Mean errors Emean are orders of magnitude smaller because quantization produces over- and under-estimated values in balance. When prc=7, quantization of single-precision values is ineffective, because all available bits are used to represent the maximum precision of seven digits. The maximum and mean absolute errors Emabs and Emebs are nearly identical across algorithms, precisions, and dataset types. This is consistent with both the artificial data and empirical data being random, and thus exercising equally strengths and weaknesses of the algorithms over the course of millions of input values. We generated artificial arrays with many different starting values and interval spacing and all gave qualitatively similar results. The results presented are the worst obtained.

The artificial data has much smaller mean error Emean than the observational analysis. The reason why is unclear. It may be because the temperature field is concentrated in particular ranges of values (and associated quantization errors) prevalent on Earth, e.g., 200 < T < 320. It is worth noting that the mean error Emean < 0.01 for 1 <= prc < 6, and that Emean is typically at least two or more orders of magnitude less than Emabs. Thus quantized values with precisions as low as prc=1 still yield highly significant statistics by contemporary scientific standards.

Testing shows that PPC quantization enhances compression of typical climate datasets. The degree of enhancement depends, of course, on the required precision. Model results are often computed as NC_DOUBLE then archived as NC_FLOAT to save space. This table summarizes the performance of lossless and lossy compression on two typical, or at least random, netCDF data files. The files were taken from representative model-simulated and satellite-retrieved datasets. Only floating point data were compressed. No attempt was made to compress integer-type variables as they occupy an insignificant fraction of every dataset. The columns are


File-type: N3 for netCDF CLASSIC, N4 for NETCDF4, N7 for NETCDF4_CLASSIC (which comprises netCDF3 data types and structures with netCDF4 storage features like compression), H4 for HDF4, and H5 for HDF5. N4/7 means results apply to both N4 and N7 filetypes.


Type of lossless compression employed, if any. Bare numbers refer to the strength of the DEFLATE algorithm employed internally by netCDF4/HDF5, while numbers prefixed with B refer to the block size employed by the Burrows-Wheeler algorithm in bzip2.


Number of significant digits retained by the precision-preserving compression NSD algorithm.


Y if the default ncpdq packing algorithm (convert floating point types to NC_SHORT) was employed.


Resulting filesize in MB.


Compression ratio, i.e., resulting filesize relative to original size, in percent. In some cases the original files is already losslessly compressed. The compression ratios reported are relative to the size of the original file as distributed, not as optimally losslessly compressed.

A dash (-) indicates the associated compression feature was not employed.

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

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

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

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

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

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

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3.28 Deflation

Availability: ncap2, ncbo, nces, ncecat, ncflint, ncks, ncpdq, ncra, ncrcat, ncwa
Short options: ‘-L
Long options: ‘--dfl_lvl’, ‘--deflate

All NCO operators that define variables support the netCDF4 feature of storing variables compressed with the lossless DEFLATE compression algorithm. DEFLATE combines the Lempel-Ziv encoding with Huffman coding. The specific version used by netCDF4/HDF5 is that implemented in the zlib library used by gzip. Activate deflation with the -L dfl_lvl short option (or with the same argument to the ‘--dfl_lvl’ or ‘--deflate’ long options). Specify the deflation level dfl_lvl on a scale from no deflation (dfl_lvl = 0) to maximum deflation (dfl_lvl = 9). Under the hood, this selects the compression blocksize. Minimal deflation (dfl_lvl = 1) achieves considerable storage compression with little time penalty. Higher deflation levels require more time for compression. File sizes resulting from minimal (dfl_lvl = 1) and maximal (dfl_lvl = 9) deflation levels typically differ by less than 10% in size.

To compress an entire file using deflation, use

ncks -4 -L 0 # No deflation (fast, no time penalty)
ncks -4 -L 1 # Minimal deflation (little time penalty)
ncks -4 -L 9 # 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 # Minimal deflation (~30-60% compression)
ncks  -4 -L 9 # Maximal deflation (~31-63% compression)
ncpdq # Standard packing  (~50% compression)
ncpdq -4 -L 9 # Deflated packing  (~80% compression)

ncks prints deflation parameters, if any, to screen (see ncks netCDF Kitchen Sink).

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3.29 MD5 digests

Availability: ncecat, ncks, ncrcat
Short options:
Long options: ‘--md5_dgs’, ‘--md5_digest’, ‘--md5_wrt_att’, ‘--md5_write_attribute

As of NCO version 4.1.0 (April, 2012), NCO supports data integrity verification using the MD5 digest algorithm. This support is currently implemented in ncks and in the multifile concantenators ncecat and ncrcat. Activate it with the ‘--md5_dgs’ or ‘--md5_digest’ long options. As of NCO version 4.3.3 (July, 2013), NCO will write the MD5 digest of each variable as an NC_CHAR attribute named MD5. This support is currently implemented in ncks and in the multifile concantenators ncecat and ncrcat. Activate it with the ‘--md5_wrt_att’ or ‘--md5_write_attribute’ long options.

The behavior and verbosity of the MD5 digest is operator-dependent. 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 only 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 for 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/
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/
ncks: INFO MD5(md5_a) = 0cc175b9c0f1b6a831c399e269772661
md5_a = 'a' 
ncks: INFO MD5(md5_abc) = 0cc175b9c0f1b6a831c399e269772661
lev[0]=100 md5_abc[0--0]='a' 

In fact these examples demonstrate the validity of the hash algorithm since the MD5 hashes of the strings “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 act only on variable data, not on metadata.

When activating MD5 digests with ncecat or ncrcat it is assumed that the user wishes to verify that every variable written to disk has the same MD5 digest as when it is subsequently read from disk. This incurs the major additional overhead of reading in each variable after it is written and performing the hash algorithm again on that to compare to the original hash. Moreover, it is assumed that such operations are generally done “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 ~/ | 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 ~/ | grep MD5
ncrcat: INFO MD5(wnd_spd) = 74699bb0a72b7f16456badb2c995f1a1
ncrcat: INFO MD5 digests of RAM and disk contents for wnd_spd agree

Regardless of the debugging level, an error is returned when the digests of the variable read from the source file and from the output file disagree.

These rules are evolving and as NCO pays more attention to data integrity. We welcome feedback and suggestions from users.

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3.30 Buffer sizes

Availability: All operators
Short options:
Long options: ‘--bfr_sz_hnt’, ‘--buffer_size_hint

As of NCO version 4.2.0 (May, 2012), NCO allows the user to request specific buffer sizes to allocate for reading and writing files. This buffer size determines how many system calls the netCDF layer must invoke to read and write files. By default, netCDF uses the preferred I/O block size returned as the ‘st_blksize’ member of the ‘stat’ structure returned by the stat() system call 41. Otherwise, netCDF uses twice the system pagesize. Larger sizes can increase access speed by reducing the number of system calls netCDF makes to read/write data from/to disk. Because netCDF cannot guarantee the buffer size request will be met, the actual buffer size granted by the system is printed as an INFO statement.

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

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3.31 RAM disks

Availability: All operators
Short options:
Long options: ‘--ram_all’, ‘--create_ram’, ‘--open_ram’, ‘--diskless_all

As of NCO version 4.2.1 (August, 2012), NCO supports the use of diskless files, aka RAM disks, for file access and creation. Two independent switches, ‘--open_ram’ and ‘--create_ram’, control this feature. Before describing the specifics of these switches, we describe why many NCO operations will not benefit from them. Essentially, reading/writing from/to RAM rather than disk only hastens the task when reads/writes to disk are avoided. Most NCO operations are simple enough that they require a single read-from/write-to disk for every block of input/output. Diskless access does not change this, but it does add an extra read-from/write-to RAM. However this extra RAM write/read does avoid contention for limited system resources like disk-head access. Operators which may benefit from RAM disks include ncwa, which may need to read weighting variables multiple times, the multi-file operators ncra, ncrcat, and ncecat, which may try to write output at least once per input file, and ncap2 scripts which may be arbitrarily long and convoluted.

The ‘--open_ram’ switch causes input files to copied to RAM when opened. All further metadata and data access occurs in RAM and thus avoids access time delays caused by disk-head movement. Usually input data is read at most once so it is unlikely that requesting input files be stored in RAM will save much time. The likeliest exceptions are files that are accessed numerous times, such as those analyzed extensively analyzed by ncap2.

Invoking ‘--open_ram’, ‘--ram_all’, or ‘--diskless_all’ uses much more system memory. To copy the input file to RAM increases the sustained memory use by exactly the on-disk filesize of the input file, i.e., MS += FT. For large input files this can be a huge memory burden that starves the rest of the NCO analysis of sufficient RAM. To be safe, use ‘--open_ram’, ‘--ram_all’, or ‘--diskless_all’ only on files that are much (say at least a factor of four) smaller than your available system RAM. See Memory Requirements for further details.

The ‘--create_ram’ switch causes output files to be created in RAM, rather than on disk. These files are copied to disk only when closed, i.e., when the operator completes. Creating files in RAM may save time, especially with ncap2 computations that are iterative, e.g., loops, and for multi-file operators that write output every record (timestep) or file. RAM files provide many of the same benefits as RAM variables in such cases (see RAM variables).

Two switches, ‘--ram_all’ and ‘--diskless_all’, are convenient shortcuts for specifying both ‘--create_ram’ and ‘--diskless_ram’. Thus

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

It is straightforward to demonstrate the efficacy of RAM disks. For NASA we constructed a test that employs ncecat an arbitrary number (set to one hundred thousand) of files are all symbolically linked to the same file. Everything is on the local filesystem (not DAP).

# Create symbolic links for benchmark
cd ${DATA}/nco # Do all work here
for idx in {1..99999}; do
  idx_fmt=`printf "%05d" ${idx}`
  /bin/ln -s ${DATA}/nco/ \
# Benchmark time to ncecat one hundred thousand files
time ncecat --create_ram -O -u time -v ts -d Latitude,40.0 \ 
 -d Longitude,-105.0 -p ${DATA}/nco -n 99999,5,1 ~/

Run normally on a laptop in 201303, this completes in 21 seconds. The ‘--create_ram’ reduces the elapsed time to 9 seconds. Some of this speed may be due to using symlinks and caching. However, the efficacy of ‘--create_ram’ is clear. Placing the output file in RAM avoids thousands of disk writes. It is not unreasonable to for NCO to process a million files like this in a few minutes. However, there is no substitute for benchmarking with real files.

A completely independent way to reduce time spent writing files is to refrain from writing temporary output files. This is accomplished with the ‘--no_tmp_fl’ switch (see Temporary Output Files).

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3.32 Packed data

Availability: ncap2, ncbo, nces, ncflint, ncpdq, ncra, ncwa
Short options: None
Long options: ‘--hdf_upk’, ‘--hdf_unpack

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 Algorithm

Packing The standard netCDF packing algorithm (described here) produces data with the same dynamic range as the original but which requires no more than half the space to store. Like all packing algorithms, it is lossy. 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 42. Let min and max be the minimum and maximum values of x.

scale_factor = (max-min)/ndrv
add_offset = 0.5*(min+max)
pck = (upk-add_offset)/scale_factor = (upk-0.5*(min+max))*ndrv/(max-min)

where ndrv is the number of discrete representable values for given type of packed variable. The theoretical maximum value for ndrv is two raised to the number of bits used to store the packed variable. Thus if the variable is packed into type NC_SHORT, a two-byte datatype, then there are at most 2^{16} = 65536 distinct values representable. In practice, the number of discretely representible values is taken to be two less than the theoretical maximum. This leaves space for a missing value and solves potential problems with rounding that may occur during the unpacking of the variable. Thus for NC_SHORT, ndrv = 65536 - 2 = 65534. Less often, the variable may be packed into type NC_CHAR, where ndrv = 2^{8} - 2 = 256 - 2 = 254, or type NC_INT where where ndrv = 2^{32} - 2 = 4294967295 - 2 = 4294967293. One useful feature of (lossy) netCDF packing algorithm is that additional, loss-less packing algorithms perform well on top of it.

Unpacking Algorithm

Unpacking The unpacking algorithm depends on the presence of two attributes, scale_factor and add_offset. If scale_factor is present for a variable, the data are multiplied by the value scale_factor after the data are read. If add_offset is present for a variable, then the add_offset value is added to the data after the data are read. If both scale_factor and add_offset attributes are present, the data are first scaled by scale_factor before the offset add_offset is added.

upk = scale_factor*pck + add_offset = (max-min)*pck/ndrv + 0.5*(min+max)

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.

Default Handling of Packed Data

Most files originally written in HDF format use the HDF packing/unpacking algorithm. This algorithm is incompatible with the netCDF packing algorithm described above. The unpacking component of the HDF algorithm (described here) is

upk = scale_factor*(pck - add_offset)

Confusingly, the (incompatible) netCDF and HDF algorithms both store their parameters in attributes with the same names (scale_factor and add_offset). Data packed with one algorithm should never be unpacked with the other; doing so will result in incorrect answers. Unfortunately, few users are aware that their datasets may be packed, and fewer know the details of the packing algorithm employed. This is what we in the “bizness” call an interoperability issue because it hampers data analysis performed on heterogeneous systems.

As described below, NCO automatically unpacks data before performing arithmetic. This automatic unpacking occurs silently since there is usually no reason to bother users with these details. There is as yet no generic way for NCO to know which packing convention was used, so NCO assumes the netCDF convention was used. NCO uses the same convention for unpacking unless explicitly told otherwise with the ‘--hdf_upk’ (also ‘--hdf_unpack’) switch. Until and unless a method of automatically detecting the packing method is devised, it must remain the user’s responsibility to tell NCO when to use the HDF convention instead of the netCDF convention to unpack.

If your data originally came from an HDF file (e.g., NASA EOS) then it was likely packed with the HDF convention and must be unpacked with the same convention. Our recommendation is to only request HDF unpacking when you are certain. Most packed datasets encountered by NCO will have used the netCDF convention. Those that were not will hopefully produce noticeably weird values when unpacked by the wrong algorithm. Before or after panicking, treat this as a clue to re-try your commands with the ‘--hdf_upk’ switch. See ncpdq netCDF Permute Dimensions Quickly for an easy technique to unpack data packed with the HDF convention, and then re-pack it with the netCDF convention.

Default Handling of Packed Data

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). Pack and unpack individual variables with ncpdq and the ncap2 pack() and unpack() functions (see Methods and functions).

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3.33 Operation Types

Availability: ncap2, ncra, nces, ncwa
Short options: ‘-y
Long options: ‘--operation’, ‘--op_typ

The ‘-y op_typ’ switch allows specification of many different types of operations Set op_typ to the abbreviated key for the corresponding operation:


Mean value


Square of the mean


Mean of sum of squares


Maximum value


Minimum value


Maximum absolute value


Mean absolute value


Minimum absolute value


Root-mean-square (normalized by N)


Root-mean square (normalized by N-1)


Square root of the mean


Sum of values

NCO assumes coordinate variables represent grid axes, e.g., longitude. The only rank-reduction which makes sense for coordinate variables is averaging. Hence NCO implements the operation type requested with ‘-y’ on all non-coordinate variables, not on coordinate variables. When an operation requires a coordinate variable to be reduced in rank, i.e., from one dimension to a scalar or from one dimension to a degenerate (single value) array, then NCO always averages the coordinate variable regardless of the arithmetic operation type performed on the non-coordinate variables.

The mathematical definition of each arithmetic operation is given below. 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 HTML users:
The definition of mathematical operations involving rank reduction (e.g., averaging) relies heavily on mathematical expressions which cannot easily be represented in HTML. See the printed 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).


Suppose you wish to examine the variable prs_sfc(time,lat,lon) which contains a time series of the surface pressure as a function of latitude and longitude. Find the minimum value of prs_sfc over all dimensions:

ncwa -y min -v prs_sfc 

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

ncwa -y max -v prs_sfc -a lon

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

ncra -y rms -v prs_sfc
ncwa -y rms -v prs_sfc -a time

The previous two commands give the same answer but ncra is preferred because it has a smaller memory footprint. A dimension of size one is said to be degenerate. By default, ncra leaves the (degenerate) time dimension in the output file (which is usually useful) whereas ncwa removes the time dimension (unless ‘-b’ is given).

These operations work as expected in multi-file operators. Suppose that prs_sfc is stored in multiple timesteps per file across multiple files, say,, We can now find the three month maximum surface pressure at every point.

nces -y max -v prs_sfc

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 involves three steps.

ncwa -O -v prs_sfc -a time
ncbo -O -v prs_sfc 
ncra -O -y rmssdn

First construct the temporal mean of prs_sfc in the file Next overwrite with the anomaly (deviation from the mean). Finally overwrite 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))'

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))' \

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)'

These instrinsic functions compute the answer quickly and concisely.

The procedure to compute the spatial standard deviation of a field in a single file involves three steps.

ncwa -O -v prs_sfc,gw -a lat,lon -w gw
ncbo -O -v prs_sfc,gw
ncwa -O -y rmssdn -v prs_sfc -a lat,lon -w gw

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)' \

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
ncwa -O -a time
ncbo -O -v tpt
ncra -O -y rmssdn

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.

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3.34 Type Conversion

Availability (automatic type conversion): ncap2, ncbo, nces, ncflint, ncra, ncwa
Short options: None (it’s automatic)
Availability (manual type conversion): nces, ncra, ncwa
Short options: None
Long options: ‘--dbl’, ‘--flt’, ‘--rth_dbl’, ‘--rth_flt

Type conversion refers to the casting or coercion of one fundamental or atomic data type to another, e.g., converting NC_SHORT (two bytes) to NC_DOUBLE (eight bytes). Type conversion always promotes or demotes the range and/or precision of the values a variable can hold. Type conversion is automatic when the language carries out this promotion according to an internal set of rules without explicit user intervention. In contrast, manual type conversion refers to explicit user commands to change the type of a variable or attribute. Most type conversion happens automatically, yet there are situations in which manual type conversion is advantageous.

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3.34.1 Automatic type conversion

There are at least two reasons to avoid type conversions. First, type conversions are expensive since they require creating (temporary) buffers and casting each element of a variable from its storage type to some other type and then, often, converting it back. Second, a dataset’s creator perhaps had a good reason for storing data as, say, NC_FLOAT rather than NC_DOUBLE. In a scientific framework there is no reason to store data with more precision than the observations merit. Normally this is single-precision, which guarantees 6–9 digits of precision. Reasons to engage in type conversion include avoiding rounding errors and out-of-range limitations of less-precise types. This is the case with most integers. Thus NCO defaults to automatically promote integer types to floating point when performing lengthy arithmetic, yet NCO defaults to not promoting single to double-precision floats.

Before discussing the more subtle floating point issues, we first examine integer promotion. We will show how following parsimonious conversion rules dogmatically can cause problems, and what NCO does about that. That said, there are situations in which implicit conversion of single- to double-precision is also warranted. Understanding the narrowness of these situations takes time, and we hope the reader appreciates the following detailed discussion.

Consider the average of the two NC_SHORTs 17000s and 17000s. A straightforward average without promotion results in garbage since the intermediate value which holds their sum is also of type NC_SHORT and thus overflows on (i.e., cannot represent) values greater than 32,767 43. There are valid reasons for expecting this operation to succeed and the NCO philosophy is to make operators do what you want, not what is purest. Thus, unlike C and Fortran, but like many other higher level interpreted languages, NCO arithmetic operators will perform automatic type conversion on integers when all the following conditions are met 44:

  1. The requested operation is arithmetic. This is why type conversion is limited to the operators ncap2, ncbo, nces, ncflint, ncra, and ncwa.
  2. The arithmetic operation could benefit from type conversion. Operations that could benefit include averaging, summation, or any “hard” arithmetic that could overflow or underflow. Larger representable sums help avoid overflow, and more precision helps to avoid underflow. Type conversion does not benefit searching for minima and maxima (‘-y min’, or ‘-y max’).
  3. The variable on disk is of type NC_BYTE, NC_CHAR, NC_SHORT, or NC_INT. Type NC_DOUBLE is not promoted because there is no type of higher precision. Conversion of type NC_FLOAT is discussed in detail below. When it occurs, it follows the same procedure (promotion then arithmetic then demotion) as conversion of integer types.

When these criteria are all met, the operator promotes the variable in question to type NC_DOUBLE, performs all the arithmetic operations, casts the NC_DOUBLE type back to the original type, and finally writes the result to disk. The result written to disk may not be what you expect, because of incommensurate ranges represented by different types, and because of (lack of) rounding. First, continuing the above example, the average (e.g., ‘-y avg’) of 17000s and 17000s is written to disk as 17000s. The type conversion feature of NCO makes this possible since the arithmetic and intermediate values are stored as NC_DOUBLEs, i.e., 34000.0d and only the final result must be represented as an NC_SHORT. Without the type conversion feature of NCO, the average would have been garbage (albeit predictable garbage near -15768s). Similarly, the total (e.g., ‘-y ttl’) of 17000s and 17000s written to disk is garbage (actually -31536s) since the final result (the true total) of 34000 is outside the range of type NC_SHORT.

After arithmetic is computed in double-precision for promoted variables, the intermediate double-precision values must be demoted to the variables’ original storage type (e.g., from NC_DOUBLE to NC_SHORT). NCO has handled this demotion in three ways in its history. Prior to October, 2011 (version 4.0.8), NCO employed the C library truncate function, trunc() 45. Truncation rounds x to the nearest integer not larger in absolute value. For example, truncation rounds 1.0d, 1.5d, and 1.8d to the same value, 1s. Clearly, truncation does not round floating point numbers to the nearest integer! Yet truncation is how the C language performs implicit conversion of real numbers to integers.

NCO stopped using truncation for demotion when an alert user (Neil Davis) informed us that this caused a small bias in the packing algorithm employed by ncpdq. This led to NCO adopting rounding functions for demotion. Rounding functions eliminated the small bias in the packing algorithm.

From February, 2012 through March, 2013 (versions 4.0.9–4.2.6), NCO employed the C library family of rounding functions, lround(). These functions round x to the nearest integer, halfway cases away from zero. The problem with lround() is that it always rounds real values ending in .5 away from zero. This rounds, for example, 1.5d and 2.5d to 2s and 3s, respectively.

Since April, 2013 (version 4.3.0), NCO has employed the other C library family of rounding functions, lrint(). This algorithm rounds x to the nearest integer, using the current rounding direction. Halfway cases are rounded to the nearest even integer. This rounds, for example, both 1.5d and 2.5d to the same value, 2s, as recommended by the IEEE. This rounding is symmetric: up half the time, down half the time. This is the current and hopefully final demotion algorithm employed by NCO.

Hence because of automatic conversion, NCO will compute the average of 2s and 3s in double-precision arithmetic as (2.0d + 3.0d)/2.0d) = 2.5d. It then demotes this intermediate result back to NC_SHORT and stores it on disk as trunc(2.5d) = 2s (versions up to 4.0.8), lround(2.5d) = 3s (versions 4.0.9–4.2.6), and lrint(2.5d) = 2s (versions 4.3.0 and later).

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3.34.2 Promoting Single-precision to Double

Promotion of real numbers from single- to double-precision is fundamental to scientific computing. When it should occur depends on the precision of the inputs and the number of operations. Single-precision (four-byte) numbers contain about seven significant figures, while double-precision contain about sixteen. More, err, precisely, the IEEE single-precision representation gives from 6 to 9 significant decimal digits precision 46. And the IEEE double-precision representation gives from 15 to 17 significant decimal digits precision 47. Hence double-precision numbers represent about nine digits more precision than single-precision numbers.

Given these properties, there are at least two possible arithmetic conventions for the treatment of real numbers:

  1. Conservative, aka Fortran Convention Automatic type conversion during arithmetic in the Fortran language is, by default, performed only when necessary. All operands in an operation are converted to the most precise type involved the operation before the arithmetic operation. Expressions which involve only single-precision numbers are computed entirely in single-precision. Expressions involving mixed precision types are computed in the type of higher precision. NCO by default employs the Fortan Convention for promotion.
  2. Aggressive, aka C Convention The C language is by default much more aggressive (and thus wasteful) than Fortran, and will always implicitly convert single- to double-precision numbers for no good reason. All real-number standard C library functions are double-precision, and C programmers must take extra steps to only utilize single precision arithmetic. The high level interpreted data analysis languages IDL, Matlab, and NCL all adopt the C Convention.

NCO does not automatically promote NC_FLOAT because, in our judgement, the performance penalty of always doing so would outweigh the potential benefits. The now-classic text “Numerical Recipes in C” discusses this point under the section “Implicit Conversion of Float to Double” 48. That said, such promotion is warranted in some circumstances.

For example, rounding errors can accumulate to worrisome levels during arithmetic performed on large arrays of single-precision floats. This use-case occurs often in geoscientific studies of climate where thousands-to-millions of gridpoints may contribute to a single average. If the inputs are all single-precision, then so should be the output. However the intermediate results where running sums are accumulated may suffer from too much rounding or from underflow unless computed in double-precision.

The order of operations matters to floating point math even when the analytic expressions are equal. Cautious users feel disquieted when results from equally valid analyses differ in the final bits instead of agreeing bit-for-bit. For example, averaging arrays in multiple stages produces different answers than averaging them in one step. This is easily seen in the computation of ensemble averages by two different methods. The NCO test file contains single- and double-precision representations of the same temperature timeseries as tpt_flt and tpt_dbl. Pretend each datapoint in this timeseries represents a monthly-mean temperature. We will mimic the derivation of a fifteen-year ensemble-mean January temperature by concatenating the input file five times, and then averaging the datapoints representing January two different ways. In Method 1 we derive the 15-year ensemble January average in two steps, as the average of three five-year averages. This method is naturally used when each input file contains multiple years and multiple input files are needed 49. In Method 2 we obtain 15-year ensemble January average in a single step, by averaging all 15 Januaries at one time:

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

Although the two methods are arithmetically equivalent, they produce slightly different answers due to the different order of operations. Moreover, it appears at first glance that the single-precision answers suffer from greater error than the double-precision answers. In fact both precisions suffer from non-zero rounding errors. The answers differ negligibly to machine precision, which is about seven significant figures for single precision floats (tpt_flt), and sixteen significant figures for double precision (tpt_dbl). The input precision determines the answer precision.

IEEE arithmetic guarantees that two methods will produce bit-for-bit identical answers only if they compute the same operations in the same order. Bit-for-bit identical answers may also occur by happenstance when rounding errors exactly compensate one another. This is demonstrated by repeating the example above with the ‘--dbl’ (or ‘--rth_dbl’ for clarity) option which forces conversion of single-precision numbers to double-precision prior to arithmetic. Now ncra will treat the first value of tpt_flt, 273.1000f, as 273.1000000000000d. Arithmetic on tpt_flt then proceeds in double-precision until the final answer, which is converted back to single-precision for final storage.

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

The ‘--dbl’ switch has no effect on the results computed from double-precision inputs. But now the two methods produce bit-for-bit identical results from the single-precision inputs! This is due to the happenstance of rounding along with the effects of the ‘--dbl’ switch. The ‘--flt’ and ‘--rth_flt’ switches are provided for symmetry. They enforce the traditional NCO and Fortran convention of keeping single-precision arithmetic in single-precision unless a double-precision number is explicitly involved.

We have shown that forced promotion of single- to double-precision prior to arithmetic has advantages and disadvantages. The primary disadvantages are speed and size. Double-precision arithmetic is 10–60% slower than, and requires twice the memory of single-precision arithmetic. The primary advantage is that rounding errors in double-precision are much less likely to accumulate to values near the precision of the underlying geophysical variable.

For example, if we know temperature to five significant digits, then a rounding error of 1-bit could affect the least precise digit of temperature after 1,000–10,000 consecutive one-sided rounding errors under the worst possible scenario. Many geophysical grids have tens-of-thousands to millions of points that must be summed prior to normalization to compute an average. It is possible for single-precision rouding errors to accumulate and degrade the precision in such situtations. Double-precision arithmetic mititgates this problem, so ‘--dbl’ would be warranted.

This can be seen with another example, averaging a global surface temperature field with ncwa. The input contains a single-precision global temperature field (stored in TREFHT) produced by the CAM3 general circulation model (GCM) run and stored at 1.9 by 2.5 degrees resolution. This requires 94 latitudes and 144 longitudes, or 13,824 total surface gridpoints, a typical GCM resolution these days. These input characteristics are provided only to show the context to the interested reader, equivalent results would be found in statistics of any dataset of comparable size. Models often represent Earth on a spherical grid where global averages must be created by weighting each gridcell by its latitude-dependent weight (i.e., the Gaussian weight stored in gw), or by the surface area of each contributing gridpoint (stored in area).

Like many geophysical models and most GCMs, CAM3 runs completely in double-precision yet stores its archival output in single-precision to save space. In practice such models usually save multi-dimensional prognostic and diagnostic fields (like TREFHT(lat,lon) and area(lat,lon)) as single-precision, while saving all one-dimensional coordinates and weights (here lat, lon, and gw(lon)) as double-precision. To obtain pure double-precision arithmetic and storage of the globla mean temperature, we first create and store double-precision versions of the single-precision fields:

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

The single- and double-precision temperatures may each be averaged globally using four permutations for the precision of the weight and of the intermediate arithmetic representation:

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

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

Second, the single-precision arithmetic averages of the single-precision input TREFHT differ by 289.246735 - 289.226135 = 0.0206 from eachother, and, more importantly, by as much as 289.239964 - 289.226135 = 0.013829 from the correct (double-precision) answer. These averages differ in the fifth digit, i.e., they agree only to four significant figures! Given that climate scientists are concerned about global temperature variations of a tenth of a degree or less, this difference is large. It means that the global mean temperature changes scientists are looking for are comparable in size to the numerical artifacts produced by the averaging procedure.

Why are the single-precision numerical artifacts so large? Each global average is the result of multiplying almost 15,000 elements each by its weight, summing those, and then dividing by the summed weights. Thus about 50,000 single-precision floating point operations caused the loss of two to three significant digits of precision. The net error of a series of independent rounding errors is a random walk phenomena 50. Successive rounding errors displace the answer further from the truth. An ensemble of such averages will, on average, have no net bias. In other words, the expectation value of a series of IEEE rounding errors is zero. And the error of any given sequence of rounding errors obeys, for large series, a Gaussian distribution centered on zero.

Single-precision numbers use three of their four eight-bit bytes to represent the mantissa so the smallest representable single-precision mantissa is \epsilon \equiv 2^{-23} = 1.19209 \times 10^{-7}. This \epsilon is the smallest x such that 1.0 + x \ne 1.0. This is the rounding error for non-exact precision-numbers. Applying random walk theory to rounding, it can be shown that the expected rounding error after n inexact operations is \sqrt{2n/\pi} for large n. The expected (i.e., mean absolute) rounding error in our example with 13,824 additions is about \sqrt{2 \times 13824 / \pi} = 91.96. Hence, addition alone of about fifteen thousand single-precision floats is expected to consume about two significant digits of precision. This neglects the error due to the inner product (weights times values) and normalization (division by tally) aspects of a weighted average. the ratio of two numbers each containing a numerical bias can magnify the size of the bias. In summary, a global mean number computed from about 15,000 gridpoints each with weights can be expected to lose up to three significant digits. Since single-precision starts with about seven significant digits, we should not expect to retain more than four significant digits after computing weighted averages in single-precision. The above example with TREFHT shows the expected four digits of agreement.

The NCO results have been independently validated to the extent possible in three other languages: C, Matlab, and NCL. C and NCO are the only languages that permit single-precision numbers to be treated with single precision arithmetic:

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

All languages tested (C, Matlab, NCL, and NCO) agree to machine precision with double-precision arithmetic. Users are fortunate to have a variety of high quality software that liberates them from the drudgery of coding their own. Many packages are free (as in beer)! As shown above NCO permits one to shift to their float-promotion preferences as desired. No other language allows this with a simple switch.

To summarize, until version 4.3.6 (September, 2013), the default arithmetic convention of NCO adhered to Fortran behavior, and automatically promoted single-precision to double-precision in all mixed-precision expressions, and left-alone pure single-precision expressions. This is faster and more memory efficient than other conventions. However, pure single-precision arithmetic can lose too much precision when used to condense (e.g., average) large arrays. Statistics involving about n = 10,000 single-precision inputs will lose about 2–3 digits if not promoted to double-precision prior to arithmetic. The loss scales with the squareroot of n. For larger n, users should promote floats with the ‘--dbl’ option if they want to preserve more than four significant digits in their results.

The ‘--dbl’ and ‘--flt’ switches are only available with the NCO arithmetic operators that could potentially perform more than a few single-precision floating point operations per result. These are nces, ncra, and ncwa. Each is capable of thousands to millions or more operations per result. By contrast, the arithmetic operators ncbo and ncflint perform at most one floating point operation per result. Providing the ‘--dbl’ option for such trivial operations makes little sense, so the option is not currently made available.

We are interested in users’ opinions on these matters. The default behavior was changed from ‘--flt’ to ‘--dbl’ with the release of NCO version 4.3.6 (October 2013). We will change the default back to ‘--flt’ if users prefer. Or we could set a threshold (e.g., n \ge 10000) after which single- to double-precision promotion is automatically invoked. Or we could make the default promotion convention settable via an environment variable (GSL does this a lot). Please let us know what you think of the selected defaults and options.

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3.34.3 Manual type conversion

ncap2 provides intrinsic functions for performing manual type conversions. This, for example, converts variable tpt to external type NC_SHORT (a C-type short), and variable prs to external type NC_DOUBLE (a C-type double).

ncap2 -s 'tpt=short(tpt);prs=double(prs)'

See ncap2 netCDF Arithmetic Processor, for more details.

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3.35 Batch Mode

Availability: All operators
Short options: ‘-O’, ‘-A
Long options: ‘--ovr’, ‘--overwrite’, ‘--apn’, ‘--append

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.

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3.36 History Attribute

Availability: All operators
Short options: ‘-h
Long options: ‘--hst’, ‘--history

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’. 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 To 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).

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3.37 File List Attributes

Availability: nces, ncecat, ncra, ncrcat
Short options: ‘-H
Long options: ‘--fl_lst_in’, ‘--file_list

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.

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3.38 CF Conventions

Availability: ncbo, nces, ncecat, ncflint, ncpdq, ncra, ncwa
Short options: None

NCO recognizes some Climate and Forecast (CF) metadata conventions, and applies special rules to such data. NCO was contemporaneous with COARDS and still contains some rules to handle older model datasets that pre-date CF, such as NCAR CCM and early CCSM datasets. Such datasets may not contain an explicit Conventions attribute (e.g., ‘CF-1.0’). Nevertheless, we refer to all such metadata collectively as CF metadata. Skip this section if you never work with CF metadata.

The latest CF netCDF conventions are described here. Most CF netCDF conventions are transparent to NCO. There are no known pitfalls associated with using any NCO operator on files adhering to these conventions. To increase user friendliness, NCO applies special rules to certain variables in CF files. Rules not required by CF are because experience shows that they simplify data analysis.

Here is a general sense of NCO’s CF-support: Understanding and implementing NUG recommendations such as the history attribute, packing conventions, and attention to units. Special handling of variables designated as coordinates, bounds, or ancillary variables, so that users subsetting a certain variable automatically obtain all related variables. Special handling and prevention of meaningless operations (e.g., the root-mean-square of latitude) so that coordinates and bounds preserve meaningful information even as normal fields are statistically transformed. Understanding units and certain calendars so that hyperslabs may be specified in physical units, and so that user needs not manually decode per-file time specifications. Understanding auxiliary coordinates so that irregular hyperslabs may be specified on complex geometric grids. Checking for CF-compliance on netCDF3 and netCDF4 and HDF files. Converting netCDF4 and HDF files to netCDF3 for strict CF-compliance. Finally, a main use of NCO is to “produce CF”, i.e., to improve CF-compliance by annotating metadata, renaming objects (attributes, variables, and dimensions), permuting and inverting dimensions, recomputing values, and data compression.

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.

Furthermore, the size and rank-preserving arithmetic operators try not to operate on certain grid properties. These operators are ncap2, ncbo, nces, ncflint, and ncpdq (when used for packing, not for permutation). These operators do not operate, by default, on (i.e., add, subtract, pack, etc.) the following variables: ORO, area, datesec, date, gw, hyai, hyam, hybi. hybm, lat_bnds, lon_bnds, msk_*. These variables represent Gaussian weights, land/sea masks, time fields, hybrid pressure coefficients, and latititude/longitude boundaries. We call these fields non-coordinate grid properties. Coordinate grid properties are easy to identify because they are coordinate variables such as latitude and longitude.

Users usually want all grid properties to remain unaltered in the output file. To be treated as a grid property, the variable name must exactly match a name in the above list, or be a coordinate variable. 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.

As of NCO version 4.0.8 (April, 2011), NCO supports the CF bounds convention for cell boundaries described here. This convention allows coordinate variables (including multidimensional coordinates) to describe the boundaries of their cells. This is done by naming the variable which contains the bounds in in the bounds attribute. Note that coordinates of rank N have bounds of rank N+1. NCO-generated subsets of CF-compliant files with bounds attributes will include the coordinates specified by the bounds attribute, if any. Hence the subsets will themselves be CF-compliant. Bounds are subject to the user-specified override switches (including ‘-c’ and ‘-C’) described in Subsetting Coordinate Variables.

As of NCO version 4.4.5 (July, 2014), NCO supports the CF ancillary_variables convention for described here. This convention allows ancillary variables to be associated with one or more primary variables. NCO attaches any such variables to the extraction list along with the primary variable and its usual (one-dimensional) coordinates, if any. Ancillary variables are subject to the user-specified override switches (including ‘-c’ and ‘-C’) described in Subsetting Coordinate Variables.

As of NCO version 3.9.6 (January, 2009), NCO supports the CF coordinates convention described here. This convention allows variables to specify additional coordinates (including mult-idimensional coordinates) in a space-separated string attribute named coordinates. NCO attaches any such coordinates to the extraction list along with the variable and its usual (one-dimensional) coordinates, if any. These auxiliary coordinates are subject to the user-specified override switches (including ‘-c’ and ‘-C’) described in Subsetting Coordinate Variables.

Elimination of reduced dimensions from the coordinates attribute helps ensure that rank-reduced variables become completely independent from their former dimensions. As of NCO version 4.4.9 (March, 2015), NCO may modify the coordinates attribute to assist this. In particular, ncwa eliminates from the coordinates attribute any dimension that it collapses, e.g., by averaging. The former presence of this dimension will usually be indicated by the CF cell_methods convention described here. Hence the CF cell_methods and coordinates conventions can be said to work in tandem to characterize the state and history of a variable’s analysis.

As of NCO version 4.4.2 (February, 2014), NCO supports some of the CF cell_methods convention to describe the analysis procedures that have been applied to data. The convention creates (or appends to an existing) cell_methods attribute a space-separated list of couplets of the form dmn: op where dmn is a comma-separated list of dimensions previously contained in the variable that have been reduced by the arithmetic operation op. For example, the cell_methods value time: mean says that the variable in question was averaged over the time dimension. In such cases time will either be a scalar variable or a degenerate dimension or coordinate. This simply means that it has been averaged-over. The value time, lon: mean lat: max says that the variable in question is the maximum zonal mean of the time averaged original variable. Which is to say that the variable was first averaged over time and longitude, and then the residual latitudinal array was reduced by choosing the maximum value. Since the cell methods convention may alter metadata in an undesirable (or possibly incorrect) fashion, we provide switches to ensure it is always or never used. Use long-options ‘--cll_mth’ or ‘--cell_methods’ to invoke the algorithm (true by default), and options ‘--no_cll_mth’ or ‘--no_cell_methods’ to turn it off. These options are only available in the operators ncwa and ncra.

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3.39 ARM Conventions

Availability: ncrcat
Short options: None

ncrcat has been programmed to correctly handle data files which utilize the Atmospheric Radiation Measurement (ARM) Program convention for time and time offsets. If you do not work with ARM data then you may skip this section. ARM data files store time information in two variables, a scalar, base_time, and a record variable, time_offset. Subtle but serious problems can arise when these type of files are blindly concatenated without CF or ARM support. NCO implements rebasing (see Rebasing Time Coordinate) as necessary on both CF and ARM files. Rebasing chains together consecutive input-files and produces an output-file which contains the correct time information. For ARM files this is expecially complex because the time coordinates are often stored as type NC_CHAR. Currently, ncrcat determines whether a datafile is an ARM datafile simply by testing for the existence of the variables base_time, time_offset, and the dimension time. If these are found in the input-file then ncrcat will automatically perform two non-standard, but hopefully useful, procedures. First, ncrcat will ensure that values of time_offset appearing in the output-file are relative to the base_time appearing in the first input-file (and presumably, though not necessarily, also appearing in the output-file). Second, if a coordinate variable named time is not found in the input-files, then ncrcat automatically creates the time coordinate in the output-file. The values of time are defined by the ARM conventions time = base_time + time_offset. Thus, if output-file contains the time_offset variable, it will also contain the time coordinate. A short message is added to the history global attribute whenever these ARM-specific procedures are executed.

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3.40 Operator Version

Availability: All operators
Short options: ‘-r
Long options: ‘--revision’, ‘--version’, or ‘--vrs

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.

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4 Reference Manual

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

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4.1 ncap2 netCDF Arithmetic Processor

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.


ncap2 [-3] [-4] [-6] [-7] [-A] [-C] [-c] 
[-D dbg] [-F] [-f] [-h] [--hdf] [--hdr_pad nbr] [-L dfl_lvl] [-l path]
[--no_tmp_fl] [-O] [-o output-file] [-p path] [-R] [-r] [--ram_all]
[-s algebra] [-S fl.nco] [-t thr_nbr] [-v]
input-file [output-file]  


ncap2 arithmetically processes netCDF files 51. The processing instructions are contained either in the NCO script file fl.nco or in a sequence of command line arguments. The options ‘-s’ (or long options ‘--spt’ or ‘--script’) are used for in-line scripts and ‘-S’ (or long options ‘--fl_spt’ 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. See 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 Files). 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. NB: As of 20120515, ncap2 is unable to append to files that already contain the appended dimensions.

Defining new variables in terms of existing variables is a powerful feature of ncap2. Derived fields inherit the metadata (i.e., attributes) of their ancestors, if any, in the script or input file. When the derived field is completely new (no identically-named ancestors exist), then it inherits the metadata (if any) of the left-most variable on the right hand side of the defining expression. This metadata inheritance is called attribute propagation. Attribute propagation is intended to facilitate well-documented data analysis, and we welcome suggestions to improve this feature.

The only exception to this rule of attribute propagation is in cases of left hand casting (see Left hand casting). The user must manually define the proper metadata for variables defined using left hand casting.

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4.1.1 Syntax of ncap2 statements

Mastering ncap2 is relatively simple. Each valid statement statement consists of standard forward algebraic expression. The fl.nco, if present, is simply a list of such statements, whitespace, and comments. The syntax of statements is most like the computer language C. The following characteristics of C are preserved:

Array syntax

Arrays elements are placed within [] characters;

Array indexing

Arrays are 0-based;

Array storage

Last dimension is most rapidly varying;

Assignment statements

A semi-colon ‘;’ indicates the end of an assignment statement.


Multi-line comments are enclosed within /* */ characters. Single line comments are preceded by // characters.


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 syntax

The at-sign @ is used to delineate an attribute name from a variable name.

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4.1.2 Expressions

Expressions are the fundamental building block of ncap2. Expressions are composed of variables, numbers, literals, and attributes. The 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



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.

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:

PZ0 is made to conform to P and the result is
PZ0 =

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=5329 and is type int 

Binary Operators
Unlike C the binary operators return an array of values. There is no such thing as short circuiting with the AND/OR operators. Missing values are carried into the result in the same way they are with the arithmetic operators. When an expression is evaluated in an if() the missing values are treated as true.
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 Operator precedence and associativity Examples:

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

Regular Assign Operator
var1 ’=’ exp1
If var1 does not 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 +=,-=,*=./=
var1 ’ass_op’ exp1
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
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;


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 ?:
exp1 ? exp2 : exp3
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.


weight_avg@units= (weight_avg == 1 ? "kilo" : "kilos");  
PS_nw=PS-(PS.min() > 100000 ? 100000 : 0);

Clipping Operators

<< Less-than Clipping

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

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.


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

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4.1.3 Dimensions

Dimensions are defined in Output using the defdim() function.

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

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


The size method allows the dimension size to be used in an arithmetic expression: / $time.size;

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


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

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




ID Quoting
If the dimension name contains non-regular characters use ID quoting. See see ID Quoting


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)

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4.1.4 Left hand casting

The following examples demonstrate the utility of the left hand casting ability of ncap2. Consider first this simple, artificial, example. If lat and lon are one dimensional coordinates of dimensions lat and lon, respectively, then addition of these two one-dimensional arrays is intrinsically ill-defined because whether lat_lon should be dimensioned lat by lon or lon by lat is ambiguous (assuming that addition is to remain a commutative procedure, i.e., one that does not depend on the order of its arguments). Differing dimensions are said to be orthogonal to one another, and sets of dimensions which are mutually exclusive are orthogonal as a set and any arithmetic operation between variables in orthogonal dimensional spaces is ambiguous without further information.

The ambiguity may be resolved by enumerating the desired dimension ordering of the output expression inside square brackets on the left hand side (LHS) of the equals sign. This is called left hand casting because the user resolves the dimensional ordering of the RHS of the expression by specifying the desired ordering on the LHS.

ncap2 -s 'lat_lon[lat,lon]=lat+lon'
ncap2 -s 'lon_lat[lon,lat]=lat+lon'

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'

Manipulating the four fields which define the pressure in a hybrid coordinate system is easy with left hand casting.

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4.1.5 Arrays and hyperslabs

Generating a regularly spaced one-dimensional array with ncap2 is simple with the array() function. The syntax is

var_out=array(val_srt,val_ncr,$dmn_nm); // One-dimensional output
var_out=array(val_srt,val_ncr,var_tpl); // Multi-dimensional output

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. The type of var_out will be the same as val_srt. Be sure to encode this type with the appropriate decimal point and floating point suffix when val_srt is a “naked constant” rather than a variable. For example, to produce an array of shorts (signed two-byte integers), integers (signed four-byte integers), unsigned 64-bit integers, floats, or doubles use

var_out=array(1s,val_ncr,$dmn_nm); // NC_SHORT array
var_out=array(1,val_ncr,$dmn_nm); // NC_INT array
var_out=array(1ull,val_ncr,$dmn_nm); // NC_UINT64 array
var_out=array(1f,val_ncr,$dmn_nm); // NC_FLOAT array
var_out=array(1.,val_ncr,$dmn_nm); // NC_DOUBLE array

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

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

var1(arg1,arg2 ... argN);

where each hyperslab argument is of the form


and the arguments for different dimensions are separated by commas. If start is omitted, it defaults to zero. If end is omitted, it defaults to dimension size minus one. If stride is omitted, it defaults to one.

If a single value is present then it is assumed that that dimension collapses to a single value (i.e., a cross-section). The number of hyperslab arguments MUST equal the variable’s rank.

Hyperslabs on the Right Hand Side of an assign

A simple 1D example:


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:

                          {1, 2, 3, 4, 5, 6, 7, 8,
                          -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 has dimensions $lon,$lat 
// NB: the time dimension has become degenerate

The following is invalid:


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:

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

Hyperslabs on the Left Hand Side of an assign
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:


Set first element of each lon element to 1.0:


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


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


Reverse method
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 methodp
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);
// The permuted values are

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4.1.6 Attributes

Attributes are referred to by var_nm@att_nm
All the following are valid statements:

global@text="Test Attributes"; /* Assign a global variable attribute */
--a1@max; q

A value list can be used on the RHS of an assign...

a1@trip1={1,2,3} ;

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 and Inheritance

// prs_mdp inherits attributes from P0:
// th_min inherits attributes from three_dmn_var_dbl:
th_min=1.0 + 2*three_dmn_var_dbl.min($time);

If the attribute name contains non-regular characters use ID quoting. See see ID Quoting


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4.1.7 Number literals

The 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[$time]=1UL; // n1 will now by type @code{NC_UINT}
n2[$lon]=4b;   // n2 will be of type @code{NC_BYTE}
n3[$lat]=5ull; // n3 will be of type @code{NC_UINT64}  
n3@a1=6.0d;   // attribute will be type @code{NC_DOUBLE}
n3@a2=-666L;  // attribute will be type @code{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[$rlev]=0.1      // n4 will be of type @code{NC_DOUBLE}
n5[$lon_grd]=2.0   // n5 will be of type @code{NC_DOUBLE}
n6[$gds_crd]=2e3;  // n6 will be of type @code{NC_DOUBLE}
n7[$gds_crd]=2e3f;  // n7 will be of type @code{NC_FLOAT}
n6@a1=41;         // attribute will be type @code{NC_INT}
n6@a2=-21;        // attribute will be type @code{NC_INT}  
n6@units="kelvin" // attribute will be type @code{NC_CHAR}
netCDF3/4 Types

NC_BYTE, a signed 1-byte integer


NC_CHAR, an ISO/ASCII character


NC_SHORT, a signed 2-byte integer


NC_INT, a signed 4-byte integer


NC_FLOAT, a single-precision (4-byte) floating point number


NC_DOUBLE, a double-precision (8-byte) floating point number

netCDF4 Types

NC_UBYTE, an unsigned 1-byte integer


NC_USHORT, an unsigned 2-byte integer


NC_UINT, an unsigned 4-byte integer


NC_INT64, a signed 8-byte integer


NC_UINT64, an unsigned 8-byte integer

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4.1.8 if statement

The syntax of the if statement is similar to its C counterpart. The Conditional Operator (ternary operator) has also been implemented.

else if(exp2)     

# Can use code blocks as well:
}else if(exp2)     

For a variable or attribute expression to be logically true all its non-missing value elements must be logically true, i.e., non-zero. The expression can be of any type. Unlike C there is no short-circuiting of an expression with the OR (||) and AND (&&) operators. The whole expression is evaluated regardless if one of the AND/OR operands are True/False.

# Simple example
  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 {
  print("min value of time=");print(time_min,"%f");
  print("max value of time=");print(time_max,"%f");

# Example from ddra.nco
  lmn_nbr=1.0*var_nbr_apx*varsz_gcm_4D; /* [nbr] Variable size */
    lmn_nbr_avg=1.0*var_nbr_apx*varsz_gcm_4D; // Block size
    lmn_nbr_wgt=dmnsz_gcm_lat; /* [nbr] Weight size */
  } // !nco_op_typ_avg
}else if(fl_typ==fl_typ_stl){
  lmn_nbr=1.0*var_nbr_apx*varsz_stl_2D; /* [nbr] Variable size */
    lmn_nbr_avg=1.0*var_nbr_apx*varsz_stl_2D; // Block size
    lmn_nbr_wgt=dmnsz_stl_lat; /* [nbr] Weight size */
  } // !nco_op_typ_avg
} // !fl_typ

Conditional Operator

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

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4.1.9 print statement

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.




mss_val_fst@_FillValue, size = 1 NC_FLOAT, value = -999

print("This function \t is monotonic\n");
This function is 	  monotonic

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4.1.10 Missing values ncap2

Missing 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.
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); 


The following methods manipulate missing value information associated with a variable. They only work on variables in Output.


The numeric argument expr becomes the new missing value, overwriting the old missing value, if any. The argument given is converted if necessary to the variable type. NB: This only changes the missing value attribute. Missing values in the original variable remain unchanged, and thus are no long considered missing values. They are “orphaned”. Thus set_miss() is normally used only when creating new variables. The intrinsic function change_miss() (see below) is typically used to edit values of existing variables.


Sets or changes (any pre-existing) missing value attribute and missing data values to expr. NB: This is an expensive function since all values must be examined. Use this function when changing missing values for pre-existing variables.


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 the missing value associated with a variable.


Count the number of missing values a variable contains.

/* 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:

// Extract only elements divisible by 3
where (three_dmn_var_dbl%3 == 0)

// Print missing value and variable summary
print("Number of missing values in three_dmn_var_dbl: ");

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4.1.11 Methods and functions

The convention within this document is that methods can be used as functions. However, functions are not and cannot be used as methods. Methods can be daisy-chained d and their syntax is cleaner than functions. Method names are reserved words and CANNOT be used as variable names. The command ncap2 -f shows the complete list of methods available on your build.

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

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


Aggregate Methods
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.


Mean value


Square of the mean


Mean of sum of squares


Maximum value


Minimum value


Maximum absolute value


Mean absolute value


Minimum absolute value


Root-mean-square (normalize by N)


Root-mean square (normalize by N-1)

ttl() or total()

Sum of values

// Average a variable over time

Packing Methods
For more information see see Packed data and see ncpdq netCDF Permute Dimensions Quickly

pack() & pack_short()

The default packing algorithm is applied and variable is packed to NC_SHORT


Variable is packed to NC_BYTE


Variable is packed to NC_SHORT


Variable is packed to NC_INT


The standard unpacking algorithm is applied.

Basic Methods
These methods work with variables and attributes. They have no arguments


Total number of elements


Number of dimensions in variable


Returns the netcdf type (see previous section)

Utility Methods
These functions are used to manipulate missing values and RAM variables. see Missing values ncap2


Takes one argument the missing value. Sets or overwrites the existing missing value. The argument given is converted if necessary to the variable type


Changes the missing value elements of the variable to the new missing value (n.b. an expensive function).


Returns the missing value of a variable in Input or Output


Deletes the missing value associated with a variable.


Writes a RAM variable to disk i.e., converts it to a regular disk type variable


Deletes a RAM variable or an attribute

PDQ Methods
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


Type Conversion Methods
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 may 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

convert to NC_BYTE, a signed 1-byte integer


convert to NC_CHAR, an ISO/ASCII character


convert to NC_SHORT, a signed 2-byte integer


convert to NC_INT, a signed 4-byte integer


convert to NC_FLOAT, a single-precision (4-byte) floating point number


convert to NC_DOUBLE, a double-precision (8-byte) floating point number

netCDF4 Types

convert to NC_UBYTE, an unsigned 1-byte integer


convert to NC_USHORT, an unsigned 2-byte integer


convert to NC_UINT, an unsigned 4-byte integer


convert to NC_INT64, a signed 8-byte integer


convert to NC_UINT64, an unsigned 8-byte integer

Intrinsic Mathematical Methods
The list of mathematical methods is system dependant. For the full list see Intrinsic mathematical methods

All the mathematical methods take a single argument except atan2() and pow() which take two. If the operand type is less than float then the result will be of type float. Arguments of type double yield results of type double. Like the other methods, you are free to use the mathematical methods as functions.

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

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4.1.12 RAM variables

Unlike regular variables, RAM variables are never written to disk. Hence using RAM variables in place of regular variables (especially within loops) significantly increases execution speed. Variables that are frequently accessed within for or where clauses provide the greatest opportunities for optimization. To declare and define a RAM variable simply prefix the variable name with an asterisk (*) when the variable is declared/initialized. To delete 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().

*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
// 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 

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4.1.13 Where statement

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)

// Multiple assigns

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). This type of structure is often found in regional weather/climate model (such as WRF) output, and in satellite swath data. For this reason we call it “Swath-like Data”, or SLD. To find the average temperature in a region bounded by [lat_min,lat_max] and [lon_min,lon_max]:

where((lat >= lat_min && lat <= lat_max) && (lon >= lon_min && lon <= lon_max))


For North American Regional Reanalysis (NARR) data (example dataset) the procedure looks like this

ncap2 -O -v -S ~/narr.nco ${DATA}/hdf/ ~/

where narr.nco is an ncap2 script like this:

/* North American Regional Reanalysis (NARR) Statistics
   NARR stores grids with 2-D latitude and longitude, aka Swath-like Data (SLD) 
   Here we work with three variables:
   lat(y,x), lon(y,x), and uwnd(time,level,y,x);
   To study sub-regions of SLD, we use masking techniques:
   1. Define mask as zero times variable to be masked
      Then mask automatically inherits variable attributes
      And average below will inherit mask attributes
   2. Optionally, create mask as RAM variable (as below with asterisk *)
      NCO does not write RAM variable to output
      Masks are often unwanted, and can be big, so this speeds execution
   3. Example could be extended to preserve mean lat and lon of sub-region
      Follow uwnd example to do this: lat_msk=0.0*lat ... lat_avg=lat.avg($y,$x) */
where((lat >= 35.6 && lat <= 37.0) && (lon >= -100.5 && lon <= -99.0))

// Average only over horizontal dimensions x and y (preserve level and time)

Stripped of comments and formatting, this example is a three-statement script executed by a one-line command. NCO needs only this meagre input to unpack and copy the input data and attributes, compute the statistics, and then define and write the output file. Unless the comments pointed out that wind variable (uwnd) was four-dimensional and the latitude/longitude grid variables were both two-dimensional, there would be no way to tell. This shows how NCO hides from the user the complexity of analyzing multi-dimensional SLD. We plan to extend such SLD features to more operators soon.

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4.1.14 Loops

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

    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?

  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	

  if(lat(idx)%2) sum+=lat(idx);

print("Total of odd elements ");print(sum);print("\n"); 

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4.1.15 Include files

The 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

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4.1.16 sort methods

In ncap2 there are multiple ways to sort data. Beginning with NCO 4.1.0 (March, 2012), ncap2 support six sorting functions:

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

The first two functions, sort() and asort() sort, in ascending order, all the elements of var_in (which can be a variable or attribute) without regard to any dimensions. The third function, dsort() does the same but sorts in descending order. Remember that ascending and descending sorts are specified by asort() and dsort(), respectively.

These three functions are overloaded to take a second, optional argument called the sort map srt_map, which should be supplied as a call-by-reference variable, i.e., preceded with an ampersand. If the sort map does not yet exist, then it will be created and returned as an integer type the same shape as the input variable.

The output var_out of each sort function is a sorted version of the input, var_in. The output var_out of the two mapping functions the result of applying (with remap() or un-applying (with unmap()) the sort map srt_map to the input var_in. To apply the sort map with remap() the size of the variable must be exactly divisible by the size of the sort map.

The final function invert_map() returns the so-called de-sorting map dsr_map which is 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.

// 1, 2, 3, 3, 4, 4, 5, 6, 7, 10;

// 1, 2, 3, 4
// 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).


 1, 2, 3, 4, 
 5, 6, 7, 8,

// 0, 2, 1, 3;

// 1, 3, 2, 4,
// 5, 7, 6, 8,
// 9,11,10,12,
// 13,15,14,16,
// 17,19,18,20;


// 3, 5, 4, 2, 1
// 8, 10, 9,7, 6, 
// 13,15,14,12,11, 
// 18,20,19,17,16

As in the above example you may create your own sort map. To sort in descending order, apply the reverse() method after the sort().

Here is an extended example of how to use ncap2 features to hyperslab an irregular region based on the values of a variable not a coordinate. The distinction is crucial: hyperslabbing based on dimensional indices or coordinate values is straightforward. Using the values of single or multi-dimensional variable to define a hyperslab is quite different.

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

// Included in NCO User Guide at

/* 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)
   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 */
*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 */

/* 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 */

// Count number of valid points in mask by summing the one's

// Define output dimension equal in size to number of valid points

/* 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 */

/* Hyperslab last msk_nbr values of variable(s) of interest */

/* 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 ~/ ~/
ncap2 -O -v -S ~/ncap2_foo.nco ~/nco/data/ ~/
ncks -O -x -v time ~/ ~/
ncks ~/

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

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

// Included in NCO User Guide at

// Randomize the time coordinate
//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 */

//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
// Multiply base offset by factorial of lesser dimensions

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

// Use remap() to re-map the data

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

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

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4.1.17 Irregular Grids

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).

The preferred NCO technique to analyze data on non-standard coordinate grids is to create a region mask with ncap2, and then to use the mask within ncap2 for variable-specific processing, and/or with other operators (e.g., ncwa, ncdiff) for entire file processing.

Before describing the construction of masks, let us review how irregularly gridded geoscience data are described. Say that latitude and longitude are stored as R-dimensional arrays and the product of the dimension sizes is the total number of elements N in the other variables. Geoscience applications tend to use R=1, R=2, and R=3.

If the grid is has no simple representation (e.g., discontinuous) then it makes sense to store all coordinates as 1D arrays with the same size as the number of grid points. These gridpoints can be completely independent of all the other (own weight, area, etc.).

R=1: lat(number_of_gridpoints) and lon(number_of_gridpoints)

If the horizontal grid is time-invariant then R=2 is common:

R=2: lat(south_north,east_west) and lon(south_north,east_west)

The Weather and Research Forecast (WRF) model uses R=3:

R=3: lat(time,south_north,east_west), lon(time,south_north,east_west)

and so supports grids that change with time.

Grids with R > 1 often use missing values to indicated empty points. For example, so-called “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);'

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()'
ncwa -a lat,lon -m mask_var -w area

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 > << 'EOF'
mask_var = (lat >= lat_min && lat <= lat_max) && (lon >= lon_min && > lon <= lon_max);
if( > 0){ // Check that mask contains some valid values
  temperature_avg=(temperature*mask_var).avg(); // Average temperature
  temperature_max=(temperature*mask_var).max(); // Maximum temperature
ncap2 -S

Grids like those produced by the WRF model are complex because one must use global metadata to determine the grid staggering and offsets to translate XLAT and XLONG into real latitudes, longitudes, and missing points. The WRF grid documentation should describe this. For WRF files creating regional masks looks, in general, like

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

A few notes: Irregular regions are the union of arrays lat/lon_min/max’s. The mask procedure is identical for all R.

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4.1.18 Bilinear interpolation

As of version 4.0.0 NCO has internal routines to perform bilinear interpolation on gridded data sets. In mathematics, bilinear interpolation is an extension of linear interpolation for interpolating functions of two variables on a regular grid. The idea is to perform linear interpolation first in one direction, and then again in the other direction.

Suppose we have an irregular grid of data temperature[lat,lon], with co-ordinate vars lat[lat], lon[lon]. We wish to find the temperature at an arbitary point [X,Y] within the grid. If we can locate lat_min,lat_max and lon_min,lon_max such that lat_min <= X <= lat_max and lon_min <= Y <= lon_max then we can interpolate in two dimensions the temperature at [X,Y].

The general form of the ncap2 interpolation function is




Input function data. Usually a two dimensional variable. It must be of size grid_in_x.size()*grid_in_y.size()


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()


X output values


Y output values


X input values values. Must be monotonic (increasing or decreasing).


Y input values values. Must be monotonic (increasing or decreasing).

Prior to calculations all arguments are converted to type NC_DOUBLE. After calculations var_out is converted to the input type of grid_in.

Suppose the first part of an ncap2 script is


// Temperature
 {100, 200, 300, 400, 500,
  101, 202, 303, 404, 505,
  102, 204, 306, 408, 510,
  103, 206, 309, 412, 515.0 };

// Coordinate variables

Now we interpolate with the following variables:


// 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:

// 513.920594059406

Wrapping and Extrapolation
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:


// Coordinate input vars
lon_in[$lon_in]={30, 110, 190, 270, 350.0};

    20,55,60,35,20.0 };

// Coordinate variables


// 13.4375, 49.5, 14.09375,
// 16.25, 54, 16.625,
// 19.25, 58.8, 19.325,
// 20.15, 60.24, 20.135 ;

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4.1.19 GSL special functions

As of version 3.9.6 (released January, 2009), NCO can link to the GNU Scientific Library (GSL). ncap2 can access most GSL special functions including Airy, Bessel, error, gamma, beta, hypergeometric, and Legendre functions and elliptical integrals. GSL must be version 1.4 or later. To list the GSL functions available with your NCO build, use ncap2 -f | grep ^gsl.

The function names used by ncap2 mirror their GSL names. The NCO wrappers for GSL functions automatically call the error-handling version of the GSL function when available 52. 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).

Consider the gamma function.
The GSL function prototype is
int gsl_sf_gamma_e(const double x, gsl_sf_result * result) The ncap2 script would be:

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).

Consider a call to a Bessel function with GSL prototype
int gsl_sf_bessel_Jn_e(int n, double x, gsl_sf_result * result)

An ncap2 script would be

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.

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.

Consider the elliptical integral with prototype int gsl_sf_ellint_RD_e(double x, double y, double z, gsl_mode_t mode, gsl_sf_result * result)


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.


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


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
int gsl_sf_legendre_Pl_array( int lmax, double x, double *result_array);

status=gsl_sf_legendre_Pl_array(a1.size()-1, x,&a1);  

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.


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4.1.20 GSL interpolation

As of version 3.9.9 (released July, 2009), NCO has wrappers to the GSL interpolation functions.

Given a set of data points (x1,y1)...(xn, yn) the GSL functions computes a continuous interpolating function Y(x) such that Y(xi) = yi. The interpolation is piecewise smooth, and its behavior at the end-points is determined by the type of interpolation used. For more information consult the GSL manual.

Interpolation with ncap2 is a two stage process. In the first stage, a RAM variable is created from the chosen interpolating function and the data set. This RAM variable holds in memory a GSL interpolation object. In the second stage, points along the interpolating function are calculated. If you have a very large data set or are interpolating many sets then consider deleting the RAM variable when it is redundant. Use the command ram_delete(var_nm).

A simple example


// Ram variable is declared and defined here 



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
All the interpolation functions have been implemented. These are:

Evaluation of Interpolating Types

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4.1.21 GSL least-squares fitting

Least Squares fitting is a method of calculating a straight line through a set of experimental data points in the XY plane. The data maybe weighted or unweighted. For more information please refer to the GSL manual.

These GSL functions fall into three categories:
A) Fitting data to Y=c0+c1*X
B) Fitting data (through the origin) Y=c1*X
C) Multi-parameter fitting (not yet implemented)

Section A

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
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
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


This function calculates y values along the line Y=c0+c1*X

Section B


Input variables: data_x, stride_x, data_y, stride_y, n
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.

Output variables: c1,cov11,sumsq

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


This function calculates y values along the line Y=c1*X

The below example shows gsl_fit_linear() in action

print(c0);  // 0.2
print(c1);  // 2.98545454545


yout=gsl_fit_linear_est(xout, c0,c1, cov00,cov01, cov11, sumsq);

print(yout);  // 3.18545454545 ,9.15636363636, ,12.1418181818 ,33.04

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4.1.22 GSL statistics

Wrappers for most of the GSL Statistical functions have been implemented. The GSL function names include a type specifier (except for type double functions). To obtain the equivalent NCO name simply remove the type specifier; then depending on the data type the appropriate GSL function is called. The weighed statistical functions e.g., gsl_stats_wvariance() are only defined in GSL for floating point types; so your data must of type float or double otherwise ncap2 will emit an error message. To view the implemented functions use the shell command ncap2 -f|grep _stats

GSL Functions

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

Equivalent ncap2 wrapper functions

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

GSL has no notion of missing values or dimensionality beyond one. If your data has missing values which you want ignored in the calculations then use the ncap2 built in aggregate functions( Methods and functions ). The GSL functions operate on a vector of values created from the var_data/stride/n arguments. The ncap wrappers check that there is no bounding error with regard to the size of the data and the final value in the vector.

Some examples


print(a1_avg); // 5.5

print(a1_var); // 16.0

// bounding error, vector attempts to access element a1(10)

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

// Following statements are equvalent

// Following statements are NOT equvalent
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


// Fill with random numbers [0.0,10.0)

// Create a weighting variable



// number of values in data that are greater than 4;

// print min/max of data 

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4.1.23 GSL random number generation

The GSL library has a large number of random number generators. In addition there are a large set of functions for turning uniform random numbers into discrete or continuous probabilty distributions. The random number generator algorithms vary in terms of quality numbers output, speed of execution and maximum number output. For more information see the GSL documentation. The algorithm and seed are set via environment variables, these are picked up by the ncap2 code.

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.


the minimum value returned by the rng algorithm.


the maximum value returned by the rng algorithm.

Uniformly Distributed Random Numbers


This function returns var_in with integers from the chosen rng algorithm. The min and max values depend uoon the chosen rng algorthm.


This function returns var_in with random integers from 0 to n-1. The value n must be less than or equal to the maximum value of the chosen rng algorithm.


This function returns var_in with double-precision numbers in the range [0.0,1). The range includes 0.0 and excludes 1.0.


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);' 
// 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();'
// 10 random numbers from the range 0 - 20
a2 = 1, 1, 6, 9, 11, 13, 13, 15, 16, 19 ;

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

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

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

ncap2 -v -O -s 'rng_max=gsl_rng_max();print(rng_max)'

Random Number Distributions
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

//a2 = 10, 11, 12, 12, 13, 14, 14, 15, 15, 16, 16, 16, 16, 17, 22 ;
//a2 = 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 3, 4, 5 ;
//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;

// b2_avg = 0.756047976787

// b3_avg = -0.00903446534258;
// b3_rms = 0.81162979889;

// b6_avg=15.0588129413

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4.1.24 Examples ncap2

See the and scripts released with NCO for more complete demonstrations of ncap2 functionality (script available on-line at

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'

Average variables of mixed types (result is of type double):

ncap2 -s 'average=(var_float+var_double+var_int)/3' 

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

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

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.

ncap2 may be used to “grow” dimensions, i.e., to increase dimension sizes without altering existing data. Say 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",$lat.size+1); // Define new dimension sizes
new_ORO[$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

Imagine you wish to create a binary flag based on the value of an array. The flag should have value 1.0 where the array exceeds 1.0, and value 0.0 elsewhere. This example creates the binary flag ORO_flg in from the continuous array named ORO in

ncap2 -s 'ORO_flg=(ORO > 1.0)'

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'

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

ncap2 -s 'where(ORO==2.0) ORO=3.0;'

This example uses ncap2 to compute the covariance of two variables. Let the variables u and v be the horizontal wind components. 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. Sometimes this is called the eddy covariance. We will store the covariance in the variable uprmvprm.

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

We have not seen a simpler method to script and execute powerful arithmetic than ncap2.

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

ncap2 -s 'uavg=u.avg($time)' # Correct (recommended)
ncap2 -s  uavg=u.avg('$time') # Correct (and dangerous)
ncap2 -s  uavg=u.avg($time) # Fails ($time = '')
ncap2 -s "uavg=u.avg($time)" # Fails ($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.

A dimension of size one is said to be degenerate. Whether a degenerate record dimension is desirable or not depends on the application. Often a degenerate time dimension is useful, e.g., for 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. Equally 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.

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4.1.25 Intrinsic mathematical methods

ncap2 supports the standard mathematical functions supplied with most operating systems. Standard calculator notation is used for addition +, subtraction -, multiplication *, division /, exponentiation ^, and modulus %. The available elementary mathematical functions are:


Absolute value Absolute value of x. Example: abs(-1) = 1


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


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


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


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


Arc-tangent Arc-tangent of x where x is specified in radians between -pi/2 and pi/2. Example: atan(1.0) = 0.78539816339744830961


Arc-tangent2 Arc-tangent of y/x :Example atan2(1,3) = 0.321689857


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


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


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


Error function Error function of x where x is specified between -1 and 1. Example: erf(1.0) = 0.842701


Complementary error function Complementary error function of x where x is specified between -1 and 1. Example: erfc(1.0) = 0.15729920705


Exponential Exponential of x, e^x. Example: exp(1.0) = 2.71828182845904523536


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


Gamma function Gamma function of x, Gamma(x). The well-known and loved continuous factorial function. Example: gamma(0.5) = sqrt(pi)


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


Natural Logarithm Natural logarithm of x, ln(x). Example: ln(2.71828182845904523536) = 1.0


Natural Logarithm Exact synonym for ln(x).


Base 10 Logarithm Base 10 logarithm of x, log10(x). Example: log(10.0) = 1.0


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


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


Round exactly Nearest integer to x is returned in floating point format. Exceptions are raised for inexact conversions. Example: rint(0.1) = 0


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


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


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


Square Root Square Root of x, sqrt(x). Example: sqrt(4.0) = 2.0


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


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


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 53. and are indicated with an asterisk. Use the ‘-f’ (or ‘fnc_tbl’ or ‘prn_fnc_tbl’) switch to print a complete list of functions supported on your platform. 54

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4.1.26 Operator precedence and associativity

This page lists the ncap2 operators in order of precedence (highest to lowest). Their associativity indicates in what order operators of equal precedence in an expression are applied.

++ --Postfix Increment/DecrementRight to Left
()Parentheses (function call)
.Method call
++ --Prefix Increment/DecrementRight to Left
+ -Unary Plus/Minus
!Logical Not
^Power of OperatorRight to Left
* / %Multiply/Divide/ModulusLeft To Right
+ -Addition/SubtractionLeft To Right
>> <<Fortran style array clippingLeft to Right
< <=Less than/Less than or equal toLeft to Right
> >=Greater than/Greater than or equal to
== !=Equal to/Not equal toLeft to Right
&&Logical ANDLeft to Right
||Logical ORLeft to Right
?:Ternary OperatorRight to Left
=AssignmentRight to Left
+= -=Addition/subtraction assignment
*= /=Multiplication/division assignment

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4.1.27 ID Quoting

In 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:
’avg’ , ’10_+10’,’set_miss’ ’+-90field’ , ’–test’=10.0d

Quote a attribute:
’three@10’, ’set_mss@+10’, ’666@hell’, ’t1@+units’="kelvin"

Quote a dimension:
’$10’, ’$t1–’, ’$–odd’, c1[’$10’,’$t1–’]=23.0d

The following comments are from the netCDF library definitions and detail the naming conventions for each release. netcdf-3.5.1

 * ( [a-zA-Z]|[0-9]|'_'|'-'|'+'|'.'|'|':'|'@'|'('|')' )+
 * Verify that name string is valid CDL syntax, i.e., all characters are
 * alphanumeric, '-', '_', '+', or '.'.
 * Also permit ':', '@', '(', or ')' in names for chemists currently making 
 * use of these characters, but don't document until ncgen and ncdump can 
 * also handle these characters in names.

netcdf-4.0 Final 2008/08/28

 * 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.

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4.2 ncatted netCDF Attribute Editor


ncatted [-a att_dsc] [-a …] [-D dbg] [-h] [--hdr_pad nbr]
[-l path] [-O] [-o output-file] [-p path] [-R] [-r] [--ram_all]
input-file [[output-file]]


ncatted edits attributes in a netCDF file. If you are editing attributes then you are spending too much time in the world of metadata, and ncatted was written to get you back out as quickly and painlessly as possible. ncatted can append, create, delete, modify, and overwrite attributes (all explained below). ncatted allows each editing operation to be applied to every variable in a file. This saves time when changing attribute conventions throughout a file. ncatted is for writing attributes. To read attribute values in plain text, use ncks -m -M, or define something like ncattget as a shell command (see Filters for ncks).

Because 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.

According to the netCDF User Guide, altering metadata in netCDF files does not incur the penalty of recopying the entire file when the new metadata occupies less space than the old metadata. Thus ncatted may run much faster (at least on netCDF3 files) if judicious use of header padding (see Metadata Optimization) was made when producing the input-file. Similarly, using the ‘--hdr_pad’ option with ncatted helps ensure that future metadata changes to output-file occur as swiftly as possible.

When ncatted is used to change the _FillValue attribute, it changes the associated missing data self-consistently. If the internal floating point representation of a missing value, e.g., 1.0e36, differs between two machines then netCDF files produced on those machines will have incompatible missing values. This allows ncatted to change the missing values in files from different machines to a single value so that the files may then be concatenated, e.g., by ncrcat, without losing information. See Missing Values, for more information.

To master ncatted one must understand the meaning of the structure that describes the attribute modification, att_dsc specified by the required option ‘-a’ or ‘--attribute’. This option is repeatable and may be used multiple time in a single ncatted invocation to increase the efficiency of altering multiple attributes. Each att_dsc contains five elements. This makes using ncatted somewhat complicated, though powerful. The att_dsc fields are in the following order:

att_dsc = att_nm, var_nm, mode, att_type, att_val


Attribute name. Example: units


Variable name. Example: pressure, '^H2O'. Regular expressions (see Subsetting Files) are accepted and will select any matching variable (not attribute) names. The names global and group have special meaning.


Edit mode abbreviation. Example: a. See below for complete listing of valid values of mode.


Attribute type abbreviation. Example: c. See below for complete listing of valid values of att_type.


Attribute value. Example: pascal.

There should be no empty space between these five consecutive arguments. The description of these arguments follows in their order of appearance.

The value of att_nm is the name of the attribute to edit. This meaning of this should be clear to all ncatted users. Recall, as mentioned above, that var_nm (but not att_nm) may be specified as a regular expression. 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.

The value of var_nm is the name of the variable containing the attribute (named att_nm) that you want to edit. There are three very important and useful exceptions to this rule. The value of var_nm can also be used to direct ncatted to edit global attributes, or to repeat the editing operation for every group or variable in a file. A value of var_nm of global indicates that att_nm refers to a global (i.e., root-level) attribute, rather than to a particular variable’s attribute. This is the method ncatted supports for editing global attributes. A value of var_nm of group indicates that att_nm refers to all groups, rather than to a particular variable’s or group’s attribute. The operation will proceed to edit group metadata for every group. Finally, if var_nm is left blank, then ncatted attempts to perform the editing operation on every variable in the file. This option may be convenient to use if you decide to change the conventions you use for describing the data.

The value of mode is a single character abbreviation (a, c, d, m, or o) standing for one of five editing modes:


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.


Create. Create variable var_nm attribute att_nm with att_val if att_nm does not yet exist. If var_nm already has an attribute att_nm, there is no effect, so the existing attribute is preserved without change.


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.


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.


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:


Float. Value(s) specified in att_val will be stored as netCDF intrinsic type NC_FLOAT.


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.


Short. Value(s) specified in att_val will be stored as netCDF intrinsic type NC_SHORT.


Char. Value(s) specified in att_val will be stored as netCDF intrinsic type NC_CHAR.


Byte. Value(s) specified in att_val will be stored as netCDF intrinsic type NC_BYTE.


Unsigned Byte. Value(s) specified in att_val will be stored as netCDF intrinsic type NC_UBYTE.


Unsigned Short. Value(s) specified in att_val will be stored as netCDF intrinsic type NC_USHORT.

u, ui, ul

Unsigned Int. Value(s) specified in att_val will be stored as netCDF intrinsic type NC_UINT.

ll, int64

Int64. Value(s) specified in att_val will be stored as netCDF intrinsic type NC_INT64.

ull, uint64

Uint64. Value(s) specified in att_val will be stored as netCDF intrinsic type NC_UINT64.

sng, string

String. Value(s) specified in att_val will be stored as netCDF intrinsic type NC_STRING. Note that ncatted handles type NC_STRING attributes correctly beginning with version 4.3.3 released in July, 2013. Earlier versions fail when asked to handle NC_STRING attributes.

In Delete mode the specification of att_type is optional (and is ignored if supplied).

The value of att_val is what you want to change attribute att_nm to contain. The specification of att_val is optional in Delete (and is ignored) mode. Attribute values for all types besides NC_CHAR must have an attribute length of at least one. Thus att_val may be a single value or one-dimensional array of elements of type att_type. If the att_val is not set or is set to empty space, and the att_type is NC_CHAR, e.g., -a units,T,o,c,"" or -a units,T,o,c,, then the corresponding attribute is set to have zero length. When specifying an array of values, it is safest to enclose att_val in single or double quotes, e.g., -a levels,T,o,s,"1,2,3,4" or -a levels,T,o,s,'1,2,3,4'. The quotes are strictly unnecessary around att_val except when att_val contains characters which would confuse the calling shell, such as spaces, commas, and wildcard characters.

NCO processing of NC_CHAR attributes is a bit like Perl in that it attempts to do what you want by default (but this sometimes causes unexpected results if you want unusual data storage). 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. 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.

Analogous 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.

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' 

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

NCO arithmetic operators may not work as expected on IEEE NaN (short for Not-a-Number) and NaN-like numbers such as positive infinity and negative infinity 55. One way to work-around this problem is to change IEEE NaNs to normal missing values. As of NCO 4.1.0 (March, 2012), ncatted works with NaNs. First set the missing value (i.e., the value of the _FillValue attribute) for the variable(s) in question to the IEEE NaN value.

ncatted -a _FillValue,,o,f,NaN

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

ncatted -a _FillValue,,m,f,1.0e36

Delete all existing units attributes:

ncatted -a units,,d,,

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.

Delete all attributes associated with the tpt variable, and delete all global attributes

ncatted -a ,tpt,d,, -a ,global,d,,

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.

Modify all existing units attributes to meter second-1:

ncatted -a units,,m,c,'meter second-1'

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

ncatted -a units,'^H2O',c,c,'kilogram kilogram-1'

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

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

As of NCO 3.9.6 (January, 2009), ncatted accepts extended regular expressions as arguments for variable names, though not for attribute names. Create isotope attributes for all variables containing ‘H2O’ in their names.

ncatted -a isotope,'^H2O*',c,s,'18'

See Subsetting Files for more details.

As of NCO 4.3.8 (November, 2013), ncatted accepts full and partial group paths in names of attributes, variables, dimensions, and groups.

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

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'

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.

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4.3 ncbo netCDF Binary Operator


ncbo [-3] [-4] [-6] [-7] [-A] [-C] [-c]
[--cnk_dmn nm,sz] [--cnk_map map] [--cnk_min sz] [--cnk_plc plc] [--cnk_scl sz]
[-D dbg] [-d dim,[min][,[max][,[stride]]] [-F] 
[-G gpe_dsc] [-g grp[,…]] [-h] [--hdr_pad nbr]
[-L dfl_lvl] [-l path] [--no_tmp_fl]
[-O] [-o file_3] [-p path] [-R] [-r] [--ram_all]
[-t thr_nbr] [--unn] [-v var[,…]] [-X ...] [-x] [-y op_typ]
file_1 file_2 [file_3]


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’). The valid binary operations for ncbo, their definitions, corresponding values of the op_typ key, and alternate invocations are:


Definition: file_3 = file_1 + file_2
Alternate invocation: ncadd
op_typ key values: ‘add’, ‘+’, ‘addition
Examples: ‘ncbo --op_typ=add’, ‘ncadd


Definition: file_3 = file_1 - file_2
Alternate invocations: ncdiff, ncsub, ncsubtract
op_typ key values: ‘sbt’, ‘-’, ‘dff’, ‘diff’, ‘sub’, ‘subtract’, ‘subtraction
Examples: ‘ncbo --op_typ=-’, ‘ncdiff


Definition: file_3 = file_1 * file_2
Alternate invocations: ncmult, ncmultiply
op_typ key values: ‘mlt’, ‘*’, ‘mult’, ‘multiply’, ‘multiplication
Examples: ‘ncbo --op_typ=mlt’, ‘ncmult


Definition: file_3 = file_1 / file_2
Alternate invocation: ncdivide
op_typ key values: ‘dvd’, ‘/’, ‘divide’, ‘division
Examples: ‘ncbo --op_typ=/’, ‘ncdivide

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 56. Place these characters inside quotes to keep them from being interpreted (globbed) by the shell 57. For example, the following commands are equivalent

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

Normally, ncbo will fail unless an operation type is specified with ‘-y’ (equivalent to ‘--op_typ’). You may create exceptions to this rule to suit your particular tastes, in conformance with your site’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 58. The alternatate invocations supported for each operation type are listed above. Alternatively, users may always define ‘ncadd’ as an alias to ‘ncbo --op_typ=add59.

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. Variables in file_1 or file_2 are broadcast to conform to the corresponding variable in the other input file if necessary60. Now ncbo is completely symmetric with respect to file_1 and file_2, i.e., file_1 - file_2 = - (file_2 - file_1.

Broadcasting a variable means creating data in non-existing dimensions by copying data in existing dimensions. For example, a two dimensional variable in file_2 can be subtracted from a four, three, or two (not one or zero) dimensional variable (of the same name) in file_1. This functionality allows the user to compute anomalies from the mean. In the future, we will broadcast variables in file_1, if necessary to conform to their counterparts in file_2. Thus, presently, the number of dimensions, or rank, of any processed variable in file_1 must be greater than or equal to the rank of the same variable in file_2. Of course, the size of all dimensions common to both file_1 and file_2 must be equal.

When computing anomalies from the mean it is often the case that file_2 was created by applying an averaging operator to a file with initially the same dimensions as file_1 (often file_1 itself). In these cases, creating file_2 with ncra rather than ncwa will cause the ncbo operation to fail. For concreteness say the record dimension in file_1 is time. If file_2 was created by averaging file_1 over the time dimension with the ncra operator (rather than with the ncwa operator), then file_2 will have a time dimension of size 1 rather than having no time dimension at all 61. 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.

ncbo never operates on coordinate variables or variables of type NC_CHAR or NC_STRING. This ensures that coordinates like (e.g., latitude and longitude) are physically meaningful in the output file, file_3. This behavior is hardcoded. ncbo applies special rules to some CF-defined (and/or NCAR CCSM or NCAR CCM fields) such as ORO. See CF Conventions for a complete description. Finally, we note that ncflint (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.

Beginning with NCO version 4.3.1 (May, 2013), ncbo supports group broadcasting. Group broadcasting means processing data based on group patterns in the input file(s) and automatically transferring or transforming groups to the output file. Consider the case where file_1 contains multiple groups each with the variable v1, while file_2 contains v1 only in its top-level (i.e., root) group. Then ncbo will replicate the group structure of file_1 in the output file, file_3. Each group in file_3 contains the output of the corresponding group in file_1 operating on the data in the single group in file_2. An example is provided below.


Say files and each contain 12 months of data. Compute the change in the monthly averages from 1985 to 1986:

ncbo --op_typ=sub
ncbo --op_typ='-'

These commands are all different ways of expressing the same thing.

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.

ncwa -O -a time

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

ncwa -a time

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

ncdiff -v T

Each of the 12 records in 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, the zonal mean of Then we use ncbo to subtract the zonal annual means from the monthly gridpoint data:

ncwa -a lon

This examples works assuming has dimensions time and lon, and that has no time or lon dimension.

Group broadcasting simplifies evaluation of multiple models against observations. Consider the input file which contains multiple top-level groups cesm, ecmwf, and giss, each of which contains the surface air temperature field tas. We wish to compare these models to observations stored in which contains tas only in its top-level (i.e., root) group. It is often the case that many models and/or model simulations exist, whereas only one observational dataset does. We evaluate the models and obtain the bias (difference) between models and observations by subtracting from Then ncbo “broadcasts” (i.e., replicates) the observational data to match the group structure of, subtracts, and then stores the results in the output file, which has the same group structure as

% ncbo -O
% ncks -H -v tas -d time,3
time[3] tas[3]=-1 
time[3] tas[3]=0 
time[3] tas[3]=1 

As a final example, say we have five years of monthly data (i.e., 60 months) stored in 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

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


Now use ncks to group together the five January anomalies in one file, and use ncra to create the average anomaly for all five Januarys. These commands are embedded in a shell loop so they are repeated for all twelve months:

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 foo.${idx}
  ncra foo.${idx} t_anm_8589_${idx}.nc
for idx in 01 02 03 04 05 06 07 08 09 10 11 12; do # Bourne Shell
  ncks -F -d time,${idx},,12 foo.${idx}
  ncra foo.${idx} t_anm_8589_${idx}.nc
foreach idx (01 02 03 04 05 06 07 08 09 10 11 12) # C Shell
  ncks -F -d time,${idx},,12 foo.${idx}
  ncra foo.${idx} t_anm_8589_${idx}.nc

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,${idx},,12 foo.${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

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4.4 nces netCDF Ensemble Statistics


nces [-3] [-4] [-6] [-7] [-A] [-C] [-c]
[--cnk_dmn nm,sz] [--cnk_map map] [--cnk_min sz] [--cnk_plc plc] [--cnk_scl sz]
[-D dbg] [-d dim,[min][,[max][,[stride]]] [-F]
[-G gpe_dsc] [-g grp[,…]] [-h] [--hdf] [--hdr_pad nbr] 
[-L dfl_lvl] [-l path] [-n loop] [--no_tmp_fl] [--nsm_fl|grp] [--nsm_sfx sfx]
[-O] [-o output-file] [-p path] [--ppc ...] [-R] [-r] [--ram_all] [--rth_dbl|flt]
[-t thr_nbr] [--unn] [-v var[,…]] [-X ...] [-x] [-y op_typ]
[input-files] [output-file]


nces performs gridpoint statistics on variables across an arbitrary number (an ensemble) of input-files and/or of input groups within each file. Each file (or group) receives an equal weight. nces was formerly (until NCO version 4.3.9, released December, 2013) known as ncea (netCDF Ensemble Averager)62. For example, nces will average a set of files or groups, weighting each file or group evenly. This is distinct from ncra, which performs statistics only over the record dimension(s) (e.g., time), and weights each record in each record dimension evenly.

The file or group is the logical unit of organization for the results of many scientific studies. Often one wishes to generate a file or group which is the statistical product (e.g., average) of many separate files or groups. This may be to reduce statistical noise by combining the results of a large number of experiments, or it may simply be a step in a procedure whose goal is to compute anomalies from a mean state. In any case, when one desires to generate a file whose statistical properties are equally influenced by all the inputs, then nces is the operator to use.

Variables in the output-file are the same size as the variable hyperslab in each input file or group, and each input file or group must be the same size after hyperslabbing 63 nces does allow files to differ in the record dimension size if the requested record hyperslab (see Hyperslabs) resolves to the same size for all files. nces recomputes the record dimension hyperslab limits for each input file so that coordinate limits may be used to select equal length timeseries from unequal length files. This simplifies analysis of unequal length timeseries from simulation ensembles (e.g., the CMIP3 IPCC AR4 archive).

nces works in one of two modes, file ensembles or group ensembles. File ensembles are the default (equivalent to the old ncea) and may also be explicitly specified by the ‘--nsm_fl’ or ‘--ensemble_file’ switches. To perform statistics on ensembles of groups, a newer feature, use ‘--nsm_grp’ or ‘--ensemble_group’. Members of a group ensemble are groups that share the same structure, parent group, and nesting level. Members must be leaf groups, i.e., not contain any sub-groups. Their contents usually have different values because they are realizations of replicated experiments. In group ensemble mode nces computes the statistics across the ensemble, which may span multiple input files. Files may contain members of multiple, distinct ensembles. However, all ensembles must have at least one member in the first input file. Group ensembles behave as an unlimited dimension of datasets: they may contain an arbitrary and extensible number of realizations in each file, and may be composed from multiple files.

Output statistics in group ensemble mode are stored in the parent group by default. If the ensemble members are /cesm/cesm_01 and /cesm/cesm_02, then the computed statistic will be in /cesm in the output file. The ‘--nsm_sfx’ option instructs nces to instead store output in a new child group of the parent created by attaching the suffix to the parent group’s name, e.g., ‘--nsm_sfx='_avg'’ would store results in the output group /cesm/cesm_avg:

nces --nsm_grp        
nces --nsm_grp --nsm_sfx='_avg'

See Statistics vs. Concatenation, for a description of the distinctions between the statistics tools and concatenators. As a multi-file operator, nces will read the list of input-files from stdin if they are not specified as positional arguments on the command line (see Large Numbers of Files).

Like ncra and ncwa, nces treats coordinate variables as a special case. Coordinate variables are assumed to be the same in all ensemble members, so nces simply copies the coordinate variables that appear in ensemble members directly to the output file. This has the same effect as averaging the coordinate variable across the ensemble, yet does not incur the time- or precision- penalties of actually averaging them. ncra and ncwa allow coordinate variables to be processed only by the linear average operation, regardless of the arithmetic operation type performed on the non-coordinate variables (see Operation Types). Thus it can be said that the three operators (ncra, ncwa, and nces) all average coordinate variables (even though nces simply copies them). All other requested arithmetic operations (e.g., maximization, square-root, RMS) are applied only to non-coordinate variables. In these cases the linear average of the coordinate variable will be returned.


Consider a model experiment which generated five realizations of one year of data, say 1985. Imagine that the experimenter slightly perturbs the initial conditions of the problem before generating each new solution. Assume each file contains all twelve months (a seasonal cycle) of data and we want to produce a single file containing the ensemble average (mean) seasonal cycle. Here the numeric filename suffix denotes the realization number (not the month):

nces 85_0[1-5].nc
nces -n 5,2,1

These three commands produce identical answers. See Specifying Input Files, for an explanation of the distinctions between these methods. The output file,, is the same size as the inputs files. It contains 12 months of data (which might or might not be stored in the record dimension, depending on the input files), but each value in the output file is the average of the five values in the input files.

In the previous example, the user could have obtained the ensemble average values in a particular spatio-temporal region by adding a hyperslab argument to the command, e.g.,

nces -d time,0,2 -d lat,-23.5,23.5 85_??.nc

In this case the output file would contain only three slices of data in the time dimension. These three slices are the average of the first three slices from the input files. Additionally, only data inside the tropics is included.

As of NCO version 4.3.9 (released December, 2013) nces also works with groups (rather than files) as the fundamental unit of the ensemble. Consider two ensembles, /ecmwf and /cesm stored across three input files,, and Ensemble members would be leaf groups with names like /ecmwf/01, /ecmwf/02 etc. and /cesm/01, /cesm/02, etc. These commands average both ensembles:

nces --nsm_grp
nces --nsm_grp --nsm_sfx='_min' --op_typ=min -n 3,1,1
nces --nsm_grp -g cesm -v tas -d time,0,3 -n 3,1,1

The first command stores averages in the output groups /cesm and /ecmwf, while the second stores minima in the output groups /cesm/cesm_min and /ecmwf/ecmwf_min: The third command demonstrates that sub-setting and hyperslabbing work as expected. Note that each input file may contain different numbers of members of each ensemble, as long as all distinct ensembles contain at least one member in the first file.

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4.5 ncecat netCDF Ensemble Concatenator


ncecat [-3] [-4] [-6] [-7] [-A] [-C] [-c]
[--cnk_dmn nm,sz] [--cnk_map map] [--cnk_min sz] [--cnk_plc plc] [--cnk_scl sz]
[-D dbg] [-d dim,[min][,[max][,[stride]]] [-F]
[-G gpe_dsc] [-g grp[,…]] [--gag] [-h] [--hdf] [--hdr_pad nbr]
[-L dfl_lvl] [-l path] [-M] [--md5_digest] [--mrd] [-n loop] [--no_tmp_fl] 
[-O] [-o output-file] [-p path] [--ppc ...] [-R] [-r] [--ram_all] 
[-t thr_nbr] [-u ulm_nm] [--unn] [-v var[,…]] [-X ...] [-x] 
[input-files] [output-file]


ncecat aggregates an arbitrary number of input files into a single output file using using one of two methods. Record AGgregation (RAG), the traditional method employed on (flat) netCDF3 files and still the default method, stores input-files as consecutive records in the output-file. Group AGgregation (GAG) stores input-files as top-level groups in the netCDF4 output-file. Record Aggregation (RAG) makes numerous assumptions about the structure of input files whereas Group Aggregation (GAG) makes none. Both methods are described in detail below. Since ncecat aggregates all the contents of the input files, it can easily produce large output files so it is often helpful to invoke subsetting simultaneously (see Subsetting Files).

RAG makes each variable (except coordinate variables) in each input file into a single record of the same variable in the output file. Coordinate variables are not concatenated, they are instead simply copied from the first input file to the output-file. All input-files must contain all extracted variables (or else there would be “gaps” in the output file).

A new record dimension is the glue which binds together the input file data. The new record dimension is defined in the root group of the output file so it is visible to all sub-groups. Its name is, by default, “record”. This default name can be overridden with the ‘-u ulm_nm’ short option (or the ‘--ulm_nm’ or ‘rcd_nm’ long options).

Each extracted variable must be constant in size and rank across all input-files. The 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. This allows easier gluing/averaging of unequal length timeseries from simulation ensembles (e.g., the CMIP rchive).

Classic (i.e., all netCDF3 and NETCDF4_CLASSIC) output files can contain only one record dimension. ncecat makes room for the new glue record dimension by changing the pre-existing record dimension, if any, in the input files into a fixed dimension in the output file. netCDF4 output files may contain any number of record dimensions, so ncecat need not and does not alter the record dimensions, if any, of the input files as it copies them to the output file.

Group AGgregation (GAG) stores input-files as top-level groups in the output-file. No assumption is made about the size or shape or type of a given object (variable or dimension or group) in the input file. The entire contents of the extracted portion of each input file is placed in its own top-level group in output-file, which is automatically made as a netCDF4-format file.

GAG has two methods to specify group names for the output-file. The ‘-G’ option, or its long-option equivalent ‘--gpe’, takes as argument a group path editing description gpe_dsc of where to place the results. Each input file needs a distinct output group name to avoid namespace conflicts in the output-file. Hence ncecat automatically creates unique output group names based on either the input filenames or the gpe_dsc arguments. When the user provides gpe_dsc (i.e., with ‘-G’), then the output groups are formed by enumerating sequential two-digit numeric suffixes starting with zero, and appending them to the specified group path (see Group Path Editing). When gpe_dsc is not provided (i.e., user requests GAG with ‘--gag’ instead of ‘-G’), then ncecat forms the output groups by stripping the input file name of any type-suffix (e.g., .nc), and all but the final component of the full filename.

ncecat --gag # Output groups 85, 86, 87
ncecat -G 85_ # Output groups 85_00, 85_01, 85_02
ncecat -G 85/ # Output groups 85/00, 85/01, 85/02

With both RAG and GAG the output-file size is the sum of the sizes of the extracted variables in the input files. See Statistics vs. Concatenation, for a description of the distinctions between the various statistics tools and concatenators. As a multi-file operator, ncecat will read the list of input-files from stdin if they are not specified as positional arguments on the command line (see Large Numbers of Files).

Suppress global metadata copying. By default NCO’s multi-file operators copy the global metadata from the first input file into output-file. This helps to preserve the provenance of the output data. However, the use of metadata is burgeoning and 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_spp’ and ‘--global_metadata_suppress’).

Consider five realizations,,, … of 1985 predictions from the same climate model. Then ncecat 85?.nc glues together the individual realizations into the single file, 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.

Concatenating a variable packed with different scales across multiple datasets is beyond the capabilities of ncecat (and ncrcat, the other concatenator (Concatenation). ncecat does not unpack data, it simply copies the data from the input-files, and the metadata from the first input-file, to the output-file. This means that data compressed with a packing convention must use the identical packing parameters (e.g., scale_factor and add_offset) for a given variable across all input files. Otherwise the concatenated dataset will not unpack correctly. The workaround for cases where the packing parameters differ across input-files requires three steps: First, unpack the data using ncpdq. Second, concatenate the unpacked data using ncecat, Third, re-pack the result with ncpdq.


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_0[1-5].nc
ncecat -n 5,2,1

These three commands produce identical answers. See Specifying Input Files, for an explanation of the distinctions between these methods. The output file,, 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

Users are more likely to understand the data processing history when such descriptive coordinates are used.

Consider a file with an existing record dimension named time. and suppose the user wishes to convert time from a record dimension to a non-record dimension. This may be useful, for example, when the user has another use for the record variable. The simplest method is to use ‘ncks --fix_rec_dmn’ but another possibility is to use ncecat followed by ncwa:

ncecat # Convert time to non-record dimension
ncwa -a record # 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.

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4.6 ncflint netCDF File Interpolator


ncflint [-3] [-4] [-6] [-7] [-A] [-C] [-c]
[--cnk_dmn nm,sz] [--cnk_map map] [--cnk_min sz] [--cnk_plc plc] [--cnk_scl sz]
[-D dbg] [-d dim,[min][,[max][,[stride]]]
[-F] [--fix_rec_crd] [-G gpe_dsc] [-g grp[,…]] [-h] [--hdr_pad nbr]
[-i var,val3] [-L dfl_lvl] [-l path] [--no_tmp_fl] 
[-O] [-o file_3] [-p path] [--ppc ...] [-R] [-r] [--ram_all] 
[-t thr_nbr] [--unn] [-v var[,…]] [-w wgt1[,wgt2]] [-X ...] [-x]
file_1 file_2 [file_3]


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. 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 the inverse of the first method in the following sense: When the user specifies the weights directly, ncflint has no work to do besides multiplying the input values by their respective weights and adding together the results to produce the output values. It makes sense to use this when the weights are known a priori.

Another class of problems has the arrival value (i.e., val3) of a particular variable var known a priori. In this case, the implied weights can always be inferred by examining the values of var in the input files. This results in one equation in two unknowns, wgt1 and wgt2: val3 = wgt1*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. Thus any dimensions associated with var must be degenerate, i.e., of size one.

If neither ‘-i’ nor ‘-w’ is specified on the command line, ncflint defaults to weighting each input file equally in the output file. This is equivalent to specifying ‘-w 0.5’ or ‘-w 0.5,0.5’. Attempting to specify both ‘-i’ and ‘-w’ methods in the same command is an error.

ncflint does not interpolate variables of type NC_CHAR and NC_STRING. This behavior is hardcoded.

By default ncflint interpolates or multiplies record coordinate variables (e.g., time is often stored as a record coordinate) not other coordinate variables (e.g., latitude and longitude). This is because ncflint is often used to time-interpolate between existing files, but is rarely used to spatially interpolate. Sometimes however, users wish to multiply entire files by a constant that does not multiply any coordinate variables. The ‘--fix_rec_crd’ switch was implemented for this purpose in NCO version 4.2.6 (March, 2013). It prevents ncflint from multiplying or interpolating any coordinate variables, including record coordinate variables.

Depending on your intuition, ncflint may treat missing values unexpectedly. Consider a point where the value in one input file, say val1, equals the missing value mss_val_1 and, at the same point, the corresponding value in the other input file val2 is not misssing (i.e., does not equal mss_val_2). There are three plausible answers, and this creates ambiguity.

Option one is to set val3 = mss_val_1. The rationale is that ncflint is, at heart, an interpolator and interpolation involving a missing value is intrinsically undefined. ncflint currently implements this behavior since it is the most conservative and least likely to lead to misinterpretation.

Option two is to output the weighted valid data point, i.e., val3 = wgt2*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.


Although it has other uses, the interpolation feature was designed to interpolate file_3 to a time between existing files. Consider input files and 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 which describes the state of the system at time at time = 86, we would use

ncflint -i time,86

Say you have observational data covering January and April 1985 in two files named and, respectively. Then you can estimate the values for February and March by interpolating the existing data as follows. Combine and in a 2:1 ratio to make

ncflint -w 0.667
ncflint -w 0.667,0.333

Multiply by 3 and by -2 and add them together to make

ncflint -w 3,-2

This is an example of a null operation, so should be identical (within machine precision) to

Multiply all the variables except the coordinate variables in the file by by 0.8:

ncflint --fix_rec_crd -w 0.8,0.0

The use of ‘--fix_rec_crd’ ensures, e.g., that the time coordinate, if any, is not scaled (i.e., multiplied).

Add to to obtain, then subtract from to obtain

ncflint -w 1,1
ncflint -w 1,-1

Thus ncflint can be used to mimic some ncbo operations. However 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.

Rescale the dimensional units of the surface pressure prs_sfc from Pascals to hectopascals (millibars)

ncflint -C -v prs_sfc -w 0.01,0.0
ncatted -a units,prs_sfc,o,c,millibar

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4.7 ncks netCDF Kitchen Sink


ncks [-3] [-4] [-5] [-6] [-7] [-A] [-a] [-b binary-file] [-C] [-c] [--cdl]
[--cnk_dmn nm,sz] [--cnk_map map] [--cnk_min sz] [--cnk_plc plc] [--cnk_scl sz]
[-D dbg] [-d dim,[min][,[max][,[stride]]] [-F] [--fix_rec_dmn dim] 
[-G gpe_dsc] [-g grp[,…]] [--grp_xtr_var_xcl] [-H] [-h] [--hdn] [--hdr_pad nbr]
[-L dfl_lvl] [-l path] [-M] [-m] [--mk_rec_dmn dim] [--md5_digest]
[--no_blank] [--no_tmp_fl] [-O] [-o output-file] [-P] [-p path] [--ppc ...]
[-Q] [-q] [-R] [-r] [--rad] [--ram_all] [-s format] 
[-u] [--unn] [-V] [-v var[,…]] [-X ...] [-x] [--xml]
input-file [[output-file]]


The nickname “kitchen sink” is a catch-all because ncks combines most features of ncdump and nccopy with extra features to extract, hyperslab, multi-slab, sub-set, and translate into one versatile utility. ncks extracts (a subset of the) data from input-file and and writes (or pastes) it in netCDF format to output-file, and optionally writes it in flat binary format to binary-file, and optionally prints it to screen.

ncks prints netCDF input data in ASCII, CDL, or NcML text formats stdout, like (an extended version of) ncdump. By default 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’) permits the user to format data using C-style format strings, while option ‘--cdl’ outputs CDL and option ‘--xml’ outputs NcML. ncks exposes many flexible controls over printed output, including CDL and NcML.

Options ‘-5’, ‘-a’, ‘--cdl’, ‘-F’ , ‘-H’, ‘--hdn’, ‘-M’, ‘-m’, ‘-P’, ‘-Q’, ‘-q’, ‘-s’, ‘-u’, ‘-V’, and ‘--xml’ (and their long option counterparts) control the formatted appearance of the data.

ncks extracts (and optionally creates a new netCDF file comprised of) only selected variables from the input file (similar to the old ncextr specification). Only variables and coordinates may be specifically included or excluded—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.

ncks will append variables and attributes from the input-file to output-file if output-file is a pre-existing netCDF file whose relevant dimensions conform to dimension sizes of input-file. The append features of ncks are intended to provide a rudimentary means of adding data from one netCDF file to another, conforming, netCDF file. If naming conflicts exist between the two files, data in output-file is usually overwritten by the corresponding data from input-file. Thus, when appending, the user should backup output-file in case valuable data are inadvertantly overwritten.

If output-file exists, the user will be queried whether to overwrite, append, or exit the ncks call completely. Choosing overwrite destroys the existing output-file and create an entirely new one from the output of the ncks call. Append has differing effects depending on the uniqueness of the variables and attributes output by ncks: If a variable or attribute extracted from input-file does not have a name conflict with the members of output-file then it will be added to output-file without overwriting any of the existing contents of output-file. In this case the relevant dimensions must agree (conform) between the two files; new dimensions are created in output-file as required. When a name conflict occurs, a global attribute from input-file will overwrite the corresponding global attribute from output-file. If the name conflict occurs for a non-record variable, then the dimensions and type of the variable (and of its coordinate dimensions, if any) must agree (conform) in both files. Then the variable values (and any coordinate dimension values) from input-file will overwrite the corresponding variable values (and coordinate dimension values, if any) in output-file 64.

Since there can only be one record dimension in a file, the record dimension must have the same name (though not necessarily the same size) in both files if a record dimension variable is to be appended. If the record dimensions are of differing sizes, the record dimension of output-file will become the greater of the two record dimension sizes, the record variable from input-file will overwrite any counterpart in output-file and fill values will be written to any gaps left in the rest of the record variables (I think). In all cases variable attributes in output-file are superseded by attributes of the same name from input-file, and left alone if there is no name conflict.

Some users may wish to avoid interactive ncks queries about whether to overwrite existing data. For example, batch scripts will fail if ncks does not receive responses to its queries. Options ‘-O’ and ‘-A’ are available to force overwriting existing files and variables, respectively.

Options specific to ncks

The following summarizes features unique to ncks. Features common to many operators are described in Shared features.


Print data to screen alphabetically by group, and alphabetically by variable within each group. This ordering here is used by default in CDL-mode printing, and may be selected for traditional mode printing with ‘-5’ (The switch for invocation may change to something more descriptive in the future).


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’.

-b file

Activate native machine binary output writing to binary file file. Also ‘--fl_bnr’ and ‘--binary-file’. Writing packed variables in binary format is not supported. Metadata is never output to the binary file. Examine the netCDF output file to see the variables in the binary file. Use the ‘-C’ switch, if necessary, to avoid wanting unwanted coordinates to the binary file:

% ncks -O -v one_dmn_rec_var -b ~/bnr.dat -p ~/nco/data ~/
% ls -l ~/bnr.dat | cut -d ' ' -f 5 # 200 B contains time and one_dmn_rec_var
% ls -l ~/bnr.dat
% ncks -C -O -v one_dmn_rec_var -b ~/bnr.dat -p ~/nco/data ~/
% ls -l ~/bnr.dat | cut -d ' ' -f # 40 B contains one_dmn_rec_var only

Change record dimension dim in the input file into a fixed dimension in the output file. Also ‘--no_rec_dmn’. Before NCO version 4.2.5 (January, 2013), the syntax for --fix_rec_dmn did not permit or require the specification of the dimension name dim. This is because the feature only worked on netCDF3 files, which support only one record dimension, so specifying its name was not necessary. netCDF4 files allow an arbitrary number of record dimensions, so the user must specify which record dimension to fix. The decision was made that starting with NCO version 4.2.5 (January, 2013), it is always required to specify the dimension name to fix regardless of the netCDF file type. This keeps the code simple, and is symmetric with the syntax for --mk_rec_dmn, described next.

As of NCO version 4.4.0 (January, 2014), the argument all may be given to ‘--fix_rec_dmn’ to convert all record dimensions to fixed dimensions in the output file. Previously, ‘--fix_rec_dmn’ only allowed one option, the name of a single record dimension to be fixed. Now it is simple to simultaneously fix all record dimensions. This is useful (and nearly mandatory) when flattening netCDF4 files that have multiple record dimensions per group into netCDF3 files (which are limited to at most one record dimension) (see Group Path Editing).

As of NCO version 4.4.0 (January, 2014), the ‘--hdn’ or ‘--hidden’ options print hidden (aka special) attributes. This is equivalent to ‘ncdump -s’. Hidden attributes include: _Format, _DeflateLevel, _Shuffle, _Storage, _ChunkSizes, _Endianness, _Fletcher32, and _NOFILL. Previously ncks ignored all these attributes in CDL/XML modes. Now it prints these attributes as appropriate. As of NCO version 4.4.6 (September, 2014), ‘--hdn’ also prints the extended file format (i.e., the format of the file or server supplying the data) as _SOURCE_FORMAT. Users are referred to the Unidata netCDF Documentation, or the man pages for ncgen or ncdump, for detailed descriptions of the meanings of these attributes.


As of NCO version 4.3.3 (July, 2013), ncks can print extracted data and metadata to screen (i.e., stdout) as valid CDL (network Common data form Description Language). CDL is the human-readable “lingua franca” of netCDF ingested by ncgen and excreted by ncdump. Compare ncks “traditional” with CDL printing:

zender@roulee:~$ ncks -v one ~/nco/data/
one: type NC_FLOAT, 0 dimensions, 1 attribute, chunked? no, compressed? no, packed? no
one size (RAM) = 1*sizeof(NC_FLOAT) = 1*4 = 4 bytes
one attribute 0: long_name, size = 3 NC_CHAR, value = one

one = 1 

zender@roulee:~$ ncks --cdl -v one ~/nco/data/
netcdf in {

    float one ;
    one:long_name = "one" ;

    one = 1 ;

} // group /

ncgen converts CDL-mode output into a netCDF file:

ncks --cdl -v one ~/nco/data/ > ~/in.cdl
ncgen -k netCDF-4 -b -o ~/ ~/in.cdl
ncks -v one ~/

The HDF version of ncgen, often named hncgen or ncgen-hdf, converts netCDF3 CDL into an HDF file:

/usr/hdf4/bin/ncgen -b -o ~/in.hdf ~/in.cdl # HDF ncgen (local builds)
/usr/bin/hncgen     -b -o ~/in.hdf ~/in.cdl # Same as HDF ncgen (RPM packages?)
/usr/bin/ncgen-hdf  -b -o ~/in.hdf ~/in.cdl # Same as HDF ncgen (Debian packages?)
hdp dumpsds ~/in.hdf                        # ncdump/h5dump-equivalent for HDF4

Note that HDF4 does not support netCDF-style groups, so the above commands fail when the input file contains groups. Only netCDF4 and HDF5 support groups. In our experience the HDF ncgen command, by whatever name installed, is not robust and can fail on valid netCDF3 CDL.

--mk_rec_dmn dim

Change existing dimension dim to a record dimension in the output file. This is the most straightforward way of changing a dimension to a/the record dimension, and works fine in most cases. See ncecat netCDF Ensemble Concatenator and ncpdq netCDF Permute Dimensions Quickly for other methods of changing variable dimensionality, including the record dimension.


Turn-on printing to screen or turn-off copying data (not metadata). 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. Also use ‘-H’ to turn-off copying data (not metadata) to an output file. 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
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
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
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
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 

Turn-on printing to screen or turn-off copying global and group metadata. This includes file summary information and global and group attributes. Also ‘--Mtd’ and ‘--Metadata’. By default ncks prints global metadata to screen if no netCDF output file and no variable extraction list is specified (with ‘-v’). Use ‘-M’ to print global metadata to screen if a netCDF output is specified, or if a variable extraction list is specified (with ‘-v’). Use ‘-M’ to turn-off copying of global and group metadata when copying, subsetting, or appending to an output file.

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               # Print  VAs and GAs
ncks          -v one        # Print  VAs not GAs
ncks    -M    -v one        # Print  GAs only
ncks       -m -v one        # Print  VAs only
ncks    -M -m -v one        # Print  VAs and GAs
ncks -O     # Copy   VAs and GAs
ncks -O       -v one # Copy   VAs and GAs
ncks -O -M    -v one # Copy   VAs not GAs
ncks -O    -m -v one # Copy   GAs not VAs
ncks -O -M -m -v one # Copy   only data (no atts)
ncks -A     # Append VAs and GAs
ncks -A       -v one # Append VAs not GAs
ncks -A -M    -v one # Append VAs and GAs
ncks -A    -m -v one # Append only data (no atts)
ncks -A -M -m -v one # Append GAs not VAs

where VAs and GAs denote variable and group/global attributes, respectively.


Turn-on printing to screen or turn-off copying variable metadata. Using ‘-m’ will print variable metadata to screen (similar to ncdump -h). This displays all metadata pertaining to each variable, one variable at a time. This includes information on the storage properties of the variable, such as whether it employs chunking, compression, or packing. Also activated using ‘--mtd’ and ‘--metadata’. The ncks default behavior is to print variable metadata to screen if no netCDF output file is specified. Use ‘-m’ to print variable metadata to screen if a netCDF output is specified. Also use ‘-m’ to turn-off copying of variable metadata to an output file.


Print numeric representation of missing values. As of NCO As of NCO version 4.2.2 (October, 2012), NCO prints missing values as blanks (i.e., the underscore character ‘_’) by default. To enable the old behavior of printing the numeric representation of missing values (e.g., 1.0e36), use the ‘--no_blank’ switch. Also activated using ‘--noblank’ or ‘--no-blank’.


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.


Print quietly, meaning omit dimension names, indices, and coordinate values when printing arrays. Variable (not dimension) indices are printed. Variable names appear flush left in the output:

zender@roulee:~$ ncks -Q -v three_dmn_rec_var -C -H ~/nco/data/              

This helps locate specific variables in lists with many variables and different dimensions. See also the ‘-V’ option, which omits all names and indices and prints only variable values.


Quench (turn-off) all printing to screen. This overrides the setting of all print-related switches, equivalent to -H -M -m when in single-file printing mode. When invoked with -R (see Retaining Retrieved Files), ncks automatically sets -q. This allows ncks to retrieve remote files without automatically trying to print them. Also ‘--quench’.


Retain all dimensions. When invoked with --rad (Retain All Dimensions), ncks copies each dimension in the input file to the output file, regardless of whether the dimension is utilized by any variables. Normally ncks discards “orphan dimensions”, i.e., dimensions not referenced by any variables. This switch allows users to keep non-referenced dimensions in the workflow. When invoked in printing mode, causes orphaned dimensions to be printed (they are not printed by default). Also ‘--retain_all_dimensions’, ‘--orphan_dimensions’, and ‘--rph_dmn’.

-s format

String format for text output. Accepts C language escape sequences and printf() formats. Also ‘--string’ and ‘--sng_fmt’.


Print summary of ncks hidden features. These hidden or secret features are used mainly by developers. They are not supported for general use and may change at any time. This demonstrates conclusively that I cannot keep a secret. Also ‘--ssh’ and ‘--scr’.


Toggle the printing of a variable’s units attribute, if any, with its values. Also ‘--units’.


Print variable values only. Do not print variable and dimension names, indices, and coordinate values when printing arrays.

zender@roulee:~$ ncks -V -v three_dmn_rec_var -C -H ~/nco/data/

See also the ‘-Q’ option, which prints variable names and indices, but not dimension names, indices, or coordinate values when printing arrays. Using ‘-V’ is the same as specifying ‘-Q --no_nm_prn’.

--xml, --ncml

As of NCO version 4.3.3 (July, 2013), ncks can print extracted metadata to screen (i.e., stdout) as XML in NcML, the netCDF Markup Language. ncks supports for XML more completely than of ‘ncdump -x’. With ncks one can translate entire netCDF3 and netCDF4 files into NcML, including metadata and data, using all NCO’s subsetting and hyperslabbing capabilities. Compare ncks “traditional” with XML printing:

zender@roulee:~$ ncks -v one ~/nco/data/
one: type NC_FLOAT, 0 dimensions, 1 attribute, chunked? no, compressed? no, packed? no
one size (RAM) = 1*sizeof(NC_FLOAT) = 1*4 = 4 bytes
one attribute 0: long_name, size = 3 NC_CHAR, value = one

one = 1 

zender@roulee:~$ ncks --xml -v one ~/nco/data/
<?xml version="1.0" encoding="UTF-8"?>
<netcdf xmlns="" location="/home/zender/nco/data/">
  <variable name="one" type="float" shape="">
    <attribute name="long_name" separator="*" value="one" />

XML-mode prints variable metadata and, as of NCO version 4.3.7 (October, 2013), variable data and, as of NCO version 4.4.0 (January, 2014), hidden attributes. That ncks produces correct NcML translations of CDM files for all supported datatypes is verified by comparison to output from Unidata’s toolsUI Java program. Please let us know how to improve XML/NcML features.

ncks provides additional options to configure NcML output: ‘--xml_no_location’, ‘--xml_spr_chr’, and ‘--xml_spr_nmr’. Every NcML configuration option automatically triggers NcML printing, so that specifying ‘--xml’ in addition to a configuration option is redundant and unnecessary. The ‘--xml_no_location’ switch prevents output of the NcML location element. By default the location element is printed with a value equal to the location of the input dataset, e.g., location="/home/zender/". The ‘--xml_spr_chr’ and ‘--xml_spr_nmr’ options customize the strings used as NcML separators for attributes and variables of character-type and numeric-type, respectively. Their default separators are * and “ ” (a space):

zender@roulee:~$ ncks --xml -d time,0,3 -v two_dmn_rec_var_sng
   <values separator="*">abc*bcd*cde*def</values>
 zender@roulee:~$ ncks --xml_spr_chr=', ' -v two_dmn_rec_var_sng
<values separator=", ">abc, bcd, cde, def, efg, fgh, ghi, hij, jkl, klm</values>
zender@roulee:~$ ncks --xml -v one_dmn_rec_var
<values>1 2 3 4 5 6 7 8 9 10</values>
zender@roulee:~$ ncks --xml_spr_nmr=', ' -v one_dmn_rec_var
<values separator=", ">1, 2, 3, 4, 5, 6, 7, 8, 9, 10</values>

Separator elements for strings are a thorny issue. One must be sure that the separator element is not mistaken as a portion of the string. NCO attempts to produce valid NcML and supplies the ‘--xml_spr_chr’ option to work around any difficulties. NCO performs precautionary checks with strstr(val,spr) to identify presence of the separator string (spr) in data (val) and, when it detects a match, automatically switches to a backup separator string (*|*). However limitations of strstr() may lead to false negatives when the separator string occurs in data beyond the first string in multi-dimensional NC_CHAR arrays. Hence, results may be ambiguous to NcML parsers. If problems arise, use ‘--xml_spr_chr’ to specify a multi-character separator that does not appear in the string array and that does not include an NcML formatting characters (e.g., commas, angles, quotes).

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4.7.1 Filters for ncks

We encourage the use of standard UNIX pipes and filters to narrow the verbose output of ncks into more precise targets. For example, to obtain an uncluttered listing of the variables in a file try

ncks -m | 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 this example, ncks -m -v z is too verbose so a robust grep and cut filter is desirable, such as

ncks -M -m | 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
Height stored with a monotonically increasing coordinate
% ncattget Purpose Z
Height stored with a monotonically increasing coordinate
% ncattget history z
% ncattget history global
History global attribute.

Note that case sensitivity has been turned off for the variable and attribute names (and could be turned on by removing the ‘-i’ switch to grep). Furthermore, extended regular expressions may be used for both the variable and attribute names. The next two commands illustrate this by searching for the values of attribute purpose in all variables, and then for all attributes of the variable z:

% ncattget purpose .+
1-D latitude coordinate referred to by geodesic grid variables
1-D longitude coordinate referred to by geodesic grid variables
% ncattget .+ Z
Height stored with a monotonically increasing coordinate

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
# ncattget $att_nm $var_nm $fl_nm : What attributes does variable have?
function ncattget { ncks -M -m ${3} | grep -E -i "^${2} attribute [0-9]+: ${1}" | cut -f 11- -d ' ' | sort ; }
# ncunits $att_val $fl_nm : Which variables have given units?
function ncunits { ncks -M -m ${2} | grep -E -i " attribute [0-9]+: units.+ ${1}" | cut -f 1 -d ' ' | sort ; }
# ncavg $var_nm $fl_nm : What is mean of variable?
function ncavg { ncwa -y avg -O -C -v ${1} ${2} ~/ ; ncks -H -C -v ${1} ~/ | cut -f 3- -d ' ' ; }
# ncavg $var_nm $fl_nm : What is mean of variable?
function ncavg { ncap2 -O -C -v -s "foo=${1}.avg();print(foo)" ${2} ~/ | cut -f 3- -d ' ' ; }
# ncdmnsz $dmn_nm $fl_nm : What is dimension size?
function ncdmnsz { ncks -m -M ${2} | grep -E -i ": ${1}, size =" | cut -f 7 -d ' ' | uniq ; }
# nclist $fl_nm : What variables are in file?
function nclist { ncks -m ${1} | grep -E ': type' | cut -f 1 -d ' ' | sed 's/://' | sort ; }
# ncmax $var_nm $fl_nm : What is maximum of variable?
function ncmax { ncwa -y max -O -C -v ${1} ${2} ~/ ; ncks -H -C -v ${1} ~/ | cut -f 3- -d ' ' ; }
# ncmax $var_nm $fl_nm : What is maximum of variable?
function ncmax { ncap2 -O -C -v -s "foo=${1}.max();print(foo)" ${2} ~/ | cut -f 3- -d ' ' ; }
# ncmdn $var_nm $fl_nm : What is median of variable?
function ncmdn { ncap2 -O -C -v -s "foo=gsl_stats_median_from_sorted_data(${1}.sort());print(foo)" ${2} ~/ | cut -f 3- -d ' ' ; }
# ncrng $var_nm $fl_nm : What is range of variable?
function ncrng { ncap2 -O -C -v -s "foo_min=${1}.min();foo_max=${1}.max();print(foo_min,\"%f\");print(\" to \");print(foo_max,\"%f\")" ${2} ~/ ; }
# ncmode $var_nm $fl_nm : What is mode of variable?
function ncmode { ncap2 -O -C -v -s "foo=gsl_stats_median_from_sorted_data(${1}.sort());print(foo)" ${2} ~/ | cut -f 3- -d ' ' ; }
# ncrecsz $fl_nm : What is record dimension size?
function ncrecsz { ncks -M ${1} | grep -E -i "^Record dimension:" | cut -f 8- -d ' ' ; }
# Csh shell (/bin/csh) users place these in .cshrc
ncattget() { ncks -M -m ${3} | grep -E -i "^${2} attribute [0-9]+: ${1}" | cut -f 11- -d ' ' | sort ; }
ncdmnsz() { ncks -m -M ${2} | grep -E -i ": ${1}, size =" | cut -f 7 -d ' ' | uniq ; }
nclist() { ncks -m ${1} | grep -E ': type' | cut -f 1 -d ' ' | sed 's/://' | sort ; }
ncrecsz() { ncks -M ${1} | grep -E -i "^Record dimension:" | cut -f 8- -d ' ' ; }
# Sh shell (/bin/sh) users place these in .profile
ncattget() { ncks -M -m ${3} | grep -E -i "^${2} attribute [0-9]+: ${1}" | cut -f 11- -d ' ' | sort ; }
ncdmnsz() { ncks -m -M ${2} | grep -E -i ": ${1}, size =" | cut -f 7 -d ' ' | uniq ; }
nclist() { ncks -m ${1} | grep -E ': type' | cut -f 1 -d ' ' | sed 's/://' | sort ; }
ncrecsz() { ncks -M ${1} | grep -E -i "^Record dimension:" | cut -f 8- -d ' ' ; }


View all data in netCDF, printed with Fortran indexing conventions:

ncks -F

Copy the netCDF file to file


Now the file contains all the data from There are, however, two differences between and First, the history global attribute (see History Attribute) will contain the command used to create Second, the variables in 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).

Copy all global attributes (and no variables) from to

ncks -A -x ~/nco/data/ ~/

The ‘-x’ switch tells NCO to use the complement of the extraction list (see Subsetting Files). Since no extraction list is explicitly specified (with ‘-v’), the default is to extract all variables. The complement of all variables is no variables. Without 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.

Copy/append metadata (not data) from variables in one file to variables in a second file. When copying/subsetting/appending files (as opposed to printing them), the copying of data, variable metadata, and global/group metadata are now turned OFF by ‘-H’, ‘-m’, and ‘-M’, respectively. This is the opposite sense in which these switches work when printing a file. One can use these switches to easily replace data or metadata in one file with data or metadata from another:

# Extract naked (data-only) copies of two variables
ncks -h -M -m -O -C -v one,three_dmn_rec_var ~/nco/data/ ~/
# Change values to be sure original values are not copied in following step
ncap2 -O -v -s 'one*=2;three_dmn_rec_var*=0' ~/nco/data/ ~/
# Append metadata (not data!) to
ncks -A -C -H -v one,three_dmn_rec_var ~/ ~/

Variables in now contain data (not metadata) from and metadata (not data) from

Print variable three_dmn_var from file 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
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
% ncks -s '%+16.10f\n' -C -v three_dmn_var
% ncks -s '%f, ' -C -v three_dmn_var
0.000000, 1.000000, ..., 23.000000,

Programmers will recognize these as the venerable C language printf() formatting strings. The second and third options are useful when pasting data into text files like reports or papers. See ncatted netCDF Attribute Editor, for more details on string formatting and special characters.

As of NCO version 4.2.2 (October, 2012), NCO prints missing values as blanks (i.e., the underscore character ‘_’) by default:

% ncks -C -H -v mss_val
lon[0]=0 mss_val[0]=73 
lon[1]=90 mss_val[1]=_ 
lon[2]=180 mss_val[2]=73 
lon[3]=270 mss_val[3]=_ 
% ncks -s '%+5.1f, ' -H -C -v mss_val
+73.0, _, +73.0, _, 

To print the numeric value of the missing value instead of a blank, use the ‘--no_blank’ option.

ncks prints in a verbose fashion by default and supplies a number of switches to pare-down (or even spruce-up) the output. The interplay of the ‘-Q’, ‘-V’, and (otherwise undocumented) ‘--no_nm_prn’ switches yields most desired verbosities:

% ncks -v three_dmn_rec_var -C -H ~/nco/data/
time[0]=1 lat[0]=-90 lon[0]=0 three_dmn_rec_var[0]=1 
% ncks -Q -v three_dmn_rec_var -C -H ~/nco/data/              
% ncks -V -v three_dmn_rec_var -C -H ~/nco/data/
% ncks -Q --no_nm_prn -v three_dmn_rec_var -C -H ~/nco/data/
% ncks --no_nm_prn -v three_dmn_rec_var -C -H ~/nco/data/
1 -90 0 1

One dimensional arrays of characters stored as netCDF variables are automatically printed as strings, whether or not they are NUL-terminated, e.g.,

ncks -v fl_nm

The %c formatting code is useful for printing multidimensional arrays of characters representing fixed length strings

ncks -s '%c' -v fl_nm_arr

Using the %s format code on strings which are not NUL-terminated (and thus not technically strings) is likely to result in a core dump.

Create netCDF containing all variables, and any associated coordinates, except variable time, from netCDF

ncks -x -v time

As a special case of this, consider how to remove a variable such as time_bounds that is identified in a CF Convention (see CF Conventions) compliant ancillary_variables, bounds, or coordinates attribute. NCO subsetting assumes the user wants all ancillary variables, axes, bounds and coordinates associated with all extracted variables (see Subsetting Coordinate Variables). Hence to exclude a ancillary_variables, bounds, or coordinates variable while retaining the “parent” variable (here time), one must use the ‘-C’ switch:

ncks -C -x -v time_bounds

The ‘-C’ switch tells the operator NOT to necessarily include all the CF ancillary variables, axes, bounds, and coordinates. Hence the output file will contain time and not time_bounds.

Extract variables time and pressure from netCDF If does not exist it will be created. Otherwise the you will be prompted whether to append to or to overwrite

ncks -v time,pressure
ncks -C -v time,pressure

The first version of the command creates an which contains time, pressure, and any coordinate variables associated with pressure. The from the second version is guaranteed to contain only two variables time and pressure.

Create netCDF containing all variables from file 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 0.3 < 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 

Assume the domain of the monotonically increasing longitude coordinate lon is 0 < lon < 360. Here, lon is an example of a wrapped coordinate. ncks will extract a hyperslab which crosses the Greenwich meridian simply by specifying the westernmost longitude as min and the easternmost longitude as max, as follows:

ncks -d lon,260.0,45.0

For more details See Wrapped Coordinates.

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4.8 ncpdq netCDF Permute Dimensions Quickly


ncpdq [-3] [-4] [-6] [-7] [-A] [-a [-]dim[,…]] [-C] [-c]
[--cnk_dmn nm,sz] [--cnk_map map] [--cnk_min sz] [--cnk_plc plc] [--cnk_scl sz]
[-D dbg] [-d dim,[min][,[max][,[stride]]]
[-F] [-G gpe_dsc] [-g grp[,…]] [-h] [--hdf] [--hdr_pad nbr]
[-L dfl_lvl] [-l path] [-M pck_map] [--mrd] [--no_tmp_fl] 
[-O] [-o output-file] [-P pck_plc] [-p path] [--ppc ...]
[-R] [-r] [--ram_all] [-t thr_nbr] [-U] [--unn] [-v var[,…]] [-X ...] [-x]
input-file [output-file]


ncpdq performs one (not both) of two distinct functions: packing or dimension permutation. 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.

Packing and Unpacking Functions

The ncpdq packing (and unpacking) algorithms are described in Methods and functions, and are also implemented in ncap2. ncpdq extends the functionality of these algorithms by providing high level control of the packing policy so that users can consistently pack (and unpack) entire files with one command. The user specifies the desired packing policy with the ‘-P’ switch (or its long option equivalents, ‘--pck_plc’ and ‘--pack_policy’) and its pck_plc argument. Four packing policies are currently implemented:

Packing (and Re-Packing) Variables [default]

Definition: Pack unpacked variables, re-pack packed variables
Alternate invocation: ncpack
pck_plc key values: ‘all_new’, ‘pck_all_new_att

Packing (and not Re-Packing) Variables

Definition: Pack unpacked variables, copy packed variables
Alternate invocation: none
pck_plc key values: ‘all_xst’, ‘pck_all_xst_att

Re-Packing Variables

Definition: Re-pack packed variables, copy unpacked variables
Alternate invocation: none
pck_plc key values: ‘xst_new’, ‘pck_xst_new_att


Definition: Unpack packed variables, copy unpacked variables
Alternate invocation: ncunpack
pck_plc key values: ‘upk’, ‘unpack’, ‘pck_upk

Equivalent key values are fully interchangeable. Multiple equivalent options are provided to satisfy disparate needs and tastes of NCO users working with scripts and from the command line.

Regardless of the packing policy selected, ncpdq no longer (as of NCO version 4.0.4 in October, 2010) packs coordinate variables, or the special variables, weights, and other grid properties described in CF Conventions. Prior ncpdq versions treated coordinate variables and grid properties no differently from other variables. However, coordinate variables are one-dimensional, so packing saves little space on large files, and the resulting files are difficult for humans to read. ncpdq will, of course, unpack coordinate variables and weights, for example, in case some other, non-NCO software packed them in the first place.

Concurrently, Gaussian and area weights and other grid properties are often used to derive fields in re-inflated (unpacked) files, so packing such grid properties causes a considerable loss of precision in downstream data processing. If users express strong wishes to pack grid properties, we will implement new packing policies. An immediate workaround for those needing to pack grid properties now, is to use the ncap2 packing functions or to rename the grid properties prior to calling ncpdq. We welcome your feedback.

To reduce required memorization of these complex policy switches, ncpdq may also be invoked via a synonym or with switches that imply a particular policy. ncpack is a synonym for ncpdq and behaves the same in all respects. Both ncpdq and ncpack assume a default packing policy request of ‘all_new’. Hence ncpack may be invoked without any ‘-P’ switch, unlike ncpdq. Similarly, ncunpack is a synonym for ncpdq except that ncpack implicitly assumes a request to unpack, i.e., ‘-P pck_upk’. Finally, 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. Note 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.

The ncpdq packing algorithms must know to which type particular types of input variables are to be packed. The correspondence between the input variable type and the output, packed type, is called the packing map. The user specifies the desired packing map with the ‘-M’ switch (or its long option equivalents, ‘--pck_map’ and ‘--map’) and its pck_map argument. Five packing maps are currently implemented:

Pack Floating Precisions to NC_SHORT [default]

Definition: Pack floating precision types to NC_SHORT
pck_map key values: ‘flt_sht’, ‘pck_map_flt_sht

Pack Floating Precisions to NC_BYTE

Definition: Pack floating precision types to NC_BYTE
pck_map key values: ‘flt_byt’, ‘pck_map_flt_byt

Pack Higher Precisions to NC_SHORT

Definition: Pack higher precision types to NC_SHORT
Types copied instead of packed: [NC_SHORT,NC_USHORT,NC_CHAR,NC_BYTE,NC_UBYTE]
pck_map key values: ‘hgh_sht’, ‘pck_map_hgh_sht

Pack Higher Precisions to NC_BYTE

Definition: Pack higher precision types to NC_BYTE
Types copied instead of packed: [NC_CHAR,NC_BYTE,NC_UBYTE]
pck_map key values: ‘hgh_byt’, ‘pck_map_hgh_byt

Pack to Next Lesser Precision

Definition: Pack each type to type of next lesser size
Types copied instead of packed: [NC_CHAR,NC_BYTE,NC_UBYTE]
pck_map key values: ‘nxt_lsr’, ‘pck_map_nxt_lsr

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%.

The 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.

Dimension Permutation

ncpdq re-shapes variables in input-file by re-ordering and/or reversing dimensions specified in the dimension list. The dimension list is a whitespace-free, comma separated list of dimension names, optionally prefixed by negative signs, that follows the ‘-a’ (or long options ‘--arrange’, ‘--permute’, ‘--re-order’, or ‘--rdr’) switch. To re-order variables by a subset of their dimensions, specify these dimensions in a comma-separated list following ‘-a’, e.g., ‘-a lon,lat’. To reverse a dimension, prefix its name with a negative sign in the dimension list, e.g., ‘-a -lat’. Re-ordering and reversal may be performed simultaneously, e.g., ‘-a lon,-lat,time,-lev’.

Users may specify any permutation of dimensions, including permutations which change the record dimension identity. The record dimension is re-ordered like any other dimension. This unique ncpdq capability makes it possible to concatenate files along any dimension. See Concatenation for a detailed example. The 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 65.

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. Second, 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.


Pack and unpack all variables in file and store the results in

ncpdq # Same as ncpack
ncpdq -P all_new -M flt_sht # Defaults
ncpdq -P all_xst
ncpdq -P upk # Same as ncunpack
ncpdq -U # Same as ncunpack

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

Many different packing maps may be used to construct a given file by performing the packing on subsets of variables (e.g., with ‘-v’) and using the append feature with ‘-A’ (see Appending Variables).

Users may wish to unpack data packed with the HDF convention, and then re-pack it with the netCDF convention so that all their datasets use the same packing convention prior to intercomparison.

# One-step procedure: For NCO 4.4.0+, netCDF 4.3.1+
# 1. Convert, unpack, and repack HDF file into netCDF file
ncpdq --hdf_upk -P xst_new modis.hdf # HDF4 files
ncpdq --hdf_upk -P xst_new modis.h5 # HDF5 files

# One-step procedure: For NCO 4.3.7--4.3.9
# 1. Convert, unpack, and repack HDF file into netCDF file
ncpdq --hdf4 --hdf_upk -P xst_new modis.hdf # HDF4
ncpdq        --hdf_upk -P xst_new modis.h5 # HDF5

# Two-step procedure: For NCO 4.3.6 and earlier
# 1. Convert HDF file to netCDF file
ncl_convert2nc modis.hdf
# 2. Unpack using HDF convention and repack using netCDF convention
ncpdq --hdf_upk -P xst_new

NCO now 66 automatically detects HDF4 files. In this case it produces an output file which preserves the HDF packing used in the input file. The ncpdq command first unpacks all packed variables using the HDF unpacking algorithm (as specified by ‘--hdf_upk’), and then repacks those same variables using the netCDF algorithm (because that is the only algorithm NCO packs with). As described above the ‘--P xst_new’ packing policy only repacks variables that are already packed. Not-packed variables are copied directly without loss of precision 67.

Re-order file so that the dimension lon always precedes the dimension lat and store the results in

ncpdq -a lon,lat
ncpdq -v three_dmn_var -a lon,lat

The first command re-orders every variable in the input file. The second command extracts and re-orders only the variable three_dmn_var.

Suppose the dimension lat represents latitude and monotonically increases increases from south to north. Reversing the lat dimension means re-ordering the data so that latitude values decrease monotonically from north to south. Accomplish this with

% ncpdq -a -lat
% ncks -C -v lat
% ncks -C -v lat

This operation reversed the latitude dimension of all variables. Whitespace immediately preceding the negative sign that specifies dimension reversal may be dangerous. Quotes and long options can help protect negative signs that should indicate dimension reversal from being interpreted by the shell as dashes that indicate new command line switches.

ncpdq -a -lat # Dangerous? Whitespace before "-lat"
ncpdq -a '-lat' # OK. Quotes protect "-" in "-lat"
ncpdq -a lon,-lat # OK. No whitespace before "-"
ncpdq --rdr=-lat # Preferred. Uses "=" not whitespace

To create the mathematical transpose of a variable, place all its dimensions in the dimension re-order list in reversed order. This example creates the transpose of three_dmn_var:

% ncpdq -a lon,lev,lat -v three_dmn_var
% ncks -C -v three_dmn_var
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
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

To completely reverse the storage order of a variable, include all its dimensions in the re-order list, each prefixed by a negative sign. This example reverses the storage order of three_dmn_var:

% ncpdq -a -lat,-lev,-lon -v three_dmn_var
% ncks -C -v three_dmn_var
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
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 # Create degenerate record dimension named "time"

Now consider a file with all dimensions, including time, fixed (non-record). Suppose the user wishes to convert time from a fixed dimension to a record dimension. This may be useful, for example, when the user wishes to append additional time slices to the data. As of NCO version 4.0.1 (April, 2010) the preferred method for doing this is with ncks:

ncks -O --mk_rec_dmn time # Change "time" to record dimension

Prior to 4.0.1, the procedure to change an existing fixed dimension into a record dimension required three separate commands, ncecat followed by ncpdq, and then ncwa. The recommended method is now to use ‘ncks --fix_rec_dmn’, yet it is still instructive to present the original procedure, as it shows how multiple operators can achieve the same ends by different means:

ncecat -O # Add degenerate record dimension named "record"
ncpdq -O -a time,record # Switch "record" and "time"
ncwa -O -a record # 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.

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4.9 ncra netCDF Record Averager


ncra [-3] [-4] [-6] [-7] [-A] [-C] [-c]
[--cnk_dmn nm,sz] [--cnk_map map] [--cnk_min sz] [--cnk_plc plc] [--cnk_scl sz]
[-D dbg] [-d dim,[min][,[max][,[stride][,[subcycle]]]] [-F]
[-G gpe_dsc] [-g grp[,…]] [-h] [--hdf] [--hdr_pad nbr] 
[-L dfl_lvl] [-l path] [--mro] [-n loop] [--no_tmp_fl]
[-O] [-o output-file] [-p path] [--ppc ...] [-R] [-r] [--ram_all] [--rec_apn] [--rth_dbl|flt]
[-t thr_nbr] [--unn] [-v var[,…]] [-X ...] [-x] [-y op_typ]
[input-files] [output-file]


ncra averages record variables across an arbitrary number of input-files. The record dimension is, by default, retained as a degenerate (size 1) dimension in the output variables. See Statistics vs. Concatenation, for a description of the distinctions between the various statistics tools and concatenators. As a multi-file operator, ncra will read the list of input-files from stdin if they are not specified as positional arguments on the command line (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). Hyperslabs of the record dimension which include more than one file work correctly. ncra 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. ncra always averages coordinate variables regardless of the arithmetic operation type performed on the non-coordinate variables. (see Operation Types).


Average files,, … along the record dimension, and store the results in

ncra 8[56789].nc
ncra -n 5,2,1

These three methods produce identical answers. See Specifying Input Files, for an explanation of the distinctions between these methods.

Assume the files,, … each contain a record coordinate time of length 12 defined such that the third record in 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
ncra -F -d time,12,14

The file is superfluous, but does not cause an error. The ‘-F’ turns on the Fortran (1-based) indexing convention. The following uses the stride option to average all the March temperature data from multiple input files into a single output file

ncra -F -d time,3,,12 -v temperature

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

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4.10 ncrcat netCDF Record Concatenator


ncrcat [-3] [-4] [-6] [-7] [-A] [-C] [-c]
[--cnk_dmn nm,sz] [--cnk_map map] [--cnk_min sz] [--cnk_plc plc] [--cnk_scl sz]
[-D dbg] [-d dim,[min][,[max][,[stride][,[subcycle]]]] [-F]
[-G gpe_dsc] [-g grp[,…]] [-h] [--hdr_pad nbr]
[-L dfl_lvl] [-l path] [--md5_digest] [-n loop] [--no_tmp_fl]
[-O] [-o output-file] [-p path] [--ppc ...] [-R] [-r] [--ram_all] [--rec_apn]
[-t thr_nbr] [--unn] [-v var[,…]] [-X ...] [-x] 
[input-files] [output-file]


ncrcat concatenates record variables across an arbitrary number of input-files. The final record dimension is by default the sum of the lengths of the record dimensions in the input files. See Statistics vs. Concatenation, for a description of the distinctions between the various statistics tools and concatenators. As a multi-file operator, ncrcat will read the list of input-files from stdin if they are not specified as positional arguments on the command line (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). Hyperslabs along the record dimension that span more than one file are handled correctly. ncra supports the stride argument to the ‘-d’ hyperslab option for the record dimension only, stride is not supported for non-record dimensions.

Concatenating a variable packed with different scales multiple datasets is beyond the capabilities of ncrcat (and ncecat, the other concatenator (Concatenation). ncrcat does not unpack data, it simply copies the data from the input-files, and the metadata from the first input-file, to the output-file. This means that data compressed with a packing convention must use the identical packing parameters (e.g., scale_factor and add_offset) for a given variable across all input files. Otherwise the concatenated dataset will not unpack correctly. The workaround for cases where the packing parameters differ across input-files requires three steps: First, unpack the data using ncpdq. Second, concatenate the unpacked data using ncrcat, Third, re-pack the result with ncpdq.

ncrcat applies special rules to ARM convention time fields (e.g., time_offset). See ARM Conventions for a complete description.


Concatenate files,, … along the record dimension, and store the results in

ncrcat 8[56789].nc
ncrcat -n 5,2,1

These three methods produce identical answers. See Specifying Input Files, for an explanation of the distinctions between these methods.

Assume the files,, … each contain a record coordinate time of length 12 defined such that the third record in 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
ncrcat -F -d time,12,14

The file 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.

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

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

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4.11 ncrename netCDF Renamer


ncrename [-a old_name,new_name] [-a …] [-D dbg] 
[-d old_name,new_name] [-d …] [-g old_name,new_name] [-g …] 
[-h] [--hdf] [--hdr_pad nbr] [-l path] [-O] [-o output-file] [-p path] [-R] [-r] 
[-v old_name,new_name] [-v …]
input-file [[output-file]]


ncrename renames netCDF dimensions, variables, attributes, and groups. Each object that has a name in the list of old names is renamed using the corresponding name in the list of new names. All the new names must be unique. Every old name must exist in the input file, unless the old name is preceded by the period (or “dot”) character ‘.’. The validity of old_name is not checked prior to the renaming. Thus, if old_name is specified without the ‘.’ prefix that indicates the presence of old_name is optional, and old_name is not present in input-file, then ncrename will abort. The new_name should never be prefixed by a ‘.’ (or else the period will be included as part of the new name). As of NCO version 4.4.6 (released October, 2014), the old_name and new_name arguments may include (or be, for groups) partial or full group paths. The OPTIONS and EXAMPLES show how to select specific variables whose attributes are to be renamed.

Caveat lector: Unforunately from 2007–present (October, 2014) the netCDF library (versions 4.0.0–4.3.2) contained bugs or limitations that sometimes prevent NCO from correctly renaming coordinate variables, dimensions, and groups in netCDF4 files. (To our knowledge the netCDF library calls for renaming always work well on netCDF3 files so one workaround to some netCDF4 issues is convert to netCDF3, rename, then convert back). To understand the renaming limitations associated with particular netCDF versions, read the ncrename documentation below in its entirety.

Although ncrename supports full pathnames for both old_name and new_name, this is really “window dressing”. The full-path to new_name must be identical to the full-path to old_name in all classes of objects (attributes, variables, dimensions, or groups). In other words, ncrename can change only the local names of objects, it cannot change the location of the object in the group hierarchy within the file. Hence using a full-path in new_name is redundant. The object name is the terminal path component of new_name and this object must already exist in the group specified by the old_name path.

ncrename is an exception to the normal NCO rule that the user will be interactively prompted before an existing file is changed, and that a temporary copy of an output file is constructed during the operation. If only input-file is specified, then ncrename changes object names in the input-file in place without prompting and without creating a temporary copy of input-file. This is because the renaming operation is considered reversible if the user makes a mistake. The new_name can easily be changed back to old_name by using ncrename one more time.

Note that renaming a dimension to the name of a dependent variable can be used to invert the relationship between an independent coordinate variable and a dependent variable. In this case, the named dependent variable must be one-dimensional and should have no missing values. Such a variable will become a coordinate variable.

According to the netCDF User Guide, renaming objects in netCDF files does not incur the penalty of recopying the entire file when the new_name is shorter than the old_name. Thus ncrename may run much faster (at least on netCDF3 files) if judicious use of header padding (see Metadata Optimization) was made when producing the input-file. Similarly, using the ‘--hdr_pad’ option with ncrename helps ensure that future metadata changes to output-file occur as swifly as possible.


-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 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 from the attribute name. If the attribute is uniquely named (no other variables contain the attribute) then the old_var_name@old_att_name syntax is redundant. The old_var_name variable names global and group have special significance. They indicate that old_att_nm should only be renamed where it occurs as a global (i.e., root group) metadata attribute (for global), or (for group) as any group attribute, and not where it occurs as a variable attribute. The var_name@att_name syntax is accepted, though not required, for the new_name.

-d old_name,new_name

Dimension renaming. The old and new names of the dimension are specified with ‘-d’ (or ‘--dmn’, ‘--dimension’) by the associated old_name and new_name values. This option may be specified more than once.

-g old_name,new_name

Group renaming. The old and new names of the group are specified with ‘-g’ (or ‘--grp’, ‘--group’) by the associated old_name and new_name values. This option may be specified more than once. This functionality is only available in NCO version 4.3.7 (October, 2013) or later, and only when built on netCDF library version 4.3.1-rc1 (August, 2013) or later.

-v old_name,new_name

Variable renaming. The old and new names of the variable are specified with ‘-v’ (or ‘--variable’) by the associated old_name and new_name values. This option may be specified more than once.


Rename the variable p to pressure and t to temperature in netCDF 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

Rename the attribute long_name to largo_nombre in the variable u, and no other variables in netCDF

ncrename -a u@long_name,largo_nombre

Rename the group g8 to g20 in netCDF4 file

ncrename -g g8,g20

Rename the variable /g1/lon to longitude in netCDF4

ncrename -v /g1/lon,longitude
ncrename -v /g1/lon,/g1/longitude # Alternate

ncrename does not automatically attach dimensions to variables of the same name. This is done to make renaming an easy way to change whether a variable is a coordinate. If you want to rename a coordinate variable so that it remains a coordinate variable, you must separately rename both the dimension and the variable:

ncrename -d lon,longitude -v lon,longitude

Unfortunately, the netCDF4 library had a longstanding bug (all versions until 4.3.1-rc5 released in December, 2013) that crashed NCO when performing this operation. Simultaneously renaming variables and dimensions in netCDF4 files with earlier versions of netCDF is impossible; it must instead be done in two separate ncrename invocations (e.g., first rename the variable, then rename the dimension) to avoid triggering the libary bug.

A related bug causes unintended side-effects with ncrename also built with all versions of the netCDF4 library until 4.3.1-rc5 released in December, 2013): This bug caused renaming either a dimension or its associated coordinate variable (not both, which would fail as above) in a netCDF4 file to inadvertently rename both:

# Demonstate bug in netCDF4/HDF5 library prior to netCDF-4.3.1-rc5
ncks -O -h -m -M -4 -v lat_T42 ~/nco/data/ ~/
ncrename -O -v lat_T42,lat ~/ ~/ # Also renames dimension
ncrename -O -d lat_T42,lat ~/ ~/ # Also renames variable

To avoid this faulty behavior, either build NCO with netCDF version 4.3.1-rc5 or later, or convert the file to netCDF3 first, then rename as intended, then convert back. Unforunately while this bug and the related coordinate renaming bug were fixed in 4.3.1-rc5 (released in December, 2013), a new and related bug was discovered in October 2014.

Another netCDF bug that causes unintended side-effects with ncrename affects (at least) versions 4.3.1–4.3.2 and all snapshots of the netCDF4 library until January, 2015. This bug (fixed in 4.3.3 in February, 2015) corrupts values or renamed netCDF4 coordinate variables (i.e., variables with underlying dimensions of the same name) and other (non-coordinate) variables that include an underlying dimension that was renamed. In other words, renaming coordinate variables and dimensions succeeds yet it corrupts the values contained by the affected array variables. This bug corrupts affected variables by replacing their values with the default _FillValue for that variable’s type:

# Demonstate bug in netCDF4/HDF5 library prior to netCDF-4.3.3
ncks -O -4 -C -M -v lat ~/nco/data/ ~/
ncrename -O -v lat,tal ~/ ~/ # Broken until netCDF-4.3.3
ncrename -O -d lat,tal ~/ ~/ # Broken until netCDF-4.3.3
ncrename -O -d lat,tal -v lat,tal ~/ ~/ # Broken too
ncks --cdl ~/

To avoid this faulty behavior, either build NCO with netCDF version 4.3.3 or later, or convert the file to netCDF3 first, then rename as intended, then convert back. This bug does not affect renaming of groups or of attributes.

The final netCDF bug that causes unintended side-effects with ncrename affects only snapshots from January–February, 2015, and released version 4.3.3 (February, 2015). It was fixed in netCDF version (March, 2015). This bug causes renamed attributes of coordinate variables in netCDF4 to files to disappear:

# Demonstate bug in netCDF4/HDF5 library netCDF-4.3.3
ncrename -O -h -a /g1/lon@units,new_units ~/nco/data/ ~/ 
ncks -v /g1/lon ~/ # Shows units and new_units are both gone

As of netCDF version (March, 2015) there are no known renaming bugs.

Create netCDF identical to 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 

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.

Rename the global or group attribute Convention to Conventions

ncrename -a Convention,Conventions # Variable and group atts.
ncrename -a .Convention,Conventions # Variable and group atts.
ncrename -a @Convention,Conventions # Group atts. only
ncrename -a @.Convention,Conventions # Group atts. only
ncrename -a global@Convention,Conventions # Group atts. only
ncrename -a .global@.Convention,Conventions # Group atts. only
ncrename -a global@Convention,Conventions # Global atts. only
ncrename -a .global@.Convention,Conventions # Global atts. only

The examples without the @ character attempt to change the attribute name in both Global or Group and variable attributes. The examples with the @ character attempt to change only global and group Convention attributes, and leave unchanged any Convention attributes attached directly to variables. Attributes prefixed with a period (.Convention) need not be present. Attributes not prefixed with a period (Convention) must be present. Variables prefixed with a period (. or .global) need not be present. Variables not prefixed with a period (global) must be present.

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4.12 ncwa netCDF Weighted Averager


ncwa [-3] [-4] [-6] [-7] [-A] [-a dim[,…]] [-B mask_cond] [-b] [-C] [-c]
[--cnk_dmn nm,sz] [--cnk_map map] [--cnk_min sz] [--cnk_plc plc] [--cnk_scl sz]
[-D dbg] [-d dim,[min][,[max][,[stride]]] [-F]
[-G gpe_dsc] [-g grp[,…]] [-h] [--hdr_pad nbr] [-I]
[-L dfl_lvl] [-l path] [-M mask_val] [-m mask_var] [-N] [--no_tmp_fl]
[-O] [-o output-file] [-p path] [--ppc ...] [-R] [-r] [--ram_all] [--rth_dbl|flt]
[-T mask_comp] [-t thr_nbr] [--unn] [-v var[,…]] [-w weight]
[-X ...] [-x] [-y op_typ]
input-file [output-file]


ncwa averages variables in a single file over arbitrary dimensions, with options to specify weights, masks, and normalization. See Statistics vs. Concatenation, for a description of the distinctions between the various statistics tools and concatenators. The default behavior of ncwa is to arithmetically average every numerical variable over all dimensions and to produce a scalar result for each.

Averaged dimensions are, by default, eliminated as dimensions. Their corresponding coordinates, if any, are output as scalar variables. The ‘-b’ switch (and its long option equivalents ‘--rdd’ and ‘--retain-degenerate-dimensions’) causes ncwa to retain averaged dimensions as degenerate (size 1) dimensions. This maintains the association between a dimension (or coordinate) and variables after averaging and simplifies, for instance, later concatenation along the degenerate dimension.

To average variables over only a subset of their dimensions, specify these dimensions in a comma-separated list following ‘-a’, e.g., ‘-a time,lat,lon’. 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.

mask_var and weight, if specified, are broadcast to conform to the variables being averaged. The rank of variables is reduced by the number of dimensions which they are averaged over. Thus arrays which are one dimensional in the input-file and are averaged by ncwa appear in the output-file as scalars. This allows the user to infer which dimensions may have been averaged. Note that that it is impossible for ncwa to make make a weight or mask_var of rank W conform to a var of rank V if W > V. This situation often arises when coordinate variables (which, by definition, are one dimensional) are weighted and averaged. ncwa assumes you know this is impossible and so ncwa does not attempt to broadcast weight or mask_var to conform to var in this case, nor does ncwa print a warning message telling you this, because it is so common. Specifying dbg > 2 does cause ncwa to emit warnings in these situations, however.

Non-coordinate variables are always masked and weighted if specified. Coordinate variables, however, may be treated specially. By default, an averaged coordinate variable, e.g., latitude, appears in output-file averaged the same way as any other variable containing an averaged dimension. In other words, by default ncwa weights and masks coordinate variables like all other variables. This design decision was intended to be helpful but for some applications it may be preferable not to weight or mask coordinate variables just like all other variables. Consider the following arguments to ncwa: -a latitude -w lat_wgt -d latitude,0.,90. where lat_wgt is a weight in the latitude dimension. Since, by default ncwa weights coordinate variables, the value of latitude in the output-file depends on the weights in lat_wgt and is not likely to be 45.0, the midpoint latitude of the hyperslab. Option ‘-I’ overrides this default behavior and causes ncwa not to weight or mask coordinate variables 68. 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. 69.

As 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).

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4.12.1 Mask condition

The mask condition has the syntax mask_var mask_comp mask_val. The preferred method to specify the mask condition is in one string with the ‘-B’ or ‘--mask_condition’ switches. The older method is to use the three switches ‘-m’, ‘-T’, and ‘-M’ to specify the mask_var, mask_comp, and mask_val, respectively. 70. The mask_condition string is automatically parsed into its three constituents mask_var, mask_comp, and mask_val.

Here mask_var is the name of the masking variable (specified with ‘-m’, ‘--mask-variable’, ‘--mask_variable’, ‘--msk_nm’, or ‘--msk_var’). The truth mask_comp argument (specified with ‘-T’, ‘--mask_comparator’, ‘--msk_cmp_typ’, or ‘--op_rlt’ may be any one of the six arithmetic comparators: eq, ne, gt, lt, ge, le. These are the Fortran-style character abbreviations for the logical comparisons ==, !=, >, <, >=, <=. The mask comparator defaults to eq (equality). The mask_val argument to ‘-M’ (or ‘--mask-value’, or ‘--msk_val’) is the right hand side of the mask condition. Thus for the i’th element of the hyperslab to be averaged, the mask condition is mask(i) mask_comp mask_val.

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4.12.2 Normalization and Integration

ncwa has one switch which controls the normalization of the averages appearing in the output-file. Short option ‘-N’ (or long options ‘--nmr’ or ‘--numerator’) prevents ncwa from dividing the weighted sum of the variable (the numerator in the averaging expression) by the weighted sum of the weights (the denominator in the averaging expression). Thus ‘-N’ tells ncwa to return just the numerator of the arithmetic expression defining the operation (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]. The vertical integral of lev, weighted by lev_wgt, is the dot product of lev and lev_wgt. That this is is 3000.0 can be seen by inspection and verified with the integration command

ncwa -N -a lev -v lev -w lev_wgt;ncks


Given file

netcdf 85_0112 {
        lat = 64 ;
        lev = 18 ;
        lon = 128 ;
        time = UNLIMITED ; // (12 currently)
        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 over all dimensions and store results in


All variables in are reduced to scalars in since ncwa averages over all dimensions unless otherwise specified (with ‘-a’).

Store the zonal (longitudinal) mean of in

ncwa -a lon
ncwa -a lon -b

The first command turns lon into a scalar and the second retains lon as a degenerate dimension in all variables.

% ncks -C -H -v lon
lon = 135
% ncks -C -H -v lon
lon[0] = 135

In either case the tally is simply the size of lon, i.e., 180 for the file described by the sample header above.

Compute the meridional (latitudinal) mean, with values weighted by the corresponding element of gw 71:

ncwa -w gw -a lat

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.

Here the tally is 64 times 128 = 8192.

Compute the area-mean over the globe using only points for which ORO < 0.5 72:

ncwa -B 'ORO < 0.5'      -w gw -a lat,lon
ncwa -m ORO -M 0.5 -T lt -w gw -a lat,lon

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 ‘-M73. 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
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

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

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 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 # Wrap in degenerate "record" dimension
ncwa -O -a record -B "${msk_var} <= ${thr_max}"
ncecat -O # Wrap in degenerate "record" dimension
ncwa -O -a record -B "${msk_var} >= ${thr_min}"

After the first use of ncwa, 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 contains valid data only where thr_min <= msk_var <= thr_max.

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5 Contributing

We welcome contributions from anyone. The project homepage at contains more information on how to contribute.

Financial contributions to NCO development may be made through PayPal. NCO has been shared for over 10 years yet only two users have contributed any money to the developers 74. So you could be the third!

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5.1 Contributors

NCO would not exist without the dedicated efforts of the remarkable software engineers who conceive, develop, and maintain netCDF, UDUnits, and OPeNDAP. Since 1995 NCO has received support from, I believe, the entire staff of all these projects, including Russ Rew, John Caron, Glenn Davis, Steve Emmerson, James Gallagher, Ed Hartnett, and Dennis Heimbigner. In addition to their roles in maintaining the software stack on which NCO perches, Yertl-like, some of these gentlemen have advised or contributed to NCO specifically. That support is acknowledged separately below.

The primary contributors to NCO development have been:

Charlie Zender

All concept, design and implementation from 1995–2000. Since then autotools, bug-squashing, CDL, chunking, documentation, anchoring, recursion, GPE, packing, NCO library redesign, ncap2 features, ncbo, ncpdq, SMP threading and MPI parallelization, netCDF4 integration, external funding, project management, science research, releases.

Henry Butowsky

Non-linear operations and min(), max(), total() support in ncra and ncwa. Type conversion for arithmetic. Migration to netCDF3 API. ncap2 parser, lexer, GSL-support, and I/O. Multislabbing algorithm. Variable wildcarding. Numerous hacks. ncap2 language.

Rorik Peterson

Original autotools build support. Long command-line options. Original UDUnits support. Debianization. Numerous bug-fixes.

Joe Hamman

Python bindings (NCOpy).

Daniel Wang

Script Workflow Analysis for MultiProcessing (SWAMP). RPM support.

Harry Mangalam

Benchmarking. OPeNDAP configuration.

Pedro Vicente

Windows Visual Studio support. netCDF4 groups.

Russ Rew

Advice on NCO structural algorithms

Brian Mays

Original packaging for Debian GNU/Linux, nroff man pages.

George Shapovalov

Packaging for Gentoo GNU/Linux.

Bill Kocik

Memory management.

Len Makin

NEC SX architecture support.

Jim Edwards

AIX architecture support.

Juliana Rew

Compatibility with large PIDs.

Karen Schuchardt

Auxiliary coordinate support.

Gayathri Venkitachalam

MPI implementation.

Scott Capps

Large work-load testing

Peter Campbell, Martin Dix, Mark Flanner, Markus Liebig, Keith Lindsay, Mike Page, Martin Schmidt, Michael Schulz, Lori Sentman, Rich Signell, Gary Strand, George White Andrew Wittenberg, Remik Ziemlinski

Excellent bug reports and feature requests.

Daniel Baumann, Nick Bower, Luk Claebs, Bas Couwenberg, Barry deFreese, Aleksandar Jelenak, Francesco Lovergine, Matej Vela

Debian packaging

Patrice Dumas, Ed Hill, Orion Poplawski

RedHat packaging

George Shapavalov, Patrick Kursawe

Gentoo packaging

Filipe Fernandes

OpenSuse packaging

Takeshi Enomoto, Alexander Hansen

Mac OS packaging

Eric Blake

Autoconf/M4 help

Gavin Burris, Kyle Wilcox

RHEL and CentOS build scripts and bug reports.

Andrea Cimatoribus

NCO Spiral Logo

Martin Otte, Etienne Tourigny

Single bug reports and fixes

Wenshan Wang

CMIP5 and MODIS processing documentation.

Please let me know if your name was omitted!

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5.2 Citation

The recommended citations for NCO software are

Zender, C. S. (2008), Analysis of Self-describing Gridded Geoscience
Data with netCDF Operators (NCO), Environ. Modell. Softw., 23(10),
1338-1342, doi:10.1016/j.envsoft.2008.03.004. 

Zender, C. S. (2014), netCDF Operator (NCO) User Guide, Version 4.4.3, 

Use the former when referring to overall design, purpose, and optimization of NCO, and use the latter when referring to specific features and/or the User Guide itself.

Additional information on citing NCO is in the User Guide at A complete list of NCO publications and presentations is at This list links to the full papers and seminars themselves.

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5.3 Proposals for Institutional Funding

NSF has funded a project to improve Distributed Data Reduction & Analysis (DDRA) by evolving NCO into a suite of Scientific Data Operators called SDO. The 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.

With 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 other interesting ideas for still more proposals. Please contact us if you wish to be involved with any future NCO-related proposals. Comments on the proposals and letters of support are also very welcome.

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6 Quick Start

Simple examples in Bash shell scripts showing how to average data with different file structures. Here we include monthly, seasonal and annual average with daily or monthly data in either one file or multiple files.

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6.1 Daily data in one file

Suppose we have daily data from Jan 1st, 1990 to Dec. 31, 2005 in the file of with the record dimension as time.

Monthly average:

for yyyy in {1990..2005}; do      # Loop over years
  for moy in {1..12}; do          # Loop over months
    mm=$( printf "%02d" ${moy} )  # Change to 2-digit format

    # Average specific month yyyy-mm
    ncra -O -d time,"${yyyy}-${mm}-01","${yyyy}-${mm}-31" \ in_${yyyy}${mm}.nc

# Concatenate monthly files together
ncrcat -O in_??????.nc

Annual average:

for yyyy in {1990..2005}; do      # Loop over years
  ncra -O -d time,"${yyyy}-01-01","${yyyy}-12-31" in_${yyyy}.nc

# Concatenate annual files together
ncrcat -O in_????.nc

The -O switch means to overwrite the pre-existing files (see Batch Mode). The -d option is to specify the range of hyperslabs (see Hyperslabs). There are detailed instructions on ncra (see ncra netCDF Record Averager and ncrcat (see ncrcat netCDF Record Concatenator). NCO supports UDUnits so that we can use readable dates as time dimension (see UDUnits Support).

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6.2 Monthly data in one file

Inside the input file, the record dimension time is from Jan 1990 to Dec 2005.

Seasonal average (e.g., DJF):

ncra -O --mro -d time,"1990-12-01",,12,3

Annual average:

ncra -O --mro -d time,,,12,12

Here we use the subcycle feature (i.e., the number after the fourth comma: ‘3’ in the seasonal example and the second ‘12’ in the annual example) to retrieve groups of records separated by regular intervals (see Subcycle). The option --mro switches ncra to produce a Multi-Record Output instead of a single-record output. For example, assume snd is a 3D array with dimensions time * latitude * longitude and time includes every month from Jan. 1990 to Dec. 2005, 192 months as total, which are 16 years. Let’s look at the following two command lines.

ncra --mro -v snd -d time,"1990-12-01",,12,3
ncra -v snd -d time,"1990-12-01",,12,3

In the first output file,, snd is still a 3D array with dimensions time * latitude * longitude, but the length of time now is 16, meaning 16 winters. In the second output file,, the length of time is only 1. It is now the average of all the 16 winters.

when using ‘-d dim,min[,max]’ to specify the hyperslabs, you can leave it blank if you want to include the minimum or the maximum of the data, like we did above.

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6.3 One time point one file

This means if you have daily data of 30 days, there will be 30 data files. Or if you have monthly data of 12 months, there will be 12 data files. Dealing with this kind of files, you need to specify the file names in shell scripts and pass them to NCO operators. For example, your daily data files may look like,, ... If you want to know the monthly average of Jan 1990, you can write like,

ncra -O snd_199001??.nc

You might want to use loop if you need the average of each month.

for moy in {1..12}; do          # Loop over months
  mm=$( printf "%02d" ${moy} )  # Change to 2-digit format

  ncra -O snd_????${mm}??.nc out_${mm}.nc

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6.4 Multiple files with multiple time points

Similar as the last one, it’s more about shell scripts. Suppose you have daily data with one month of them in one data file. The monthly average is simply to apply ncra on the specific data file. And for seasonal averages, you can specify the three months by shell scripts.

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7 CMIP5 Example

The fifth phase of the Coupled Model Intercomparison Project (CMIP5) provides a multi-model framework for comparing the mechanisms and responses of climate models from around the world. However, it is a tremendous workload to retrieve a single climate statistic from all these models, each of which includes several ensemble members. Not only that, it is too often a tedious process that impedes new research and hypothesis testing. Our NASA ACCESS 2011 project simplified and accelerated this process.

Traditional geoscience data analysis requires users to work with numerous flat (data in one level or namespace) files. In that paradigm instruments or models produce, and then repositories archive and distribute, and then researchers request and analyze, collections of flat files. NCO works well with that paradigm, yet it also embodies the necessary algorithms to transition geoscience data analysis from relying solely on traditional (or “flat”) datasets to allowing newer hierarchical (or “nested”) datasets.

Hierarchical datasets support and enable combining all datastreams that meet user-specified criteria into a single or small number of files that hold all the science-relevant data. NCO (and no other software to our knowledge) exploits this capability now. Data and metadata may be aggregated into and analyzed in hierarchical structures. We call the resulting data storage, distribution, and analysis paradigm Group-Oriented Data Analysis and Distribution (GODAD). GODAD lets the scientific question organize the data, not the ad hoc granularity of all relevant datasets. This chapter illustrates GODAD techniques applied to analysis of the CMIP5 dataset.

To begin, we document below a prototypical example of CMIP5 analysis and evaluation using traditional NCO commands on netCDF3-format model and HDF-EOS format observational (NASA MODIS satellite instrument) datasets. These examples complement the NCO User Guide by detailing in-depth data analysis in a frequently encountered “real world” context. Graphical representations of the results (NCL scripts available upon request) are provided to illustrate physical meaning of the analysis. Since NCO can process hierarchical datasets, i.e., datasets stored with netCDF4 groups, we present sample scripts illustrating group-based processing as well.

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7.1 Combine Files

Sometimes, the data of one ensemble member will be stored in several files to reduce single file size. It is more convenient to concatenate these files into a single timeseries, and the following script illustrates how. Key steps include:

  1. Obtain number and names (or partial names) of files in a directory
  2. Concatenate files along the record dimension (usually time) using ncrcat (see ncrcat netCDF Record Concatenator).
#!/bin/bash      # shell type
shopt -s extglob # enable extended globbing

# Some of the models cut one ensemble member into several files, 
#  which include data of different time periods.
# We'd better concatenate them into one at the beginning so that 
#  we won't have to think about which files we need if we want 
#  to retrieve a specific time period later.
# Method:
#	- Make sure 'time' is the record dimension (i.e., left-most)
#	- ncrcat
# Input files like:
# /data/cmip5/
# /data/cmip5/
# Output files like:
# /data/cmip5/
# Online:
# Execute this script: bash

drc_in='/home/wenshanw/data/cmip5/' # Directory of input files

var=( 'snc' 'snd' )                 # Variables
rlm='LImon'                         # Realm
xpt=( 'historical' )                # Experiment ( could be more )

for var_id in {0..1}; do            # Loop over two variables
  # Names of all the models (ls [get file names]; 
  #  cut [get model names]; 
  #  sort; uniq [remove duplicates]; awk [print])
  mdl_set=$( ls ${drc_in}${var[var_id]}_${rlm}_*_${xpt[0]}_*.nc | \
    cut -d '_' -f 3 | sort | uniq -c | awk '{print $2}' )
  # Number of models (echo [print contents]; wc [count])
  mdl_nbr=$( echo ${mdl_set} | wc -w )
  echo "=============================="
  echo "There are" ${mdl_nbr} "models for" ${var[var_id]}.
  for mdl in ${mdl_set}; do	        # Loop over models
    # Names of all the ensemble members
    nsm_set=$( ls ${drc_in}${var[var_id]}_${rlm}_${mdl}_${xpt[0]}_*.nc | \
      cut -d '_' -f 5 | sort | uniq -c | awk '{print $2}' )
    # Number of ensemble members in each model
    nsm_nbr=$( echo ${nsm_set} | wc -w )		
    echo "------------------------------"
    echo "Model" ${mdl} "includes" ${nsm_nbr} "ensemble member(s):"
    echo ${nsm_set}"."
    for nsm in ${nsm_set}; do	      # Loop over ensemble members
      # Number of files in this ensemble member
      fl_nbr=$( ls ${drc_in}${var[var_id]}_${rlm}_${mdl}_${xpt[0]}_${nsm}_*.nc \
        | wc -w ) 
      # If there is only 1 file, continue to next loop
      if [ ${fl_nbr} -le 1 ]			
      	echo "There is only 1 file in" ${nsm}.
      echo "There are" ${fl_nbr} "files in" ${nsm}.
      # Starting date of data 
      #   (sed [the name of the first file includes the starting date])
      yyyymm_str=$( ls ${drc_in}${var[var_id]}_${rlm}_${mdl}_${xpt[0]}_${nsm}_*.nc\
        | sed -n '1p' | cut -d '_' -f 6 | cut -d '-' -f 1 )
      # Ending date of data 
      #   (sed [the name of the last file includes the ending date])
      yyyymm_end=$( ls ${drc_in}${var[var_id]}_${rlm}_${mdl}_${xpt[0]}_${nsm}_*.nc\
        | sed -n "${fl_nbr}p" | cut -d '_' -f 6 | cut -d '-' -f 2 )
      # Concatenate one ensemble member files 
      #   into one along the record dimension (now is time)
      ncrcat -O ${drc_in}${var[var_id]}_${rlm}_${mdl}_${xpt[0]}_${nsm}_*.nc \
      # Remove useless files
      rm ${drc_in}${var[var_id]}_${rlm}_${mdl}_${xpt[0]}_${nsm}_\

CMIP5 model data downloaded from the Earth System Grid Federation (ESGF) does not contain group features yet. Therefore users must aggregate flat files into hierarchical ones themselves. The following script shows how. Each dataset becomes a group in the output file. There can be several levels of groups. In this example, we employ two experiments (“scenarios”) as the top-level. The second-level comprises different models (e.g., CCSM4, CESM1-BGC). Many models are run multiple times with slight perturbed initial conditions to produce an ensemble of realizations. These ensemble members comprise the third level of the hierarchy. The script selects two variables, snc and snd (snow cover and snow depth).

# Aggregate models to one group file
# Method:
# - Create files with groups by ncecat --gag
# - Append groups level by level using ncks
# Input files like:
# Output files like:
# Online:
# Execute this script: bash

# Directories

# Constants
rlm='LImon'         # Realm: LandIce; Time frequency: monthly
tms='200001-200512' # Timeseries
flt='nc'            # File Type

# Geographical weights
# Can be skipped when ncap2 works on group data
# Loop over all snc files
for fn in $( ls ${drc_in}snc_${rlm}_*_${tms}.${flt} ); do
  ncap2 -O -s \
    'gw = float(cos(lat*3.1416/180.)); gw@long_name="geographical weight";'\
    ${fn} ${fn}

var=( 'snc' 'snd' )
xpt=( 'esmHistorical' 'historical' )
mdl=( 'CCSM4' 'CESM1-BGC' 'CESM1-CAM5' )

for i in {0..1}; do     # Loop over variables
  for j in {0..1}; do   # Loop over experiments
    for k in {0..2}; do # Loop over models
      ncecat -O --glb_mtd_spp -G ${xpt[j]}/${mdl[k]}/${mdl[k]}_ \
        ${drc_in}${var[i]}_${rlm}_${mdl[k]}_${xpt[j]}_*_${tms}.${flt} \
      ncks -A \
        ${drc_out}${var[i]}_${rlm}_${mdl[k]}_${xpt[j]}_all-nsm_${tms}.${flt} \
    done                # Loop done: models
    ncks -A \
      ${drc_out}${var[i]}_${rlm}_${mdl[0]}_${xpt[j]}_all-nsm_${tms}.${flt} \
  done                  # Loop done: experiments
  ncks -A \
    ${drc_out}${var[i]}_${rlm}_${mdl[0]}_${xpt[0]}_all-nsm_${tms}.${flt} \
done                    # Loop done: variables

# Rename output file
mv ${drc_out}${var[0]}_${rlm}_${mdl[0]}_${xpt[0]}_all-nsm_${tms}.${flt} \
# Remove temporary files
rm ${drc_out}sn?_${rlm}*.nc

#- Rename Group:
#   E.g., file
#   is now group /historical/CESM1-CAM5/CESM1-CAM5_00.
#   You can rename it to /historical/CESM1-CAM5/r1i1p1 to make more sense.
#  Note: You don't need to write the full path of the new name.
ncrename -g ${xpt}/${mdl}/${mdl}_00,r1i1p1 \

# Output file structure
# esmHistorical 
# {
#   CESM1-BGC 
#   {
#     CESM1-BGC_00 
#     {
#       snc(time, lat, lon)
#       snd(time, lat, lon)
#     }
#   }
# }
# historical
# {
#    CCSM4
#    {
#      CCSM4_00
#      {
#       snc(time, lat, lon)
#       snd(time, lat, lon)
#      }
#      CCSM4_01
#      {
#       snc(time, lat, lon)
#       snd(time, lat, lon)
#      }
#      CCSM4_02 { ... }
#      CCSM4_03 { ... }
#      CCSM4_04 { ... }
#    }
#    CESM1-BGC
#    {
#      CESM1-BGC_00 { ... }
#    }
#    CESM1-CAM5
#    {
#      r1i1p1 { ... }
#      CESM1-CAM5_01 { ... }
#      CESM1-CAM5_02 { ... }
#    }
# }

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7.2 Global Distribution of Long-term Average


Figure 7.1: Global Distribution of Long-term Average.

This section illustrates how to calculate the global distribution of long-term average (see Figure 7.1) with either flat files or group file. Key steps include:

  1. Average ensemble members of each model using nces (see nces netCDF Ensemble Statistics)
  2. Average the record dimension using ncra (see ncra netCDF Record Averager)
  3. Store results of each model as a distinct group in a single output file using ncecat (see ncrcat netCDF Record Concatenator) with the --gag option

The first example shows how to process flat files.


# After
# Example: Long-term average of each model globally
# Input files like:
# /data/cmip5/
# Output files like:
# /data/cmip5/output/snc/
# Online: 
# Execute this script: bash

# Parameters
drc_in='/home/wenshanw/data/cmip5/'         # Directory of input files
drc_out='/home/wenshanw/data/cmip5/output/' # Directory of output files

var=( 'snc' 'snd' )                         # Variables
rlm='LImon'                                 # Realm
xpt=( 'historical' )                        # Experiment ( could be more )

fld_out=( 'snc/' 'snd/' )                   # Folders of output files

for var_id in {0..1}; do	                  # Loop over two variables
  # Names of all models 
  #   (ls [get file names]; cut [get the part for model names]; 
  #   sort; uniq [remove duplicates]; awk [print])
  mdl_set=$( ls ${drc_in}${var[var_id]}_${rlm}_*_${xpt[0]}_*.nc | \
    cut -d '_' -f 3 | sort | uniq -c | awk '{print $2}' )
  # Number of models (echo [print contents]; wc [count])
  mdl_num=$( echo ${mdl_set} | wc -w )		
  for mdl in ${mdl_set}; do				          # Loop over models
  	# Average all the ensemble members of each model
    # Use nces file ensembles mode: --nsm_fl
  	nces --nsm_fl -O -4 -d time,"1956-01-01 00:00:0.0","2005-12-31 23:59:9.9" \
      ${drc_in}${var[var_id]}_${rlm}_${mdl}_${xpt[0]}_*.nc \
  	# Average along time
  	ncra -O ${drc_out}${fld_out[var_id]}${var[var_id]}_${rlm}_${mdl}_${xpt[0]}\ \
  	echo Model ${mdl} done!

	# Remove temporary files
	rm ${drc_out}${fld_out[var_id]}${var[var_id]}*historical*.nc
  # Store models as groups in the output file
  ncecat -O --gag ${drc_out}${fld_out[var_id]}${var[var_id]}_*.nc \

	echo Var ${var[var_id]} done!

With the use of group, the above script will be shortened to ONE LINE.

# Data from
# ensemble averaging
nces -O --nsm_grp --nsm_sfx='_avg' \ \

The input file,, produced by, includes all the ensemble members as groups. The option ‘--nsm_grp’ denotes that we are using group ensembles mode of nces, instead of file ensembles mode, ‘--nsm_fl’. The option ‘--nsm_sfx='_avg'’ instructs nces to store the output as a new child group /[model]/[model name]_avg/var; otherwise, the output will be stored directly in the parent group /[model]/var. In the final output file,, sub-groups with a suffix of ‘avg’ are the long-term averages of each model. One thing to notice is that for now, ensembles with only one ensemble member will be left untouched.

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7.3 Annual Average over Regions


Figure 7.2: Annual Average over Regions.

This section illustrates how to calculate the annual average over specific regions (see Figure 7.2). Key steps include:

  1. Spatial average using ncap2 (see ncap2 netCDF Arithmetic Processor) and ncwa (see ncwa netCDF Weighted Averager);
  2. Change dimension order using ncpdq (see ncpdq netCDF Permute Dimensions Quickly);
  3. Annual average using ncra (see ncra netCDF Record Averager);
  4. Anomaly from long-term average using ncbo (see ncbo netCDF Binary Operator);
  5. Standard deviation using ncbo (see ncbo netCDF Binary Operator) and nces (see nces netCDF Ensemble Statistics);
  6. Rename variables using ncrename (see ncrename netCDF Renamer);
  7. Edit attributions using ncatted (see ncatted netCDF Attribute Editor);
  8. Linear regression using ncap2 (see ncap2 netCDF Arithmetic Processor);
  9. Use ncap2 (see ncap2 netCDF Arithmetic Processor) with nco script file (i.e., .nco file);
  10. Move variables around using ncks (see ncks netCDF Kitchen Sink).

Flat files example

# Includes gsl_rgr.nco

# After
# Example: Annual trend of each model over Greenland and Tibet 
#   ( time- and spatial-average, standard deviation, 
#   anomaly and linear regression)
# Input files:
# /data/cmip5/
# Output files:
# /data/cmip5/outout/snc/
# Online:
# Execute this script: bash

# Parameters
drc_in='/home/wenshanw/data/cmip5/'         # Directory of input files
drc_out='/home/wenshanw/data/cmip5/output/' # Directory of output files

var=( 'snc' 'snd' )                         # Variables
rlm='LImon'                                 # Realm
xpt=( 'historical' )                        # Experiment ( could be more )

fld_out=( 'snc/' 'snd/' )                   # Folders of output files
# ------------------------------------------------------------

for var_id in {0..1}; do                    # Loop over two variables
  # Names of all models 
  #   (ls [get file names]; cut [get the part for model names]; 
  #   sort; uniq [remove duplicates]; awk [print])
  mdl_set=$( ls ${drc_in}${var[var_id]}_${rlm}_*_${xpt[0]}_*.nc | \
    cut -d '_' -f 3 | sort | uniq -c | awk '{print $2}' )
  for mdl in ${mdl_set}; do		              # Loop over models
  	# Loop over ensemble members
    for fn in $( ls ${drc_in}${var[var_id]}_${rlm}_${mdl}_${xpt[0]}_*.nc ); do
      pfx=$( echo ${fn} | cut -d'/' -f6 | cut -d'_' -f1-5 )
      # Two regions
      # Geographical weight
      ncap2 -O -s 'gw = cos(lat*3.1415926/180.); gw@long_name="geographical weight"\
        ;gw@units="ratio"' ${fn} ${drc_out}${fld_out[var_id]}${pfx}
      # Greenland
      ncwa -O -w gw -d lat,60.0,75.0 -d lon,300.0,340.0 -a lat,lon \
        ${drc_out}${fld_out[var_id]}${pfx} \
      # Tibet
      ncwa -O -w gw -d lat,30.0,40.0 -d lon,80.0,100.0 -a lat,lon \
        ${drc_out}${fld_out[var_id]}${pfx} \
      # Aggregate 2 regions together
      ncecat -O -u rgn ${drc_out}${fld_out[var_id]}${pfx}_gw_?.nc \

      # Change dimensions order
      ncpdq -O -a time,rgn ${drc_out}${fld_out[var_id]}${pfx} \

      # Remove temporary files (optional)
      rm ${drc_out}${fld_out[var_id]}${pfx}_gw_?.nc \
      # Annual average (use the feature of 'Duration')
      ncra -O --mro -d time,"1956-01-01 00:00:0.0","2005-12-31 23:59:9.9",12,12 \
        ${drc_out}${fld_out[var_id]}${pfx} \
      # Anomaly
      # Long-term average
      ncwa -O -a time ${drc_out}${fld_out[var_id]}${pfx} \
      # Subtract long-term average
      ncbo -O --op_typ=- ${drc_out}${fld_out[var_id]}${pfx} \
        ${drc_out}${fld_out[var_id]}${pfx} \
    rm ${drc_out}${fld_out[var_id]}${var[var_id]}_${rlm}_${mdl}_${xpt[0]}_*
    # Average over all the ensemble members
    ncea -O -4 ${drc_out}${fld_out[var_id]}${var[var_id]}_\
      ${rlm}_${mdl}_${xpt[0]}_* ${drc_out}${fld_out[var_id]}\
    # Standard deviation ------------------------------
    for fn in $( ls ${drc_out}${fld_out[var_id]}${var[var_id]}_${rlm}_${mdl}_\
      ${xpt[0]}_* ); do
      pfx=$( echo ${fn} | cut -d'/' -f8 | cut -d'_' -f1-5 )
      # Difference between each ensemble member and the average of all members
      ncbo -O --op_typ=- ${fn} \
        ${rlm}_${mdl}_${xpt[0]} \
    # RMS
    ncea -O -y rmssdn ${drc_out}${fld_out[var_id]}${var[var_id]}_${rlm}_\
      ${mdl}_${xpt[0]}_* \
    # Rename variables
    ncrename -v ${var[var_id]},sdv \
    # Edit attributions
    ncatted -a standard_name,sdv,a,c,"_standard_deviation_over_ensemble" \
      -a long_name,sdv,a,c," Standard Deviation over Ensemble" \
      -a original_name,sdv,a,c," sdv" \
    # Linear regression -----------------------------------------
    # Have to change the name of variable in the commands file 
    #   of gsl_rgr.nco manually (gsl_rgr.nco is listed below)
    ncap2 -O -S gsl_rgr.nco \
      ${mdl}_${xpt[0]} ${drc_out}${fld_out[var_id]}${var[var_id]}\

    # Get rid of temporary variables
    ncks -O -v c0,c1,pval,${var[var_id]},gw \
      ${xpt[0]} \
    # Move the variable 'sdv' into the anomaly files (i.e., * files)
    ncks -A -v sdv \
      ${mdl}_${xpt[0]} \
    rm ${drc_out}${fld_out[var_id]}${var[var_id]}_*historical*
    echo Model ${mdl} done!
  # Store models as groups in the output file
  ncecat -O --gag ${drc_out}${fld_out[var_id]}${var[var_id]}_*.nc 

  echo Var ${var[var_id]} done!


// Linear Regression
// Called by
// Caution: make sure the variable name is 
//  in agreement with the main script (now is 'snd')
// Online:

// Declare variables
*c0[$rgn]=0.;        // Intercept
*c1[$rgn]=0.;        // Slope
*sdv[$rgn]=0.;       // Standard deviation
*covxy[$rgn]=0.;     // Covariance
*x = double(time);

for (*rgn_id=0;rgn_id<$rgn.size;rgn_id++)   // Loop over regions
	gsl_fit_linear(time,1,snd(:,rgn_id),1,$time.size, \
    &tc0, &tc1, &cov00, &cov01,&cov11,&sumsq); // Linear regression function
	c0(rgn_id) = tc0;    // Output results
	c1(rgn_id) = tc1;
	covxy(rgn_id) = gsl_stats_covariance(time,1,\
    $time.size,double(snd(:,rgn_id)),1,$time.size); // Covariance function
	sdv(rgn_id) = gsl_stats_sd(snd(:,rgn_id), \
    1, $time.size);   // Standard deviation function

// P value------------------------------------------------------------
*time_sdv = gsl_stats_sd(time, 1, $time.size);
*r_value = covxy/(time_sdv*sdv); 
*t_value = r_value/sqrt((1-r_value^2)/($time.size-2));
pval = abs(gsl_cdf_tdist_P(t_value, $time.size-2) - \
  gsl_cdf_tdist_P(-t_value, $time.size-2));

// Write RAM variables to disk
// Usually NCO writes the outputs directly to disk
// Using RAM variables, declared by *, will shorten running time
// Output the final results using ram_write()

With the group feature, all the loops over experiments, models and ensemble members can be omitted. As we are working on implementing group feature in all NCO operators, some functions (e.g., regression and standard deviation over ensemble members) may have to wait until the new versions.

# Group data output by
# Annual trend of each model over Greenland and Tibet 
# Time- and spatial-average, standard deviation and anomaly
# No regression yet (needs ncap2)
# Input files:
# Online:
# Execute this script: bash
# Input and output directory

# Constants
tms='200001-200512'           # Time series

# Greenland
ncwa -O -w gw -d lat,60.0,75.0 -d lon,300.0,340.0 -a lat,lon \
  ${drc}${pfx}_${tms}.nc \
# Tibet
ncwa -O -w gw -d lat,30.0,40.0 -d lon,80.0,100.0 -a lat,lon \
  ${drc}${pfx}_${tms}.nc \

# Aggregate 2 regions together
ncecat -O -u rgn ${drc}${pfx}_${tms}_???.nc \

# Change dimensions order
ncpdq -O -a time,rgn ${drc}${pfx}_${tms} \

# Remove temporary files (optional)
rm ${drc}${pfx}_${tms}_???.nc

#Annual average
ncra -O --mro -d time,,,12,12 ${drc}${pfx}_${tms} \

# Anomaly
# Long-term average
ncwa -O -a time ${drc}${pfx}_${tms} \
# Subtract
ncbo -O --op_typ=- ${drc}${pfx}_${tms} \
  ${drc}${pfx}_${tms} \

# Standard Deviation: inter-annual variability 
# RMS of the above anomaly
ncra -O -y rmssdn ${drc}${pfx}_${tms} \

Next: , Previous: , Up: CMIP5 Example   [Contents][Index]

7.4 Monthly Cycle


Figure 7.3: Monthly Cycle.

This script illustrates how to calculate the monthly anomaly from the annual average (see Figure 7.3). In order to keep only the monthly cycle, we will subtract the annual average of each year from the monthly data, instead of subtracting the long-term average. This is a little more complicated in coding since we need to loop over years.

Flat files example


# After
# Example: Monthly cycle of each model in Greenland
# Input files:
# /data/cmip5/
# Output files:
# /data/cmip5/snc/
# Online:
# Execute this script: bash

# Parameters
drc_in='/home/wenshanw/data/cmip5/'		# Directory of input files
drc_out='/home/wenshanw/data/cmip5/output/'	# Directory of output files

var=( 'snc' 'snd' )		# Variables
rlm='LImon'			# Realm
xpt=( 'historical' )		# Experiment ( could be more )

fld_out=( 'snc/' 'snd/' )		# Folders of output files

for var_id in {0..1}; do		# Loop over two variables
  # names of all models 
  #  (ls [get file names]; cut [get the part for model names]; 
  #  sort; uniq [remove duplicates]; awk [print])
  mdl_set=$( ls ${drc_in}${var[var_id]}_${rlm}_*_${xpt[0]}_*.nc | \
    cut -d '_' -f 3 | sort | uniq -c | awk '{print $2}' )

  for mdl in ${mdl_set}; do		## Loop over models
    # Average all the ensemble members of each model
    ncea -O -4 -d time,"1956-01-01 00:00:0.0","2005-12-31 23:59:9.9" \
      ${drc_in}${var[var_id]}_${rlm}_${mdl}_${xpt[0]}_*.nc \
    # Greenland
    # Geographical weight
    ncap2 -O -s \
      'gw = cos(lat*3.1415926/180.); \
      gw@long_name="geographical weight";gw@units="ratio"' \
      ${drc_out}${fld_out[var_id]}${var[var_id]}_${rlm}_${mdl}_${xpt[0]} \
    ncwa -O -w gw -d lat,60.0,75.0 -d lon,300.0,340.0 -a lat,lon \
      ${drc_out}${fld_out[var_id]}${var[var_id]}_${rlm}_${mdl}_${xpt[0]} \
    # Anomaly----------------------------------------
    for moy in {1..12}; do		# Loop over months
      mm=$( printf "%02d" ${moy} )	# Change to 2-digit format
      for yr in {1956..2005}; do		# Loop over years
        # If January, calculate the annual average
        if [ ${moy} -eq 1 ]; then	 
        	ncra -O -d time,"${yr}-01-01 00:00:0.0","${yr}-12-31 23:59:9.9" \
            ${xpt[0]} ${drc_out}${fld_out[var_id]}${var[var_id]}_\
        # The specific month
        ncks -O -d time,"${yr}-${mm}-01 00:00:0.0","${yr}-${mm}-31 23:59:9.9" \
          ${rlm}_${mdl}_${xpt[0]} \
        # Subtract the annual average from the monthly data
        ncbo -O --op_typ=- ${drc_out}${fld_out[var_id]}${var[var_id]}_\
          ${rlm}_${mdl}_${xpt[0]}_all-nsm_GN_${yr}${mm}.nc \
          all-nsm_GN_${yr}.nc ${drc_out}${fld_out[var_id]}${var[var_id]}_${rlm}_\
      # Average over years
      ncra -O ${drc_out}${fld_out[var_id]}${var[var_id]}_${rlm}_${mdl}_\
        ${xpt[0]}_all-nsm_GN_????${mm} \
    # Concatenate months together
    ncrcat -O ${drc_out}${fld_out[var_id]}${var[var_id]}_${rlm}_${mdl}_\
      ${xpt[0]}_all-nsm_GN_?? \
    echo Model ${mdl} done!
  rm -f ${drc_out}${fld_out[var_id]}${var[var_id]}*historical*
  # Store models as groups in the output file
  ncecat -O --gag -v ${var[var_id]} \
    ${drc_out}${fld_out[var_id]}${var[var_id]}_*.nc \
  echo Var ${var[var_id]} done!

Using group feature and hyperslabs of ncbo, the script will be shortened.


# Monthly cycle of each ensemble member in Greenland
# Input file from cmb_fl_grpsh
# Online:
# Execute this script in command line: bash
# Input and output directory

# Constants

# Greenland
ncwa -O -w gw -d lat,60.0,75.0 -d lon,300.0,340.0 -a lat,lon \
  ${drc}${pfx}.nc ${drc}${pfx}

# Anomaly from annual average of each year 
for yyyy in {2000..2005}; do
  # Annual average
  ncwa -O -d time,"${yyyy}-01-01","${yyyy}-12-31" \
    ${drc}${pfx} ${drc}${pfx}_grl_${yyyy}.nc

  # Anomaly
  ncbo -O --op_typ=- -d time,"${yyyy}-01-01","${yyyy}-12-31" \
    ${drc}${pfx} ${drc}${pfx}_grl_${yyyy}.nc \

# Monthly cycle
for moy in {1..12}; do
  mm=$( printf "%02d" ${moy} )      # Change to 2-digit format
  ncra -O -d time,"2000-${mm}-01",,12 \
    ${drc}${pfx}_grl_???? ${drc}${pfx}_grl_${mm}
# Concatenate 12 months together
ncrcat -O ${drc}${pfx}_grl_?? \

Next: , Previous: , Up: CMIP5 Example   [Contents][Index]

7.5 Regrid MODIS Data

In order to compare the results between MODIS and CMIP5 models, one usually regrids one or both datasets so that the spatial resolutions match. Here, the script illustrates how to regrid MODIS data. Key steps include:

  1. Regrid using bilinear interpolation (see Bilinear interpolation)
  2. Rename variables, dimensions and attributions using ncrename (see ncrename netCDF Renamer).

Main Script

# include bi_interp.nco

# Example for
#	- regrid (using bi_interp.nco): the spatial resolution of MODIS data 
#		is much finer than those of CMIP5 models. In order to compare
#		the two, we can regrid MODIS data to comform to CMIP5.
# Input files (Note: the .hdf files downloaded have to be converted to .nc at
# the present):
# /modis/mcd43c3/
# Output files:
# /modis/mcd43c3/cesm-grid/
# Online:
# Execute this script: bash

var=( 'MCD43C3' )     # Variable 
fld_in=( 'monthly/' )     # Folder of input files
fld_out=( 'cesm-grid/' )      # Folder of output files
drc_in='/media/grele_data/wenshan/modis/mcd43c3/'     # Directory of input files

for fn in $( ls ${drc_in}${fld_in}${var}.*.nc ); do		# Loop over files
  sfx=$( echo $fn | cut -d '/' -f 8 | cut -d '.' -f 2 ) # Part of file names
  # Regrid
  ncap2 -O -S bi_interp.nco ${fn} ${drc_in}${fld_out}${var}.${sfx}
  # Keep only the new variables
  ncks -O -v wsa_sw_less,bsa_sw_less ${drc_in}${fld_out}${var}.${sfx} \
  # Rename the new variables, dimensions and attributions
  ncrename -O -d latn,lat -d lonn,lon -v latn,lat -v lonn,lon \
    -v wsa_sw_less,wsa_sw -v bsa_sw_less,bsa_sw -a missing_value,_FillValue \
  echo $sfx done.


// Bilinear interpolation
// Included by
// Online:

defdim("latn",192);		// Define new dimension: latitude
defdim("lonn",288);		// Define new dimension: longitude
latn[$latn] = {90,89.0576 ,88.1152 ,87.1728 ,86.2304 ,85.288  ,\
  84.3456 ,83.4031 ,82.4607 ,81.5183 ,80.5759 ,79.6335 ,78.6911 ,\
  77.7487 ,76.8063 ,75.8639 ,74.9215 ,73.9791 ,73.0367 ,72.0942 ,\
  71.1518 ,70.2094 ,69.267  ,68.3246 ,67.3822 ,66.4398 ,65.4974 ,\
  64.555  ,63.6126 ,62.6702 ,61.7277 ,60.7853 ,59.8429 ,58.9005 ,\
  57.9581 ,57.0157 ,56.0733 ,55.1309 ,54.1885 ,53.2461 ,52.3037 ,\
  51.3613 ,50.4188 ,49.4764 ,48.534  ,47.5916 ,46.6492 ,45.7068 ,\
  44.7644 ,43.822  ,42.8796 ,41.9372 ,40.9948 ,40.0524 ,39.11   ,\
  38.1675 ,37.2251 ,36.2827 ,35.3403 ,34.3979 ,33.4555 ,32.5131 ,\
  31.5707 ,30.6283 ,29.6859 ,28.7435 ,27.8011 ,26.8586 ,25.9162 ,\
  24.9738 ,24.0314 ,23.089  ,22.1466 ,21.2042 ,20.2618 ,19.3194 ,\
  18.377  ,17.4346 ,16.4921 ,15.5497 ,14.6073 ,13.6649 ,12.7225 ,\
  11.7801 ,10.8377 ,9.89529 ,8.95288 ,8.01047 ,7.06806 ,6.12565 ,\
  5.18325 ,4.24084 ,3.29843 ,2.35602 ,1.41361 ,0.471204,-0.471204,\
  -14.6073,-15.5497,-16.4921,-17.4346,-18.377 ,-19.3194,-20.2618,\
  -21.2042,-22.1466,-23.089 ,-24.0314,-24.9738,-25.9162,-26.8586,\
  -34.3979,-35.3403,-36.2827,-37.2251,-38.1675,-39.11  ,-40.0524,\
  -40.9948,-41.9372,-42.8796,-43.822 ,-44.7644,-45.7068,-46.6492,\
  -47.5916,-48.534 ,-49.4764,-50.4188,-51.3613,-52.3037,-53.2461,\
  -60.7853,-61.7277,-62.6702,-63.6126,-64.555 ,-65.4974,-66.4398,\
  -67.3822,-68.3246,-69.267 ,-70.2094,-71.1518,-72.0942,-73.0367,\
  -87.1728,-88.1152,-89.0576,-90};		// Copy of CCSM4 latitude
lonn[$lonn] = {-178.75,-177.5,-176.25,-175,-173.75,-172.5,-171.25,\
  172.5,173.75,175,176.25,177.5,178.75,180};	// Copy of CCSM4 longitude

*out[$time,$latn,$lonn]=0.0;		// Output structure

// Bi-linear interpolation

// Add attributions
latn@units = "degree_north";
lonn@units = "degree_east";
latn@long_name = "latitude";
lonn@long_name = "longitude";
bsa_sw_less@hdf_name = "Albedo_BSA_shortwave";
bsa_sw_less@calibrated_nt = 5;
bsa_sw_less@missing_value = 32767.0;
bsa_sw_less@units = "albedo, no units";
bsa_sw_less@long_name = "Global_Albedo_BSA_shortwave";
wsa_sw_less@hdf_name = "Albedo_WSA_shortwave";
wsa_sw_less@calibrated_nt = 5;
wsa_sw_less@missing_value = 32767.0;
wsa_sw_less@units = "albedo, no units";
wsa_sw_less@long_name = "Global_Albedo_WSA_shortwave";

Next: , Previous: , Up: CMIP5 Example   [Contents][Index]

7.6 Add Coordinates to MODIS Data

Main Script


# Example for
#	- regrid (using bi_interp.nco): the spatial resolution of MODIS data 
#		is much finer than those of CMIP5 models. In order to compare
#		the two, we can regrid MODIS data to comform to CMIP5.
#	- add coordinates (using coor.nco): there is no coordinate information
#		in MODIS data. We have to add it manually now.
# Input files:
# /modis/mcd43c3/cesm-grid/
# Output files:
# /modis/mcd43c3/cesm-grid/
# Online:
# Execute this script: bash

var=( 'MOD10CM' )     # Variable
fld_in=( 'snc/nc/' )  # Folder of input files
drc_in='/media/grele_data/wenshan/modis/' # directory of input files

for fn in $( ls ${drc_in}${fld_in}${var}*.nc ); do		# Loop over files
  sfx=$( echo ${fn} | cut -d '/' -f 8 | cut -d '.' -f 2-4 )	# Part of file names
  echo ${sfx} 
  # Rename dimension names
  ncrename -d YDim_MOD_CMG_Snow_5km,lat -d XDim_MOD_CMG_Snow_5km,lon -O \
    ${drc_in}${fld_in}${var}.${sfx}.nc ${drc_in}${fld_in}${var}.${sfx}.nc
  # Add coordinates
  ncap2 -O -S crd.nco ${drc_in}${fld_in}${var}.${sfx}.nc \


// Add coordinates to MODIS HDF data
// Included by
// Online:

lon = array(0.f, 0.05, $lon) - 180;
lat = 90.f- array(0.f, 0.05, $lat);

Previous: , Up: CMIP5 Example   [Contents][Index]

7.7 Permute MODIS Coordinates

MODIS orders latitude data from 90°N to -90°N, and longitude from -180°E to 180°E. However, CMIP5 orders latitude from -90°N to 90°N, and longitude from 0°E to 360°E. This script changes the MODIS coordinates to follow the CMIP5 convention.


## Example for
##	- permute coordinates: the grid of MODIS is 
##		from (-180 degE, 90 degN), the left-up corner, to
##		(180 degE, -90 degN), the right-low corner. However, CMIP5 is
##		from (0 degE, -90 degN) to (360 degE, 90 degN). The script
##		here changes the MODIS grid to CMIP5 grid.
## Input files:
## /modis/mcd43c3/cesm-grid/
## Output files:
## /modis/mcd43c3/cesm-grid/
## Online:
## Execute this script: bash

## Permute coordinates
##	- Inverse lat from (90,-90) to (-90,90)
##	- Permute lon from (-180,180) to (0,360)
for fn in $( ls MCD43C3.*.nc ); do      # Loop over files
  sfx=$( echo ${fn} | cut -d '.' -f 1-3 )     # Part of file names
  echo ${sfx}
  ## Lat
  ncpdq -O -a -lat ${fn} ${fn}      # Inverse latitude (NB: there is '-' before 'lat')
  ## Lon
  ncks -O --msa -d lon,0.0,180.0 -d lon,-180.0,-1.25 ${fn} ${fn}

  ## Add new longitude coordinates
  ncap2 -O -s 'lon=array(0.0,1.25,$lon)' ${fn} ${fn}

Next: , Previous: , Up: Top   [Contents][Index]

8 Parallel

This section will describe scripting strategies, including the use of GNU Parallel, to NCO.

ls *historical*.nc | parallel ncks -O -d time,"1950-01-01","2000-01-01" {} 50y/{}

Next: , Previous: , Up: Top   [Contents][Index]

9 CCSM Example

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:


Each component model has its own subdirectory, e.g., 


within which model output is tagged with the particular model name




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.

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 \
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 \
done # end loop over mth

Method 3: Construct input filename list explicitly
for mth in {1..12}; do
  mm=`printf "%02d" $mth`
  for yr in {1..100}; do
    yyyy=`printf "%04d" $yr`
    fl_lst_in="${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} \
done # end loop over mth

Make sure the output file averages correct input files!
ncks -M prints global metadata: 

  ncks -M ${drc_out}/${caseid}

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}

Finally, create climatological means for reference.
The climatological time-mean:

  ncra -O -D 1 \
    ${drc_out}/${caseid} ${drc_out}/${caseid}

The climatological zonal-mean:

  ncwa -O -D 1 -a lon \
    ${drc_out}/${caseid} ${drc_out}/${caseid}

The climatological time- and spatial-mean:

  ncwa -O -D 1 -a lon,lat,time -w gw \
    ${drc_out}/${caseid} ${drc_out}/${caseid}

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} \
    ${drc_out}/${caseid} ${drc_out}/${caseid}

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/ \
done # end loop over mth
ncecat -O -D 1 -p ${drc_out} -n 12,2,2 foo_${mm}.nc
ncrename -O -D 1 -d record,time ${drc_out}/
ncatted -O -h \
  -a long_name,dpm,o,c,"Days per month" \
  -a units,dpm,o,c,"days" \
ncks -A -v dpm ${drc_out}/ ${drc_out}/${caseid}

Method 3: Create small netCDF file using ncgen
cat > foo.cdl << 'EOF'
netcdf foo { 
	float dpm(time);
	dpm:long_name="Days per month";
ncgen -b -o foo.cdl
ncks -A -v dpm ${drc_out}/ ${drc_out}/${caseid}

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} \

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
ncrcat -O -C -D 3 \
  -p \
  -l /tmp ~/

Unidata DAP servers:
ncks -M -p
ncrcat -O -C -D 3 \
  -p \
  -l /tmp ~/

NOAA DAP servers:
ncwa -O -C -a lat,lon,time -d lon,-10.,10. -d lat,-10.,10. -l /tmp -p \ \ ~/

ncks -M -p

Earth System Grid (ESG):

CCSM3.0 1% increasing CO2 run, T42_gx1v3, 200 years starting in year 400
Atmospheric post-processed data, monthly averages, e.g.,

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 \
  -l /tmp ~/ 

# 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${scn}/atm/mo/${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 | m
bds -x 096 -y 073 -m 33 -o ${DATA}/data/ # ukmo_hadcm3
ncview ${DATA}/data/

# 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/ \

Template for standardized data:

e.g., raw data
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

Level I: Add all variables (not standardized in time)
         to file containing msk_rgn and area
Template: ${scn}_${mdl}_${run}_${yyyymm}_${yyyymm}.nc

ncks -A -v tas \
ncks -A -v pr \

If already have file then:
ncks -A -v tas,pr

Level II: Correct # years, months
Template: ${scn}_${mdl}_${run}_${var}_${yyyymm}_${yyyymm}.nc

ncks -d time,....... 

Level III: Many derived products from level II, e.g., 

      A. Global mean timeseries
      ncwa -w area -a lat,lon \

      B. Califoria average timeseries
      ncwa -m msk_rgn -w area -a lat,lon \

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10 References

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General Index

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Index Entry  Section

" (double quote): ncatted netCDF Attribute Editor

#include: Syntax of ncap2 statements

$ (wildcard character): Subsetting Files

% (modulus): Intrinsic mathematical methods

' (end quote): ncatted netCDF Attribute Editor

*: ncbo netCDF Binary Operator
* (filename expansion): Subsetting Files
* (multiplication): Intrinsic mathematical methods
* (wildcard character): Subsetting Files

+: ncbo netCDF Binary Operator
+ (addition): Intrinsic mathematical methods
+ (wildcard character): Subsetting Files

-: ncbo netCDF Binary Operator
- (subtraction): Intrinsic mathematical methods
--3: File Formats and Conversion
--4: File Formats and Conversion
--5: File Formats and Conversion
--6: File Formats and Conversion
--64bit: File Formats and Conversion
--7: File Formats and Conversion
--abc: ncks netCDF Kitchen Sink
--alphabetize: ncks netCDF Kitchen Sink
--apn: Temporary Output Files
--apn: Batch Mode
--apn: Filters for ncks
--append: Temporary Output Files
--append: Batch Mode
--append: Filters for ncks
--auxiliary: Auxiliary Coordinates
--auxiliary lon_min,lon_max,lat_min,lat_max: Auxiliary Coordinates
--bfr_sz_hnt: Buffer sizes
--binary: ncks netCDF Kitchen Sink
--bnr: ncks netCDF Kitchen Sink
--cdl: ncks netCDF Kitchen Sink
--cell_methods: CF Conventions
--chunk_byte: Chunking
--chunk_dimension: Chunking
--chunk_map: Chunking
--chunk_min: Chunking
--chunk_policy: Chunking
--chunk_scalar: Chunking
--cll_mth: CF Conventions
--cnk_byt: Chunking
--cnk_dmn: Chunking
--cnk_map: Chunking
--cnk_map cnk_map: Chunking
--cnk_min: Chunking
--cnk_plc: Chunking
--cnk_scl: Chunking
--coords: Subsetting Coordinate Variables
--coords: CF Conventions
--crd: Subsetting Coordinate Variables
--crd: CF Conventions
--create_ram: Temporary Output Files
--create_ram: RAM disks
--data: ncks netCDF Kitchen Sink
--dbg_lvl debug-level: Help Requests and Bug Reports
--dbg_lvl debug-level: Large Datasets
--dbg_lvl debug-level: Command Line Options
--dbl: Promoting Single-precision to Double
--debug-level debug-level: Help Requests and Bug Reports
--debug-level debug-level: Large Datasets
--deflate: Deflation
--dfl_lvl: Deflation
--dimension dim,[min],[max],stride: Stride
--dimension dim,[min],[max],[stride],[subcycle]: Subcycle
--dimension dim,[min][,[max][,[stride]]]: Hyperslabs
--dimension dim,[min][,[max][,[stride]]]: Multislabs
--dimension dim,[min][,[max][,[stride]]]: Wrapped Coordinates
--dimension dim,[min][,[max][,[stride]]]: UDUnits Support
--diskless_all: Memory Requirements
--diskless_all: RAM disks
--dmn dim,[min],[max],stride: Stride
--dmn dim,[min],[max],[stride],subcycle]: Subcycle
--dmn dim,[min][,[max][,[stride]]]: Hyperslabs
--dmn dim,[min][,[max][,[stride]]]: Multislabs
--dmn dim,[min][,[max][,[stride]]]: Wrapped Coordinates
--dmn dim,[min][,[max][,[stride]]]: UDUnits Support
--ensemble_file: nces netCDF Ensemble Statistics
--ensemble_group: nces netCDF Ensemble Statistics
--ensemble_suffix: nces netCDF Ensemble Statistics
--exclude: Subsetting Files
--exclude: Filters for ncks
--file_format: File Formats and Conversion
--file_list: File List Attributes
--fix_rec_dmn all: Autoconversion
--fix_rec_dmn dim: ncks netCDF Kitchen Sink
--flt: Promoting Single-precision to Double
--fl_bnr: ncks netCDF Kitchen Sink
--fl_fmt: File Formats and Conversion
--fl_lst_in: File List Attributes
--fl_out fl_out: Specifying Output Files
--fl_spt: ncap2 netCDF Arithmetic Processor
--fnc_tbl: Intrinsic mathematical methods
--fortran: C and Fortran Index Conventions
--gag: ncecat netCDF Ensemble Concatenator
--gag: Combine Files
--glb_mtd_spp: ncecat netCDF Ensemble Concatenator
--gpe gpe_dsc: Group Path Editing
--group grp: Subsetting Files
--grp grp: Subsetting Files
--grp_xtr_var_xcl: Subsetting Files
--gxvx: Subsetting Files
--hdf4: netCDF2/3/4 and HDF4/5 Support
--hdf_unpack: Packed data
--hdf_upk: Packed data
--hdn: ncks netCDF Kitchen Sink
--hdr_pad hdr_pad: Metadata Optimization
--header_pad hdr_pad: Metadata Optimization
--hidden: ncks netCDF Kitchen Sink
--hieronymus: ncks netCDF Kitchen Sink