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

   Copyright (C) 1995-2013 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 `http://www.gnu.org/copyleft/fdl.html'

   The original author of this software, Charlie Zender, wants to
improve it with the help of your suggestions, improvements,
bug-reports, and patches.
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
*****************

NCO User Guide
Foreword
Summary
1 Introduction
  1.1 Availability
  1.2 Operating systems compatible with NCO
    1.2.1 Compiling NCO for Microsoft Windows OS
  1.3 Symbolic Links
  1.4 Libraries
  1.5 netCDF2/3/4 and HDF4/5 Support
  1.6 Help Requests and Bug Reports
2 Operator Strategies
  2.1 Philosophy
  2.2 Climate Model Paradigm
  2.3 Temporary Output Files
  2.4 Appending Variables
  2.5 Simple Arithmetic and Interpolation
  2.6 Averagers vs. Concatenators
    2.6.1 Concatenators `ncrcat' and `ncecat'
    2.6.2 Averagers `ncea', `ncra', and `ncwa'
    2.6.3 Interpolator `ncflint'
  2.7 Large Numbers of Files
  2.8 Large Datasets
  2.9 Memory Requirements
    2.9.1 Single and Multi-file Operators
    2.9.2 Memory for `ncap2'
  2.10 Performance
3 NCO Features
  3.1 Internationalization
  3.2 Metadata Optimization
  3.3 OpenMP Threading
  3.4 Command Line Options
  3.5 Specifying Input Files
  3.6 Specifying Output Files
  3.7 Accessing Remote Files
    3.7.1 OPeNDAP
  3.8 Retaining Retrieved Files
  3.9 Selecting Output File Format
  3.10 Large File Support
  3.11 Subsetting Files
  3.12 Subsetting Coordinate Variables
  3.13 Group Path Editing
  3.14 C and Fortran Index conventions
  3.15 Hyperslabs
  3.16 Stride
  3.17 Record Appending
  3.18 Duration
  3.19 Multislabs
  3.20 Wrapped Coordinates
  3.21 Auxiliary Coordinates
  3.22 UDUnits Support
  3.23 Rebasing Time Coordinate
  3.24 Missing values
  3.25 Chunking
  3.26 Deflation
  3.27 MD5 digests
  3.28 Buffer sizes
  3.29 RAM disks
  3.30 Packed data
    Packing Algorithm
    Unpacking Algorithm
    Default Handling of Packed Data
    Default Handling of Packed Data
  3.31 Operation Types
  3.32 Type Conversion
    3.32.1 Automatic type conversion
    3.32.2 Promoting Single-precision to Double
    3.32.3 Manual type conversion
  3.33 Batch Mode
  3.34 History Attribute
  3.35 File List Attributes
  3.36 CF Conventions
  3.37 ARM Conventions
  3.38 Operator Version
4 Operator Reference Manual
  4.1 `ncap2' netCDF Arithmetic Processor
    4.1.1 Syntax of `ncap2' statements
    4.1.2 Expressions
    4.1.3 Dimensions
    4.1.4 Left hand casting
    4.1.5 Arrays and hyperslabs
    4.1.6 Attributes
    4.1.7 Number literals
    4.1.8 if statement
    4.1.9 print statement
    4.1.10 Missing values ncap2
    4.1.11 Methods and functions
    4.1.12 RAM variables
    4.1.13 Where statement
    4.1.14 Loops
    4.1.15 Include files
    4.1.16 `sort' methods
    4.1.17 Irregular Grids
    4.1.18 Bilinear interpolation
    4.1.19 GSL special functions
    4.1.20 GSL interpolation
    4.1.21 GSL least-squares fitting
    4.1.22 GSL statistics
    4.1.23 GSL random number generation
    4.1.24 Examples ncap2
    4.1.25 Intrinsic mathematical methods
    4.1.26 Operator precedence and associativity
    4.1.27 ID Quoting
  4.2 `ncatted' netCDF Attribute Editor
  4.3 `ncbo' netCDF Binary Operator
  4.4 `ncea' netCDF Ensemble Averager
  4.5 `ncecat' netCDF Ensemble Concatenator
  4.6 `ncflint' netCDF File Interpolator
  4.7 `ncks' netCDF Kitchen Sink
    Options specific to `ncks'
    4.7.1 Filters for `ncks'
  4.8 `ncpdq' netCDF Permute Dimensions Quickly
    Packing and Unpacking Functions
    Dimension Permutation
  4.9 `ncra' netCDF Record Averager
  4.10 `ncrcat' netCDF Record Concatenator
  4.11 `ncrename' netCDF Renamer
  4.12 `ncwa' netCDF Weighted Averager
    4.12.1 Mask condition
    4.12.2 Normalization and Integration
5 Contributing
  5.1 Contributors
  5.2 Proposals for Institutional Funding
6 Quick Start
  6.1 Daily data in one file
  6.2 Monthly data in one file
  6.3 One time point one file
  6.4 Multiple files with multiple time points
7 CMIP5 Example
  7.1 Combine Files
  7.2 Global Distribution of Long-term Average
  7.3 Annual Average over Regions
  7.4 Monthly Cycle
  7.5 Regrid MODIS Data
  7.6 Add Coordinates to MODIS Data
  7.7 Permute MODIS Coordinates
  7.8 Hierarchical Data Files
8 Parallel
9 CCSM Example
10 References
General Index


NCO User Guide
**************

_Note to readers of the NCO User Guide in Info format_: _The NCO User
Guide in PDF format (./nco.pdf) (also on SourceForge
(http://nco.sf.net/nco.pdf)) contains the complete NCO documentation._
This Info documentation is equivalent except it refers you to the
printed (i.e., DVI, PostScript, and PDF) documentation for description
of complex mathematical expressions. 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
(`http://www.unidata.ucar.edu/packages/netcdf').  Each NCO operator
(e.g., ncks) takes netCDF input file(s), performs an operation (e.g.,
averaging, hyperslabbing, or renaming), and outputs a processed netCDF
file.  Although most users of netCDF data are involved in scientific
research, these data formats, and thus NCO, are generic and are equally
useful in fields from agriculture to zoology.  The NCO User Guide
illustrates NCO use with examples from the field of climate modeling
and analysis.  The NCO homepage is `http://nco.sf.net', and there is a
mirror at `http://dust.ess.uci.edu/nco'.

   This documentation is for NCO version 4.3.5.  It was last updated
20 September 2013.  Corrections, additions, and rewrites of this
documentation are very welcome.

   Enjoy,
Charlie Zender

Foreword
********

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
(http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=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 recentely the NASA ACCESS program
commenced funding NCO support for netCDF4 group functionality.
Thus NCO will grow and evade bit-rot for the foreseeable future.

   On a personal level, 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, sometimes rather
effusively, these time-saving tools.  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
Summary
*******

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

1 Introduction
**************

1.1 Availability
================

The complete NCO source distribution is currently distributed as a
"compressed tarfile" from `http://sf.net/projects/nco' and from
`http://dust.ess.uci.edu/nco/nco.tar.gz'.  The compressed tarfile must
be uncompressed and untarred before building NCO.  Uncompress the file
with `gunzip nco.tar.gz'.  Extract the source files from the resulting
tarfile with `tar -xvf nco.tar'.  GNU `tar' lets you perform both
operations in one step with `tar -xvzf nco.tar.gz'.

   The documentation for NCO is called the `NCO User Guide'.  The `User
Guide' is available in Postscript, HTML, DVI, TeXinfo, and Info formats.
These formats are included in the source distribution in the files
`nco.ps', `nco.html', `nco.dvi', `nco.texi', and `nco.info*',
respectively.  All the documentation descends from a single source file,
`nco.texi' (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 `http://nco.sf.net'.  The HTML version of the `User Guide'
is also available online through the World Wide Web at URL
`http://nco.sf.net/nco.html'.  To build and use NCO, you must have
netCDF installed.  The netCDF homepage is
`http://www.unidata.ucar.edu/packages/netcdf'.

   New NCO releases are announced on the netCDF list and on the
`nco-announce' mailing list
`http://lists.sf.net/mailman/listinfo/nco-announce'.

   ---------- Footnotes ----------

   (1) To produce these formats, `nco.texi' was simply run through the
freely available programs `texi2dvi', `dvips', `texi2html', and
`makeinfo'.  Due to a bug in TeX, the resulting Postscript file,
`nco.ps', contains the Table of Contents as the final pages.  Thus if
you print `nco.ps', remember to insert the Table of Contents after the
cover sheet before you staple the manual.

1.2 Operating systems compatible with NCO
=========================================

NCO has been successfully ported and tested and is known to work on the
following 32- and 64-bit platforms: IBM AIX 4.x, 5.x, FreeBSD 4.x,
GNU/Linux 2.x, LinuxPPC, LinuxAlpha, LinuxARM, LinuxSparc64, SGI IRIX
5.x and 6.x, MacOS X 10.x, NEC Super-UX 10.x, DEC OSF, Sun SunOS 4.1.x,
Solaris 2.x, Cray UNICOS 8.x-10.x, and MS Windows95 and all later
versions.  If you port the code to a new operating system, please send
me a note and any patches you required.

   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) (1), 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 (2).

   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 (*note 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.

   ---------- Footnotes ----------

   (1) The `_BSD_SOURCE' token is required on some Linux platforms where
`gcc' dislikes the network header files like `netinet/in.h').

   (2) NCO may still build with an ANSI or ISO C89 or C94/95-compliant
compiler if the C pre-processor undefines the `restrict' type
qualifier, e.g., by invoking the compiler with `-Drestrict='''.

1.2.1 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 (1), 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 (*note
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.

   ---------- Footnotes ----------

   (1) The Cygwin package is available from
`http://sourceware.redhat.com/cygwin'
Currently, Cygwin 20.x comes with the GNU C/C++ compilers (`gcc', `g++'.
These GNU compilers may be used to build the netCDF distribution itself.

1.3 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, `ncea' and `ncrcat' are symbolically linked to the
`ncra' executable.  The `ncra' executable behaves slightly differently
based on its invocation name (i.e., `argv[0]'), which can be `ncea',
`ncra', or `ncrcat'.  Logically, these are three different operators
that happen to share the same executable.

   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 ncecat    # ncra --pseudonym='ncecat'
     ln -s -f ncra ncrcat    # ncra --pseudonym='ncrcat'
     ln -s -f ncbo ncadd     # ncbo --op_typ='+'
     ln -s -f ncbo ncsubtract # ncbo --op_typ='-'
     ln -s -f ncbo ncmultiply # ncbo --op_typ='*'
     ln -s -f ncbo ncdivide   # ncbo --op_typ='/'
     ln -s -f ncpdq ncpack    # ncpdq
     ln -s -f ncpdq ncunpack  # ncpdq --unpack
     # NB: Cygwin executable (and link) names have an '.exe' suffix, e.g.,
     ln -s -f ncbo.exe ncdiff.exe
     ...
   The imputed command called by the link is given after the comment.
As can be seen, some these links impute the passing of a command line
argument to further modify the behavior of the underlying executable.
For example, `ncdivide' is a pseudonym for `ncbo --op_typ='/''.

1.4 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
`ld.so.1: /usr/local/bin/ncea: fatal: libsunmath.so.1: can't open file:
errno=2'.  If you received an error message like this, ask your system
administrator to diagnose whether the library is truly missing (1), 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.

   ---------- Footnotes ----------

   (1) The `ldd' command, if it is available on your system, will tell
you where the executable is looking for each dynamically loaded
library. Use, e.g., `ldd `which ncea`'.

1.5 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
eager to reap the performance advantages of the newer netCDF
implementation.  One netCDF3 interface call (`nc_inq_libvers') was
added to NCO in January, 1998, to aid in maintainance and debugging.
In March, 2001, the final conversion of NCO to netCDF3 was completed
(coincidentally on the same day netCDF 3.5 was released).  NCO
versions 2.0 and higher are built with the `-DNO_NETCDF_2' flag to
ensure no netCDF2 interface calls are used.  

   However, the ability to compile NCO with only netCDF2 calls is worth
maintaining because HDF version 4 (1) (available from HDF
(http://hdf.ncsa.uiuc.edu)) supports only the netCDF2 library calls
(see `http://hdf.ncsa.uiuc.edu/UG41r3_html/SDS_SD.fm12.html#47784').
Note that there are multiple versions of HDF.  Currently HDF
version 4.x supports netCDF2 and thus NCO version 1.2.x.  If NCO
version 1.2.x (or earlier) is built with only netCDF2 calls then all
NCO operators should work with HDF4 files as well as netCDF files (2).  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.  The NCO 1.2.x branch will be maintained
with bugfixes only (no new features) until HDF begins to fully support
the netCDF3 interface (which is employed by NCO 2.x).  If, at
compilation time, `NETCDF2_ONLY' is defined, then NCO version 1.2.x
will not use any netCDF3 calls and, if linked properly, the resulting
NCO operators will work with HDF4 files.  The `Makefile' supplied with
NCO 1.2.x is written to simplify building in this HDF capability.  When
NCO is built with `make HDF4=Y', the `Makefile' sets all required
preprocessor flags and library links to build with the HDF4 libraries
(which are assumed to reside under `/usr/local/hdf4', edit the
`Makefile' to suit your installation).

   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.  In 2004, Unidata and NCSA began a project
to implement the HDF5 features necessary to support the netCDF API.
NCO version 3.0.3 added support for reading/writing netCDF4-formatted
HDF5 files in October, 2005.  See *note Selecting Output File Format::
for more details.

   HDF support for netCDF was completed with HDF5 version version 1.8
in 2007.  The netCDF front-end that uses this HDF5 back-end was
completed and released soon after as netCDF version 4.  Download it
from the netCDF4
(http://my.unidata.ucar.edu/content/software/netcdf/netcdf-4) 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.

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

   NCO version 3.9.1 (June, 2007) added support for netCDF4 Lempel-Ziv
deflation.  Lempel-Ziv deflation is a lossless compression technique.
See *note 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 *note
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 *note Group Path Editing:: for
more details.

   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 *note 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 *note Selecting Output File Format:: for more details.

   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.

   ---------- Footnotes ----------

   (1) The Hierarchical Data Format, or HDF, is another self-describing
data format similar to, but more elaborate than, netCDF.

   (2) One must link the NCO code to the HDF4 MFHDF library instead of
the usual netCDF library.  Does `MF' stands for Mike Folk?  Perhaps.
In any case, the MFHDF library only supports netCDF2 calls.  Thus I
will try to keep this capability in NCO as long as it is not too much
trouble.

1.6 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 (https://sf.net/account/register.php) your name and email
address with SourceForge.net or else all of your postings will be
attributed to "nobody".  Once registered you may choose to "monitor"
any forum and to receive (or not) email when there are any postings
including responses to your questions.  We usually reply to the forum
message, not to the original poster.

   If you want us to include a new feature in NCO, check first to see
if that feature is already on the TODO (file:./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 (http://sf.net/projects/nco/forums/forum/9829).

   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
(http://sf.net/projects/nco/forums/forum/9830).

   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 (http://sf.net/bugs/?group_id=3331).

   Build failures count as bugs.  Our limited machine access means we
cannot fix all build failures.  The information we need to diagnose,
and often fix, build failures are the three files output by GNU build
tools, `nco.config.log.${GNU_TRP}.foo', `nco.configure.${GNU_TRP}.foo',
and `nco.make.${GNU_TRP}.foo'.  The file `configure.eg' shows how to
produce these files.  Here `${GNU_TRP}' is the "GNU architecture
triplet", the CHIP-VENDOR-OS string returned by `config.guess'.  Please
send us your improvements to the examples supplied in `configure.eg'.  The
regressions archive at `http://dust.ess.uci.edu/nco/rgr' contains the
build output from our standard test systems.  You may find you can
solve the build problem yourself by examining the differences between
these files and your own.

2 Operator Strategies
*********************

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 *note Memory Requirements::.

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.

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 (1).  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 (2).  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 (*note 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 (*note RAM disks::)
then it is still possible to have the same name for both OUTPUT-FILE
and INPUT-FILE.
     ncks in.nc out.nc # Default: create out.pid.tmp.nc then move to out.nc
     ncks --wrt_tmp_fl in.nc out.nc # Same as default
     ncks --no_tmp_fl in.nc out.nc # Create out.nc directly on disk
     ncks --no_tmp_fl in.nc in.nc # ERROR-prone! Overwrite in.nc with itself
     ncks --create_ram --no_tmp_fl in.nc in.nc # Create in RAM, write to disk
     ncks --open_ram --no_tmp_fl in.nc in.nc # Read into RAM, write to disk
   There is no reason to expect the fourth example to work.  The
behavior of overwriting a file while reading from the same file is
undefined, much as is the shell command `cat foo > foo'.  Although it
may "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, *Note 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, but 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.

   ---------- Footnotes ----------

   (1) The `ncrename' and `ncatted' operators are exceptions to this
rule.  *Note ncrename netCDF Renamer::.

   (2) The OS-specific system move command is used.  This is `mv' for
UNIX, and `move' for Windows.

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" (1) 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 (2).
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, *Note ncks netCDF Kitchen
Sink::.

   The simplest way to create the union of two files is
     ncks -A fl_1.nc fl_2.nc
   This puts the contents of `fl_1.nc' into `fl_2.nc'.  The `-A' is
optional.  On output, `fl_2.nc' is the union of the input files,
regardless of whether they share dimensions and variables, or are
completely disjoint.  The append fails if the input files have
differently named record dimensions (since netCDF supports only one),
or have dimensions of the same name but different sizes.

   ---------- Footnotes ----------

   (1) The terminology "merging" is reserved for an (unwritten)
operator which replaces hyperslabs of a variable in one file with
hyperslabs of the same variable from another file

   (2) Yes, the terminology is confusing.  By all means mail me if you
think of a better nomenclature.  Should NCO use "paste" instead of
"append"?

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' (*note ncbo netCDF Binary Operator::) does file
addition, subtraction, multiplication, division, and broadcasting.  It
even does group broadcasting.  `ncflint' (*note ncflint netCDF File
Interpolator::) does file addition, subtraction, multiplication and
interpolation.  Sequences of these commands can accomplish simple but
powerful operations from the command line.

2.6 Averagers vs. Concatenators
===============================

The most frequently used operators of NCO are probably the averagers
and concatenators.  Because there are so many permutations of averaging
(e.g., across files, within a file, over the record dimension, over
other dimensions, with or without weights and masks) and of
concatenating (across files, along the record dimension, along other
dimensions), there are currently no fewer than five operators which
tackle these two purposes: `ncra', `ncea', `ncwa', `ncrcat', and
`ncecat'.  These operators do share many capabilities (1), but each has
its unique specialty.  Two of these operators, `ncrcat' and `ncecat',
are for concatenating hyperslabs across files.  The other two
operators, `ncra' and `ncea', are for averaging hyperslabs across files
(2).  First, let's describe the concatenators, then the averagers.

   ---------- Footnotes ----------

   (1) Currently `ncea' and `ncrcat' are symbolically linked to the
`ncra' executable, which behaves slightly differently based on its
invocation name (i.e., `argv[0]').  These three operators share the
same source code, but merely have different inner loops.

   (2) The third averaging operator, `ncwa', is the most sophisticated
averager in NCO.  However, `ncwa' is in a different class than `ncra'
and `ncea' because it can only operate on a single file per invocation
(as opposed to multiple files).  On that single file, however, `ncwa'
provides a richer set of averaging options--including weighting,
masking, and broadcasting.

2.6.1 Concatenators `ncrcat' and `ncecat'
-----------------------------------------

Joining independent files together along a record dimension is called
"concatenation".  `ncrcat' is designed for concatenating record
variables, while `ncecat' is designed for concatenating fixed length
variables.  Consider five files, `85.nc', `86.nc', ... `89.nc' each
containing a year's worth of data.  Say you wish to create from them a
single file, `8589.nc' containing all the data, i.e., spanning all five
years.  If the annual files make use of the same record variable, then
`ncrcat' will do the job nicely with, e.g., `ncrcat 8?.nc 8589.nc'.
The number of records in the input files is arbitrary and can vary from
file to file.  *Note 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,
`85a.nc', `85b.nc', ... `85e.nc' of 1985 predictions from the same
climate model.  Then `ncecat 85?.nc 85_ens.nc' glues the individual
realizations together into the single file, `85_ens.nc'.  If an input
variable was dimensioned [`lat',`lon'], it will have dimensions
[`record',`lat',`lon'] in the output file.  A restriction of `ncecat'
is that the hyperslabs of the processed variables must be the same from
file to file.  Normally this means all the input files are the same
size, and contain data on different realizations of the same variables.
*Note 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
`x_01.nc', `x_02.nc', ... `x_10.nc' contain time-evolving datasets from
spatially adjacent regions.  The time and spatial coordinates are
`time' and `x', respectively.  Initially the record dimension is `time'.
Our goal is to create a single file that contains joins all the
spatially adjacent regions into one single time-evolving dataset.
     for idx in 01 02 03 04 05 06 07 08 09 10; do # Bourne Shell
       ncpdq -a x,time x_${idx}.nc foo_${idx}.nc # Make x record dimension
     done
     ncrcat foo_??.nc out.nc       # Concatenate along x
     ncpdq -a time,x out.nc out.nc # Revert to time as record dimension

   Note that `ncrcat' will not concatenate fixed-length variables,
whereas `ncecat' concatenates both fixed-length and record variables
along a new record variable.  To conserve system memory, use `ncrcat'
where possible.

2.6.2 Averagers `ncea', `ncra', and `ncwa'
------------------------------------------

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

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 (*note 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.

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 (*note 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 (*note History
Attribute::).  However, the `-n LOOP' switch is useless when there is no
simple, arithmetic pattern to the input filenames (e.g., `h00001.nc',
`h00002.nc', ... `h90210.nc').  Moreover, filename globbing does not
work when the input files are too numerous or their names are too
lengthy (when strung together as a single argument) to be passed by the
calling shell to the NCO operator (1).  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 foo.nc
   This method avoids all constraints on command line size imposed by
the operating system.  A drawback to this method is that the `history'
attribute (*note 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 (*note
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' (*note Subsetting Files::).
We use `grep' to filter the directory listing for this pattern and to
pipe the results to `xargs' which, in turn, passes the matching
filenames to an NCO multi-file operator, e.g., `ncecat'.
     # Use xargs to transfer filenames on the command line
     /bin/ls | grep ${CASEID}_'......'.nc | xargs -x ncecat -o foo.nc
   The single quotes protect the only sensitive parts of the extended
regular expression (the `grep' argument), and allow shell interpolation
(the `${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 foo.nc' switch ensures that the input files supplied
by `xargs' are not confused with the output file name.  `xargs' does,
unfortunately, have its own limit (usually about 20,000 characters) on
the size of command lines it can pass.  Give `xargs' the `-x' switch to
ensure it dies if it reaches this internal limit.  When this occurs,
use either the `stdin' method above, or the symbolic link presented
next.

   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 `000001.nc' to `999999.nc': 
     # Create enumerated symbolic links
     /bin/ls | grep \.nc | perl -e \
     '$idx=1;while(<STDIN>){chop;symlink $_,sprintf("%06d.nc",$idx++);}'
     ncecat -n 999999,6,1 000001.nc foo.nc
     # Remove symbolic links when finished
     /bin/rm ??????.nc
   The `-n LOOP' option tells the NCO operator to automatically
generate the filnames of the symbolic links.  This circumvents any OS
and shell limits on command line size.  The symbolic links are easily
removed once NCO is finished.  One drawback to this method is that the
`history' attribute (*note 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.

   ---------- Footnotes ----------

   (1) The exact length which exceeds the operating system internal
limit for command line lengths varies from OS to OS and from shell to
shell.  GNU `bash' may not have any arbitrary fixed limits to the size
of command line arguments.  Many OSs cannot handle command line
arguments (including results of file globbing) exceeding 4096
characters.

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
`ncea' 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 (*note 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.
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.  If
you get a memory-related core dump (e.g., `Error exit (core dumped)')
on a GNU/Linux system, try increasing the process-available memory with
`ulimit'.

   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., `ncea -D 2 85.nc 86.nc 8586.nc'.  A full description
of how to estimate the actual amount of memory the multi-file NCO
operators consume is given in *note Memory Requirements::.

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 (*note 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'.

2.9.1 Single and Multi-file Operators
-------------------------------------

The multi-file operators currently comprise the record operators,
`ncra' and `ncrcat', and the ensemble operators, `ncea' and `ncecat'.
The record operators require _much less_ memory than the ensemble
operators.  This is because the record operators operate on one single
record (i.e., time-slice) at a time, 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.
`ncea' 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 (*note 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 (*note Type Conversion::).  For
example, `ncra' and `ncea' 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.  Single-precision
floating point variables however are not by default promoted to
double-precision prior to arithmetic.  Hence, the sustained memory
required for integer variables are two or four-times their on-disk,
uncompressed, unpacked sizes if they meet the rules for automatic
internal promotion.

   `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 (*note OpenMP Threading::).  If this causes problems
then reduce (with `-t THR_NBR') the number of threads.

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 (*note 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.

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
`http://dust.ess.uci.edu/ppr/ppr_Zen08.pdf'.

   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 `http://dust.ess.uci.edu/ppr/ppr_ZeM07.pdf'.

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

3 NCO 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.

3.1 Internationalization
========================

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

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.

   This optimization exploits the netCDF library `nc__enddef()'
function, which behaves differently with different versions of netCDF.
It will improve speed of future metadata expansion with `CLASSIC' and
`64bit' netCDF files, but not necessarily with `NETCDF4' files, i.e.,
those created by the netCDF interface to the HDF5 library (*note
Selecting Output File Format::).

3.3 OpenMP Threading
====================

Availability: `ncap2', `ncbo', `ncea', `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 could 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.

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 (1).

   The syntax of "short options" (single letter options) is `-KEY
VALUE' (dash-key-space-value).  Here, KEY is the single letter option
name, e.g., `-D 2'.

   The syntax of "long options" (multi-letter options) is `--LONG_NAME
VALUE' (dash-dash-key-space-value), e.g., `--dbg_lvl 2' or
`--LONG_NAME=VALUE' (dash-dash-key-equal-value), e.g., `--dbg_lvl=2'.
Thus the following are all valid for the `-D' (short version) or
`--dbg_lvl' (long version) command line option.
     ncks -D 3 in.nc        # Short option
     ncks --dbg_lvl=3 in.nc # Long option, preferred form
     ncks --dbg_lvl 3 in.nc # Long option, alternate form
   The last example is preferred for two reasons.  First, `--dbg_lvl'
is more specific and less ambiguous than `-D'.  The long option form
makes scripts more self documenting and less error prone.  Often long
options are named after the source code variable whose value they carry.
Second, the equals sign `=' joins the key (i.e., LONG_NAME) to the
value in an uninterruptible text block.  Experience shows that users
are less likely to mis-parse commands when restricted to this form.

   GNU implements a superset of the POSIX standard which allows any
unambiguous truncation of a valid option to be used.
     ncks -D 3 in.nc        # Short option
     ncks --dbg_lvl=3 in.nc # Long option, full form
     ncks --dbg=3 in.nc     # Long option, unambiguous truncation
     ncks --db=3 in.nc      # Long option, unambiguous truncation
     ncks --d=3 in.nc       # Long option, ambiguous truncation
   The first four examples are equivalent and will work as expected.
The final example will exit with an error since `ncks' cannot
disambiguate whether `--d' is intended as a truncation of `--dbg_lvl',
of `--dimension', or of some other long option.

   NCO provides many long options for common switches.  For example,
the debugging level may be set in all operators with any of the
switches `-D', `--debug-level', or `--dbg_lvl'.  This flexibility
allows users to choose their favorite mnemonic.  For some, it will be
`--debug' (an unambiguous truncation of `--debug-level', and other will
prefer `--dbg'.  Interactive users usually prefer the minimal amount of
typing, i.e., `-D'.  We recommend that scripts which are re-usable
employ some form of the long options for future maintainability.

   This manual generally uses the short option syntax 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.

   ---------- Footnotes ----------

   (1) If a `getopt_long' function cannot be found on the system, NCO
will use the `getopt_long' from the `my_getopt' package by Benjamin
Sittler <bsittler@iname.com>.  This is BSD-licensed software available
from `http://www.geocities.com/ResearchTriangle/Node/9405/#my_getopt'.

3.5 Specifying Input Files
==========================

Availability (`-n'): `ncea', `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 (*note
Large Numbers of Files::).

   To illustrate these methods, consider the simple problem of using
`ncra' to average five input files, `85.nc', `86.nc', ... `89.nc', and
store the results in `8589.nc'.  Here are the four methods in order.
They produce identical answers.
     ncra 85.nc 86.nc 87.nc 88.nc 89.nc 8589.nc
     ncra 8[56789].nc 8589.nc
     ncra -p INPUT-PATH 85.nc 86.nc 87.nc 88.nc 89.nc 8589.nc
     ncra -n 5,2,1 85.nc 8589.nc
   The first method (explicitly specifying all filenames) works by brute
force.  The second method relies on the operating system shell to "glob"
(expand) the "regular expression" `8[56789].nc'.  The shell passes
valid filenames which match the expansion to `ncra'.  The third method
uses the `-p INPUT-PATH' argument to specify the directory where all
the input files reside.  NCO prepends INPUT-PATH (e.g.,
`/data/usrname/model') to all INPUT-FILES (but not to OUTPUT-FILE).
Thus, using `-p', the path to any number of input files need only be
specified once.  Note INPUT-PATH need not end with `/'; the `/' is
automatically generated if necessary.

   The last method passes (with `-n') syntax concisely describing the
entire set of filenames (1).  This option is only available with the
"multi-file operators": `ncra', `ncrcat', `ncea', and `ncecat'.  By
definition, multi-file operators are able to process an arbitrary
number of INPUT-FILES.  This option is very useful for abbreviating
lists of filenames representable as
ALPHANUMERIC_PREFIX+NUMERIC_SUFFIX+`.'+FILETYPE where
ALPHANUMERIC_PREFIX is a string of arbitrary length and composition,
NUMERIC_SUFFIX is a fixed width field of digits, and FILETYPE is a
standard filetype indicator.  For example, in the file `ccm3_h0001.nc',
we have ALPHANUMERIC_PREFIX = `ccm3_h', NUMERIC_SUFFIX = `0001', and
FILETYPE = `nc'.

   NCO is able to decode lists of such filenames encoded using the `-n'
option.  The simpler (3-argument) `-n' usage takes the form `-n
FILE_NUMBER,DIGIT_NUMBER,NUMERIC_INCREMENT' where FILE_NUMBER is the
number of files, DIGIT_NUMBER is the fixed number of numeric digits
comprising the NUMERIC_SUFFIX, and NUMERIC_INCREMENT is the constant,
integer-valued difference between the NUMERIC_SUFFIX of any two
consecutive files.  The value of ALPHANUMERIC_PREFIX is taken from the
input file, which serves as a template for decoding the filenames.  In
the example above, the encoding `-n 5,2,1' along with the input file
name `85.nc' tells NCO to construct five (5) filenames identical to the
template `85.nc' except that the final two (2) digits are a numeric
suffix to be incremented by one (1) for each successive file.
Currently FILETYPE may be either be empty, `nc', `cdf', `hdf', or `hd5'.
If present, these FILETYPE suffixes (and the preceding `.') are ignored
by NCO as it uses the `-n' arguments to locate, evaluate, and compute
the NUMERIC_SUFFIX component of filenames.

   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 85_06.nc 85_0608.nc
     ncra -n 3,2,1,12 85_12.nc 85_1202.nc
     ncra -n 3,2,1,12,1 85_12.nc 85_1202.nc
   The first example shows that three arguments to the `-n' option
suffice to specify consecutive months (`06, 07, 08') which do not
"wrap" back to a minimum value.  The second example shows how to use
the optional fourth and fifth elements of the `-n' option to specify a
wrap value to NCO.  The fourth argument to `-n', if present, specifies
the maximum integer value of NUMERIC_SUFFIX.  In this case the maximum
value is 12, and will be formatted as `12' in the filename string.  The
fifth argument to `-n', if present, specifies the minimum integer value
of NUMERIC_SUFFIX.  The default minimum filename suffix is 1, which is
formatted as `01' in this case.  Thus the second and third examples
have the same effect, that is, they automatically generate, in order,
the filenames `85_12.nc', `85_01.nc', and `85_02.nc' as input to NCO.

   ---------- Footnotes ----------

   (1) The `-n' option is a backward compatible superset of the
`NINTAP' option from the NCAR CCM Processor.

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 (*note Large Numbers of Files::).  Making the input file list
the final argument to an operator facilitates using `xargs' for this
purpose.  Some alternatives to `xargs' are very ugly and undesirable.
Finally, many users are more comfortable specifying output files with
`-o FL_OUT' near the beginning of an argument list.  Compilers and
linkers are usually invoked this way.

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

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, supply the remote
file's URL.  FTP is an intrinsically insecure protocol because it
transfers passwords in plain text format.  Users should access sites
using anonymous FTP, or better yet, secure FTP when possible.  Some FTP
servers require a login/password combination for a valid user account.
NCO allows these transactions so long as the required information is
stored in the `.netrc' file.  Usually this information is the remote
machine name, login, and password, in plain text, separated by those
very keywords, e.g.,
     machine dust.ess.uci.edu login zender password bushlied
   Eschew using valuable passwords for FTP transactions, since `.netrc'
passwords are potentially exposed to eavesdropping software (1).

   SFTP, i.e., secure FTP, uses SSH-based security protocols that solve
the security issues associated with plain FTP.  NCO supports SFTP
protocol access to files specified with a homebrew syntax of the form
     sftp://machine.domain.tld:/path/to/filename
   Note the second colon following the top-level-domain, `tld'.  This
syntax is a hybrid between an FTP URL and a standard remote file syntax.

   To access a file using `rcp' or `scp', specify the Internet address
of the remote file.  Of course in this case you must have `rcp' or `scp'
privileges which allow transparent (no password entry required) access
to the remote machine.  This means that `~/.rhosts' or
`~/ssh/authorized_keys' must be set accordingly on both local and
remote machines.

   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' (2).

   The following examples show how one might analyze files stored on
remote systems.
     ncks -l . ftp://dust.ess.uci.edu/pub/zender/nco/in.nc
     ncks -l . sftp://dust.ess.uci.edu:/home/ftp/pub/zender/nco/in.nc
     ncks -l . dust.ess.uci.edu:/home/zender/nco/data/in.nc
     ncks -l . /ZENDER/nco/in.nc
     ncks -l . /home/zender/nco/in.nc
     ncks -l . http://motherlode.ucar.edu:8080/thredds/dodsC/testdods/in.nc
   The first example works verbatim if your system is connected to the
Internet and is not behind a firewall.  The second example works if you
have `sftp' access to the machine `dust.ess.uci.edu'.  The third
example works if you have `rcp' or `scp' access to the machine
`dust.ess.uci.edu'.  The fourth and fifth examples work on NCAR
computers with local access to the HPSS `hsi get' command (3).  The
sixth command works if your local version of NCO is OPeNDAP-enabled
(this is fully described in *note 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 ftp://dust.ess.uci.edu/pub/zender/nco -l . in.nc
     ncks -p sftp://dust.ess.uci.edu:/home/ftp/pub/zender/nco -l . in.nc
     ncks -p dust.ess.uci.edu:/home/zender/nco -l . in.nc
     ncks -p /ZENDER/nco -l . in.nc
     ncks -p /home/zender/nco -l . in.nc # HPSS
     ncks -p http://motherlode.ucar.edu:8080/thredds/dodsC/testdods \
          -l . in.nc
   Using `-p' is recommended because it clearly separates the
INPUT-PATH from the filename itself, sometimes called the "stub".  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.

   ---------- Footnotes ----------

   (1) NCO does not implement command line options to specify FTP
logins and passwords because copying those data into the `history'
global attribute in the output file (done by default) poses an
unacceptable security risk.

   (2) The `hsi' command must be in the user's path in one of the
following directories: `/usr/local/bin', `/opt/hpss/bin', or
`/ncar/opt/hpss/hsi'.  Tell us if the HPSS installation at your site
places the `hsi' command in a different location, and we will add that
location to the list of acceptable paths to search for `hsi'.

   (3) NCO supported the old NCAR Mass Storage System (MSS) until
version 4.0.7 in April, 2011.  NCO supported MSS-retrievals via a
variety of mechanisms including the `msread', `msrcp', and `nrnet'
commands invoked either automatically or with sentinels like `ncks -p
mss:/ZENDER/nco -l . in.nc'.  Once the MSS was decommissioned in March,
2011, support for these retrieval mechanisms was replaced by support
for HPSS in NCO.

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
(1).

   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 (2).  The `./configure' mechanism automatically
enables NCO as OPeNDAP clients if it can find the required OPeNDAP
libraries (3).   in the usual locations.  The `$DODS_ROOT' environment
variable may be used to override the default OPeNDAP library location
at NCO compile-time.  Building NCO with `bld/Makefile' and the command
`make DODS=Y' adds the (non-intuitive) commands to link to the OPeNDAP
libraries installed in the `$DODS_ROOT' directory.  The file
`doc/opendap.sh' contains a generic script intended to help users
install OPeNDAP before building NCO.  The documentation at the OPeNDAP
Homepage (http://www.opendap.org) is voluminous.  Check there and on the
DODS mail lists
(http://www.unidata.ucar.edu/packages/dods/home/mailLists/).  to learn
more about the extensive capabilities of OPeNDAP (4).

   Once NCO is DAP-enabled the operators are OPeNDAP clients.  All
OPeNDAP clients have network transparent access to any files controlled
by a OPeNDAP server.  Simply specify the input file path(s) in URL
notation and all NCO operations may be performed on remote files made
accessible by a OPeNDAP server.  This command tests the basic
functionality of OPeNDAP-enabled NCO clients:
     % ncks -O -o ~/foo.nc -C -H -v one -l /tmp \
       -p http://motherlode.ucar.edu:8080/thredds/dodsC/testdods in.nc
     % ncks -H -v one ~/foo.nc
     one = 1
   The `one = 1' outputs confirm (first) that `ncks' correctly
retrieved data via the  OPeNDAP protocol and (second) that `ncks'
created a valid local copy of the subsetted remote file.  With minor
changes to the above command, netCDF4 can be used as both the input and
output file format:
     % ncks -4 -O -o ~/foo.nc -C -H -v one -l /tmp \
       -p http://motherlode.ucar.edu:8080/thredds/dodsC/testdods in_4.nc
     % ncks -H -v one ~/foo.nc
     one = 1
   And, of course, OPeNDAP-enabled NCO clients continue to support
other, orthogonal features such as UDUnits (*note UDUnits Support::):
     % ncks -u -C -H -v wvl -d wvl,'0.4 micron','0.7 micron' \
       -p http://motherlode.ucar.edu:8080/thredds/dodsC/testdods in_4.nc
     % wvl[0]=5e-07 meter

   The next command is a more advanced example which demonstrates the
real power of OPeNDAP-enabled NCO clients.  The `ncwa' client requests
an equatorial hyperslab from remotely stored NCEP reanalyses data of
the year 1969.  The NOAA OPeNDAP server (hopefully!) serves these data.
The local `ncwa' client then computes and stores (locally) the regional
mean surface pressure (in Pa).
     ncwa -C -a lat,lon,time -d lon,-10.,10. -d lat,-10.,10. -l /tmp -p \
     http://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets/ncep.reanalysis.dailyavgs/surface \
       pres.sfc.1969.nc ~/foo.nc
   All with one command!  The data in this particular input file also
happen to be packed (*note Methods and functions::), although this is
completely transparent to the user since NCO automatically unpacks data
before attempting arithmetic.

   NCO obtains remote files from the OPeNDAP server (e.g.,
`www.cdc.noaa.gov') rather than the local machine.  Input files are
first copied to the local machine, then processed.  The OPeNDAP server
performs data access, hyperslabbing, and transfer to the local machine.  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.

   ---------- Footnotes ----------

   (1) DODS is being deprecated because it is ambiguous, referring both
to a protocol and to a collection of (oceanography) data.  It is
superceded by two terms.  DAP is the discipline-neutral Data Access
Protocol at the heart of DODS.  The National Virtual Ocean Data System
(NVODS) refers to the collection of oceanography data and oceanographic
extensions to DAP.  In other words, NVODS is implemented with OPeNDAP.
OPeNDAP is _also_ the open source project which maintains, develops,
and promulgates the DAP standard.  OPeNDAP and DAP really are
interchangeable.  Got it yet?

   (2) Automagic support for DODS version 3.2.x was deprecated in
December, 2003 after NCO version 2.8.4.  NCO support for OPeNDAP
versions 3.4.x commenced in December, 2003, with NCO version 2.8.5.
NCO support for OPeNDAP versions 3.5.x commenced in June, 2005, with
NCO version 3.0.1.  NCO support for OPeNDAP versions 3.6.x commenced in
June, 2006, with NCO version 3.1.3.  NCO support for OPeNDAP versions
3.7.x commenced in January, 2007, with NCO version 3.1.9.

   (3) The minimal set of libraries required to build NCO as OPeNDAP
clients, where OPeNDAP is supplied as a separate library apart from
`libnetcdf.a', are, in link order, `libnc-dap.a', `libdap.a', and
`libxml2' and `libcurl.a'.

   (4) We are most familiar with the OPeNDAP ability to enable
network-transparent data access.  OPeNDAP has many other features,
including sophisticated hyperslabbing and server-side processing via
"constraint expressions".  If you know more about this, please consider
writing a section on "OPeNDAP Capabilities of Interest to NCO Users"
for incorporation in the `NCO User Guide'.

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 *note 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 ftp://weather.edu/pub/pix/jpeg -l . storm.jpg
   In this example, `ncks' automatically performs an anonymous FTP
login to the remote machine and retrieves the specified file.  When
`ncks' attempts to read the local copy of `storm.jpg' as a netCDF file,
it fails and exits, leaving  `storm.jpg' in the current directory.

   If your NCO is DAP-enabled (*note OPeNDAP::), then you may use NCO
to retrieve any files (including netCDF, HDF, etc.) served by an
OPeNDAP server to your local machine.  For example,
     ncks -R -l . -p \
     http://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets/ncep.reanalysis.dailyavgs/surface \
       pres.sfc.1969.nc
   It may occasionally be useful to use NCO to transfer files when your
other preferred methods are not available locally.

3.9 Selecting Output File Format
================================

Availability: `ncap2', `ncbo', `ncea', `ncecat', `ncflint', `ncks',
`ncpdq', `ncra', `ncrcat', `ncwa'
Short options: `-3', `-4'
Long options: `--3', `--4', `--64bit', `--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.)

   netCDF supports four types of files: `CLASSIC', `64BIT', `NETCDF4',
and `NETCDF4_CLASSIC', The `CLASSIC' format is the traditional 32-bit
offset written by netCDF2 and netCDF3.  As of 2005, most netCDF
datasets are in `CLASSIC' format.  The `64BIT' format was added in
Fall, 2004.

   The `NETCDF4' format uses HDF5 as the file storage layer.  The files
are (usually) created, accessed, and manipulated using the traditional
netCDF3 API (with numerous extensions).  The `NETCDF4_CLASSIC' format
refers to netCDF4 files created with the `NC_CLASSIC_MODEL' mask.  Such
files use HDF5 as the back-end storage format (unlike netCDF3), though
they incorporate only netCDF3 features.  Hence `NETCDF4_CLASSIC' files
are perfectly readable by applications which use only the netCDF3 API
and library.  NCO must be built with netCDF4 to write files in the new
`NETCDF4' and `NETCDF4_CLASSIC' formats, and to read files in the new
`NETCDF4' format.  Users are advised to use the default `CLASSIC'
format or the `NETCDF4_CLASSIC' format until netCDF4 is more widespread.
Widespread support for `NETCDF4' format files is not expected for a few
more years, 2013-2014, say.  If performance or coolness are issues,
then use `NETCDF4_CLASSIC' instead of `CLASSIC' format files.

   As mentioned above, all operators write use the input file format for
output files unless told otherwise.  Toggling the long option `--64bit'
switch (or its KEY-VALUE equivalent `--fl_fmt=64bit') produces the
netCDF3 64-bit offset format named `64BIT'.  NCO must be built with
netCDF 3.6 or higher to produce a `64BIT' file.  Using the `-4' switch
(or its long option equivalents `--4' or `--netcdf4'), or setting its
KEY-VALUE equivalent `--fl_fmt=netcdf4' produces a `NETCDF4' file
(i.e., HDF).  Casual users are advised to use the default (netCDF3)
`CLASSIC' format until netCDF 3.6 and netCDF 4.0 are more widespread.
Conversely, operators given the `-3' (or `--3') switch without
arguments will (attempt to) produce netCDF3 `CLASSIC' output, even from
netCDF4 input files.

   These examples demonstrate converting a file from any netCDF format
into any other netCDF format (subject to limits of the format):
     ncks --fl_fmt=classic in.nc foo_3c.nc # netCDF3 classic
     ncks --fl_fmt=64bit in.nc foo_364.nc # netCDF3 64bit
     ncks --fl_fmt=netcdf4_classic in.nc foo_4c.nc # netCDF4 classic
     ncks --fl_fmt=netcdf4 in.nc foo_4.nc # netCDF4
     ncks -3 in.nc foo_3c.nc # netCDF3 classic
     ncks --3 in.nc foo_3c.nc # netCDF3 classic
     ncks --64 in.nc foo_364.nc # netCDF3 64bit
     ncks -4 in.nc foo_4.nc # netCDF4
     ncks --4 in.nc foo_4.nc # netCDF4
   Of course since most operators support these switches, the
"conversions" can be done at the output stage of arithmetic or metadata
processing rather than requiring a separate step.  Producing (netCDF3)
`CLASSIC' or `64BIT' files from `NETCDF4_CLASSIC' files will always
work.  However, producing netCDF3 files from `NETCDF4' files will only
work if the output files are not required to contain netCDF4-specific
features.

   Note that `NETCDF4' and `NETCDF4_CLASSIC' are the same binary format.
The latter simply causes a writing application to fail if it attempts to
write a `NETCDF4' file that cannot be completely read by the netCDF3
library.  Conversely, `NETCDF4_CLASSIC' indicates to a reading
application that all of the file contents are readable with the netCDF3
library.  As of October, 2005, NCO writes no netCDF4-specific data
structures and so always succeeds at writing `NETCDF4_CLASSIC' files.

   There are at least three ways to discover the format of a netCDF
file, i.e., whether it is a classic (32-bit offset) or newer 64-bit
offset netCDF3 format, or is netCDF4 format.  Each method returns the
information using slightly different terminology that becomes easier to
understand with practice.

   First, examine the end of the first line of global metadata output by
`ncks -M': 
     % ncks -M foo_3c.nc
     Opened file foo_3c.nc: dimensions = 23, variables = 296,
     global atts. = 5, type = NC_FORMAT_CLASSIC
     % ncks -M foo_364.nc
     Opened file foo_364.nc: dimensions = 23, variables = 296,
     global atts. = 5, type = NC_FORMAT_64BIT
     % ncks -M foo_4c.nc
     Opened file foo_4c.nc: dimensions = 23, variables = 296,
     global atts. = 5, type = NC_FORMAT_NETCDF4_CLASSIC
     % ncks -M foo_4.nc
     Opened file foo_4.nc: dimensions = 23, variables = 296,
     global atts. = 5, type = NC_FORMAT_NETCDF4
   This method requires a netCDF4-enabled NCO version 3.9.0+ (i.e.,
from 2007 or later).

   Second, query the file with `ncdump -k': 
     % ncdump -k foo_3.nc
     classic
     % ncdump -k foo_364.nc
     64-bit-offset
     % ncdump -k foo_4c.nc
     netCDF-4 classic model
     % ncdump -k foo_4.nc
     netCDF-4
   This method requires a netCDF4-enabled netCDF 3.6.2+ (i.e., from
2007 or later).

   The third option uses the POSIX-standard `od' (octal dump) command: 
     % od -An -c -N4 foo_3c.nc
        C   D   F 001
     % od -An -c -N4 foo_364.nc
        C   D   F 002
     % od -An -c -N4 foo_4c.nc
      211   H   D   F
     % od -An -c -N4 foo_4.nc
      211   H   D   F
   This option works without NCO and `ncdump'.  Values of `C D F 001'
and `C D F 002' indicate 32-bit (classic) and 64-bit netCDF3 formats,
respectively, while values of `211 H D F' indicate the newer netCDF4
file format.

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) which now support 64-bit files directly
through the netCDF interface.  See *note Selecting Output File Format::
for instructions on accessing the different file formats, including
64-bit files, supported by the modern netCDF interface.

   If you are still 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 (1) and netCDF support issues.
The netCDF LFS FAQ
(http://my.unidata.ucar.edu/content/software/netcdf/faq-lfs.html)
describes the various file size limitations imposed by different
versions of the netCDF standard.  NCO and netCDF automatically attempt
to configure LFS at build time.

   ---------- Footnotes ----------

   (1) Linux and AIX do support LFS.

3.11 Subsetting Files
=====================

Options `--unn'
Availability: `ncbo', `ncea', `ncecat', `ncflint', `ncks', `ncpdq',
`ncra', `ncrcat', `ncwa'
Short options:
Long options: `--unn' and `--union'
Options `-g GRP'
Availability: `ncbo', `ncea', `ncecat', `ncflint', `ncks', `ncpdq',
`ncra', `ncrcat', `ncwa'
Short options: `-g'
Long options: `--grp' and `--group'
Options `-v VAR' and `-x'
Availability: (`ncap2'), `ncbo', `ncea', `ncecat', `ncflint', `ncks',
`ncpdq', `ncra', `ncrcat', `ncwa'
Short options: `-v', `-x'
Long options: `--variable', `--exclude' or `--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.

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

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

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

   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
(1).  The `--unn' option causes NCO to extract the union, rather than
the intersection, of the specified groups and variables.  Consider the
following commands (which may be assumed to end with `in.nc out.nc'):
     # Intersection-mode subsetting (default)
     ncks -g  g1  -v v1 # Yes: /g1/v1, /g2/g1/v1. No: /v1, /g2/v1
     ncks -g /g1  -v v1 # Yes: /g1/v1, /g1/g2/v1. No: /v1, /g2/v1, /g2/g1/v1
     ncks -g  g1/ -v v1 # Yes: /g1/v1, /g2/g1/v1. No: /v1, /g2/v1, /g1/g2/v1
     ncks -v  g1/v1     # Yes: /g1/v1, /g2/g1/v1. No: /v1, /g2/v1, /g1/g2/v1
     ncks -g /g1/ -v v1 # Yes: /g1/v1. No: /g2/g1/v1, /v1, /g2/v1 ...
     ncks -v /g1/v1     # Yes: /g1/v1. No: /g2/g1/v1, /v1, /g2/v1 ...

     # Union-mode subsetting (invoke with --unn or --union)
     ncks -g  g1  -v v1 --unn # All variables in  g1 or progeny, or named v1
     ncks -g /g1  -v v1 --unn # All variables in /g1 or progeny, or named v1
     ncks -g  g1/ -v v1 --unn # All variables in  g1 or named v1
     ncks -g /g1/ -v v1 --unn # All variables in /g1 or named v1
   The first command (`-g g1 -v v1') extracts the variable `v1' from
any group named `g1' or descendent `g1'.  The second command extracts
`v1' from any root group named `g1' and any descendent groups as well.
The third and fourth commands are equivalent ways of extracting `v1'
only from the root group named `g1' (but 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 (*note Memory Requirements::).
     ncks          in.nc out.nc # Extract all groups and variables
     ncks -v scl   # Extract variable scl from all groups
     ncks -g g1    # Extract group g1 and descendents
     ncks -x -g g1 # Extract all groups except g1 and descendents
     ncks -g g2,g3 -v scl # Extract scl from groups g2 and g3
   Overwriting and appending work as expected:
     # Replace scl in group g2 in out.nc with scl from group g2 from in.nc
     ncks -A -g g2 -v scl in.nc out.nc

   Due to its special capabilities, `ncap2' interprets the `-v' switch
differently (*note 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 `in_grp.nc' with groups `g0'-`g9', and subgroups
`s0'-`s9', in each of those groups, and file `in.nc' with variables `Q',
`Q01'-`Q99', `Q100', `QAA'-`QZZ', `Q_H2O', `X_H2O', `Q_CO2', `X_CO2'.
     ncks -v '.+' in.nc               # All variables (default)
     ncks -v 'Q.?' in.nc              # Variables that contain Q
     ncks -v '^Q.?' in.nc             # Variables that start with Q
     ncks -v '^Q+.?.' in.nc           # Q, Q0--Q9, Q01--Q99, QAA--QZZ, etc.
     ncks -v '^Q..' in.nc             # Q01--Q99, QAA--QZZ, etc.
     ncks -v '^Q[0-9][0-9]' in.nc     # Q01--Q99, Q100
     ncks -v '^Q[[:digit:]]{2}' in.nc # Q01--Q99
     ncks -v 'H2O$' in.nc             # Q_H2O, X_H2O
     ncks -v 'H2O$|CO2$' in.nc        # Q_H2O, X_H2O, Q_CO2, X_CO2
     ncks -v '^Q[0-9][0-9]$' in.nc    # Q01--Q99
     ncks -v '^Q[0-6][0-9]|7[0-3]' in.nc # Q01--Q73, Q100
     ncks -v '(Q[0-6][0-9]|7[0-3])$' in.nc # Q01--Q73
     ncks -v '^[a-z]_[a-z]{3}$' in.nc # Q_H2O, X_H2O, Q_CO2, X_CO2
     ncks -g 'g.' in_grp.nc           # 10 Groups g0-g9
     ncks -g 's.' in_grp.nc       # 100 sub-groups g0/s0, g0/s1, ... g9/s9
     ncks -g 'g.' -v 'v.' in_grp.nc   # All variables 'v.' in groups 'g.'
   Beware--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?? in.nc out.nc   # Error: Shell attempts to glob wildcards
     ncra -v '^Q+..' in.nc out.nc # Correct: NCO interprets wildcards
     ncra -v '^Q+..' in*.nc out.nc # Correct: NCO interprets, Shell globs
   The final example shows that commands may use a combination of
variable wildcarding and shell filename expansion (globbing).  For
globbing, `*' and `?' _have nothing to do_ with the preceding regular
expression!  In shell-level filename expansion, `*' matches any string,
including the null string and `?' matches any single character.
Documentation for `bash' and `csh' describe the rules of filename
expansion (globbing).

   ---------- Footnotes ----------

   (1) Intersection-mode can also be explicitly invoked with the `--nsx'
or `--intersection' switches.  These switches are supplied for clarity
and consistency and do absolutely nothing since intersection-mode is
the default.

3.12 Subsetting Coordinate Variables
====================================

Availability: `ncap2', `ncbo', `ncea', `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 3.9.6 (January, 2009) both `-c' and `-C' honor the CF
`coordinates' convention described in *note CF Conventions::.  As of
NCO version 4.0.8 (April, 2011) both `-c' and `-C' honor the CF `bounds'
convention described in *note CF Conventions::.

3.13 Group Path Editing
=======================

Options `-G GPE_DSC'
Availability: `ncbo', `ncecat', `ncea', `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

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

LVL_NBR
     The number of levels to delete (from the head) or truncate (from
     the tail) of the input path.
   If both components of the argument are present, then a single
character, either the colon or at-sign (`:' or `@'), 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.

   GPE has three editing modes: Deletion, Truncation, and Flattening.
Select one of GPE's three editing modes by supplying a LVL_NBR that is
positive, negative, or zero for Deletion-, Truncation- and
Flattening-mode, respectively.

   In Deletion-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 Truncation-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 Flattening-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 Deletion Mode) or Appends (in
Truncation-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 (*note Subsetting Files::) yields
powerful control over the extracted (or excluded) variables and groups
and their placement in the output file as shown by the following
commands.  All commands below may be assumed to end with `in.nc out.nc'.
     # Prepending paths without editing:
     ncks                   # /g?/v? -> /g?/v?
     ncks             -v v1 # /g?/v1 -> /g?/v1
     ncks       -g g1       # /g1/v? -> /g1/v?
     ncks -G o1             # /g?/v? -> /o1/g?/v?
     ncks -G o1 -g g1       # /g1/v? -> /o1/g1/v?
     ncks       -g g1 -v v1 # /g1/v1 -> /g1/v1
     ncks -G o1       -v v1 # /g?/v1 -> /o1/g?/v1
     ncks -G o1 -g g1 -v v1 # /g1/v1 -> /o1/g1/v1
     ncks -G g1 -g /  -v v1 # /v1    -> /g1/v1
     ncks -G g1/g2    -v v1 # /g?/v1 -> /g1/g2/g?/v1
     # Deletion-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  -g g1/g1 -v v1 # /g1/g1/v1 -> /g2/g1/v1
     # Flattening-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
     # Truncation-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          -v v1 # /g?/v1    -> /g2/v1
     ncks -G g2:-1 -g g1    -v v1 # /g1/v1    -> /g1/g2/v1
     ncks -G g1:-1 -g g1/g2 -v v1 # /g1/g2/v1 -> /g1/g1/v1

   Until fall 2013 (netCDF version 4.3.1), netCDF contained no library
function for renaming groups.  NCO built on earlier versions of netCDF
than 4.3.1 therefore provides a workaround mechanism that utilizes GPE
to rename groups.  The GPE mechanism used is actually to move groups, a
more arduous procedure than renaming.  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.
     ncks -O -G f4:1 -g g4 ~/nco/data/in_grp.nc ~/tmp.nc # Move /g4 to /f4
     ncks -O -x -g g4 ~/nco/data/in_grp.nc ~/out.nc # Excise /g4
     ncks -A ~/tmp.nc ~/out.nc # Add /f4 to new file
   If the original group `g4' is not excised from `out.nc' (step two
above), then the final output file would contain both `g4' and a copy
named `f4'.  Thus GPE can be used to both "rename" and copy groups.
The recommended way to rename groups when when netCDF version 4.3.1 is
availale is to use `ncrename' (*note 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/cmip5.nc ~/cesm.nc # Extract /cesm to /
   The `-3' switch (1) specifies the output dataset should be in netCDF3
format, the `-G :' switch flattens all extracted groups, and the `-g
cesm' switch extracts only the `cesm' group and leaves all other groups
(e.g., `ecmwf', `giss').

   ---------- Footnotes ----------

   (1) Note that the `-3' switch should appear _after_ the `-G' and
`-g' switches.  This is due to an artifact of the GPE implementation
which we will hopefully remove in the future.

3.14 C and Fortran Index conventions
====================================

Availability: `ncbo', `ncea', `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 *note ncpdq netCDF Permute
Dimensions Quickly:: for techniques to re-order (including transpose)
dimensions and to reverse data storage order.

   Consider a file `85.nc' containing 12 months of data in the record
dimension `time'.  The following hyperslab operations produce identical
results, a June-July-August average of the data:
     ncra -d time,5,7 85.nc 85_JJA.nc
     ncra -F -d time,6,8 85.nc 85_JJA.nc

   Printing variable THREE_DMN_VAR in file `in.nc' first with the
C indexing convention, then with Fortran indexing convention results in
the following output formats:
     % ncks -v three_dmn_var in.nc
     lat[0]=-90 lev[0]=1000 lon[0]=-180 three_dmn_var[0]=0
     ...
     % ncks -F -v three_dmn_var in.nc
     lon(1)=0 lev(1)=100 lat(1)=-90 three_dmn_var(1)=0
     ...

3.15 Hyperslabs
===============

Availability: `ncbo', `ncea', `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]]]'
A "hyperslab" is a subset of a variable's data.  The coordinates of a
hyperslab are specified with the `-d DIM,[MIN][,[MAX][,[STRIDE]]]' short
option (or with the same arguments to the `--dimension' or `--dmn' long
options).  At least one hyperslab argument (MIN, MAX, or STRIDE) must
be present.  The bounds of the hyperslab to be extracted are specified
by the associated MIN and MAX values.  A half-open range is specified
by omitting either the MIN or MAX parameter.  The separating comma must
be present to indicate the omission of one of these arguments.  The
unspecified limit is interpreted as the maximum or minimum value in the
unspecified direction.  A cross-section at a specific coordinate is
extracted by specifying only the MIN limit and omitting a trailing
comma.  Dimensions not mentioned are passed with no reduction in range.
The dimensionality of variables is not reduced (in the case of a
cross-section, the size of the constant dimension will be one).
     # First and second indices of lon dimension
     ncks -F -d lon,1,2 in.nc out.nc
     # Second and third indices of lon dimension
     ncks -d lon,1,2 in.nc out.nc

   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.
*Note Stride::, for more information on the STRIDE option.
     # All longitude values between 1 and 2 degrees
     ncks -d lon,1.0,2.0 in.nc out.nc
     # All longitude values between 1 and 2 degrees
     ncks -F -d lon,1.0,2.0 in.nc out.nc
     # Every other longitude value between 0 and 90 degrees
     ncks -F -d lon,0.0,90.0,2 in.nc out.nc

   As 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).  Previously, for example, `-d
time,-2,-1' caused a domain error.  Now it means select the
second-to-last and penultimate timesteps.  Negative integers work for
MIN and MAX indices but not for STRIDE.
     # Last two indices of lon dimension
     ncks -F -d lon,1,-2 in.nc out.nc
     # First to penultimate indices of lon dimension
     ncks -F -d lon,1,-2 in.nc out.nc
     # Third-to-last to last index of lon dimension
     ncks -F -d lon,-3,-1 in.nc out.nc
     # Third-to-last to last index of lon dimension
     ncks -F -d lon,-3, in.nc out.nc
   As shown, we recommend using a full floating point suffix of `.0'
instead of simply `.' in order to make obvious the selection of
hyperslab elements based on coordinate value rather than index.

   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_BYTE' and `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" (*note 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).

3.16 Stride
===========

Availability: `ncbo', `ncea', `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 (*note
Multislabs::).  Using the STRIDE option for the record dimension with
`ncra' and `ncrcat' makes it possible, for instance, to average or
concatenate regular intervals across multi-file input data sets.

   The STRIDE is specified as the optional fourth argument to the `-d'
hyperslab specification: `-d DIM,[MIN][,[MAX][,[STRIDE]]]'.  Specify
STRIDE as an integer (i.e., no decimal point) following the third comma
in the `-d' argument.  There is no default value for STRIDE.  Thus
using `-d time,,,2' is valid but `-d time,,,2.0' and `-d time,,,' are
not.  When STRIDE is specified but MIN is not, there is an ambiguity as
to whether the extracted hyperslab should begin with (using C-style,
0-based indexes) element 0 or element `stride-1'.  NCO must resolve
this ambiguity and it chooses element 0 as the first element of the
hyperslab when MIN is not specified.  Thus `-d time,,,STRIDE' is
syntactically equivalent to `-d time,0,,STRIDE'.  This means, for
example, that specifying the operation `-d time,,,2' on the array
`1,2,3,4,5' selects the hyperslab `1,3,5'.  To obtain the hyperslab
`2,4' instead, simply explicitly specify the starting index as 1, i.e.,
`-d time,1,,2'.

   For example, consider a file `8501_8912.nc' which contains 60
consecutive months of data.  Say you wish to obtain just the March data
from this file.  Using 0-based subscripts (*note 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,${idx} 8501_8912.nc foo.${idx}
     done
     foreach idx (02 14 26 38 50) # C Shell
       ncks -d time,${idx} 8501_8912.nc foo.${idx}
     end
     ncrcat foo.?? 8589_03.nc
     rm foo.??
   With the STRIDE option, `ncks' performs this hyperslab extraction in
one operation:
     ncks -d time,2,,12 8501_8912.nc 8589_03.nc
   *Note ncks netCDF Kitchen Sink::, for more information on `ncks'.

   Applying the STRIDE option to the record dimension in `ncra' and
`ncrcat' makes it possible, for instance, to average or concatenate
regular intervals across multi-file input data sets.
     ncra -F -d time,3,,12 85.nc 86.nc 87.nc 88.nc 89.nc 8589_03.nc
     ncrcat -F -d time,3,,12 85.nc 86.nc 87.nc 88.nc 89.nc 8503_8903.nc

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 useful when a
target file is to be augmented, rather than constructed from scratch.
For example, when a timeseries is concatenated from input data that
becomes available in stages rather than all at once.  This switch
enables an optimization that significantly speeds writing in such cases.

   Consider the use case where wants to preserve the contents of
`fl_1.nc', and add to them new records contained in `fl_2.nc'.
Previously the output had to be placed in a third file, `fl_3.nc'
(which could also safely be named `fl_2.nc'), via
     ncrcat -O fl_1.nc fl_2.nc fl_3.nc
   Under the hood this operation copies all information in `fl_1.nc'
and `fl_2.nc' not once but twice.  The first copy is performed through
the netCDF interface, as all data from `fl_1.nc' and `fl_2.nc' are
extracted and placed in the output file.  The second copy occurs
(usually much) more quickly as the (by default) temporary output file
is copied (sometimes a quick re-link suffices) to the final output file
(*note Temporary Output Files::).  All this copying is expensive for
large files.

   The new `--record_append' switch causes all records in `fl_1.nc' to
be appended to the end of the corresponding records in `fl_2.nc':
     ncrcat --rec_apn fl_1.nc fl_2.nc
   The contents of `fl_2.nc' are completely preserved, and only values
in `fl_1.nc' are copied.  This switch avoids the necessity of copying
all of `fl_2.nc' through the netCDF interface to a new output file.
The `--rec_apn' switch automatically puts NCO into append mode (*note
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 (*note Temporary
Output Files::):
     ncrcat --rec_apn --no_tmp_fl fl_1.nc fl_2.nc
   This avoids creating an intermediate file, and copies only the
minimal amount of data (i.e., all of `fl_1.nc').  Hence, it is fast.
We recommend users try to understand the safety trade-offs involved.

3.18 Duration
=============

Availability: `ncra', `ncrcat'
Short options: `-d DIM,[MIN][,[MAX][,[STRIDE][,[DURATION]]]]'
Long options: `--mro' `--dimension
DIM,[MIN][,[MAX][,[STRIDE][,[DURATION]]]]'
`--dmn DIM,[MIN][,[MAX][,[STRIDE][,[DURATION]]]]'
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 physically connected
to _sub-cycles_ of a periodic nature, e.g., seasons of a year.  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
"duration feature", sometimes abbreviated DRN.

   The duration 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 DRN 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:
`1850.nc', `1851.nc', ... `1999.nc'.  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 clm06.nc..clm08.nc
     for mth in {6..8}; do
       mm=`printf "%02d" $mth`
       ncra -O -F -d time,${mm},,12 -n 150,4,1 1850.nc clm${mm}.nc
     done
     # Step 2: Average climatological monthly files into summertime mean
     ncra -O clm06 clm07.nc clm08.nc clm_JJA.nc
   So far, nothing is unusual and this task can be performed by any NCO
version.  The DRN feature makes obsolete the need for the shell loop
used in Step 1 above.

   The new DRN 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 a the climatological
summertime mean to one step:
     # Step 1: Compute climatological summertime mean
     ncra -O -F -d time,6,,12,3 -n 150,4,1 1850.nc clm_JJA.nc
   The DRN option instructs `ncra' (or `ncrcat') to process files in
groups of three 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 all the sub-groups together, and operates on
their ensemble, producing 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 1850.nc 1850_2009_JJA.nc
     # ...or all (150) annual means...
     ncra --mro -O -d time,,,12,12 -n 150,4,1 1850.nc 1850_2009.nc
   These operations generated and required no intermediate files.  This
contrasts to the 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.

3.19 Multislabs
===============

Availability: `ncbo', `ncea', `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
(*note 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. in.nc out.nc
     ncks -d lon,10.,15. -d lon,15.,20. -d lon,80.,90. in.nc out.nc
   Any number of hyperslabs specifications may be chained together to
specify the multislab.  MSA creates an output dimension equal in size
to the sum of the sizes of the multislabs.  This can be used to extend
and or pad coordinate grids.

   Users may specify redundant ranges of indices in a multislab, e.g.,
     ncks -d lon,0,4 -d lon,2,9,2 in.nc out.nc
   This command retrieves the first five longitudes, and then every
other longitude value up to the tenth.  Elements 0, 2, and 4 are
specified by both hyperslab arguments (hence this is redundant) but
will count only once if an arithmetic operation is being performed.
This example uses index-based (not coordinate-based) multislabs because
the STRIDE option only supports index-based hyper-slabbing.  *Note
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 in.nc out.nc
   Multislabs are powerful because they may be specified for every
dimension at the same time.  Thus multislabs obsolete the need to
execute multiple `ncks' commands to gather the desired range of data.

   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 in.nc
     Lon[0]=-180
     Lon[1]=-90
     Lon[2]=0
     Lon[3]=90
   Although simple in theory, this task requires both mathematics to
change the numerical value of the longitude coordinate, data
hyperslabbing to split the input on-disk arrays at Greenwich, and data
re-ordering within to stitch the western hemisphere onto the eastern
hemisphere at the date-line.  The `--msa' user-order switch overrides
the default that data are output in the same order in which they are
stored on-disk in the input file, and instead stores them in the same
order as the multi-slabs are given to the command line.  This default
is intuitive and is not important in most uses.  However, the MSA
user-order switch allows users to meet their output order needs by
specifying multi-slabs in a certain order.  Compare the results of
default ordering to user-ordering for longitude:
     % ncks -O -H       -v Lon -d Lon,0.,180. -d Lon,-180.,-1.0 in.nc
     Lon[0]=-180
     Lon[1]=-90
     Lon[2]=0
     Lon[3]=90
     % ncks -O -H --msa -v Lon -d Lon,0.,180. -d Lon,-180.,-1.0 in.nc
     Lon[0]=0
     Lon[1]=90
     Lon[2]=-180
     Lon[3]=-90
   The two multi-slabs are the same but they can be presented to screen,
or to an output file, in either order.  The second example shows how to
place the western hemisphere after the eastern hemisphere, although
they are stored in the opposite order in the input file.

   With this background, one sees that the following commands suffice to
rotate the input file by 180 degrees longitude:
     % ncks -O -v LatLon --msa -d Lon,0.,180. -d Lon,-180.,-1.0 in.nc out.nc
     % ncap2 -O -s 'where(Lon < 0) Lon=Lon+360' out.nc out.nc
     % ncks -C -H -v LatLon ~/nco/data/in.nc
     Lat[0]=-45 Lon[0]=-180 LatLon[0]=0
     Lat[0]=-45 Lon[1]=-90 LatLon[1]=1
     Lat[0]=-45 Lon[2]=0 LatLon[2]=2
     Lat[0]=-45 Lon[3]=90 LatLon[3]=3
     Lat[1]=45 Lon[0]=-180 LatLon[4]=4
     Lat[1]=45 Lon[1]=-90 LatLon[5]=5
     Lat[1]=45 Lon[2]=0 LatLon[6]=6
     Lat[1]=45 Lon[3]=90 LatLon[7]=7
     % ncks -C -H -v LatLon ~/out.nc
     Lat[0]=-45 Lon[0]=0 LatLon[0]=2
     Lat[0]=-45 Lon[1]=90 LatLon[1]=3
     Lat[0]=-45 Lon[2]=180 LatLon[2]=0
     Lat[0]=-45 Lon[3]=270 LatLon[3]=1
     Lat[1]=45 Lon[0]=0 LatLon[4]=6
     Lat[1]=45 Lon[1]=90 LatLon[5]=7
     Lat[1]=45 Lon[2]=180 LatLon[6]=4
     Lat[1]=45 Lon[3]=270 LatLon[7]=5
   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.

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

   Assume the domain of the monotonically increasing longitude
coordinate `lon' is 0 < LON < 360.  `ncks' will extract a hyperslab
which crosses the Greenwich meridian simply by specifying the
westernmost longitude as MIN and the easternmost longitude as MAX.  The
following commands extract a hyperslab containing the Saharan desert:
     ncks -d lon,340.,50. in.nc out.nc
     ncks -d lon,340.,50. -d lat,10.,35. in.nc out.nc
   The first example selects data in the same longitude range as the
Sahara.  The second example further constrains the data to having the
same latitude as the Sahara.  The coordinate `lon' in the OUTPUT-FILE,
`out.nc', will no longer be monotonic!  The values of `lon' will be,
e.g., `340, 350, 0, 10, 20, 30, 40, 50'.  This can have serious
implications should you run `out.nc' through another operation which
expects the `lon' coordinate to be monotonically increasing.
Fortunately, the chances of this happening are slim, since `lon' has
already been hyperslabbed, there should be no reason to hyperslab `lon'
again.  Should you need to hyperslab `lon' again, be sure to give
dimensional indices as the hyperslab arguments, rather than coordinate
values (*note Hyperslabs::).

3.21 Auxiliary Coordinates
==========================

Availability: `ncbo', `ncea', `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 (*note CF Conventions::) `coordinates' and
`standard_name' attributes described here
(http://cf-pcmdi.llnl.gov/documents/cf-conventions/1.6/cf-conventions.html#coordinate-system).
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 (*note
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 (*note CF Conventions::).

   Putting it all together, consider a variable GDS_3DVAR output from
simulations on a cell-based geodesic grid.  Although the variable
contains three dimensions of data (time, latitude, and longitude), it
is stored in the netCDF file with only two dimensions, `time' and
`gds_crd'.
     % ncks -m -C -v gds_3dvar ~/nco/data/in.nc
     gds_3dvar: type NC_FLOAT, 2 dimensions, 4 attributes, chunked? no, \
      compressed? no, packed? no, ID = 41
     gds_3dvar RAM size is 10*8*sizeof(NC_FLOAT) = 80*4 = 320 bytes
     gds_3dvar dimension 0: time, size = 10 NC_DOUBLE, dim. ID = 20 \
      (CRD)(REC)
     gds_3dvar dimension 1: gds_crd, size = 8 NC_FLOAT, dim. ID = 17 (CRD)
     gds_3dvar attribute 0: long_name, size = 17 NC_CHAR, value = \
      Geodesic variable
     gds_3dvar attribute 1: units, size = 5 NC_CHAR, value = meter
     gds_3dvar attribute 2: coordinates, size = 15 NC_CHAR, value = \
      lat_gds lon_gds
     gds_3dvar attribute 3: purpose, size = 64 NC_CHAR, value = \
      Test auxiliary coordinates like those that define geodesic grids
   The `coordinates' attribute lists the names of the latitude and
longitude coordinates, `lat_gds' and `lon_gds', respectively.  The
`coordinates' attribute is recommended though optional.  With it, the
user can immediately identify which variables contain the latitude and
longitude coordinates.  Without a `coordinates' attribute it would be
unclear at first glance whether a variable resides on a cell-based grid.
In this example, `time' is a normal record dimension and `gds_crd' is
the cell-based dimension.

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

     lon_gds: type NC_DOUBLE, 1 dimensions, 4 attributes, \
      chunked? no, compressed? no, packed? no, ID = 38
     lon_gds RAM size is 8*sizeof(NC_DOUBLE) = 8*8 = 64 bytes
     lon_gds dimension 0: gds_crd, size = 8 NC_FLOAT, dim. ID = 17 (CRD)
     lon_gds attribute 0: long_name, size = 9 NC_CHAR, value = Longitude
     lon_gds attribute 1: standard_name, size = 9 NC_CHAR, value = longitude
     lon_gds attribute 2: units, size = 6 NC_CHAR, value = degree
     lon_gds attribute 3: purpose, size = 63 NC_CHAR, value = \
      1-D longitude coordinate referred to by geodesic grid variables
   In this example `lat_gds' and `lon_gds' represent the latitude or
longitude, respectively, of cell-based variables.  These coordinates
(must) have the same single dimension (`gds_crd', in this case) as the
cell-based variables.  And the coordinates must be
one-dimensional--multidimensional coordinates will not work.

   This infrastructure allows NCO to identify, interpret, and process
(e.g., hyperslab) the variables on cell-based grids as easily as it
works with regular grids.  To time-average all the values between zero
and 180 degrees longitude and between plus and minus 30 degress
latitude, we use
     ncra -O -X 0.,180.,-30.,30. -v gds_3dvar in.nc out.nc
   NCO accepts multiple `-X' arguments for cell-based grid multi-slabs,
just as it accepts multiple `-d' arguments for multi-slabs of regular
coordinates.
     ncra -O -X 0.,180.,-30.,30. -X 270.,315.,45.,90. in.nc out.nc
   The arguments to `-X' are always interpreted as floating point
numbers, i.e., as coordinate values rather than dimension indices so
that these two commands produce identical results
     ncra -X 0.,180.,-30.,30. in.nc out.nc
     ncra -X 0,180,-30,30 in.nc out.nc
   In contrast, arguments to `-d' require decimal places to be
recognized as coordinates not indices (*note Hyperslabs::).  We
recommend always using decimal points with `-X' arguments to avoid
confusion.

3.22 UDUnits Support
====================

Availability: `ncbo', `ncea', `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
(http://www.unidata.ucar.edu/packages/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" in.nc
     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" in.nc
     time_udunits[1]=876018 hours since 1900-01-01 00:00:0.0
   Here, the user invokes the stride (*note 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 (*note Hyperslabs::) options and in all operators
which support hyperslabbing.  The following example shows how one might
average data in a time period spread across multiple input files
     ncra -d time,"1939-09-09 12:00:0.0","1945-05-08 00:00:0.0" \
       in1.nc in2.nc in3.nc out.nc
   Note that there is no excess whitespace before or after the
individual elements of the `-d' argument.  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 (*note 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
(http://www.unidata.ucar.edu/software/udunits/udunits-2/udunits2.html)
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 (http://www.unidata.ucar.edu/packages/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 (http://cf-pcmdi.llnl.gov) and the
Cooperative Ocean/Atmosphere Research Data Service (COARDS) Conventions
(http://ferret.wrc.noaa.gov/noaa_coop/coop_cdf_profile.html).  The
following `-d arguments' extract the same data using commonly
encountered time dimension formats:
     -d time,'1918-11-11 00:00:0.0','1939-09-09 00:00:0.0'
     -d time,'1918-11-11 00:00:0.0','1939-09-09 00:00:0.0'
     -d time,'1918-11-11T00:00:0.0Z','1939-09-09T00:00:0.0Z'
     -d time,'1918-11-11','1939-09-09'
     -d time,'1918-11-11','1939-9-9'
   All of these formats include at least one dash `-' in a non-leading
character position (a dash in a leading character position is a
negative sign).  NCO assumes that a space, colon, or non-leading dash
in a limit string indicates that a UDUnits units conversion is
requested.  Some date formats like YYYYMMDD that are valid in UDUnits
are ambiguous to NCO because it cannot distinguish a purely numerical
date (i.e., no dashes or text characters in it) from a coordinate or
index value:
     -d time,1918-11-11 # Interpreted as the date November 11, 1918
     -d time,19181111   # Interpreted as time-dimension index 19181111
     -d time,19181111.  # Interpreted as time-coordinate value 19181111.0
   Hence, use the YYYY-MM-DD format rather than YYYYMMDD for dates.

As of version 4.0.0 (January, 2010), NCO supports some calendar
attributes specified by the CF conventions.
*Supported types:*
     "365_day"/"noleap", "360_day", "gregorian", "standard"

*Unsupported types:*
     "366_day"/"all_leap","proleptic_gregorian","julian","none"
   Unsupported types default to mixed Gregorian/Julian as defined by
UDUnits.

An Example: Consider the following netCDF variable

     variables:
       double lon_cal(lon_cal) ;
         lon_cal:long_name = "lon_cal" ;
         lon_cal:units = "days since 1964-2-28 0:0:0" ;
         lon_cal:calendar = "365_day" ;
     data:
       lon_cal = 1,2,3,4,5,6,7,8,9,10;
   `ncks -v lon_cal -d lon_cal,'1964-3-1 0:00:0.0','1964-3-4 00:00:0.0''
results in `lon_cal=1,2,3,4'.

   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).

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

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

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

     // Results: time is "re-based" to the time units in "file01.nc"
     time=36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, \
          51, 52, 53, 54, 55, 56, 57, 58, 59 ;

     // If we repeat the above command but with only two input files...
     ncrcat -v tpt -d time,"1990-1-2 12:00:00","1990-1-3 11:59:59" \
           file02.nc file03 noon.nc

     // ...then output time coordinate is based on time units in "file02.nc"
     time = 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, \
          26, 27, 28, 29, 30, 31, 32, 33, 34, 35 ;
   As of NCO version 4.2.1 (August, 2012), NCO automatically rebases
not only the record coordinate (`time', here) but also any bounds
associated with the record coordinate (e.g., `time_bnds') (*note CF
Conventions::).

3.24 Missing values
===================

Availability: `ncap2', `ncbo', `ncea', `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 (1).

     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' (*note ncrename netCDF Renamer::) to rename all
     `missing_value' attributes to `_FillValue':
          ncrename -a .missing_value,_FillValue inout.nc
     Alternatively, one may use `ncatted' (*note ncatted netCDF
     Attribute Editor::) to add a `_FillValue' attribute to all
     variables
          ncatted -O -a _FillValue,,o,f,1.0e36 inout.nc

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

     It is impossible to determine and store the correct result of a
     binary operation in a single variable.  One such corner case
     occurs when both operands have differing _FILLVALUE attributes,
     i.e., attributes with different numerical values.  Since the
     output (result) of the operation can only have one _FILLVALUE,
     some information may be lost.  In this case, NCO always defines
     the output variable to have the same _FILLVALUE as the first input
     variable.  Prior to performing the arithmetic operation, all
     values of the second operand equal to the second _FILLVALUE are
     replaced with the first _FILLVALUE.  Then the arithmetic operation
     proceeds as normal, comparing each element of each operand to a
     single _FILLVALUE.  Comparing each element to two distinct
     _FILLVALUE's would be much slower and would be no likelier to
     yield a more satisfactory answer.  In practice, judicious choice
     of _FILLVALUE values prevents any important information from being
     lost.

   ---------- Footnotes ----------

   (1) The old functionality, i.e., where the ignored values are
indicated by `missing_value' not `_FillValue', may still be selected
_at NCO build time_ by compiling NCO with the token definition
`CPPFLAGS='-UNCO_USE_FILL_VALUE''.

   (2) For example, the DOE ARM program often uses ATT_TYPE = `NC_CHAR'
and _FILLVALUE = `-99999.'.

3.25 Chunking
=============

Availability: `ncap2', `ncbo', `ncea', `ncecat', `ncflint', `ncks',
`ncpdq', `ncra', `ncrcat', `ncwa'
Short options: none
Long options: `--cnk_dmn DMN_NM,CNK_SZ', `--chunk_dimension
DMN_NM,CNK_SZ'
, `--cnk_map CNK_MAP', `--chunk_map CNK_MAP',
`--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 in detail here
(http://www.hdfgroup.org/HDF5/doc/H5.user/Chunking.html).

   The NCO chunking implementation is designed to be flexible.  Users
control three aspects of the chunking implementation.  These are known
as the "chunking policy", "chunking map", and "chunksize".  The first
two are high-level mechanisms that apply to an entire file, while the
third allows per-dimension specification of parameters.  The
implementation is a hybrid of the `ncpdq' packing policies (*note ncpdq
netCDF Permute Dimensions Quickly::), and the hyperslab specifications
(*note Hyperslabs::).  Each aspect is intended to have a sensible
default, so that most users will only need to set one switch to obtain
sensible chunking.  Power users can tune the three switches in tandem
to obtain optimal performance.

   The user specifies the desired chunking policy with the `-P' switch
(or its long option equivalents, `--cnk_plc' and `--chunk_policy') and
its CNK_PLC argument.  Five chunking policies are currently implemented:
"Chunk All Variables [_default_]"
     Definition: Chunk all variables possible
     Alternate invocation: `ncchunk'
     CNK_PLC key values: `all', `cnk_all', `plc_all'
     Mnemonic: All
"Chunk Variables with at least Two Dimensions"
     Definition: Chunk all variables possible with at least two
     dimensions
     Alternate invocation: none
     CNK_PLC key values: `g2d', `cnk_g2d', `plc_g2d'
     Mnemonic: _G_reater than or equal to _2_ _D_imensions
"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: _G_reater than or equal to _3_ _D_imensions
"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: E_XPL_icitly specified dimensions
"Unchunking"
     Definition: Unchunk all variables
     Alternate invocation: `ncunchunk'
     CNK_PLC key values: `uck', `cnk_uck', `plc_uck', `unchunk'
     Mnemonic: _U_n_C_hun_K_
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.  Four 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: _D_i_M_e_N_sion
"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: _R_ecord _D_imension 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: E_XPL_icitly specified dimensions
"Chunksize Product Equals Scalar Size Specified"
     Definition: The product of the chunksizes for each variable
     (approximately) equals the size specified with the `--cnk_scl'
     option.  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: _PR_o_D_uct
It is possible to combine the above chunking map algorithms with
user-specified per-dimension (but not per-variable) chunksizes that
override specific chunksizes determined by the maps above.  The user
specifies the per-dimension chunksizes with the (equivalent) long
options `--cnk_dmn' or `--chunk_dimension').  The option takes two
comma-separated arguments, DMN_NM,CNK_SZ, which are the dimension name
and its chunksize, respectively.  The `--cnk_dmn' option may be used as
many times as necessary.

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

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

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

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

   It is appropriate to conclude by informing users about an aspect of
chunking that may not be expected: Record dimensions are always chunked
with a chunksize of one.  Hence all variables that contain the record
dimension are also stored as chunked (since data must be stored with
chunking either in all dimensions, or in no dimensions).  Unless
otherwise specified by the user, the other (fixed, non-record)
dimensions of such variables are assigned default chunk sizes.  The
HDF5 layer does all this automatically to optimize the on-disk
variable/file storage geometry of record variables.  Do not be
surprised to learn that files created without any explicit instructions
to activate chunking nevertheless contain chunked variables.

3.26 Deflation
==============

Availability: `ncap2', `ncbo', `ncea', `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 Lempel-Ziv deflation.  The
Lempel-Ziv algorithm is a lossless data compression technique.
Activate this deflation with the `-L DFL_LVL' short option (or with the
same argument to the `--dfl_lvl' or `--deflate' long options).  Specify
the deflation level DFL_LVL on a scale from no deflation (DFL_LVL = 0)
to maximum deflation (DFL_LVL = 9).  Minimal deflation (DFL_LVL = 1)
achieves considerable storage compression with little time penalty.
Higher deflation levels require more time for compression.  File sizes
resulting from minimal (DFL_LVL = 1) and maximal (DFL_LVL = 9)
deflation levels typically differ by a few percent in size.

   To compress an entire file using deflation, use
     ncks -4 -L 0 in.nc out.nc # No deflation (fast, no time penalty)
     ncks -4 -L 1 in.nc out.nc # Minimal deflation (little time penalty)
     ncks -4 -L 9 in.nc out.nc # Maximal deflation (much slower)

   Unscientific testing shows that deflation compresses typical climate
datasets by 30-60%.  Packing, a lossy compression technique available
for all netCDF files (see *note Packed data::), can easily compress
files by 50%.  Packed data may be deflated to squeeze datasets by about
80%:
     ncks  -4 -L 1 in.nc out.nc # Minimal deflation (~30-60% compression)
     ncks  -4 -L 9 in.nc out.nc # Maximal deflation (~31-63% compression)
     ncpdq         in.nc out.nc # Standard packing  (~50% compression)
     ncpdq -4 -L 9 in.nc out.nc # Deflated packing  (~80% compression)
   `ncks' prints deflation parameters, if any, to screen (*note ncks
netCDF Kitchen Sink::).

3.27 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/in.nc
     ...
     ncks: INFO MD5(md5_a) = 0cc175b9c0f1b6a831c399e269772661
     md5_a = 'a'
     ncks: INFO MD5(md5_abc) = 900150983cd24fb0d6963f7d28e17f72
     lev[0]=100 md5_abc[0--2]='abc'
     > ncks -O -D 2 -C -d lev,0 --md5 -v md5_a,md5_abc ~/nco/data/in.nc
     ...
     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 in.nc in.nc ~/foo.nc | grep MD5
     ...
     ncecat: INFO MD5(wnd_spd) = bec190dd944f2ce2794a7a4abf224b28
     ncecat: INFO MD5 digests of RAM and disk contents for wnd_spd agree
     > ncrcat -O -D 4 --md5 -p ~/nco/data in.nc in.nc ~/foo.nc | grep MD5
     ...
     ncrcat: INFO MD5(wnd_spd) = 74699bb0a72b7f16456badb2c995f1a1
     ncrcat: INFO MD5 digests of RAM and disk contents for wnd_spd agree
   Regardless of the debugging level, an error is returned when the
digests of the variable read from the source file and from the output
file disagree.

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

3.28 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 (1).  Otherwise, netCDF
uses twice the system pagesize.  Larger sizes can increase access speed
by reducing the number of system calls netCDF makes to read/write data
from/to disk.  Because netCDF cannot guarantee the buffer size request
will be met, the actual buffer size granted by the system is printed as
an INFO statement.
     # Request 2 MB file buffer instead of default 8 kB buffer
     > ncks -O -D 3 --bfr_sz=2097152 ~/nco/data/in.nc ~/foo.nc
     ...
     ncks: INFO nc__open() will request file buffer size = 2097152 bytes
     ncks: INFO nc__open() opened file with buffer size = 2097152 bytes
     ...

   ---------- Footnotes ----------

   (1) On modern Linux systems the block size defaults to 8192 B.  The
GLADE filesystem at NCAR has a block size of 512 kB.

3.29 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 p`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'.

   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 (*note RAM variables::).

   Two switches, `--ram_all' and `--diskless_all', are convenient
shortcuts for specifying both `--create_ram' and `--diskless_ram'.  Thus
     ncks in.nc out.nc # Default: Open in.nc on disk, write out.nc to disk
     ncks --open_ram in.nc out.nc # Open in.nc in RAM, write out.nc to disk
     ncks --create_ram in.nc out.nc # Create out.nc in RAM, write to disk
     # Open in.nc in RAM, create out.nc in RAM, then write out.nc to disk
     ncks --open_ram --create_ram in.nc out.nc
     ncks --ram_all in.nc out.nc # Same as above
     ncks --diskless_all in.nc out.nc # Same as above

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

   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 (*note Temporary Output Files::).

3.30 Packed data
================

Availability: `ncap2', `ncbo', `ncea', `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
*note 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
(http://www.unidata.ucar.edu/software/netcdf/docs/netcdf/Attribute-Conventions.html))
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 (1).  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
(http://www.hdfgroup.org/HDF5/doc/UG)) 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 *note 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' (*note ncpdq netCDF
Permute Dimensions Quickly::).  Pack and unpack individual variables
with `ncpdq' and the `ncap2' `pack()' and `unpack()' functions (*note
Methods and functions::).

   ---------- Footnotes ----------

   (1) Although not a part of the standard, NCO enforces the policy
that the `_FillValue' attribute, if any, of a packed variable is also
stored at the original precision.

3.31 Operation Types
====================

Availability: `ncap2', `ncra', `ncea', `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:
`avg'
     Mean value

`sqravg'
     Square of the mean

`avgsqr'
     Mean of sum of squares

`max'
     Maximium value

`min'
     Minimium value

`rms'
     Root-mean-square (normalized by N)

`rmssdn'
     Root-mean square (normalized by N-1)

`sqrt'
     Square root of the mean

`ttl'
     Sum of values
   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, but not on
coorniate variables.  When an operation requires a coordinate variable
to be reduced in rank, i.e., from one dimension to a scalar or from one
dimension to a degenerate (single value) array, then NCO _always
averages_ the coordinate variable regardless of the arithmetic
operation type performed on the non-coordinate variables.

   The mathematical definition of each arithmetic operation is given
below.  *Note ncwa netCDF Weighted Averager::, for additional
information on masks and normalization.  If an operation type is not
specified with `-y' then the operator performs an arithmetic average by
default.  Averaging is described first so the terminology for the other
operations is familiar.

   _Note for Info users_: The definition of mathematical operations
involving rank reduction (e.g., averaging) relies heavily on
mathematical expressions which cannot be easily represented in Info.
_See the printed manual (./nco.pdf) 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
(*note Help Requests and Bug Reports::).

EXAMPLES

Suppose you wish to examine the variable `prs_sfc(time,lat,lon)' which
contains a time series of the surface pressure as a function of
latitude and longitude.  Find the minimium value of `prs_sfc' over all
dimensions:
     ncwa -y min -v prs_sfc in.nc foo.nc
   Find the maximum value of `prs_sfc' at each time interval for each
latitude:
     ncwa -y max -v prs_sfc -a lon in.nc foo.nc
   Find the root-mean-square value of the time-series of `prs_sfc' at
every gridpoint:
     ncra -y rms -v prs_sfc in.nc foo.nc
     ncwa -y rms -v prs_sfc -a time in.nc foo.nc
   The previous two commands give the same answer but `ncra' is
preferred because it has a smaller memory footprint.  A dimension of
size one is said to be "degenerate".  By default, `ncra' leaves the
(degenerate) `time' dimension in the output file (which is usually
useful) whereas `ncwa' removes the `time' dimension (unless `-b' is
given).

These operations work as expected in multi-file operators.  Suppose
that `prs_sfc' is stored in multiple timesteps per file across multiple
files, say `jan.nc', `feb.nc', `march.nc'.  We can now find the three
month maximium surface pressure at every point.
     ncea -y max -v prs_sfc jan.nc feb.nc march.nc out.nc

It is possible to use a combination of these operations to compute the
variance and standard deviation of a field stored in a single file or
across multiple files.  The procedure to compute the temporal standard
deviation of the surface pressure at all points in a single file
`in.nc' involves three steps.
     ncwa -O -v prs_sfc -a time in.nc out.nc
     ncbo -O -v prs_sfc in.nc out.nc out.nc
     ncra -O -y rmssdn out.nc out.nc
   First construct the temporal mean of `prs_sfc' in the file `out.nc'.
Next overwrite `out.nc' with the anomaly (deviation from the mean).
Finally overwrite `out.nc' with the root-mean-square of itself.  Note
the use of `-y rmssdn' (rather than `-y rms') in the final step.  This
ensures the standard deviation is correctly normalized by one fewer
than the number of time samples.  The procedure to compute the variance
is identical except for the use of `-y var' instead of `-y rmssdn' in
the final step.

   `ncap2' can also compute statistics like standard deviations.
Brute-force implementation of formulae is one option, e.g.,
     ncap2 -s 'prs_sfc_sdn=sqrt((prs_sfc-prs_sfc.avg($time)^2).total($time)/($time.size-1))'
           in.nc out.nc
   The operation may, of course, be broken into multiple steps in order
to archive intermediate quantities, such as the time-anomalies
     ncap2 -s 'prs_sfc_anm=prs_sfc-prs_sfc.avg($time)' \
           -s 'prs_sfc_sdn=sqrt((prs_sfc_anm^2).total($time)/($time.size-1))' \
           in.nc out.nc

   `ncap2' supports intrinsic standard deviation functions (*note
Operation Types::) which simplify the above expression to
     ncap2 -s 'prs_sfc_sdn=(prs_sfc-prs_sfc.avg($time)).rmssdn($time)' in.nc out.nc
   These instrinsic functions compute the answer quickly and concisely.

   The procedure to compute the spatial standard deviation of a field
in a single file `in.nc' involves three steps.
     ncwa -O -v prs_sfc,gw -a lat,lon -w gw in.nc out.nc
     ncbo -O -v prs_sfc,gw in.nc out.nc out.nc
     ncwa -O -y rmssdn -v prs_sfc -a lat,lon -w gw out.nc out.nc
   First the appropriately weighted (with `-w gw') spatial mean values
are written to the output file.  This example includes the use of a
weighted variable specified with `-w gw'.  When using weights to
compute standard deviations one must remember to include the weights in
the initial output files so that they may be used again in the final
step.  The initial output file is then overwritten with the gridpoint
deviations from the spatial mean.  Finally the root-mean-square of the
appropriately weighted spatial deviations is taken.

   The `ncap2' solution to the spatially-weighted standard deviation
problem is
     ncap2 -s 'prs_sfc_sdn=(prs_sfc*gw-prs_sfc*gw.avg($lat,$lon)).rmssdn($lat,$lon)' \
           in.nc out.nc
   Be sure to multiply the variable by the weight prior to computing the
the anomalies and the standard deviation.

   The procedure to compute the standard deviation of a time-series
across multiple files involves one extra step since all the input must
first be collected into one file.
     ncrcat -O -v tpt in.nc in.nc foo1.nc
     ncwa -O -a time foo1.nc foo2.nc
     ncbo -O -v tpt foo1.nc foo2.nc foo3.nc
     ncra -O -y rmssdn foo3.nc out.nc
   The first step assembles all the data into a single file.  Though
this may consume a lot of temporary disk space, it is more or less
required by the `ncbo' operation in the third step.

3.32 Type Conversion
====================

Availability (automatic type conversion): `ncap2', `ncbo', `ncea',
`ncflint', `ncra', `ncwa'
Short options: None (it's _automatic_)
Availability (manual type conversion): `ncea', `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.

3.32.1 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_SHORT's `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
(1).  There are valid reasons for expecting this operation to succeed
and the NCO philosophy is to make operators do what you want, not what
is purest.  Thus, unlike C and Fortran, but like many other higher
level interpreted languages, NCO arithmetic operators will perform
automatic type conversion on integers when all the following conditions
are met (2):
  1. The requested operation is arithmetic.  This is why type
     conversion is limited to the operators `ncap2', `ncbo', `ncea',
     `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_DOUBLE's, 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()' (3).  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
`1s' and `2s', 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).

   ---------- Footnotes ----------

   (1)

   32767 = 2^15-1

   (2) Operators began performing automatic type conversions before
arithmetic in NCO version 1.2, August, 2000.  Previous versions never
performed unnecessary type conversion for arithmetic.

   (3) The actual type conversions with trunction were handled by
intrinsic type conversion, so the `trunc()' function was never
explicitly called, although the results would be the same if it were.

3.32.2 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 (1).  And the IEEE
double-precision representation gives from 15 to 17 significant decimal
digits precision (2).  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" (3).  That said, such promotion is warranted in some
circumstances.

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

   The order of operations matters to floating point math even when the
analytic expressions are equal.  Cautious users feel disquieted when
results from equally valid analyses differ in the final bits instead of
agreeing bit-for-bit.  For example, averaging arrays in multiple stages
produces different answers than averaging them in one step.  This is
easily seen in the computation of ensemble averages by two different
methods.  The NCO test file `in.nc' contains single- and
double-precision representations of the same temperature timeseries as
`tpt_flt' and `tpt_dbl'.  Pretend each datapoint in this timeseries
represents a monthly-mean temperature.  We will mimic the derivation of
a fifteen-year ensemble-mean January temperature by concatenating the
input file five times, and then averaging the datapoints representing
January two different ways.  In Method 1 we derive the 15-year ensemble
January average in two steps, as the average of three five-year
averages.  This method is naturally used when each input file contains
multiple years and multiple input files are needed (4).  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/in.nc
     # tpt_dbl = 273.1, 273.2, 273.3, 273.4, 273.5, 273.6, 273.7, 273.8, 273.9, 274
     # tpt_flt = 273.1, 273.2, 273.3, 273.4, 273.5, 273.6, 273.7, 273.8, 273.9, 274
     # Create file with five "ten-month years" (i.e., 50 timesteps) of temperature data
     ncrcat -O -v tpt_flt,tpt_dbl -p ~/nco/data in.nc in.nc in.nc in.nc in.nc ~/foo.nc
     # Average 1st five "Januaries" (elements 1, 11, 21, 31, 41)
     ncra -O -F -d time,1,,10 ~/foo.nc ~/foo_avg1.nc
     # Average 2nd five "Januaries" (elements 2, 12, 22, 32, 42)
     ncra -O -F -d time,2,,10 ~/foo.nc ~/foo_avg2.nc
     # Average 3rd five "Januaries" (elements 3, 13, 23, 33, 43)
     ncra -O -F -d time,3,,10 ~/foo.nc ~/foo_avg3.nc
     # Method 1: Obtain ensemble January average by averaging the averages
     ncra -O ~/foo_avg1.nc ~/foo_avg2.nc ~/foo_avg3.nc ~/foo_avg_mth1.nc
     # Method 2: Obtain ensemble January average by averaging the raw data
     # Employ ncra's "duration" feature (http://nco.sf.net/nco.html#drn)
     ncra -O -F -d time,1,,10,3 ~/foo.nc ~/foo_avg_mth2.nc
     # Difference the two methods
     ncbo -O ~/foo_avg_mth1.nc ~/foo_avg_mth2.nc ~/foo_avg_dff.nc
     ncks --cdl ~/foo_avg_dff.nc
     # tpt_dbl = 5.6843418860808e-14 ;
     # tpt_flt = -3.051758e-05 ;
   Although the two methods are arithmetically equivalent, they produce
slightly different answers due to the different order of operations.
Moreover, it appears at first glance that the single-precision answers
suffer from greater error than the double-precision answers.  In fact
both precisions suffer from non-zero rounding errors.  The answers
differ negligibly to machine precision, which is about seven
significant figures for single precision floats (`tpt_flt'), and
sixteen significant figures for double precision (`tpt_dbl').  The
input precision determines the answer precision.

   IEEE arithmetic guarantees that two methods will produce bit-for-bit
identical answers only if they compute the same operations in the same
order.  Bit-for-bit identical answers may also occur by happenstance
when rounding errors exactly compensate one another.  This is
demonstrated by repeating the example above with the `--dbl' (or
`--rth_dbl' for clarity) option which forces conversion of
single-precision numbers to double-precision prior to arithmetic.  Now
`ncra' will treat the first value of `tpt_flt', `273.1000f', as
`273.1000000000000d'.  Arithmetic on `tpt_flt' then proceeds in
double-precision until the final answer, which is converted back to
single-precision for final storage.
     # Average 1st five "Januaries" (elements 1, 11, 21, 31, 41)
     ncra --dbl -O -F -d time,1,,10 ~/foo.nc ~/foo_avg1.nc
     # Average 2nd five "Januaries" (elements 2, 12, 22, 32, 42)
     ncra --dbl -O -F -d time,2,,10 ~/foo.nc ~/foo_avg2.nc
     # Average 3rd five "Januaries" (elements 3, 13, 23, 33, 43)
     ncra --dbl -O -F -d time,3,,10 ~/foo.nc ~/foo_avg3.nc
     # Method 1: Obtain ensemble January average by averaging the averages
     ncra --dbl -O ~/foo_avg1.nc ~/foo_avg2.nc ~/foo_avg3.nc ~/foo_avg_mth1.nc
     # Method 2: Obtain ensemble January average by averaging the raw data
     # Employ ncra's "duration" feature (http://nco.sf.net/nco.html#drn)
     ncra --dbl -O -F -d time,1,,10,3 ~/foo.nc ~/foo_avg_mth2.nc
     # Difference the two methods
     ncbo -O ~/foo_avg_mth1.nc ~/foo_avg_mth2.nc ~/foo_avg_dff.nc
     # Show differences
     ncks --cdl ~/foo_avg_dff.nc
     # tpt_dbl = 5.6843418860808e-14 ;
     # tpt_flt = 0 ;
   The `--dbl' switch has no effect on the results computed from
double-precision inputs.  But now the two methods produce bit-for-bit
identical results from the single-precision inputs!  This is due to the
happenstance of rounding along with the effects of the `--dbl' switch.
The `--flt' and `--rth_flt' switches are provided for symmetry.  They
enforce the traditional NCO and Fortran convention of keeping
single-precision arithmetic in single-precision unless a
double-precision number is explicitly involved.

   As has been seen, 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)' in.nc in.nc
   The single- and double-precision temperatures may each be averaged
globally using four permutations for the precision of the weight and of
the intermediate arithmetic representation:
  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 out.nc
     # Single-precision weight (area), single-precision arithmetic
     ncwa -O -a lat,lon -w area in.nc out.nc
     # TREFHT     = 289.246735
     # TREFHT_dbl = 289.239964
     # Double-precision weight (gw),   single-precision arithmetic
     ncwa -O -a lat,lon -w gw   in.nc out.nc
     # TREFHT     = 289.226135
     # TREFHT_dbl = 289.239964
     # Single-precision weight (area), double-precision arithmetic
     ncwa -O --dbl -a lat,lon -w area in.nc out.nc
     # TREFHT     = 289.239960
     # TREFHT_dbl = 289.239964
     # Double-precision weight (gw),   double-precision arithmetic
     ncwa -O --dbl -a lat,lon -w gw   in.nc out.nc
     # TREFHT     = 289.239960
     # TREFHT_dbl = 289.239964
   First note that the `TREFHT_dbl' average never changes because
`TREFHT_dbl(lat,lon)' is double-precision in the input file.  As
described above, NCO automatically converts all operands involving to
the highest precision involved in the operation.  So specifying `--dbl'
is redundant for double-precision inputs.

   Second, the single-precision arithmetic averages of the
single-precision input `TREFHT' differ by 289.246735 - 289.226135 =
0.0206 from eachother, and, more importantly, by as much as 289.239964
- 289.226135 = 0.013829 from the correct (double-precision) answer.
These averages differ in the fifth digit, i.e., they agree only to four
significant figures!  Given that climate scientists are concerned about
global temperature variations of a tenth of a degree or less, this
difference is large.  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 (5).
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 \sqrt2n/\pi for large N.  The expected
(i.e., mean absolute) rounding error in our example with 13,824
additions is about \sqrt2 \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)
     ncwa_3528514.exe
     # TREFHT     = 289.240112
     # Double-precision weight (gw),   double-precision arithmetic (C)
     # TREFHT     = 289.239964
     # Single-precision weight (area), double-precision arithmetic (Matlab)
     # TREFHT     = 289.239964
     # Double-precision weight (gw),   double-precision arithmetic (Matlab)
     # TREFHT     = 289.239964
     # Single-precision weight (area), double-precision arithmetic (NCL)
     ncl < ncwa_3528514.ncl
     # TREFHT     = 289.239960
     # TREFHT_dbl = 289.239964
     # Double-precision weight (gw),   double-precision arithmetic (NCL)
     # TREFHT     = 289.239960
     # TREFHT_dbl = 289.239964
   All languages tested (C, Matlab, NCL, and NCO) agree to machine
precision with double-precision arithmetic.  Users are fortunate to
have a variety of high quality software that liberates them from the
drudgery of coding their own.  Many packages are free (as in beer)!  As
shown above NCO permits one to shift to their float-promotion
preferences as desired.  No other language allows this with a simple
switch.

   To summarize, the default arithmetic convention of NCO follows the
behavior of Fortran, and automatically promotes single-precision to
double-precision in all mixed-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' switch is only available with the NCO arithmetic
operators that could potentially perform more than a few
single-precision floating point operations per result.  These are
`ncea', `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.

   At the time of this writing (September 2013), we are interested in
users' opinions on these matters.  Currently the default behavior is
`--flt'.  We are willing to change the default to `--dbl' 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.

   ---------- Footnotes ----------

   (1) According to Wikipedia's summary of IEEE standard 754, "If a
decimal string with at most 6 significant digits is converted to IEEE
754 single-precision and then converted back to the same number of
significant decimal, then the final string should match the original;
and if an IEEE 754 single-precision is converted to a decimal string
with at least 9 significant decimal and then converted back to single,
then the final number must match the original".

   (2) According to Wikipedia's summary of IEEE standard 754, "If a
decimal string with at most 15 significant digits is converted to IEEE
754 double-precision representation and then converted back to a string
with the same number of significant digits, then the final string
should match the original; and if an IEEE 754 double precision is
converted to a decimal string with at least 17 significant digits and
then converted back to double, then the final number must match the
original".

   (3) See page 21 in Section 1.2 of the First edition for this gem:

     One does not need much experience in scientific computing to
     recognize that the implicit conversion rules are, in fact, sheer
     madness!  In effect, they make it impossible to write efficient
     numerical programs.

   (4) For example, the CMIP5 archive tends to distribute monthly
average timeseries in 50-year chunks.

   (5) Thanks to Michael J. Prather for explaining this to me.

3.32.3 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)' in.nc out.nc
   *Note ncap2 netCDF Arithmetic Processor::, for more details.

3.33 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.

3.34 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 in.nc foo.nc'.  The
full contents of an existing `history' attribute are copied from the
first INPUT-FILE to the OUTPUT-FILE.  The timestamps appear in reverse
chronological order, with the most recent timestamp appearing first in
the `history' attribute.  Since NCO and many other netCDF operators
adhere to the `history' convention, the entire data processing path of
a given netCDF file may often be deduced from examination of its
`history' attribute.  As of May, 2002, NCO is case-insensitive to the
spelling of the `history' attribute name.  Thus attributes named
`History' or `HISTORY' (which are non-standard and not recommended)
will be treated as valid history attributes.  When more than one global
attribute fits the case-insensitive search for "history", the first one
found will be used.  `history' attribute To avoid information overkill,
all operators have an optional switch (`-h', `--hst', or `--history')
to override automatically appending the `history' attribute (*note
ncatted netCDF Attribute Editor::).  Note that the `-h' switch also
turns off writing the `nco_input_file_list' attribute for multi-file
operators (*note File List Attributes::).

3.35 File List Attributes
=========================

Availability: `ncea', `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 (*note 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 (*note History Attribute::).  Hence both switches
allows space-conscious users to avoid storing what may amount to many
thousands of filenames in a metadata attribute.

3.36 CF Conventions
===================

Availability: `ncbo', `ncea', `ncecat', `ncflint', `ncpdq', `ncra',
`ncwa'
Short options: None
NCO recognizes the Climate and Forecast (CF) metadata conventions, and
applies special rules to such data.  NCO also handles older NCAR model
datasets, such as CCM and early CCSM datasets, with its CF rules even
though the earlier data may not contain an explicit `Conventions'
attribute (e.g., `CF-1.0').  We refer to all such data collectively as
CF data.  Skip this section if you never work with CF data.

   The CF netCDF conventions are described here
(http://cf-pcmdi.llnl.gov/documents/cf-conventions/1.6/cf-conventions.html#coordinate-system).
Most CF netCDF conventions are transparent to NCO (1).  There are no
known pitfalls associated with using any NCO operator on files adhering
to these conventions (2).  However, to facilitate maximum user
friendliness, NCO applies special rules to certain variables in CF
files.  The special functions are not required by the CF netCDF
conventions, yet experience shows that they simplify data analysis.

   Currently, NCO determines whether a datafile is a CF output datafile
simply by checking (case-insensitively) whether the value of the global
attribute `Conventions' (if any) equals `CF-1.0' or `NCAR-CSM' Should
`Conventions' equal either of these in the (first) INPUT-FILE, NCO will
apply special rules to certain variables because of their usual meaning
in CF files.  NCO will not average the following variables often found
in CF files: `ntrm', `ntrn', `ntrk', `ndbase', `nsbase', `nbdate',
`nbsec', `mdt', `mhisf'.  These variables contain scalar metadata such
as the resolution of the host geophysical model and it makes no sense
to change their values.

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

   Users usually want _all_ grid properties to remain unaltered in the
output file.  To be treated as a grid property, the variable name must
_exactly_ match a name in the above list, or be a coordinate variable.
The handling of `msk_*' is exceptional in that _any_ variable name
beginning with the string `msk_' is considered to be a "mask" and is
thus preserved (not operated on arithmetically).

   You must spoof NCO if you would like any grid properties or other
special CF fields processed normally.  For example rename the variables
first with `ncrename', or alter the `Conventions' attribute.

   As of NCO version 4.0.8 (April, 2011), NCO supports the CF `bounds'
convention for cell boundaries described here
(http://cf-pcmdi.llnl.gov/documents/cf-conventions/1.6/cf-conventions.html#cell-boundaries).
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.

   As of NCO version 3.9.6 (January, 2009), NCO supports the CF
`coordinates' convention described here
(http://cf-pcmdi.llnl.gov/documents/cf-conventions/1.6/cf-conventions.html#coordinate-system).
This convention allows variables to specify additional coordinates
(including multidimensional coordinates) in a space-separated string
attribute named `coordinates'.  NCO attaches any such coordinates to
the extraction list along with variable and its usual (one-dimensional)
coordinates, if any.  These auxiliary coordinates are subject to the
user-specified overrides described in *note Subsetting Coordinate
Variables::.

   ---------- Footnotes ----------

   (1) The exception is appending/altering the attributes `x_op',
`y_op', `z_op', and `t_op' for variables which have been averaged
across space and time dimensions.  This feature is scheduled for future
inclusion in NCO.

   (2) The CF conventions recommend `time' be stored in the format TIME
since BASE_TIME, e.g., the `units' attribute of `time' might be `days
since 1992-10-8 15:15:42.5 -6:00'.  A problem with this format occurs
when using `ncrcat' to concatenate multiple files together, each with a
different BASE_TIME.  That is, any `time' values from files following
the first file to be concatenated should be corrected to the BASE_TIME
offset specified in the `units' attribute of `time' from the first file.
The analogous problem has been fixed in ARM files (*note ARM
Conventions::) and could be fixed for CF files if there is sufficient
lobbying.

3.37 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
(http://www.arm.gov/data/time.stm) for time and time offsets.  If you
do not work with ARM data then you may skip this section.  ARM data
files store time information in two variables, a scalar, `base_time',
and a record variable, `time_offset'.  Subtle but serious problems can
arise when these type of files are just blindly concatenated.
Therefore `ncrcat' has been specially programmed to be able to chain
together consecutive ARM INPUT-FILES and produce and an OUTPUT-FILE
which contains the correct time information.  Currently, `ncrcat'
determines whether a datafile is an ARM datafile simply by testing for
the existence of the variables `base_time', `time_offset', and the
dimension `time'.  If these are found in the INPUT-FILE then `ncrcat'
will automatically perform two non-standard, but hopefully useful,
procedures.  First, `ncrcat' will ensure that values of `time_offset'
appearing in the OUTPUT-FILE are relative to the `base_time' appearing
in the first INPUT-FILE (and presumably, though not necessarily, also
appearing in the OUTPUT-FILE).  Second, if a coordinate variable named
`time' is not found in the INPUT-FILES, then `ncrcat' automatically
creates the `time' coordinate in the OUTPUT-FILE.  The values of `time'
are defined by the ARM conventions TIME = BASE_TIME + TIME_OFFSET.
Thus, if OUTPUT-FILE contains the `time_offset' variable, it will also
contain the `time' coordinate.  A short message is added to the
`history' global attribute whenever these ARM-specific procedures are
executed.

3.38 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.

4 Operator 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 *note Common features::, to avoid redundancy.  Only
options specific to, or most useful with, a particular operator are
described in any detail in the sections below.

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.

SYNTAX
     ncap2 [-3] [-4] [-6] [-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]

DESCRIPTION

   `ncap2' arithmetically processes netCDF files (1).  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.  *Note 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' (*note 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 (*note Left hand casting::).  The user must
manually define the proper metadata for variables defined using left
hand casting.

   ---------- Footnotes ----------

   (1) `ncap2' is the successor to `ncap' which was put into
maintenance mode in November, 2006.  This documentation refers to
`ncap2', which has a superset of the `ncap' functionality.  Eventually
`ncap' will be deprecated in favor `ncap2'.  `ncap2' may be renamed
`ncap' in 2013.

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.

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

Nesting
     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.

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_

   *Precision*
   * When both operands are variables, the result has the precision of
     the higher precision operand.

   * When one operand is a variable and the other an attribute, the
     result has the precision of the variable.

   * When both operands are attributes, the result has the precision of
     the more precise attribute.

   * The exponentiation operator "^" is an exception to the above rules.
     When both operands have type less than `NC_FLOAT', the result is
     `NC_FLOAT'.  When either type is `NC_DOUBLE', the result is also
     `NC_DOUBLE'.

   *Rank*
   * The Rank of the result is generally equal to Rank of the operand
     that has the greatest number of dimensions.

   * If the dimensions in var2 are a subset of the dimensions in var1
     then its possible to  make var2 conform to var1 through
     broadcasting and or dimension reordering.

   * Broadcasting a variable means creating data in non-existing
     dimensions by copying data in existing dimensions.

   * More specifically: If the numbers of dimensions in var1 is greater
     than or equal to the number of dimensions in var2 then an attempt
     is made to make var2 conform to var1 ,else var1 is made to conform
     to var2. If conformance  is not possible then an error message
     will be emitted and script execution will cease.

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.
Examples:

     dimensions: time=10, lat=2, lon=4
     Suppose we have the two variables:

     double  P(time,lat,lon);
     float   PZ0(lon,lat);  // PZ0=1,2,3,4,5,6,7,8;

     Consider now the expression:
      PZ=P-PZ0

     PZ0 is made to conform to P and the result is
     PZ0 =
        1,3,5,7,2,4,6,8,
        1,3,5,7,2,4,6,8,
        1,3,5,7,2,4,6,8,
        1,3,5,7,2,4,6,8,
        1,3,5,7,2,4,6,8,
        1,3,5,7,2,4,6,8,
        1,3,5,7,2,4,6,8,
        1,3,5,7,2,4,6,8,
        1,3,5,7,2,4,6,8,
        1,3,5,7,2,4,6,8,

     Once the expression is evaluated then PZ will be of type double;

     Consider now
      start=four-att_var@double_att;  // start =-69  and is of type intger;
      four_pow=four^3.0f               // four_pow=64 and is of type float
      three_nw=three_dmn_var_sht*1.0f; // type is now float
      start@n1=att_var@short_att*att_var@int_att;
                                       // start@n1=5329 and is type int

*Binary Operators*
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: *note 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;

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

*Conditional Operator ?: *
_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.

   Example:
     weight_avg=weight.avg();
     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.

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

4.1.3 Dimensions
----------------

Dimensions are defined in Output using the `defdim()' function.
     defdim("cnt",10);

   This dimension name must then be prefixed with a dollar-sign `$'
when referred to in method arguments or left-hand-casting, e.g.,
     new_var[$cnt]=time;
     temperature[$time,$lat,$lon]=35.5;
     temp_avg=temperature.avg($time);

   The `size' method allows the dimension size to be used in an
arithmetic expression:
     time_avg=time.total() / $time.size;

   Increase the size of a new variable by one and set new member to
zero:
     defdim("cnt_new",$cnt.size+1);
     new_var[$cnt_new]=0.0;
     new_var(0:($cnt_new.size-2))=old_var;

*Dimension Abbreviations
* 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);

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

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

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

*ID Quoting
* If the dimension name contains non-regular characters use ID quoting.
See *note ID Quoting::
     defdim("a--list.A",10);
     A1['$a--list.A']=30.0;

*GOTCHA
* 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)

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' in.nc out.nc
     ncap2 -s 'lon_lat[lon,lat]=lat+lon' in.nc out.nc
   The explicit list of dimensions on the LHS, `[lat,lon]' resolves the
otherwise ambiguous ordering of dimensions in LAT_LON.  In effect, the
LHS "casts" its rank properties onto the RHS.  Without LHS casting, the
dimensional ordering of LAT_LON would be undefined and, hopefully,
`ncap2' would print an error message.

   Consider now a slightly more complex example.  In geophysical
models, a coordinate system based on a blend of terrain-following and
density-following surfaces is called a "hybrid coordinate system".  In
this coordinate system, four variables must be manipulated to obtain
the pressure of the vertical coordinate: PO is the domain-mean surface
pressure offset (a scalar), PS is the local (time-varying) surface
pressure (usually two horizontal spatial dimensions, i.e. latitude by
longitude), HYAM is the weight given to surfaces of constant density
(one spatial dimension, pressure, which is orthogonal to the horizontal
dimensions), and HYBM is the weight given to surfaces of constant
elevation (also one spatial dimension).  This command constructs a
four-dimensional pressure `prs_mdp' from the four input variables of
mixed rank and orthogonality:
     ncap2 -s 'prs_mdp[time,lat,lon,lev]=P0*hyam+PS*hybm' in.nc out.nc
   Manipulating the four fields which define the pressure in a hybrid
coordinate system is easy with left hand casting.

4.1.5 Arrays and hyperslabs
---------------------------

Generating a regularly spaced one-dimensional array with `ncap2' is
simple with the `array()' function.  The syntax is
     output=array(val_srt,val_ncr,$dmn_nm); // One-dimensional output
     output=array(val_srt,val_ncr,var_tpl); // Multi-dimensional output
   where the arguments are the starting value VAL_SRT, incremental
value VAL_NCR, and, for one-dimensional output, the single dimension
`$dmn_nm', or, for multi-dimensional output, a template variable
`var_tpl', i.e., a variable with the same shape as the desired output.

   Once the associated dimensions have been defined, the start and
increment arguments may be supplied as values, mathmatical expressions,
or variables:
     var_out=array(1,1,$time); // 1,2,3,4,5,6,7,8,9,10
     var_out=array(1+2-2,one,$time); // 1,2,3,4,5,6,7,8,9,10
     var_out=array(1,2,three_dmn_rec_var); // 1,3,5,...155,157,159

   Hyperslabs in `ncap2' are more limited than hyperslabs with the
other NCO operators.  `ncap2' does not understand the shell
command-line syntax used to specify multi-slabs, wrapped co-ordinates,
negative stride or coordinate value limits.  However with a bit of
syntactic magic they are all are possible.  `ncap2' accepts (in fact,
it requires) N-hyperslab arguments for a variable of rank N:
     var1(arg1,arg2 ... argN);
   where each hyperslab argument is of the form
     start:end:stride
   and the arguments for different dimensions are separated by commas.
If "start" is omitted, it defaults to 0.  If "end" is omitted, it
defaults to dimension size minus one.  If "stride" is omitted, it
defaults to 1.

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

*Hyperslabs on the Right Hand Side of an assign
*

   A simple 1D example:
     ($time.size=10)
     od[$time]={20,22,24,26,28,30,32,34,36,38};

     od(7);     // 34
     od(7:);    // 34,36,38
     od(:7);    // 20,22,24,26,28,30,32,34
     od(::4);   // 20,28,36
     od(1:6:2)  // 22,26,30
     od(:)      // 20,22,24,26,28,30,32,34,36,38

   A more complex three dimensional example:
     ($lat.size=2,$lon.size=4)
     th[$time,$lat,$lon]=
                               {1, 2, 3, 4, 5, 6, 7, 8,
                               9,10,11,12,13,14,15,16,
                               17,18,19,20,21,22,23,24,
                               -99,-99,-99,-99,-99,-99,-99,-99,
                               33,34,35,36,37,38,39,40,
                               41,42,43,44,45,46,47,48,
                               49,50,51,52,53,54,55,56,
                               -99,58,59,60,61,62,63,64,
                               65,66,67,68,69,70,71,72,
                               -99,74,75,76,77,78,79,-99 };

     th(1,1,3);        // 16
     th(2,0,:);        // 17, 18, 19, 20
     th(:,1,3);        // 8, 16, 24, -99, 40, 48, 56, 64, 72, -99
     th(::5 ,:,0:3:2); // 1, 3, 5, 7, 41, 43, 45, 47

   If hyperslab arguments collapse to a single value (a cross-section
has been specified), then that dimension is removed from the returned
variable.  If all the values collapse then a scalar variable is
returned.  So, for example, the following is valid:
     th_nw=th(0,:,:)+th(9,:,:);
     // th_nw has dimensions $lon,$lat
     // NB: the time dimension has become degenerate

   The following is invalid:
     th_nw=th(0,:,0:1)+th(9,:,0:1);
   because the `$lon' dimension now only has two elements.  The above
can be calculated by using a LHS cast with `$lon_nw' as replacement dim
for `$lon':
     defdim("lon_nw",2);
     th_nw[$lat,$lon_nw]=th(0,:,0:1) +th(9,:,0:1);

*Hyperslabs on the Left Hand Side of an assign
* When hyperslabing on the LHS, the expression on the RHS must evaluate
to a scalar or a variable/attribute with the same number of elements as
the LHS hyperslab.  Set all elements of the last record to zero:
     th(9,:,:)=0.0;
   Set first element of each lon element to 1.0:
     th(:,:,0)=1.0;
   One may hyperslab on both sides of an assign.  For example, this
sets the last record to the first record:
     th(9,:,:)=th(0,:,:);
   Say TH0 represents pressure at height=0 and TH1 represents pressure
at height=1.  Then it is possible to insert these hyperslabs into the
records
     prs[$time,$height,$lat,$lon]=0.0;
     prs(:,0,:,:)=th0;
     prs(:,1,:,:)=th1

*Reverse method*
Use the `reverse()' method to reverse a dimension's elements in a
variable with at least one dimension.  This is equivalent to a negative
stride, e.g.,
     th_rv=th(1 ,:,:).reverse($lon); // {12,11,10,9 }, {16,15,14,13}
     od_rv=od.reverse($time);        // {38,36,34,32,30,28,26,24,22,20}

*Permute method*p
Use the `permute()' method to swap the dimensions of a variable.  The
number and names of dimension arguments must match the dimensions in
the variable.  If the first dimension in the variable is of record type
then this must remain the first dimension.  If you want to change the
record dimension then consider using `ncpdq'.

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

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[$time]=time*20;
     a1@long_name="Kelvin";
     a1@min=a1.min();
     a1@max=a1.max();
     a1@min++;
     --a1@max; q
     a1(0)=a1@min;
     a1($time.size-1)=a1@max;

   A _value list_ can be used on the RHS of an assign...  
     a1@trip1={1,2,3} ;
     a1@triplet={a1@min,(a1@min+a1@max)/2,a1@max};
   The netCDF specification allows all attribute types to have a size
greater than one.  The maximum is defined by `NC_MAX_ATTRS'.  The
following is an `ncdump' of the metadata for variable A1
     double a1(time) ;
       a1:long_name = "Kelvin" ;
       a1:max = 199. ;
       a1:min = 21. ;
       a1:trip1 = 1, 2, 3 ;
       a1:triplet = 21., 110., 199. ;

   The `size()' method can be used with attributes.  For example, to
save an attribute text string in a variable,
     defdim("sng_len", a1@long_name.size());
     sng_arr[$sng_len]=a1@long_name; // sng_arr now contains "Kelvin"
   Attributes defined in a script are stored in memory and are written
to Output after script completion.  To stop the attribute being written
use the ram_delete() method or use a bogus variable name.

*Attribute Propagation and Inheritance* 
   * Attribute propagation occurs in a regular  assign statement. The
     variable being defined on the LHS gets copies of the attributes
     from the leftermost variable on the RHS

   * Attribute Inheritance: The LHS variable "inherits" attributes from
     an Input variable with the same name

   * It is possible to have a regular assign statement for which both
     propagation and inheritance occur.

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

   If the attribute name contains non-regular characters use ID
quoting. See *note ID Quoting::
     'b..m1@c--lost'=23;

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[$time]=1UL; // n1 will now by type `NC_UINT'
     n2[$lon]=4b;   // n2 will be of type `NC_BYTE'
     n3[$lat]=5ull; // n3 will be of type `NC_UINT64'
     n3@a1=6.0d;   // attribute will be type `NC_DOUBLE'
     n3@a2=-666L;  // attribute will be type `NC_INT'

   A floating point number without a postfix will default to
`NC_DOUBLE'.  An integer without a postfix will default to type
`NC_INT'.  There is no postfix for characters, use a quoted string
instead.
     n4[$rlev]=0.1      // n4 will be of type `NC_DOUBLE'
     n5[$lon_grd]=2.0   // n5 will be of type `NC_DOUBLE'
     n6[$gds_crd]=2e3;  // n6 will be of type `NC_DOUBLE'
     n7[$gds_crd]=2e3f;  // n7 will be of type `NC_FLOAT'
     n6@a1=41;         // attribute will be type `NC_INT'
     n6@a2=-21;        // attribute will be type `NC_INT'
     n6@units="kelvin" // attribute will be type `NC_CHAR'

*netCDF3/4 Types*

b|B
     `NC_BYTE', a signed 1-byte integer

none
     `NC_CHAR', an ISO/ASCII character

s|S
     `NC_SHORT', a signed 2-byte integer

l|L
     `NC_INT', a signed 4-byte integer

f|F
     `NC_FLOAT', a single-precision (4-byte) floating point number

d|D
     `NC_DOUBLE', a double-precision (8-byte) floating point number

*netCDF4 Types*

ub|UB
     `NC_UBYTE', an unsigned 1-byte integer

us|US
     `NC_USHORT', an unsigned 2-byte integer

u|U|ul|UL
     `NC_UINT', an unsigned 4-byte integer

ll|LL
     `NC_INT64', a signed 8-byte integer

ull|ULL
     `NC_UINT64', an unsigned 8-byte integer

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.
     if(exp1)
        stmt1;
     else if(exp2)
        stmt2;
     else
        stmt3;

     # Can use code blocks as well:
     if(exp1){
        stmt1;
        stmt1a;
        stmt1b;
     } else if(exp2)
        stmt2;
     else {
        stmt3;
        stmt3a;
        stmt3b;
     }

For a variable or attribute expression to be logically true all its
non-missing value elements must be logically true, i.e., non-zero.  The
expression can be of any type.  Unlike C there is no short-circuiting
of an expression with the OR (`||') and AND (`&&') operators.  The
whole expression is evaluated regardless if one of the AND/OR operands
are True/False.
     # Simple example
     if(time>0)
       print("All values of time are greater than zero\n");
     else if( time<0)
       print("All values of time are less than zero\n");
     else {
       time_max=time.max();
       time_min=time.min();
       print("min value of time=");print(time_min,"%f");
       print("max value of time=");print(time_max,"%f");
     }

     # Example from ddra.nco
     if(fl_typ==fl_typ_gcm){
       var_nbr_apx=32;
       lmn_nbr=1.0*var_nbr_apx*varsz_gcm_4D; /* [nbr] Variable size */
       if(nco_op_typ==nco_op_typ_avg){
         lmn_nbr_avg=1.0*var_nbr_apx*varsz_gcm_4D; // Block size
         lmn_nbr_wgt=dmnsz_gcm_lat; /* [nbr] Weight size */
       } // !nco_op_typ_avg
     }else if(fl_typ==fl_typ_stl){
       var_nbr_apx=8;
       lmn_nbr=1.0*var_nbr_apx*varsz_stl_2D; /* [nbr] Variable size */
       if(nco_op_typ==nco_op_typ_avg){
         lmn_nbr_avg=1.0*var_nbr_apx*varsz_stl_2D; // Block size
         lmn_nbr_wgt=dmnsz_stl_lat; /* [nbr] Weight size */
       } // !nco_op_typ_avg
     } // !fl_typ

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

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.

examples
     print(lon);
     lon[0]=0
     lon[1]=90
     lon[2]=180
     lon[3]=270

     print(lon_2D_rrg,"%3.2f,");
     0.00,0.00,180.00,0.00,180.00,0.00,180.00,0.00,

     print(mss_val_fst@_FillValue);
     mss_val_fst@_FillValue, size = 1 NC_FLOAT, value = -999

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

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);

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

   The following methods manipulate missing value information associated
with a variable.  They only work on variables in Output.
`set_miss(expr)'
     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.

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

`get_miss()'
     Returns the missing value of a variable.   If the variable exists
     in Input and Output then the missing value of  the variable in
     Output is returned.   If the variable has no missing value then an
     error is returned.

`delete_miss()'
     Deletes the missing value associated with a variable.

`number_miss()'
     Counts the number of missing values a variable contains.

     th=three_dmn_var_dbl;
     th.change_miss(-1e10d);
     /* Set values less than 0 or greater than 50 to missing value */
     where(th < 0.0 || th > 50.0) th=th.get_miss();

     # Another example:
     new[$time,$lat,$lon]=1.0;
     new.set_miss(-997.0);

     // Extract only elements divisible by 3
     where (three_dmn_var_dbl%3 == 0)
          new=three_dmn_var_dbl;
     elsewhere
          new=new.get_miss();

     // Print missing value and variable summary
     mss_val_nbr=three_dmn_var_dbl.number_miss();
     print(three_dmn_var_dbl@_FillValue);
     print("Number of missing values in three_dmn_var_dbl: ");
     print(mss_val_nbr,"%d");
     print(three_dmn_var_dbl);

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 changed together and their syntax is cleaner than
functions.  Method names are reserved words and CANNOT be used as
variable names.  The command `ncap2 -f' shows the complete list of
methods available on your build.
     n2=sin(theta)
     n2=theta.sin()
     n2=sin(theta)^2 + cos(theta)^2
     n2=theta.sin().pow(2) + theta.cos()^2

   This statement chains methods together to convert three_dmn_var_sht
to type double, average it, then convert this back to type short:
     three_avg=three_dmn_var_sht.double().avg().short();


*Aggregate Methods
* These methods mirror the averaging types available in `ncwa'. The
arguments to the methods are the dimensions to average over. Specifying
no dimensions is equivalent to specifying all dimensions i.e.,
averaging over all dimensions. A masking variable and a weighting
variable can be manually created and applied as needed.

`avg()'
     Mean value

`sqravg()'
     Square of the mean

`avgsqr()'
     Mean of sum of squares

`max()'
     Maximum value

`min()'
     Minimum value

`rms()'
     Root-mean-square (normalize by N)

`rmssdn()'
     Root-mean square (normalize by N-1)

`ttl() or total()'
     Sum of values

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


* Packing Methods
* For more information see *note Packed data:: and *note ncpdq netCDF
Permute Dimensions Quickly::
`pack() & pack_short()'
     The default packing algorithm is applied and variable is packed to
     `NC_SHORT'

`pack_byte()'
     Variable is packed to `NC_BYTE'

`pack_short()'
     Variable is packed to `NC_SHORT'

`pack_int()'
     Variable is packed to `NC_INT'

`unpack()'
     The standard unpacking algorithm is applied.

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

`size()'
     Total number of elements

`ndims()'
     Number of dimensions in variable

`type()'
     Returns the netcdf type (see previous section)


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

`set_miss(expr)'
     Takes one argument the missing value. Sets or overwrites the
     existing missing value. The argument given is converted if
     necessary to the variable type

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

`get_miss()'
     Returns the missing value of a variable in Input or Output

`delete_miss()'
     Deletes the missing value associated with a variable.

`ram_write()'
     Writes a RAM variable to disk i.e., converts it to a regular disk
     type variable

`ram_delete()'
     Deletes a RAM variable or an attribute


*PDQ Methods
* See *note ncpdq netCDF Permute Dimensions Quickly::
`reverse(dim args)'
     Reverses the dimension ordering of elements in a variable.

`permute(dim args)'
     Re-shapes variables by re-ordering the dimensions. All the dims of
     the variable must be specified in the arguments. A limitation of
     this permute (unlike ncpdq) is that the record dimension cannot be
     re-assigned.
   // Swap dimensions about and reorder along lon
     lat_2D_rrg_new=lat_2D_rrg.permute($lon,$lat).reverse($lon);
     lat_2D_rrg_new=0,90,-30,30,-30,30,-90,0


*Type Conversion Methods
* These methods allow `ncap2' to convert variables and attributes to
the different netcdf types. For more details on automatic and manual
type conversion see (*note Type Conversion::). You can only use the new
netCDF4 types if you have compiled/links NCO with the netCDF4 library
and the Output file is HDF5.

`*netCDF3/4 Types*'

`byte()'
     convert to `NC_BYTE',  a signed 1-byte integer

`char()'
     convert to `NC_CHAR',  an ISO/ASCII character

`short()'
     convert to `NC_SHORT', a signed 2-byte integer

`int()'
     convert to `NC_INT',   a signed 4-byte integer

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

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

`*netCDF4 Types*'

`ubyte()'
     convert to `NC_UBYTE', an unsigned 1-byte integer

`ushort()'
     convert to `NC_USHORT', an unsigned 2-byte integer

`uint()'
     convert to `NC_UINT', an unsigned 4-byte integer

`int64()'
     convert to `NC_INT64', a signed 8-byte integer

`uint64()'
     convert to `NC_UINT64', an unsigned 8-byte integer

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

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

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)
     where(mask)
        var1=expr1;
     elsewhere
        var1=expr2;

     // Multiple assigns
     where(mask){
         var1=expr1;
         var2=expr2;
         ...
     }elsewhere{
         var1=expr3
         var2=expr4
         var3=expr5;
         ...
     }

   * The only expression allowed in the predicate of a where is assign,
     i.e., 'var=expr'.  This assign differs from a regular `ncap2'
     assign.  The LHS var must already exist in Input or Output.  The
     RHS expression must evaluate to a scalar or a variable/attribute of
     the same size as the LHS variable.

   * Consider when both the LHS and RHS are variables: For every
     element where mask condition is True, the corresponding LHS
     variable element is re-assigned to its partner element on the RHS.
     In the elsewhere part the mask is logically inverted and the assign
     process proceeds as before.

   * If the mask dimensions are a subset of the LHS variable's
     dimensions, then it is made to conform; if it cannot be made to
     conform then script execution halts.

   * Missing values in the mask evaluate to False in the where
     code/block statement and to True in the elsewhere block/statement.
     LHS variable elements set to missing value are not re-assigned.
     For these reasons, do not explicitly reference missing values in
     the masking condition, e.g., `where(foo=foo.get_missing()) foo=1;'
     will not work as expected.

   Example: Consider the variables `float lon_2D_rct(lat,lon);' and
`float var_msk(lat,lon);'.  Suppose we wish to multiply by two the
elements for which `var_msk' equals 1:
     where(var_msk==1) lon_2D_rct=2*lon_2D_rct;
   Suppose that we have the variable `int RDM(time)' and that we want
to set its values less than 8 or greater than 80 to 0:
     where(RDM < 8 || RDM > 80) RDM=0;

   Consider irregularly gridded data, described using rank 2
coordinates: `double lat(south_north,east_west)', `double
lon(south_north,east_west)', `double
temperature(south_north,east_west)'.  To find the average temperature
in a region bounded by [LAT_MIN,LAT_MAX] and [LON_MIN,LON_MAX]:
     temperature_msk[$south_north,$east_west]=0.0;
     where(lat >= lat_min && lat <= lat_max) && (lon >= lon_min && lon <= lon_max)
       temperature_msk=temperature;
     elsewhere
       temperature_msk=temperature@_FillValue;

     temp_avg=temperature_msk.avg();
     temp_max=temperature.max();

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
     *sz_idx=$time.size;
     *sz_jdx=$lat.size;

     for(*idx=0;idx<sz_idx;idx++)
       for(*jdx=0;jdx<sz_jdx;jdx++)
         if(temp(idx,jdx) > 100) temp(idx,jdx)=100.0;
           else if(temp(idx,jdx) < 0) temp(idx,jdx)=0.0;

     // Are values of co-ordinate variable double lat(lat) monotonic?
     *sz=$lat.size;

     for(*idx=1;idx<sz;idx++)
       if(lat(idx)-lat(idx-1) < 0.0) break;

     if(idx == sz) print("lat co-ordinate is monotonic\n");
        else print("lat co-ordinate is NOT monotonic\n");

     // Sum odd elements
     *idx=0;
     *sz=$lat_nw.size;
     *sum=0.0;

     while(idx<sz){
       if(lat(idx)%2) sum+=lat(idx);
       idx++;
     }

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

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

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.
     a1[$time]={10,2,3,4,6,5,7,3,4,1};
     a1_sort=sort(a1);
     print(a1_sort);
     // 1, 2, 3, 3, 4, 4, 5, 6, 7, 10;

     a2[$lon]={2,1,4,3};
     a2_sort=sort(a2,&a2_map);
     print(a2);
     // 1, 2, 3, 4
     print(a2_map);
     // 1, 0, 3, 2;

   If the map variable does not exist prior to the `sort()' call, then
it will be created with the same shape as the input variable and be of
type `NC_INT'.  If the map variable already exists, then the only
restriction is that it be of at least the same size as the input
variable.  To apply a map use `remap(var_in,srt_map)'.
     defdim("nlat",5);

     a3[$lon]={2,5,3,7};
     a4[$nlat,$lon]={
      1, 2, 3, 4,
      5, 6, 7, 8,
      9,10,11,12,
      13,14,15,16,
      17,18,19,20};

     a3_sort=sort(a3,&a3_map);
     print(a3_map);
     // 0, 2, 1, 3;

     a4_sort=remap(a4,a3_map);
     print(a4_sort);
     // 1, 3, 2, 4,
     // 5, 7, 6, 8,
     // 9,11,10,12,
     // 13,15,14,16,
     // 17,19,18,20;

     a3_map2[$nlat]={4,3,0,2,1};

     a4_sort2=remap(a4,a3_map2);
     print(a4_sort2);
     // 3, 5, 4, 2, 1
     // 8, 10, 9,7, 6,
     // 13,15,14,12,11,
     // 18,20,19,17,16
   As in the above example you may create your own sort map.  To sort
in descending order, apply the `reverse()' method after the `sort()'.

   Here is an extended example of how to use `ncap2' features to
hyperslab an irregular region based on the values of a variable not a
coordinate.  The distinction is crucial: hyperslabbing based on
dimensional indices or coordinate values is straightforward.  Using the
values of single or multi-dimensional variable to define a hyperslab is
quite different.
     cat > ~/ncap2_foo.nco << 'EOF'
     // Purpose: Save irregular 1-D regions based on variable values

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

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

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

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

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

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

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

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

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

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

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

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

   Here is an extended example of how to use `ncap2' features to sort
multi-dimensional arrays based on the coordinate values along a single
dimension.
     cat > ~/ncap2_foo.nco << 'EOF'
     /* Purpose: Sort multi-dimensional array based on coordinate values
        This example sorts the variable three_dmn_rec_var(time,lat,lon)
        based on the values of the time coordinate. */

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

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

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

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

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

     // Following shows how to do method one:

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

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

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

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

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

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

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 (*note 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);' in.nc out.nc
   Arbitrarily shaped regions can be defined by more complex conditional
statements.  Once defined, masks can be applied to specific variables,
and to entire files:
     ncap2 -s 'temperature_avg=(temperature*mask_var).avg()' in.nc out.nc
     ncwa -a lat,lon -m mask_var -w area in.nc out.nc
   Crafting such commands on the command line is possible though
unwieldy.  In such cases, a script is often cleaner and allows you to
document the procedure:
     cat > ncap2.in << 'EOF'
     mask_var = (lat >= lat_min && lat <= lat_max) && (lon >= lon_min && > lon <= lon_max);
     if(mask_var.total() > 0){ // Check that mask contains some valid values
       temperature_avg=(temperature*mask_var).avg(); // Average temperature
       temperature_max=(temperature*mask_var).max(); // Maximum temperature
     }
     EOF
     ncap2 -S ncap2.in in.nc out.nc

   For WRF files creating regional masks looks like
     mask_var = (XLAT >= lat_min && XLAT <= lat_max) && (XLONG >= lon_min && > XLONG <= lon_max);
   In practice with WRF it's a bit more complicated because you must
use the global metadata to determine the grid staggering and offsets to
translate XLAT and XLONG into real latitudes and longitudes and missing
points. The WRF grid documentation should describe this.

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

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
     var_out=bilinear_interp(grid_in,grid_out,grid_out_x,grid_out_y,grid_in_x,grid_in_y)
   where
`grid_in'
     Input function data.  Usually a two dimensional variable.  It must
     be of size `grid_in_x.size()*grid_in_y.size()'

`grid_out'
     This variable is the shape of `var_out'.  Usually a two
     dimensional variable.  It must be of size
     `grid_out_x.size()*grid_out_y.size()'

`grid_out_x'
     X output values

`grid_out_y'
     Y output values

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

`grid_in_y'
     Y input values values. Must be monotonic (increasing or
     decreasing).
   Prior to calculations all arguments are converted to type
`NC_DOUBLE'.  After calculations `var_out' is converted to the input
type of `grid_in'.

   Suppose the first part of an `ncap2' script is
     defdim("X",4);
     defdim("Y",5);

     // Temperature
     T_in[$X,$Y]=
      {100, 200, 300, 400, 500,
       101, 202, 303, 404, 505,
       102, 204, 306, 408, 510,
       103, 206, 309, 412, 515.0 };

     // Coordinate variables
     x_in[$X]={0.0,1.0,2.0,3.01};
     y_in[$Y]={1.0,2.0,3.0,4.0,5};
   Now we interpolate with the following variables:
     defdim("Xn",3);
     defdim("Yn",4);
     T_out[$Xn,$Yn]=0.0;
     x_out[$Xn]={0.0,0.02,3.01};
     y_out[$Yn]={1.1,2.0,3,4};

     var_out=bilinear_interp(T_in,T_out,x_out,y_out,x_in,y_in);
     print(var_out);
     // 110, 200, 300, 400,
     // 110.022, 200.04, 300.06, 400.08,
     // 113.3, 206, 309, 412 ;

   It is possible to interpolate a single point:
     var_out=bilinear_interp(T_in,0.0,3.0,4.99,x_in,y_in);
     print(var_out);
     // 513.920594059406

*Wrapping and Extrapolation*
The function `bilinear_interp_wrap()' takes the same arguments as
`bilinear_interp()' but performs wrapping (Y) and extrapolation (X) for
points off the edge of the grid.  If the given range of longitude is
say (25-335) and we have a point at 20 degrees, then the endpoints of
the range are used for the interpolation.  This is what wrapping means.
For wrapping to occur Y must be longitude and must be in the range
(0,360) or (-180,180).  There are no restrictions on the longitude (X)
values, though typically these are in the range (-90,90).  This `ncap2'
script illustrates both wrapping and extrapolation of end points:
     defdim("lat_in",6);
     defdim("lon_in",5);

     // Coordinate input vars
     lat_in[$lat_in]={-80,-40,0,30,60.0,85.0};
     lon_in[$lon_in]={30, 110, 190, 270, 350.0};

     T_in[$lat_in,$lon_in]=
       {10,40,50,30,15,
         12,43,52,31,16,
         14,46,54,32,17,
         16,49,56,33,18,
         18,52,58,34,19,
         20,55,60,35,20.0 };

     defdim("lat_out",4);
     defdim("lon_out",3);

     // Coordinate variables
     lat_out[$lat_out]={-90,0,70,88.0};
     lon_out[$lon_out]={0,190,355.0};

     T_out[$lat_out,$lon_out]=0.0;

     T_out=bilinear_interp_wrap(T_in,T_out,lat_out,lon_out,lat_in,lon_in);
     print(T_out);
     // 13.4375, 49.5, 14.09375,
     // 16.25, 54, 16.625,
     // 19.25, 58.8, 19.325,
     // 20.15, 60.24, 20.135 ;

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 (1).  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_in[lon]={-1,0.1,0,2,0.3};
     lon_out=gsl_sf_gamma(lon_in);
     lon_out= _, 9.5135, 4.5908, 2.9915

The first value is set to `_FillValue' since the gamma function is
undefined for negative integers.  If the input variable has a missing
value then this value is used.  Otherwise, the default double fill
value is used (defined in the netCDF header `netcdf.h' as
`NC_FILL_DOUBLE = 9.969e+36').

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=gsl_sf_bessel_Jn(2,lon_in);
     lon_out=0.11490, 0.0012, 0.00498, 0.011165
   This computes the Bessel function of order N=2 for every value in
`lon_in'.  The Bessel order argument, an integer, can also be a
non-scalar variable, i.e., an array.
     n_in[lon]={0,1,2,3};
     lon_out=gsl_sf_bessel_Jn(n_in,0.5);
     lon_out= 0.93846, 0.24226, 0.03060, 0.00256

Arguments to GSL wrapper functions in `ncap2' must conform to one
another, i.e., they must share the same sub-set of dimensions.  For
example: `three_out=gsl_sf_bessel_Jn(n_in,three_dmn_var_dbl)' is valid
because the variable `three_dmn_var_dbl' has a LON dimension, so `n_in'
in can be broadcast to conform to `three_dmn_var_dbl'.  However
`time_out=gsl_sf_bessel_Jn(n_in,time)' is invalid.

   Consider the elliptical integral with prototype `int
gsl_sf_ellint_RD_e(double x, double y, double z, gsl_mode_t mode,
gsl_sf_result * result)'
     three_out=gsl_sf_ellint_RD(0.5,time,three_dmn_var_dbl);

The three arguments are all conformable so the above `ncap2' call is
valid. The mode argument in the function prototype controls the
convergence of the algorithm. It also appears  in the Airy Function
prototypes. It can be set by defining the environment variable
`GSL_PREC_MODE'. If unset it defaults to the value `GSL_PREC_DOUBLE'.
See the GSL manual for more details.
     export GSL_PREC_MODE=0 // GSL_PREC_DOUBLE
     export GSL_PREC_MODE=1 // GSL_PREC_SINGLE
     export GSL_PREC_MODE=2 // GSL_PREC_APPROX

The `ncap2' wrappers to the array functions are slightly different.
Consider the following GSL prototype
`int gsl_sf_bessel_Jn_array(int nmin, int nmax, double x, double
*result_array)'
     b1=lon.double();
     x=0.5;
     status=gsl_sf_bessel_Jn_array(1,4,x,&b1);
     print(status);
     b1=0.24226,0.0306,0.00256,0.00016;
   This calculates the Bessel function of X=0.5 for N=1 to 4.  The
first three arguments are scalar values.  If a non-scalar variable is
supplied as an argument then only the first value is used.  The final
argument is the variable where the results are stored (NB: the `&'
indicates this is a call by reference).  This final argument must be of
type `double' and must be of least size NMAX-NMIN+1.  If either of
these conditions is not met then then the function returns an error
message.  The function/wrapper returns a status flag.  Zero indicates
success.

Consider another array function
`int gsl_sf_legendre_Pl_array( int lmax, double x, double
*result_array);' 
     a1=time.double();
     x=0.3;
     status=gsl_sf_legendre_Pl_array(a1.size()-1, x,&a1);
     print(status);
   This call calculates P_L(0.3) for L=0..9.  Note that |X|<=1,
otherwise there will be a domain error.  See the GSL documentation for
more details.

The GSL functions implemented in NCO are listed in the table below.
This table is correct for GSL version 1.10.  To see what functions are
available on your build run the command `ncap2 -f |grep ^gsl' .  To see
this table along with the GSL C-function prototypes look at the
spreadsheet *doc/nco_gsl.ods*.
*GSL NAME*                *I*  *NCAP FUNCTION CALL*
gsl_sf_airy_Ai_e          Y    gsl_sf_airy_Ai(dbl_expr)
gsl_sf_airy_Bi_e          Y    gsl_sf_airy_Bi(dbl_expr)
gsl_sf_airy_Ai_scaled_e   Y    gsl_sf_airy_Ai_scaled(dbl_expr)
gsl_sf_airy_Bi_scaled_e   Y    gsl_sf_airy_Bi_scaled(dbl_expr)
gsl_sf_airy_Ai_deriv_e    Y    gsl_sf_airy_Ai_deriv(dbl_expr)
gsl_sf_airy_Bi_deriv_e    Y    gsl_sf_airy_Bi_deriv(dbl_expr)
gsl_sf_airy_Ai_deriv_scaled_eY    gsl_sf_airy_Ai_deriv_scaled(dbl_expr)
gsl_sf_airy_Bi_deriv_scaled_eY    gsl_sf_airy_Bi_deriv_scaled(dbl_expr)
gsl_sf_airy_zero_Ai_e     Y    gsl_sf_airy_zero_Ai(uint_expr)
gsl_sf_airy_zero_Bi_e     Y    gsl_sf_airy_zero_Bi(uint_expr)
gsl_sf_airy_zero_Ai_deriv_eY    gsl_sf_airy_zero_Ai_deriv(uint_expr)
gsl_sf_airy_zero_Bi_deriv_eY    gsl_sf_airy_zero_Bi_deriv(uint_expr)
gsl_sf_bessel_J0_e        Y    gsl_sf_bessel_J0(dbl_expr)
gsl_sf_bessel_J1_e        Y    gsl_sf_bessel_J1(dbl_expr)
gsl_sf_bessel_Jn_e        Y    gsl_sf_bessel_Jn(int_expr,dbl_expr)
gsl_sf_bessel_Jn_array    Y    status=gsl_sf_bessel_Jn_array(int,int,double,&var_out)
gsl_sf_bessel_Y0_e        Y    gsl_sf_bessel_Y0(dbl_expr)
gsl_sf_bessel_Y1_e        Y    gsl_sf_bessel_Y1(dbl_expr)
gsl_sf_bessel_Yn_e        Y    gsl_sf_bessel_Yn(int_expr,dbl_expr)
gsl_sf_bessel_Yn_array    Y    gsl_sf_bessel_Yn_array
gsl_sf_bessel_I0_e        Y    gsl_sf_bessel_I0(dbl_expr)
gsl_sf_bessel_I1_e        Y    gsl_sf_bessel_I1(dbl_expr)
gsl_sf_bessel_In_e        Y    gsl_sf_bessel_In(int_expr,dbl_expr)
gsl_sf_bessel_In_array    Y    status=gsl_sf_bessel_In_array(int,int,double,&var_out)
gsl_sf_bessel_I0_scaled_e Y    gsl_sf_bessel_I0_scaled(dbl_expr)
gsl_sf_bessel_I1_scaled_e Y    gsl_sf_bessel_I1_scaled(dbl_expr)
gsl_sf_bessel_In_scaled_e Y    gsl_sf_bessel_In_scaled(int_expr,dbl_expr)
gsl_sf_bessel_In_scaled_arrayY    staus=gsl_sf_bessel_In_scaled_array(int,int,double,&var_out)
gsl_sf_bessel_K0_e        Y    gsl_sf_bessel_K0(dbl_expr)
gsl_sf_bessel_K1_e        Y    gsl_sf_bessel_K1(dbl_expr)
gsl_sf_bessel_Kn_e        Y    gsl_sf_bessel_Kn(int_expr,dbl_expr)
gsl_sf_bessel_Kn_array    Y    status=gsl_sf_bessel_Kn_array(int,int,double,&var_out)
gsl_sf_bessel_K0_scaled_e Y    gsl_sf_bessel_K0_scaled(dbl_expr)
gsl_sf_bessel_K1_scaled_e Y    gsl_sf_bessel_K1_scaled(dbl_expr)
gsl_sf_bessel_Kn_scaled_e Y    gsl_sf_bessel_Kn_scaled(int_expr,dbl_expr)
gsl_sf_bessel_Kn_scaled_arrayY    status=gsl_sf_bessel_Kn_scaled_array(int,int,double,&var_out)
gsl_sf_bessel_j0_e        Y    gsl_sf_bessel_J0(dbl_expr)
gsl_sf_bessel_j1_e        Y    gsl_sf_bessel_J1(dbl_expr)
gsl_sf_bessel_j2_e        Y    gsl_sf_bessel_j2(dbl_expr)
gsl_sf_bessel_jl_e        Y    gsl_sf_bessel_jl(int_expr,dbl_expr)
gsl_sf_bessel_jl_array    Y    status=gsl_sf_bessel_jl_array(int,double,&var_out)
gsl_sf_bessel_jl_steed_arrayY    gsl_sf_bessel_jl_steed_array
gsl_sf_bessel_y0_e        Y    gsl_sf_bessel_Y0(dbl_expr)
gsl_sf_bessel_y1_e        Y    gsl_sf_bessel_Y1(dbl_expr)
gsl_sf_bessel_y2_e        Y    gsl_sf_bessel_y2(dbl_expr)
gsl_sf_bessel_yl_e        Y    gsl_sf_bessel_yl(int_expr,dbl_expr)
gsl_sf_bessel_yl_array    Y    status=gsl_sf_bessel_yl_array(int,double,&var_out)
gsl_sf_bessel_i0_scaled_e Y    gsl_sf_bessel_I0_scaled(dbl_expr)
gsl_sf_bessel_i1_scaled_e Y    gsl_sf_bessel_I1_scaled(dbl_expr)
gsl_sf_bessel_i2_scaled_e Y    gsl_sf_bessel_i2_scaled(dbl_expr)
gsl_sf_bessel_il_scaled_e Y    gsl_sf_bessel_il_scaled(int_expr,dbl_expr)
gsl_sf_bessel_il_scaled_arrayY    status=gsl_sf_bessel_il_scaled_array(int,double,&var_out)
gsl_sf_bessel_k0_scaled_e Y    gsl_sf_bessel_K0_scaled(dbl_expr)
gsl_sf_bessel_k1_scaled_e Y    gsl_sf_bessel_K1_scaled(dbl_expr)
gsl_sf_bessel_k2_scaled_e Y    gsl_sf_bessel_k2_scaled(dbl_expr)
gsl_sf_bessel_kl_scaled_e Y    gsl_sf_bessel_kl_scaled(int_expr,dbl_expr)
gsl_sf_bessel_kl_scaled_arrayY    status=gsl_sf_bessel_kl_scaled_array(int,double,&var_out)
gsl_sf_bessel_Jnu_e       Y    gsl_sf_bessel_Jnu(dbl_expr,dbl_expr)
gsl_sf_bessel_Ynu_e       Y    gsl_sf_bessel_Ynu(dbl_expr,dbl_expr)
gsl_sf_bessel_sequence_Jnu_eN    gsl_sf_bessel_sequence_Jnu
gsl_sf_bessel_Inu_scaled_eY    gsl_sf_bessel_Inu_scaled(dbl_expr,dbl_expr)
gsl_sf_bessel_Inu_e       Y    gsl_sf_bessel_Inu(dbl_expr,dbl_expr)
gsl_sf_bessel_Knu_scaled_eY    gsl_sf_bessel_Knu_scaled(dbl_expr,dbl_expr)
gsl_sf_bessel_Knu_e       Y    gsl_sf_bessel_Knu(dbl_expr,dbl_expr)
gsl_sf_bessel_lnKnu_e     Y    gsl_sf_bessel_lnKnu(dbl_expr,dbl_expr)
gsl_sf_bessel_zero_J0_e   Y    gsl_sf_bessel_zero_J0(uint_expr)
gsl_sf_bessel_zero_J1_e   Y    gsl_sf_bessel_zero_J1(uint_expr)
gsl_sf_bessel_zero_Jnu_e  N    gsl_sf_bessel_zero_Jnu
gsl_sf_clausen_e          Y    gsl_sf_clausen(dbl_expr)
gsl_sf_hydrogenicR_1_e    N    gsl_sf_hydrogenicR_1
gsl_sf_hydrogenicR_e      N    gsl_sf_hydrogenicR
gsl_sf_coulomb_wave_FG_e  N    gsl_sf_coulomb_wave_FG
gsl_sf_coulomb_wave_F_arrayN    gsl_sf_coulomb_wave_F_array
gsl_sf_coulomb_wave_FG_arrayN    gsl_sf_coulomb_wave_FG_array
gsl_sf_coulomb_wave_FGp_arrayN    gsl_sf_coulomb_wave_FGp_array
gsl_sf_coulomb_wave_sphF_arrayN    gsl_sf_coulomb_wave_sphF_array
gsl_sf_coulomb_CL_e       N    gsl_sf_coulomb_CL
gsl_sf_coulomb_CL_array   N    gsl_sf_coulomb_CL_array
gsl_sf_coupling_3j_e      N    gsl_sf_coupling_3j
gsl_sf_coupling_6j_e      N    gsl_sf_coupling_6j
gsl_sf_coupling_RacahW_e  N    gsl_sf_coupling_RacahW
gsl_sf_coupling_9j_e      N    gsl_sf_coupling_9j
gsl_sf_coupling_6j_INCORRECT_eN    gsl_sf_coupling_6j_INCORRECT
gsl_sf_dawson_e           Y    gsl_sf_dawson(dbl_expr)
gsl_sf_debye_1_e          Y    gsl_sf_debye_1(dbl_expr)
gsl_sf_debye_2_e          Y    gsl_sf_debye_2(dbl_expr)
gsl_sf_debye_3_e          Y    gsl_sf_debye_3(dbl_expr)
gsl_sf_debye_4_e          Y    gsl_sf_debye_4(dbl_expr)
gsl_sf_debye_5_e          Y    gsl_sf_debye_5(dbl_expr)
gsl_sf_debye_6_e          Y    gsl_sf_debye_6(dbl_expr)
gsl_sf_dilog_e            N    gsl_sf_dilog
gsl_sf_complex_dilog_xy_e N    gsl_sf_complex_dilog_xy_e
gsl_sf_complex_dilog_e    N    gsl_sf_complex_dilog
gsl_sf_complex_spence_xy_eN    gsl_sf_complex_spence_xy_e
gsl_sf_multiply_e         N    gsl_sf_multiply
gsl_sf_multiply_err_e     N    gsl_sf_multiply_err
gsl_sf_ellint_Kcomp_e     Y    gsl_sf_ellint_Kcomp(dbl_expr)
gsl_sf_ellint_Ecomp_e     Y    gsl_sf_ellint_Ecomp(dbl_expr)
gsl_sf_ellint_Pcomp_e     Y    gsl_sf_ellint_Pcomp(dbl_expr,dbl_expr)
gsl_sf_ellint_Dcomp_e     Y    gsl_sf_ellint_Dcomp(dbl_expr)
gsl_sf_ellint_F_e         Y    gsl_sf_ellint_F(dbl_expr,dbl_expr)
gsl_sf_ellint_E_e         Y    gsl_sf_ellint_E(dbl_expr,dbl_expr)
gsl_sf_ellint_P_e         Y    gsl_sf_ellint_P(dbl_expr,dbl_expr,dbl_expr)
gsl_sf_ellint_D_e         Y    gsl_sf_ellint_D(dbl_expr,dbl_expr,dbl_expr)
gsl_sf_ellint_RC_e        Y    gsl_sf_ellint_RC(dbl_expr,dbl_expr)
gsl_sf_ellint_RD_e        Y    gsl_sf_ellint_RD(dbl_expr,dbl_expr,dbl_expr)
gsl_sf_ellint_RF_e        Y    gsl_sf_ellint_RF(dbl_expr,dbl_expr,dbl_expr)
gsl_sf_ellint_RJ_e        Y    gsl_sf_ellint_RJ(dbl_expr,dbl_expr,dbl_expr,dbl_expr)
gsl_sf_elljac_e           N    gsl_sf_elljac
gsl_sf_erfc_e             Y    gsl_sf_erfc(dbl_expr)
gsl_sf_log_erfc_e         Y    gsl_sf_log_erfc(dbl_expr)
gsl_sf_erf_e              Y    gsl_sf_erf(dbl_expr)
gsl_sf_erf_Z_e            Y    gsl_sf_erf_Z(dbl_expr)
gsl_sf_erf_Q_e            Y    gsl_sf_erf_Q(dbl_expr)
gsl_sf_hazard_e           Y    gsl_sf_hazard(dbl_expr)
gsl_sf_exp_e              Y    gsl_sf_exp(dbl_expr)
gsl_sf_exp_e10_e          N    gsl_sf_exp_e10
gsl_sf_exp_mult_e         Y    gsl_sf_exp_mult(dbl_expr,dbl_expr)
gsl_sf_exp_mult_e10_e     N    gsl_sf_exp_mult_e10
gsl_sf_expm1_e            Y    gsl_sf_expm1(dbl_expr)
gsl_sf_exprel_e           Y    gsl_sf_exprel(dbl_expr)
gsl_sf_exprel_2_e         Y    gsl_sf_exprel_2(dbl_expr)
gsl_sf_exprel_n_e         Y    gsl_sf_exprel_n(int_expr,dbl_expr)
gsl_sf_exp_err_e          Y    gsl_sf_exp_err(dbl_expr,dbl_expr)
gsl_sf_exp_err_e10_e      N    gsl_sf_exp_err_e10
gsl_sf_exp_mult_err_e     N    gsl_sf_exp_mult_err
gsl_sf_exp_mult_err_e10_e N    gsl_sf_exp_mult_err_e10
gsl_sf_expint_E1_e        Y    gsl_sf_expint_E1(dbl_expr)
gsl_sf_expint_E2_e        Y    gsl_sf_expint_E2(dbl_expr)
gsl_sf_expint_En_e        Y    gsl_sf_expint_En(int_expr,dbl_expr)
gsl_sf_expint_E1_scaled_e Y    gsl_sf_expint_E1_scaled(dbl_expr)
gsl_sf_expint_E2_scaled_e Y    gsl_sf_expint_E2_scaled(dbl_expr)
gsl_sf_expint_En_scaled_e Y    gsl_sf_expint_En_scaled(int_expr,dbl_expr)
gsl_sf_expint_Ei_e        Y    gsl_sf_expint_Ei(dbl_expr)
gsl_sf_expint_Ei_scaled_e Y    gsl_sf_expint_Ei_scaled(dbl_expr)
gsl_sf_Shi_e              Y    gsl_sf_Shi(dbl_expr)
gsl_sf_Chi_e              Y    gsl_sf_Chi(dbl_expr)
gsl_sf_expint_3_e         Y    gsl_sf_expint_3(dbl_expr)
gsl_sf_Si_e               Y    gsl_sf_Si(dbl_expr)
gsl_sf_Ci_e               Y    gsl_sf_Ci(dbl_expr)
gsl_sf_atanint_e          Y    gsl_sf_atanint(dbl_expr)
gsl_sf_fermi_dirac_m1_e   Y    gsl_sf_fermi_dirac_m1(dbl_expr)
gsl_sf_fermi_dirac_0_e    Y    gsl_sf_fermi_dirac_0(dbl_expr)
gsl_sf_fermi_dirac_1_e    Y    gsl_sf_fermi_dirac_1(dbl_expr)
gsl_sf_fermi_dirac_2_e    Y    gsl_sf_fermi_dirac_2(dbl_expr)
gsl_sf_fermi_dirac_int_e  Y    gsl_sf_fermi_dirac_int(int_expr,dbl_expr)
gsl_sf_fermi_dirac_mhalf_eY    gsl_sf_fermi_dirac_mhalf(dbl_expr)
gsl_sf_fermi_dirac_half_e Y    gsl_sf_fermi_dirac_half(dbl_expr)
gsl_sf_fermi_dirac_3half_eY    gsl_sf_fermi_dirac_3half(dbl_expr)
gsl_sf_fermi_dirac_inc_0_eY    gsl_sf_fermi_dirac_inc_0(dbl_expr,dbl_expr)
gsl_sf_lngamma_e          Y    gsl_sf_lngamma(dbl_expr)
gsl_sf_lngamma_sgn_e      N    gsl_sf_lngamma_sgn
gsl_sf_gamma_e            Y    gsl_sf_gamma(dbl_expr)
gsl_sf_gammastar_e        Y    gsl_sf_gammastar(dbl_expr)
gsl_sf_gammainv_e         Y    gsl_sf_gammainv(dbl_expr)
gsl_sf_lngamma_complex_e  N    gsl_sf_lngamma_complex
gsl_sf_taylorcoeff_e      Y    gsl_sf_taylorcoeff(int_expr,dbl_expr)
gsl_sf_fact_e             Y    gsl_sf_fact(uint_expr)
gsl_sf_doublefact_e       Y    gsl_sf_doublefact(uint_expr)
gsl_sf_lnfact_e           Y    gsl_sf_lnfact(uint_expr)
gsl_sf_lndoublefact_e     Y    gsl_sf_lndoublefact(uint_expr)
gsl_sf_lnchoose_e         N    gsl_sf_lnchoose
gsl_sf_choose_e           N    gsl_sf_choose
gsl_sf_lnpoch_e           Y    gsl_sf_lnpoch(dbl_expr,dbl_expr)
gsl_sf_lnpoch_sgn_e       N    gsl_sf_lnpoch_sgn
gsl_sf_poch_e             Y    gsl_sf_poch(dbl_expr,dbl_expr)
gsl_sf_pochrel_e          Y    gsl_sf_pochrel(dbl_expr,dbl_expr)
gsl_sf_gamma_inc_Q_e      Y    gsl_sf_gamma_inc_Q(dbl_expr,dbl_expr)
gsl_sf_gamma_inc_P_e      Y    gsl_sf_gamma_inc_P(dbl_expr,dbl_expr)
gsl_sf_gamma_inc_e        Y    gsl_sf_gamma_inc(dbl_expr,dbl_expr)
gsl_sf_lnbeta_e           Y    gsl_sf_lnbeta(dbl_expr,dbl_expr)
gsl_sf_lnbeta_sgn_e       N    gsl_sf_lnbeta_sgn
gsl_sf_beta_e             Y    gsl_sf_beta(dbl_expr,dbl_expr)
gsl_sf_beta_inc_e         N    gsl_sf_beta_inc
gsl_sf_gegenpoly_1_e      Y    gsl_sf_gegenpoly_1(dbl_expr,dbl_expr)
gsl_sf_gegenpoly_2_e      Y    gsl_sf_gegenpoly_2(dbl_expr,dbl_expr)
gsl_sf_gegenpoly_3_e      Y    gsl_sf_gegenpoly_3(dbl_expr,dbl_expr)
gsl_sf_gegenpoly_n_e      N    gsl_sf_gegenpoly_n
gsl_sf_gegenpoly_array    Y    gsl_sf_gegenpoly_array
gsl_sf_hyperg_0F1_e       Y    gsl_sf_hyperg_0F1(dbl_expr,dbl_expr)
gsl_sf_hyperg_1F1_int_e   Y    gsl_sf_hyperg_1F1_int(int_expr,int_expr,dbl_expr)
gsl_sf_hyperg_1F1_e       Y    gsl_sf_hyperg_1F1(dbl_expr,dbl_expr,dbl_expr)
gsl_sf_hyperg_U_int_e     Y    gsl_sf_hyperg_U_int(int_expr,int_expr,dbl_expr)
gsl_sf_hyperg_U_int_e10_e N    gsl_sf_hyperg_U_int_e10
gsl_sf_hyperg_U_e         Y    gsl_sf_hyperg_U(dbl_expr,dbl_expr,dbl_expr)
gsl_sf_hyperg_U_e10_e     N    gsl_sf_hyperg_U_e10
gsl_sf_hyperg_2F1_e       Y    gsl_sf_hyperg_2F1(dbl_expr,dbl_expr,dbl_expr,dbl_expr)
gsl_sf_hyperg_2F1_conj_e  Y    gsl_sf_hyperg_2F1_conj(dbl_expr,dbl_expr,dbl_expr,dbl_expr)
gsl_sf_hyperg_2F1_renorm_eY    gsl_sf_hyperg_2F1_renorm(dbl_expr,dbl_expr,dbl_expr,dbl_expr)
gsl_sf_hyperg_2F1_conj_renorm_eY    gsl_sf_hyperg_2F1_conj_renorm(dbl_expr,dbl_expr,dbl_expr,dbl_expr)
gsl_sf_hyperg_2F0_e       Y    gsl_sf_hyperg_2F0(dbl_expr,dbl_expr,dbl_expr)
gsl_sf_laguerre_1_e       Y    gsl_sf_laguerre_1(dbl_expr,dbl_expr)
gsl_sf_laguerre_2_e       Y    gsl_sf_laguerre_2(dbl_expr,dbl_expr)
gsl_sf_laguerre_3_e       Y    gsl_sf_laguerre_3(dbl_expr,dbl_expr)
gsl_sf_laguerre_n_e       Y    gsl_sf_laguerre_n(int_expr,dbl_expr,dbl_expr)
gsl_sf_lambert_W0_e       Y    gsl_sf_lambert_W0(dbl_expr)
gsl_sf_lambert_Wm1_e      Y    gsl_sf_lambert_Wm1(dbl_expr)
gsl_sf_legendre_Pl_e      Y    gsl_sf_legendre_Pl(int_expr,dbl_expr)
gsl_sf_legendre_Pl_array  Y    status=gsl_sf_legendre_Pl_array(int,double,&var_out)
gsl_sf_legendre_Pl_deriv_arrayN    gsl_sf_legendre_Pl_deriv_array
gsl_sf_legendre_P1_e      Y    gsl_sf_legendre_P1(dbl_expr)
gsl_sf_legendre_P2_e      Y    gsl_sf_legendre_P2(dbl_expr)
gsl_sf_legendre_P3_e      Y    gsl_sf_legendre_P3(dbl_expr)
gsl_sf_legendre_Q0_e      Y    gsl_sf_legendre_Q0(dbl_expr)
gsl_sf_legendre_Q1_e      Y    gsl_sf_legendre_Q1(dbl_expr)
gsl_sf_legendre_Ql_e      Y    gsl_sf_legendre_Ql(int_expr,dbl_expr)
gsl_sf_legendre_Plm_e     Y    gsl_sf_legendre_Plm(int_expr,int_expr,dbl_expr)
gsl_sf_legendre_Plm_array Y    status=gsl_sf_legendre_Plm_array(int,int,double,&var_out)
gsl_sf_legendre_Plm_deriv_arrayN    gsl_sf_legendre_Plm_deriv_array
gsl_sf_legendre_sphPlm_e  Y    gsl_sf_legendre_sphPlm(int_expr,int_expr,dbl_expr)
gsl_sf_legendre_sphPlm_arrayY    status=gsl_sf_legendre_sphPlm_array(int,int,double,&var_out)
gsl_sf_legendre_sphPlm_deriv_arrayN    gsl_sf_legendre_sphPlm_deriv_array
gsl_sf_legendre_array_sizeN    gsl_sf_legendre_array_size
gsl_sf_conicalP_half_e    Y    gsl_sf_conicalP_half(dbl_expr,dbl_expr)
gsl_sf_conicalP_mhalf_e   Y    gsl_sf_conicalP_mhalf(dbl_expr,dbl_expr)
gsl_sf_conicalP_0_e       Y    gsl_sf_conicalP_0(dbl_expr,dbl_expr)
gsl_sf_conicalP_1_e       Y    gsl_sf_conicalP_1(dbl_expr,dbl_expr)
gsl_sf_conicalP_sph_reg_e Y    gsl_sf_conicalP_sph_reg(int_expr,dbl_expr,dbl_expr)
gsl_sf_conicalP_cyl_reg_e Y    gsl_sf_conicalP_cyl_reg(int_expr,dbl_expr,dbl_expr)
gsl_sf_legendre_H3d_0_e   Y    gsl_sf_legendre_H3d_0(dbl_expr,dbl_expr)
gsl_sf_legendre_H3d_1_e   Y    gsl_sf_legendre_H3d_1(dbl_expr,dbl_expr)
gsl_sf_legendre_H3d_e     Y    gsl_sf_legendre_H3d(int_expr,dbl_expr,dbl_expr)
gsl_sf_legendre_H3d_array N    gsl_sf_legendre_H3d_array
gsl_sf_legendre_array_sizeN    gsl_sf_legendre_array_size
gsl_sf_log_e              Y    gsl_sf_log(dbl_expr)
gsl_sf_log_abs_e          Y    gsl_sf_log_abs(dbl_expr)
gsl_sf_complex_log_e      N    gsl_sf_complex_log
gsl_sf_log_1plusx_e       Y    gsl_sf_log_1plusx(dbl_expr)
gsl_sf_log_1plusx_mx_e    Y    gsl_sf_log_1plusx_mx(dbl_expr)
gsl_sf_mathieu_a_array    N    gsl_sf_mathieu_a_array
gsl_sf_mathieu_b_array    N    gsl_sf_mathieu_b_array
gsl_sf_mathieu_a          N    gsl_sf_mathieu_a
gsl_sf_mathieu_b          N    gsl_sf_mathieu_b
gsl_sf_mathieu_a_coeff    N    gsl_sf_mathieu_a_coeff
gsl_sf_mathieu_b_coeff    N    gsl_sf_mathieu_b_coeff
gsl_sf_mathieu_ce         N    gsl_sf_mathieu_ce
gsl_sf_mathieu_se         N    gsl_sf_mathieu_se
gsl_sf_mathieu_ce_array   N    gsl_sf_mathieu_ce_array
gsl_sf_mathieu_se_array   N    gsl_sf_mathieu_se_array
gsl_sf_mathieu_Mc         N    gsl_sf_mathieu_Mc
gsl_sf_mathieu_Ms         N    gsl_sf_mathieu_Ms
gsl_sf_mathieu_Mc_array   N    gsl_sf_mathieu_Mc_array
gsl_sf_mathieu_Ms_array   N    gsl_sf_mathieu_Ms_array
gsl_sf_pow_int_e          N    gsl_sf_pow_int
gsl_sf_psi_int_e          Y    gsl_sf_psi_int(int_expr)
gsl_sf_psi_e              Y    gsl_sf_psi(dbl_expr)
gsl_sf_psi_1piy_e         Y    gsl_sf_psi_1piy(dbl_expr)
gsl_sf_complex_psi_e      N    gsl_sf_complex_psi
gsl_sf_psi_1_int_e        Y    gsl_sf_psi_1_int(int_expr)
gsl_sf_psi_1_e            Y    gsl_sf_psi_1(dbl_expr)
gsl_sf_psi_n_e            Y    gsl_sf_psi_n(int_expr,dbl_expr)
gsl_sf_synchrotron_1_e    Y    gsl_sf_synchrotron_1(dbl_expr)
gsl_sf_synchrotron_2_e    Y    gsl_sf_synchrotron_2(dbl_expr)
gsl_sf_transport_2_e      Y    gsl_sf_transport_2(dbl_expr)
gsl_sf_transport_3_e      Y    gsl_sf_transport_3(dbl_expr)
gsl_sf_transport_4_e      Y    gsl_sf_transport_4(dbl_expr)
gsl_sf_transport_5_e      Y    gsl_sf_transport_5(dbl_expr)
gsl_sf_sin_e              N    gsl_sf_sin
gsl_sf_cos_e              N    gsl_sf_cos
gsl_sf_hypot_e            N    gsl_sf_hypot
gsl_sf_complex_sin_e      N    gsl_sf_complex_sin
gsl_sf_complex_cos_e      N    gsl_sf_complex_cos
gsl_sf_complex_logsin_e   N    gsl_sf_complex_logsin
gsl_sf_sinc_e             N    gsl_sf_sinc
gsl_sf_lnsinh_e           N    gsl_sf_lnsinh
gsl_sf_lncosh_e           N    gsl_sf_lncosh
gsl_sf_polar_to_rect      N    gsl_sf_polar_to_rect
gsl_sf_rect_to_polar      N    gsl_sf_rect_to_polar
gsl_sf_sin_err_e          N    gsl_sf_sin_err
gsl_sf_cos_err_e          N    gsl_sf_cos_err
gsl_sf_angle_restrict_symm_eN    gsl_sf_angle_restrict_symm
gsl_sf_angle_restrict_pos_eN    gsl_sf_angle_restrict_pos
gsl_sf_angle_restrict_symm_err_eN    gsl_sf_angle_restrict_symm_err
gsl_sf_angle_restrict_pos_err_eN    gsl_sf_angle_restrict_pos_err
gsl_sf_zeta_int_e         Y    gsl_sf_zeta_int(int_expr)
gsl_sf_zeta_e             Y    gsl_sf_zeta(dbl_expr)
gsl_sf_zetam1_e           Y    gsl_sf_zetam1(dbl_expr)
gsl_sf_zetam1_int_e       Y    gsl_sf_zetam1_int(int_expr)
gsl_sf_hzeta_e            Y    gsl_sf_hzeta(dbl_expr,dbl_expr)
gsl_sf_eta_int_e          Y    gsl_sf_eta_int(int_expr)
gsl_sf_eta_e              Y    gsl_sf_eta(dbl_expr)

   ---------- Footnotes ----------

   (1) These are the GSL standard function names postfixed with `_e'.
NCO calls these functions automatically, without the NCO command having
to specifically indicate the `_e' function suffix.

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

     x_in[$lon]={1.0,2.0,3.0,4.0};
     y_in[$lon]={1.1,1.2,1.5,1.8};

     // Ram variable is declared and defined here
     gsl_interp_cspline(&ram_sp,x_in,y_in);

     x_out[$lon_grd]={1.1,2.0,3.0,3.1,3.99};

     y_out=gsl_spline_eval(ram_sp,x_out);
     y2=gsl_spline_eval(ram_sp,1.3);
     y3=gsl_spline_eval(ram_sp,0.0);
     ram_delete(ram_sp);

     print(y_out); // 1.10472, 1.2, 1.4, 1.42658, 1.69680002
     print(y2);    // 1.12454
     print(y3);    // '_'

Note in the above example y3 is set to 'missing value' because 0.0
isn't within the input X range.

   *GSL Interpolation Types*
All the interpolation functions have been implemented. These are:
gsl_interp_linear()
gsl_interp_polynomial()
gsl_interp_cspline()
gsl_interp_cspline_periodic()
gsl_interp_akima()
gsl_interp_akima_periodic()
* Evaluation of Interpolating Types *
*Implemented*
gsl_spline_eval()
*Unimplemented*
gsl_spline_deriv()
gsl_spline_deriv2()
gsl_spline_integ()
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

   ` data_y_out=*gsl_fit_linear_est*(data_x,c0,c1,cov00,cov01,cov11) '

This function calculates y values along the line Y=c0+c1*X
*Section B*
`status=*gsl_fit_mul*(data_x,stride_x,data_y,stride_y,n,&c1,&cov11,&sumsq)
'

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

   ` data_y_out=*gsl_fit_mul_est*(data_x,c0,c1,cov11) '

This function calculates y values along the line Y=c1*X
The below example shows *gsl_fit_linear()* in action

     defdim("d1",10);
     xin[d1]={1,2,3,4,5,6,7,8,9,10.0};
     yin[d1]={3.1,6.2,9.1,12.2,15.1,18.2,21.3,24.0,27.0,30.0};
     gsl_fit_linear(xin,1,yin,1,$d1.size,&c0,&c1,&cov00,&cov01,&cov11,&sumsq);
     print(c0);  // 0.2
     print(c1);  // 2.98545454545


     defdim("e1",4);
     xout[e1]={1.0,3.0,4.0,11};
     yout[e1]=0.0;

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

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

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( *note Methods and
functions:: ). The GSL functions operate on a vector of values created
from the var_data/stride/n arguments. The ncap wrappers check that
there is no bounding error with regard to the size of the data and the
final value in the vector.

   Some examples

     a1[time]={1,2,3,4,5,6,7,8,9,10 };

     a1_avg=gsl_stats_mean(a1,1,10);
     print(a1_avg); // 5.5

     a1_var=gsl_stats_variance(a1,4,3);
     print(a1_var); // 16.0

     // bounding error, vector attempts to access element a1(10)
     a1_sd=gsl_stats_sd(a1,5,3);

For functions with the signature *func_nm(var_data,data_stride,n)*, one
may omit the second or third arguments.  The default value for STRIDE
is `1'.  The default value for N is `1+(data.size()-1)/stride'.

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

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

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

   The following example illustrates some of the weighted functions.
The data are randomly generated.  In this case the value of the weight
for each datum is either 0.0 or 1.0
     defdim("r1",2000);
     data[r1]=1.0;

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

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

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

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

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

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

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 maximium number output. For more
information see the GSL documentation. The algorithm and seed are set
via environment variables, these are picked up by the `ncap2' code.

*Setup*
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.
`gsl_rng_min()'
     the minimium value returned by the rng algorithm.

`gsl_rng_max()'
     the maximium value returned by the rng algorithm.

*Uniformly Distributed Random Numbers*
`gsl_rng_get(var_in)'
     This function returns var_in with integers from the chosen rng
     algorithm. The min and max values depend uoon the chosen rng
     algorthm.

`gsl_rng_uniform_int(var_in)'
     This function returns var_in with random integers from 0 to n-1.
     The value n must be less than or equal to the maximium value of
     the chosen rng algorithm.

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

`gsl_rng_uniform_pos(var_in)'
     This function returns var_in with double-precision numbers in the
     range (0.0,1), excluding both 0.0 and 1.0.

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

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

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

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

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

To find the maximium value of the chosen rng algorithm use the
following code snippet.
     ncap2 -v -O -s 'rng_max=gsl_rng_max();print(rng_max)' in.nc foo.nc

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

The following all return variables of type `NC_DOUBLE';
     defdim("b1",1000);
     b1[$b1]=0.8;
     b2=gsl_ran_exponential(b1);
     b2_avg=b2.avg();
     print(b2_avg);
     // b2_avg = 0.756047976787

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

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

4.1.24 Examples ncap2
---------------------

See the `ncap.in' and `ncap2.in' scripts released with NCO for more
complete demonstrations of `ncap2' functionality (script available
on-line at `http://nco.sf.net/ncap2.in').

   Define new attribute NEW for existing variable ONE as twice the
existing attribute DOUBLE_ATT of variable ATT_VAR:
     ncap2 -s 'one@new=2*att_var@double_att' in.nc out.nc

   Average variables of mixed types (result is of type `double'):
     ncap2 -s 'average=(var_float+var_double+var_int)/3' in.nc out.nc

   Multiple commands may be given to `ncap2' in three ways.  First, the
commands may be placed in a script which is executed, e.g., `tst.nco'.
Second, the commands may be individually specified with multiple `-s'
arguments to the same `ncap2' invocation.  Third, the commands may be
chained together into a single `-s' argument to `ncap2'.  Assuming the
file `tst.nco' contains the commands `a=3;b=4;c=sqrt(a^2+b^2);', then
the following `ncap2' invocations produce identical results:
     ncap2 -v -S tst.nco in.nc out.nc
     ncap2 -v -s 'a=3' -s 'b=4' -s 'c=sqrt(a^2+b^2)' in.nc out.nc
     ncap2 -v -s 'a=3;b=4;c=sqrt(a^2+b^2)' in.nc out.nc
   The second and third examples show that `ncap2' does not require
that a trailing semi-colon `;' be placed at the end of a `-s' argument,
although a trailing semi-colon `;' is always allowed.  However,
semi-colons are required to separate individual assignment statements
chained together as a single `-s' argument.

   `ncap2' may be used to "grow" dimensions, i.e., to increase
dimension sizes without altering existing data.  Say `in.nc' has
`ORO(lat,lon)' and the user wishes a new file with
`new_ORO(new_lat,new_lon)' that contains zeros in the undefined
portions of the new grid.
     defdim("new_lat",$lat.size+1); // Define new dimension sizes
     defdim("new_lon",$lon.size+1);
     new_ORO[$new_lat,$new_lon]=0.0f; // Initialize to zero
     new_ORO(0:$lat.size-1,0:$lon.size-1)=ORO; // Fill valid data
   The commands to define new coordinate variables `new_lat' and
`new_lon' in the output file follow a similar pattern.  One would might
store these commands in a script `grow.nco' and then execute the script
with
     ncap2 -v -S grow.nco in.nc out.nc

   Imagine you wish to create a binary flag based on the value of an
array.  The flag should have value 1.0 where the array exceeds 1.0, and
value 0.0 elsewhere.  This example creates the binary flag `ORO_flg' in
`out.nc' from the continuous array named `ORO' in `in.nc'.
     ncap2 -s 'ORO_flg=(ORO > 1.0)' in.nc out.nc
   Suppose your task is to change all values of `ORO' which equal 2.0
to the new value 3.0:
     ncap2 -s 'ORO_msk=(ORO==2.0);ORO=ORO_msk*3.0+!ORO_msk*ORO' in.nc out.nc
   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;' in.nc foo.nc

   This example uses `ncap2' to compute the covariance of two variables.
Let the variables U and V be the horizontal wind components.  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 in.nc foo.nc # Compute time mean of u,v
     ncrename -O -v u,uavg -v v,vavg foo.nc # Rename to avoid conflict
     ncks -A -v uavg,vavg foo.nc in.nc # Place time means with originals
     ncap2 -O -s 'uprmvprm=u*v-uavg*vavg' in.nc in.nc # Covariance
     ncra -O -v uprmvprm in.nc foo.nc # Time-mean covariance
   The mathematically inclined will note that the same covariance would
be obtained by replacing the step involving `ncap2' with
     ncap2 -O -s 'uprmvprm=(u-uavg)*(v-vavg)' foo.nc foo.nc # Covariance

   As of NCO version 3.1.8 (December, 2006), `ncap2' can compute
averages, and thus covariances, by itself:
     ncap2 -s 'uavg=u.avg($time);vavg=v.avg($time);uprmvprm=u*v-uavg*vavg' \
           -s 'uprmvrpmavg=uprmvprm.avg($time)' in.nc foo.nc
   We have not seen a simpler method to script and execute powerful
arithmetic than `ncap2'.

   `ncap2' utilizes many meta-characters (e.g., `$', `?', `;', `()',
`[]') that can confuse the command-line shell if not quoted properly.
The issues are the same as those which arise in utilizing extended
regular expressions to subset variables (*note Subsetting Files::).
The example above will fail with no quotes and with double quotes.
This is because shell globbing tries to "interpolate" the value of
`$time' from the shell environment unless it is quoted:
     ncap2 -s 'uavg=u.avg($time)'  in.nc foo.nc # Correct (recommended)
     ncap2 -s  uavg=u.avg('$time') in.nc foo.nc # Correct (and dangerous)
     ncap2 -s  uavg=u.avg($time)   in.nc foo.nc # Fails ($time = '')
     ncap2 -s "uavg=u.avg($time)"  in.nc foo.nc # Fails ($time = '')
   Without the single quotes, the shell replaces `$time' with an empty
string.  The command `ncap2' receives from the shell is `uavg=u.avg()'.
This causes `ncap2' to average over all dimensions rather than just the
TIME dimension, and unintended consequence.

   We recommend using single quotes to protect `ncap2' command-line
scripts from the shell, even when such protection is not strictly
necessary.  Expert users may violate this rule to exploit the ability
to use shell variables in `ncap2' command-line scripts (*note 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 (*note 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.

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: 
`abs(x)'
     "Absolute value" Absolute value of X.  Example: abs(-1) = 1

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

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

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

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

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

`atan2(y,x)'
     "Arc-tangent2" Arc-tangent of Y/X :Example atan2(1,3) =
     0.321689857

`atanh(x)'
     "Hyperbolic arc-tangent" Hyperbolic arc-tangent of X where X is
     specified in radians between -pi/2 and pi/2.  Example:
     atanh(3.14159265358979323844) = 1.0

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

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

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

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

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

`exp(x)'
     "Exponential" Exponential of X, e^x.  Example: exp(1.0) =
     2.71828182845904523536

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

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

`gamma_inc_P(x)'
     "Incomplete Gamma function" Incomplete Gamma function of parameter
     A and variable X, gamma_inc_P(a,x).  One of the four incomplete
     gamma functions.  Example: gamma_inc_P(1,1) = 1-1/e

`ln(x)'
     "Natural Logarithm" Natural logarithm of X, ln(x).  Example:
     ln(2.71828182845904523536) = 1.0

`log(x)'
     "Natural Logarithm" Exact synonym for `ln(x)'.

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

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

`pow(x,y)'
     "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 (*note
     Type Conversion::) to type `NC_FLOAT' before evaluation.  Example:
     pow(2,3) = 8

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

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

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

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

`sqrt(x)'
     "Square Root" Square Root of X, sqrt(x).  Example: sqrt(4.0) = 2.0

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

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

`trunc(x)'
     "Truncate" Nearest integer to X is returned in floating point
     format.  Round halfway cases toward zero, regardless of current
     IEEE rounding direction.  Example: trunc(0.5) = 0.0
   The complete list of mathematical functions supported is
platform-specific.  Functions mandated by ANSI C are _guaranteed_ to be
present and are indicated with an asterisk (1).  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.  (2)



   ---------- Footnotes ----------

   (1) ANSI C compilers are guaranteed to support double-precision
versions of these functions.  These functions normally operate on
netCDF variables of type `NC_DOUBLE' without having to perform
intrinsic conversions.  For example, ANSI compilers provide `sin' for
the sine of C-type `double' variables.  The ANSI standard does not
require, but many compilers provide, an extended set of mathematical
functions that apply to single (`float') and quadruple (`long double')
precision variables.  Using these functions (e.g., `sinf' for `float',
`sinl' for `long double'), when available, is (presumably) more
efficient than casting variables to type `double', performing the
operation, and then re-casting.  NCO uses the faster intrinsic
functions when they are available, and uses the casting method when
they are not.

   (2) Linux supports more of these intrinsic functions than other OSs.

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.

Operator      Description                                   Associativity
--------------------------------------------------------------------------- 
`++ --'       Postfix Increment/Decrement                   Right to Left
`()'          Parentheses (function call)                   
`.'           Method call                                   

`++ --'       Prefix Increment/Decrement                    Right to Left
`+ -'         Unary  Plus/Minus                             
`!'           Logical Not                                   

`^'           Power of Operator                             Right to Left

`* / %'       Multiply/Divide/Modulus                       Left To Right

`+ -'         Addition/Subtraction                          Left To Right

`>> <<'       Fortran style array clipping                  Left to Right


`< <='        Less than/Less than or equal to               Left to Right
`> >='        Greater than/Greater than or equal to         

`== !='       Equal to/Not equal to                         Left to Right

`&&'          Logical AND                                   Left to Right

`||'          Logical OR                                    Left to Right

`?:'          Ternary Operator                              Right to Left

`='           Assignment                                    Right to Left
`+= -='       Addition/subtraction assignment               
`*= /='       Multiplication/division assignment            

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
netcdf-3.6.0-p1
netcdf-3.6.1
netcdf-3.6.2
     /*
      * ( [a-zA-Z]|[0-9]|'_'|'-'|'+'|'.'|'|':'|'@'|'('|')' )+
      * Verify that name string is valid CDL syntax, i.e., all characters are
      * alphanumeric, '-', '_', '+', or '.'.
      * Also permit ':', '@', '(', or ')' in names for chemists currently making
      * use of these characters, but don't document until ncgen and ncdump can
      * also handle these characters in names.
      */

netcdf-3.6.3
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.
      */

4.2 `ncatted' netCDF Attribute Editor
=====================================

SYNTAX
     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]]

DESCRIPTION

   `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 (*note Filters for `ncks'::).

   Because repeated use of `ncatted' can considerably increase the size
of the `history' global attribute (*note History Attribute::), the `-h'
switch is provided to override automatically appending the command to
the `history' global attribute in the OUTPUT-FILE.

   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 together, e.g., by `ncrcat',
without losing any information.  *Note 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'.  Each ATT_DSC contains five
elements, which makes using `ncatted' somewhat complicated, but
powerful.  The ATT_DSC argument structure contains five arguments in the
following order:
ATT_DSC = ATT_NM, VAR_NM, MODE, ATT_TYPE, ATT_VAL
ATT_NM
     Attribute name.  Example: `units'

VAR_NM
     Variable name.  Regular expressions (*note Subsetting Files::) are
     accepted and will select any matching variable names.  Example:
     `pressure', `'^H2O''.

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

ATT_TYPE
     Attribute type abbreviation.  Example: `c'.  See below for
     complete listing of valid values of ATT_TYPE.

ATT_VAL
     Attribute value.  Example: `pascal'.
There should be no empty space between these five consecutive arguments.
The description of these arguments follows in their order of appearance.

   The value of ATT_NM is the name of the attribute you want to edit.
This meaning of this should be clear to all users of the `ncatted'
operator.  If ATT_NM is omitted (i.e., left blank) and "Delete" mode is
selected, then all attributes associated with the specified variable
will be deleted.

   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 variable in a file.  A value of
VAR_NM of "global" indicates that ATT_NM refers to a global attribute,
rather than a particular variable's attribute.  This is the method
`ncatted' supports for editing global attributes.  If VAR_NM is left
blank, on the other hand, then `ncatted' attempts to perform the
editing operation on every variable in the file.  This option may be
convenient to use if you decide to change the conventions you use for
describing the data.  Finally, as mentioned above, VAR_NM may be
specified as a regular expression.

   The value of MODE is a single character abbreviation (`a', `c', `d',
`m', or `o') standing for one of five editing modes:
`a'
     "Append".  Append value ATT_VAL to current VAR_NM attribute ATT_NM
     value ATT_VAL, if any.  If VAR_NM does not have an attribute
     ATT_NM, there is no effect.

`c'
     "Create".  Create variable VAR_NM attribute ATT_NM with ATT_VAL if
     ATT_NM does not yet exist.  If VAR_NM already has an attribute
     ATT_NM, there is no effect.

`d'
     "Delete".  Delete current VAR_NM attribute ATT_NM.  If VAR_NM does
     not have an attribute ATT_NM, there is no effect.  If ATT_NM is
     omitted (left blank), then all attributes associated with the
     specified variable are automatically deleted.  When "Delete" mode
     is selected, the ATT_TYPE and ATT_VAL arguments are superfluous
     and may be left blank.

`m'
     "Modify".  Change value of current VAR_NM attribute ATT_NM to value
     ATT_VAL.  If VAR_NM does not have an attribute ATT_NM, there is no
     effect.

`o'
     "Overwrite".  Write attribute ATT_NM with value ATT_VAL to variable
     VAR_NM, overwriting existing attribute ATT_NM, if any.  This is
     the default mode.

   The value of ATT_TYPE is a single character abbreviation (`f', `d',
`l', `i', `s', `c', `b', `u') or a short string standing for one of the
twelve primitive netCDF data types:
`f'
     "Float".  Value(s) specified in ATT_VAL will be stored as netCDF
     intrinsic type `NC_FLOAT'.

`d'
     "Double".  Value(s) specified in ATT_VAL will be stored as netCDF
     intrinsic type `NC_DOUBLE'.

`i, l'
     "Integer" or "Long".  Value(s) specified in ATT_VAL will be stored
     as netCDF intrinsic type `NC_INT'.

`s'
     "Short".  Value(s) specified in ATT_VAL will be stored as netCDF
     intrinsic type `NC_SHORT'.

`c'
     "Char".  Value(s) specified in ATT_VAL will be stored as netCDF
     intrinsic type `NC_CHAR'.

`b'
     "Byte".  Value(s) specified in ATT_VAL will be stored as netCDF
     intrinsic type `NC_BYTE'.

`ub'
     "Unsigned Byte".  Value(s) specified in ATT_VAL will be stored as
     netCDF intrinsic type `NC_UBYTE'.

`us'
     "Unsigned Short".  Value(s) specified in ATT_VAL will be stored as
     netCDF intrinsic type `NC_USHORT'.

`u, ui, ul'
     "Unsigned Int".  Value(s) specified in ATT_VAL will be stored as
     netCDF intrinsic type `NC_UINT'.

`ll, int64'
     "Int64".  Value(s) specified in ATT_VAL will be stored as netCDF
     intrinsic type `NC_INT64'.

`ull, uint64'
     "Uint64".  Value(s) specified in ATT_VAL will be stored as netCDF
     intrinsic type `NC_UINT64'.

`sng, string'
     "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 `\\'.  *Note 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" in.nc
   Note the use of embedded C language `printf()'-style escape
sequences.

   Change the value of the `long_name' attribute for variable `T' from
whatever it currently is to "temperature":
     ncatted -a long_name,T,o,c,temperature in.nc

   NCO arithmetic operators will not work as expected on IEEE NaN
(short for Not-a-Number) and NaN-like numbers such as positive infinity
and negative infinity.  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 in.nc
   Then change the missing value from the IEEE NaN value to a normal
IEEE number, like 1.0e36 (or to whatever the original missing value
was).
     ncatted -a _FillValue,,m,f,1.0e36 in.nc

   Delete all existing `units' attributes:
     ncatted -a units,,d,, in.nc
   The value of VAR_NM was left blank in order to select all variables
in the file.  The values of ATT_TYPE and ATT_VAL were left blank because
they are superfluous in "Delete" mode.

   Delete all attributes associated with the `tpt' variable, and delete
all global attributes
     ncatted -a ,tpt,d,, -a ,global,d,, in.nc
   The value of ATT_NM was left blank in order to select all attributes
associated with the variable.  To delete all global attributes, simply
replace `tpt' with `global' in the above.

   Modify all existing `units' attributes to "meter second-1":
     ncatted -a units,,m,c,"meter second-1" in.nc

   Add a `units' attribute of "kilogram kilogram-1" to all variables
whose first three characters are `H2O':
     ncatted -a units,'^H2O',c,c,"kilogram kilogram-1" in.nc

   Overwrite the `quanta' attribute of variable `energy' to an array of
four integers.
     ncatted -O -a quanta,energy,o,s,"010,101,111,121" in.nc

   As of NCO 3.9.6 (January, 2009), variable names arguments to
`ncatted' may contain "extended regular expressions".  Create `isotope'
attributes for all variables containing `H2O' in their names.
     ncatted -O -a isotope,'^H2O*',c,s,"18" in.nc
   See *note Subsetting Files:: for more details.

   Demonstrate input of C-language escape sequences (e.g., `\n') and
other special characters (e.g., `\"')
     ncatted -h -a special,global,o,c,
     '\nDouble quote: \"\nTwo consecutive double quotes: \"\"\n
     Single quote: Beyond my shell abilities!\nBackslash: \\\n
     Two consecutive backslashes: \\\\\nQuestion mark: \?\n' in.nc
   Note that the entire attribute is protected from the shell by single
quotes.  These outer single quotes are necessary for interactive use,
but may be omitted in batch scripts.

4.3 `ncbo' netCDF Binary Operator
=================================

SYNTAX
     ncbo [-3] [-4] [-6] [-A] [-C] [-c]
     [--cnk_dmn nm,sz] [--cnk_map map] [--cnk_plc plc] [--cnk_scl sz]
     [-D DBG] [-d DIM,[MIN][,[MAX][,[STRIDE]]] [-F]
     [-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]

DESCRIPTION

   `ncbo' performs binary operations on variables in FILE_1 and the
corresponding variables (those with the same name) in FILE_2 and stores
the results in FILE_3.  The binary operation operates on the entire
files (modulo any excluded variables).  *Note 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:
"Addition"
     Definition: FILE_3 = FILE_1 + FILE_2
     Alternate invocation: `ncadd'
     OP_TYP key values: `add', `+', `addition'
     Examples: `ncbo --op_typ=add 1.nc 2.nc 3.nc', `ncadd 1.nc 2.nc
     3.nc'
"Subtraction"
     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=- 1.nc 2.nc 3.nc', `ncdiff 1.nc 2.nc 3.nc'
"Multiplication"
     Definition: FILE_3 = FILE_1 * FILE_2
     Alternate invocations: `ncmult', `ncmultiply'
     OP_TYP key values: `mlt', `*', `mult', `multiply', `multiplication'
     Examples: `ncbo --op_typ=mlt 1.nc 2.nc 3.nc', `ncmult 1.nc 2.nc
     3.nc'
"Division"
     Definition: FILE_3 = FILE_1 / FILE_2
     Alternate invocation: `ncdivide'
     OP_TYP key values: `dvd', `/', `divide', `division'
     Examples: `ncbo --op_typ=/ 1.nc 2.nc 3.nc', `ncdivide 1.nc 2.nc
     3.nc'
   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 (1).  Place these characters inside
quotes to keep them from being interpreted (globbed) by the shell (2).  For
example, the following commands are equivalent
     ncbo --op_typ=* 1.nc 2.nc 3.nc # Dangerous (shell may try to glob)
     ncbo --op_typ='*' 1.nc 2.nc 3.nc # Safe ('*' protected from shell)
     ncbo --op_typ="*" 1.nc 2.nc 3.nc # Safe ('*' protected from shell)
     ncbo --op_typ=mlt 1.nc 2.nc 3.nc
     ncbo --op_typ=mult 1.nc 2.nc 3.nc
     ncbo --op_typ=multiply 1.nc 2.nc 3.nc
     ncbo --op_typ=multiplication 1.nc 2.nc 3.nc
     ncmult 1.nc 2.nc 3.nc # First do 'ln -s ncbo ncmult'
     ncmultiply 1.nc 2.nc 3.nc # First do 'ln -s ncbo ncmultiply'
   No particular argument or invocation form is preferred.  Users are
encouraged to use the forms which are most intuitive to them.

   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' (3).  The alternatate invocations
supported for each operation type are listed above.  Alternatively,
users may always define `ncadd' as an "alias" to `ncbo --op_typ=add'
(4).

   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
necessary(5).  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 (but 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 were created by averaging
FILE_1 over the `time' dimension with the `ncra' operator rather than
with the `ncwa' operator, then FILE_2 will have a `time' dimension of
size 1 rather than having no `time' dimension at all (6).  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_BYTE'.  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 *note CF Conventions:: for a complete description.  Finally, we
note that `ncflint' (*note 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.

EXAMPLES

   Say files `85_0112.nc' and `86_0112.nc' each contain 12 months of
data.  Compute the change in the monthly averages from 1985 to 1986:
     ncbo   86_0112.nc 85_0112.nc 86m85_0112.nc
     ncdiff 86_0112.nc 85_0112.nc 86m85_0112.nc
     ncbo --op_typ=sub 86_0112.nc 85_0112.nc 86m85_0112.nc
     ncbo --op_typ='-' 86_0112.nc 85_0112.nc 86m85_0112.nc
   These commands are all different ways of expressing the same thing.

   The following examples demonstrate the broadcasting feature of
`ncbo'.  Say we wish to compute the monthly anomalies of `T' from the
yearly average of `T' for the year 1985.  First we create the 1985
average from the monthly data, which is stored with the record
dimension `time'.
     ncra 85_0112.nc 85.nc
     ncwa -O -a time 85.nc 85.nc
   The second command, `ncwa', gets rid of the `time' dimension of
size 1 that `ncra' left in `85.nc'.  Now none of the variables in
`85.nc' has a `time' dimension.  A quicker way to accomplish this is to
use `ncwa' from the beginning:
     ncwa -a time 85_0112.nc 85.nc
   We are now ready to use `ncbo' to compute the anomalies for 1985:
     ncdiff -v T 85_0112.nc 85.nc t_anm_85_0112.nc
   Each of the 12 records in `t_anm_85_0112.nc' now contains the
monthly deviation of `T' from the annual mean of `T' for each gridpoint.

   Say we wish to compute the monthly gridpoint anomalies from the zonal
annual mean.  A "zonal mean" is a quantity that has been averaged over
the longitudinal (or X) direction.  First we use `ncwa' to average over
longitudinal direction `lon', creating `85_x.nc', the zonal mean of
`85.nc'.  Then we use `ncbo' to subtract the zonal annual means from the
monthly gridpoint data:
     ncwa -a lon 85.nc 85_x.nc
     ncdiff 85_0112.nc 85_x.nc tx_anm_85_0112.nc
   This examples works assuming `85_0112.nc' has dimensions `time' and
`lon', and that `85_x.nc' has no `time' or `lon' dimension.

   Group broadcasting simplifies evaluation of multiple models against
observations.  Consider the input file `cmip5.nc' which contains
multiple top-level groups `cesm', `ecmwf', and `giss', each of which
contains the surface air temperature field `tas'.  We wish to compare
these models to observations stored in `obs.nc' which contains `tas'
only in its top-level (i.e., root) group.  It is often the case that
many models and/or model simulations exist, whereas only one
observational dataset does.  We evaluate the models and obtain the bias
(difference) between models and observations by subtracting `obs.nc'
from `cmip5.nc'.  Then `ncbo' "broadcasts" (i.e., replicates) the
observational data to match the group structure of `cmip5.nc',
subtracts, and then stores the results in the output file, `bias.nc'
which has the same group structure as `cmip5.nc'.
     % ncbo -O cmip5.nc obs.nc bias.nc
     % ncks -H -v tas -d time,3 bias.nc
     /cesm/tas
     time[3] tas[3]=-1
     /ecmwf/tas
     time[3] tas[3]=0
     /giss/tas
     time[3] tas[3]=1
   As a final example, say we have five years of monthly data (i.e.,
60 months) stored in `8501_8912.nc' and we wish to create a file which
contains the twelve month seasonal cycle of the average monthly anomaly
from the five-year mean of this data.  The following method is just one
permutation of many which will accomplish the same result.  First use
`ncwa' to create the five-year mean:
     ncwa -a time 8501_8912.nc 8589.nc
   Next use `ncbo' to create a file containing the difference of each
month's data from the five-year mean:
     ncbo 8501_8912.nc 8589.nc t_anm_8501_8912.nc
   Now use `ncks' to group the five January anomalies together in one
file, and use `ncra' to create the average anomaly for all five
Januarys.  These commands are embedded in a shell loop so they are
repeated for all twelve months: 
     for idx in {1..12}; do # Bash Shell (version 3.0+)
       idx=`printf "%02d" ${idx}` # Zero-pad to preserve order
       ncks -F -d time,${idx},,12 t_anm_8501_8912.nc foo.${idx}
       ncra foo.${idx} t_anm_8589_${idx}.nc
     done
     for idx in 01 02 03 04 05 06 07 08 09 10 11 12; do # Bourne Shell
       ncks -F -d time,${idx},,12 t_anm_8501_8912.nc foo.${idx}
       ncra foo.${idx} t_anm_8589_${idx}.nc
     done
     foreach idx (01 02 03 04 05 06 07 08 09 10 11 12) # C Shell
       ncks -F -d time,${idx},,12 t_anm_8501_8912.nc foo.${idx}
       ncra foo.${idx} t_anm_8589_${idx}.nc
     end
   Note that `ncra' understands the `stride' argument so the two
commands inside the loop may be combined into the single command
     ncra -F -d time,${idx},,12 t_anm_8501_8912.nc foo.${idx}
   Finally, use `ncrcat' to concatenate the 12 average monthly anomaly
files into one twelve-record file which contains the entire seasonal
cycle of the monthly anomalies:
     ncrcat t_anm_8589_??.nc t_anm_8589_0112.nc

   ---------- Footnotes ----------

   (1) A naked (i.e., unprotected or unquoted) `*' is a wildcard
character.  A naked `-' may confuse the command line parser.  A naked
`+' and `/' are relatively harmless.

   (2) The widely used shell Bash correctly interprets all these
special characters even when they are not quoted.  That is, Bash does
not prevent NCO from correctly interpreting the intended arithmetic
operation when the following arguments are given (without quotes) to
`ncbo': `--op_typ=+', `--op_typ=-', `--op_typ=*', and `--op_typ=/'

   (3) The command to do this is `ln -s -f ncbo ncadd'

   (4) The command to do this is `alias ncadd='ncbo --op_typ=add''

   (5) Prior to NCO version 4.3.1 (May, 2013), `ncbo' would only
broadcast variables in FILE_2 to conform to FILE_1.  Variables in
FILE_1 were _never_ broadcast to conform to the dimensions in FILE_2.

   (6) This is because `ncra' collapses the record dimension to a size
of 1 (making it a "degenerate" dimension), but does not remove it,
while, unless `-b' is given, `ncwa' removes all averaged dimensions.
In other words, by default `ncra' changes variable size but not rank,
while, `ncwa' changes both variable size and rank.

4.4 `ncea' netCDF Ensemble Averager
===================================

SYNTAX
     ncea [-3] [-4] [-6] [-A] [-C] [-c]
     [--cnk_dmn nm,sz] [--cnk_map map] [--cnk_plc plc] [--cnk_scl sz]
     [-D DBG] [-d DIM,[MIN][,[MAX][,[STRIDE]]] [-F]
     [-G GPE_DSC] [-g GRP[,...]] [-h] [--hdf] [--hdr_pad NBR]
     [-L DFL_LVL] [-l PATH] [-n LOOP] [--no_tmp_fl]
     [-O] [-o OUTPUT-FILE] [-p PATH] [-R] [-r] [--ram_all] [--rth_dbl|flt]
     [-t THR_NBR] [--unn] [-v VAR[,...]] [-X ...] [-x] [-y OP_TYP]
     [INPUT-FILES] [OUTPUT-FILE]

DESCRIPTION

   `ncea' performs gridpoint arithmetic of variables across an
arbitrary number (an "ensemble") of INPUT-FILES, with each file
receiving an equal weight in the arithmetic.  For example, `ncea' will
average a set of files, weighting each file evenly.  This is distinct
from `ncra', which averages only over the record dimension(s) (e.g.,
TIME), and weights each record in each record dimension evenly,

   Variables in the OUTPUT-FILE are the same size as the variable in
each of the INPUT-FILES, and all INPUT-FILES must be the same size.  The
only exception is that `ncea' allows files to differ in the record
dimension size if the requested record hyperslab (*note Hyperslabs::)
resolves to the same size for all files.  `ncea' recomputes the record
dimension hyperslab limits for each input file so that coordinate
limits may be used to select equal length timeseries from unequal
length files.  This simplifies analysis of unequal length timeseries
from simulation ensembles (e.g., the CMIP3 IPCC AR4 archive).

   `ncea' _always averages_ coordinate variables regardless of the
arithmetic operation type performed on the non-coordinate variables.
(*note Operation Types::).  All dimensions, including the record
dimension, are treated identically and preserved in the OUTPUT-FILE.

   *Note Averaging vs. Concatenating::, for a description of the
distinctions between the various averagers and concatenators.  As a
multi-file operator, `ncea' will read the list of INPUT-FILES from
`stdin' if they are not specified as positional arguments on the
command line (*note Large Numbers of Files::).

   The file is the logical unit of organization for the results of many
scientific studies.  Often one wishes to generate a file which is the
gridpoint average of many separate files.  This may be to reduce
statistical noise by combining the results of a large number of
experiments, or it may simply be a step in a procedure whose goal is to
compute anomalies from a mean state.  In any case, when one desires to
generate a file whose properties are the mean of all the input files,
then `ncea' is the operator to use.

   `ncea' only allows coordinate variables to be processed by the
linear average, minimum, and maximum operations.  `ncea' will return
the linear average of coordinates unless extrema are explicitly
requested.  Other requested operations (e.g., square-root, RMS) are
applied only to non-coordinate variables.  In these cases the linear
average of the coordinate variable will be returned.

EXAMPLES

   Consider a model experiment which generated five realizations of one
year of data, say 1985.  You can imagine that the experimenter slightly
perturbs the initial conditions of the problem before generating each
new solution.  Assume each file contains all twelve months (a seasonal
cycle) of data and we want to produce a single file containing the
ensemble average (mean) seasonal cycle.  Here the numeric filename
suffix denotes the experiment number (_not_ the month):
     ncea 85_01.nc 85_02.nc 85_03.nc 85_04.nc 85_05.nc 85.nc
     ncea 85_0[1-5].nc 85.nc
     ncea -n 5,2,1 85_01.nc 85.nc
   These three commands produce identical answers.  *Note Specifying
Input Files::, for an explanation of the distinctions between these
methods.  The output file, `85.nc', is the same size as the inputs
files.  It contains 12 months of data (which might or might not be
stored in the record dimension, depending on the input files), but each
value in the output file is the average of the five values in the input
files.

   In the previous example, the user could have obtained the ensemble
average values in a particular spatio-temporal region by adding a
hyperslab argument to the command, e.g.,
     ncea -d time,0,2 -d lat,-23.5,23.5 85_??.nc 85.nc
   In this case the output file would contain only three slices of data
in the TIME dimension.  These three slices are the average of the first
three slices from the input files.  Additionally, only data inside the
tropics is included.

4.5 `ncecat' netCDF Ensemble Concatenator
=========================================

SYNTAX
     ncecat [-3] [-4] [-6] [-A] [-C] [-c]
     [--cnk_dmn nm,sz] [--cnk_map map] [--cnk_plc plc] [--cnk_scl sz]
     [-D DBG] [-d DIM,[MIN][,[MAX][,[STRIDE]]] [-F]
     [-G GPE_DSC] [-g GRP[,...]] [--gag] [-h] [--hdf] [--hdr_pad NBR]
     [-L DFL_LVL] [-l PATH] [-M] [--md5_digest] [-n LOOP] [--no_tmp_fl]
     [-O] [-o OUTPUT-FILE] [-p PATH] [-R] [-r] [--ram_all]
     [-t THR_NBR] [-u ULM_NM] [--unn] [-v VAR[,...]] [-X ...] [-x]
     [INPUT-FILES] [OUTPUT-FILE]

DESCRIPTION

   `ncecat' aggregates an arbitrary number of input files into a single
output file using using one of two methods.  "Record AGgregation"
(RAG), the traditional method employed on 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 and 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 (*note 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 the input file data
together.  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
(*note 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 (*note Group Path Editing::).  When GPE_DSC is not
provided (i.e., user requests GAG with `--gag' instead of `-G'), then
`ncecat' forms the output groups by stripping the input file name of
any type-suffix (e.g., `.nc'), and all but the final component of the
full filename.
     ncecat --gag 85.nc 86.nc 87.nc 8587.nc # Output groups 85, 86, 87
     ncecat -G 85_ a.nc b.nc c.nc 8589.nc # Output groups 85_00, 85_01, 85_02
     ncecat -G 85/ a.nc b.nc c.nc 8589.nc # Output groups 85/00, 85/01, 85/02

   With both RAG and GAG the OUTPUT-FILE size is the sum of the sizes
of the extracted variables in the input files.  *Note Averaging vs.
Concatenating::, for a description of the distinctions between the
various averagers 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 (*note Large
Numbers of Files::).

   Turn off global metadata copying.  By default all NCO operators copy
the global metadata of the first input file into OUTPUT-FILE.  This
helps preserve the provenance of the output data.  However, the use of
metadata is burgeoning and is not uncommon to encounter files with
excessive amounts of extraneous metadata.  Extracting small bits of
data from such files leads to output files which are much larger than
necessary due to the automatically copied metadata.  `ncecat' supports
turning off the default copying of global metadata via the `-M' switch
(or its long option equivalents, `--glb_mtd_spr' and
`--global_metadata_suppress').

   Consider five realizations, `85a.nc', `85b.nc', ... `85e.nc' of 1985
predictions from the same climate model.  Then `ncecat 85?.nc
85_ens.nc' glues the individual realizations together into the single
file, `85_ens.nc'.  If an input variable was dimensioned [`lat',`lon'],
it will by default have dimensions [`record',`lat',`lon'] in the output
file.  A restriction of `ncecat' is that the hyperslabs of the
processed variables must be the same from file to file.  Normally this
means all the input files are the same size, and contain data on
different realizations of the same variables.

   Concatenating a variable packed with different scales across multiple
datasets is beyond the capabilities of `ncecat' (and `ncrcat', the
other concatenator (*note Concatenation::).  `ncecat' does not unpack
data, it simply _copies_ the data from the INPUT-FILES, and the
metadata from the _first_ INPUT-FILE, to the OUTPUT-FILE.  This means
that data compressed with a packing convention must use the identical
packing parameters (e.g., `scale_factor' and `add_offset') for a given
variable across _all_ input files.  Otherwise the concatenated dataset
will not unpack correctly.  The workaround for cases where the packing
parameters differ across INPUT-FILES requires three steps: First,
unpack the data using `ncpdq'.  Second, concatenate the unpacked data
using `ncecat', Third, re-pack the result with `ncpdq'.

EXAMPLES

   Consider a model experiment which generated five realizations of one
year of data, say 1985.  You can imagine that the experimenter slightly
perturbs the initial conditions of the problem before generating each
new solution.  Assume each file contains all twelve months (a seasonal
cycle) of data and we want to produce a single file containing all the
seasonal cycles.  Here the numeric filename suffix denotes the
experiment number (_not_ the month):
     ncecat 85_01.nc 85_02.nc 85_03.nc 85_04.nc 85_05.nc 85.nc
     ncecat 85_0[1-5].nc 85.nc
     ncecat -n 5,2,1 85_01.nc 85.nc
   These three commands produce identical answers.  *Note Specifying
Input Files::, for an explanation of the distinctions between these
methods.  The output file, `85.nc', is five times the size as a single
INPUT-FILE.  It contains 60 months of data.

   One often prefers that the (new) record dimension have a more
descriptive, context-based name than simply "record".  This is easily
accomplished with the `-u ULM_NM' switch:
     ncecat -u realization 85_0[1-5].nc 85.nc
   Users are more likely to understand the data processing history when
such descriptive coordinates are used.

   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 in.nc out.nc # Convert time to non-record dimension
     ncwa -a record in.nc out.nc # Remove new degenerate record dimension
   The second step removes the degenerate record dimension.  See *note
ncpdq netCDF Permute Dimensions Quickly:: and *note ncks netCDF Kitchen
Sink:: for other methods of of changing variable dimensionality,
including the record dimension.

4.6 `ncflint' netCDF File Interpolator
======================================

SYNTAX
     ncflint [-3] [-4] [-6] [-A] [-C] [-c]
     [--cnk_dmn nm,sz] [--cnk_map map] [--cnk_plc plc] [--cnk_scl sz]
     [-D DBG] [-d DIM,[MIN][,[MAX][,[STRIDE]]]
     [-F] [--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] [-R] [-r] [--ram_all]
     [-t THR_NBR] [--unn] [-v VAR[,...]] [-w WGT1[,WGT2]] [-X ...] [-x]
     FILE_1 FILE_2 [FILE_3]

DESCRIPTION

   `ncflint' creates an output file that is a linear combination of the
input files.  This linear combination is a weighted average, a
normalized weighted average, or an interpolation of the input files.
Coordinate variables are not acted upon in any case, they are simply
copied from FILE_1.

   There are two conceptually distinct methods of using `ncflint'.  The
first method is to specify the weight each input file contributes to
the output file.  In this method, the value VAL3 of a variable in the
output file FILE_3 is determined from its values VAL1 and VAL2 in the
two input files according to

   VAL3 = WGT1*VAL1 + WGT2*VAL2

   .  Here at least WGT1, and, optionally, WGT2, are specified on the
command line with the `-w' (or `--weight' or `--wgt_var') switch.  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 the results together 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_BYTE'.  This behavior is hardcoded.

   By default `ncflint' interpolates or multiplies record coordinate
variables (e.g., time is often stored as a record coordinate) but 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.

EXAMPLES

   Although it has other uses, the interpolation feature was designed
to interpolate FILE_3 to a time between existing files.  Consider input
files `85.nc' and `87.nc' containing variables describing the state of
a physical system at times `time' = 85 and `time' = 87.  Assume each
file contains its timestamp in the scalar variable `time'.  Then, to
linearly interpolate to a file `86.nc' which describes the state of the
system at time at `time' = 86, we would use
     ncflint -i time,86 85.nc 87.nc 86.nc

   Say you have observational data covering January and April 1985 in
two files named `85_01.nc' and `85_04.nc', respectively.  Then you can
estimate the values for February and March by interpolating the
existing data as follows.  Combine `85_01.nc' and `85_04.nc' in a 2:1
ratio to make `85_02.nc':
     ncflint -w 0.667 85_01.nc 85_04.nc 85_02.nc
     ncflint -w 0.667,0.333 85_01.nc 85_04.nc 85_02.nc

   Multiply `85.nc' by 3 and by -2 and add them together to make
`tst.nc':
     ncflint -w 3,-2 85.nc 85.nc tst.nc
   This is an example of a null operation, so `tst.nc' should be
identical (within machine precision) to `85.nc'.

   Multiply all the variables except the coordinate variables in the
file `emissions.nc' by by 0.8:
     ncflint --fix_rec_crd -w 0.8,0.0 emissions.nc emissions.nc scaled_emissions.nc
   The use of `--fix_rec_crd' ensures, e.g., that the `time'
coordinate, if any, is not scaled (i.e., multiplied).

   Add `85.nc' to `86.nc' to obtain `85p86.nc', then subtract `86.nc'
from `85.nc' to obtain `85m86.nc'
     ncflint -w 1,1 85.nc 86.nc 85p86.nc
     ncflint -w 1,-1 85.nc 86.nc 85m86.nc
     ncdiff 85.nc 86.nc 85m86.nc
   Thus `ncflint' can be used to mimic some `ncbo' operations.  However
this is not a good idea in practice because `ncflint' does not
broadcast (*note 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 in.nc in.nc out.nc
     ncatted -a units,prs_sfc,o,c,millibar out.nc

4.7 `ncks' netCDF Kitchen Sink
==============================

SYNTAX
     ncks [-3] [-4] [-5] [-6] [-A] [-a] [-b BINARY-FILE] [-C] [-c] [--cdl]
     [--cnk_dmn nm,sz] [--cnk_map map] [--cnk_plc plc] [--cnk_scl sz]
     [-D DBG] [-d DIM,[MIN][,[MAX][,[STRIDE]]] [-F] [--fix_rec_dmn DIM]
     [-G GPE_DSC] [-g GRP[,...]] [-H] [-h] [--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]
     [-Q] [-q] [-R] [-r] [--ram_all] [-s FORMAT]
     [-u] [--unn] [-v VAR[,...]] [-X ...] [-x] [--xml]
     INPUT-FILE [[OUTPUT-FILE]]

DESCRIPTION

   The nickname "kitchen sink" is a catch-all because `ncks' combines
selected features of `ncdump' with (non-existent but easily imaginable)
operators `ncextract', `nccut', and `ncpaste', into one versatile
utility.  `ncks' extracts a subset of the data from INPUT-FILE and
prints it as ASCII text to `stdout', writes it in flat binary format to
`binary-file', and writes (or pastes) it in netCDF format to
OUTPUT-FILE.

   `ncks' will print netCDF data in ASCII format to `stdout', like
`ncdump', but with these differences: 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') lets the user format the data using C-style
format strings, while option `--cdl' outputs CDL like `ncdump' but in a
more finely tunable way.

   Options `-5', `-a', `--cdl', `-F' , `-H', `-M', `-m', `-P', `-Q',
`-q', `-s', `-u', `--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 (1).

   Since there can only be one record dimension in a file, the record
dimension must have the same name (but not necessarily the same size) in
both files if a record dimension variable is to be appended.  If the
record dimensions are of differing sizes, the record dimension of
OUTPUT-FILE will become the greater of the two record dimension sizes,
the record variable from INPUT-FILE will overwrite any counterpart in
OUTPUT-FILE and fill values will be written to any gaps left in the
rest of the record variables (I think).  In all cases variable
attributes in OUTPUT-FILE are superseded by attributes of the same name
from INPUT-FILE, and left alone if there is no name conflict.

   Some users may wish to avoid interactive `ncks' queries about
whether to overwrite existing data.  For example, batch scripts will
fail if `ncks' does not receive responses to its queries.  Options `-O'
and `-A' are available to force overwriting existing files and
variables, respectively.

Options specific to `ncks'
--------------------------

The following list provides a short summary of the features unique to
`ncks'.  Features common to many operators are described in *note
Common features::.

`-5'
     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).

`-a'
     Do not alphabetize extracted fields.  By default, the specified
     output variables are extracted, printed, and written to disk in
     alphabetical order.  This tends to make long output lists easier
     to search for particular variables.  Specifying `-a' results in
     the variables being extracted, printed, and written to disk in the
     order in which they were saved in the input file.  Thus `-a'
     retains the original ordering of the variables.  Also `--abc' and
     `--alphabetize'.

`-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.

`--fix_rec_dmn'
     Change record dimension DIM in the input file into a fixed
     dimension in the output file.  Also `--no_rec_dmn'.  Before NCO
     version 4.2.5 (January, 2013), the syntax for `--fix_rec_dmn' did
     not permit or require the specification of the dimension name DIM.
     This is because the feature only worked on netCDF3 files, which
     support only one record dimension, so specifying its name was 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.

`--cdl'
     As of NCO version 4.3.3 (July, 2013), `ncks' can print extracted
     data and metadata to screen (i.e., `stdout') as valid CDL (network
     Common data form Description Language).  CDL is the human-readable
     "lingua franca" of netCDF ingested by `ncgen' and excreted by
     `ncdump'.  Compare `ncks' "traditional" with CDL printing:
          zender@roulee:~$ ncks -v one ~/nco/data/in.nc
          one: type NC_FLOAT, 0 dimensions, 1 attribute, chunked? no, compressed? no, packed? no
          one size (RAM) = 1*sizeof(NC_FLOAT) = 1*4 = 4 bytes
          one attribute 0: long_name, size = 3 NC_CHAR, value = one

          one = 1

          zender@roulee:~$ ncks --cdl -v one ~/nco/data/in.nc
          netcdf in {

            variables:
              float one ;
              one:long_name = "one" ;

            data:
              one = 1 ;

          } // group /
     `ncgen' converts CDL-mode output into a netCDF file:
          ncks --cdl -v one ~/nco/data/in.nc > ~/in.cdl
          ncgen -k netCDF-4 -b -o ~/in.nc ~/in.cdl
          ncks -v one ~/in.nc
     The HDF version of `ncgen' converts CDL into an HDF file:
          /usr/local/hdf4/bin/ncgen -b -o ~/in.hdf ~/in.cdl # HDF ncgen
          hdp dumpsds ~/in.hdf

`--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 *note ncecat netCDF Ensemble Concatenator:: and *note ncpdq
     netCDF Permute Dimensions Quickly:: for other methods of changing
     variable dimensionality, including the record dimension.

`-H'
     Print data to screen.  Also activated using `--print' or `--prn'.
     By default `ncks' prints all metadata and data to screen if no
     netCDF output file is specified.  Use `-H' to print data to screen
     if a netCDF output is specified, or to restrict printing to data
     (no metadata) when no netCDF output is specified.  Unless
     otherwise specified (with `-s'), each element of the data
     hyperslab prints on a separate line containing the names, indices,
     and, values, if any, of all of the variables dimensions.  The
     dimension and variable indices refer to the location of the
     corresponding data element with respect to the variable as stored
     on disk (i.e., not the hyperslab).
          % ncks -C -v three_dmn_var in.nc
          lat[0]=-90 lev[0]=100 lon[0]=0 three_dmn_var[0]=0
          lat[0]=-90 lev[0]=100 lon[1]=90 three_dmn_var[1]=1
          lat[0]=-90 lev[0]=100 lon[2]=180 three_dmn_var[2]=2
          ...
          lat[1]=90 lev[2]=1000 lon[1]=90 three_dmn_var[21]=21
          lat[1]=90 lev[2]=1000 lon[2]=180 three_dmn_var[22]=22
          lat[1]=90 lev[2]=1000 lon[3]=270 three_dmn_var[23]=23
     Printing the same variable with the `-F' option shows the same
     variable indexed with Fortran conventions
          % ncks -F -C -v three_dmn_var in.nc
          lon(1)=0 lev(1)=100 lat(1)=-90 three_dmn_var(1)=0
          lon(2)=90 lev(1)=100 lat(1)=-90 three_dmn_var(2)=1
          lon(3)=180 lev(1)=100 lat(1)=-90 three_dmn_var(3)=2
          ...
     Printing a hyperslab does not affect the variable or dimension
     indices since these indices are relative to the full variable (as
     stored in the input file), and the input file has not changed.
     However, if the hyperslab is saved to an output file and those
     values are printed, the indices will change:
          % ncks -H -d lat,90.0 -d lev,1000.0 -v three_dmn_var in.nc out.nc
          ...
          lat[1]=90 lev[2]=1000 lon[0]=0 three_dmn_var[20]=20
          lat[1]=90 lev[2]=1000 lon[1]=90 three_dmn_var[21]=21
          lat[1]=90 lev[2]=1000 lon[2]=180 three_dmn_var[22]=22
          lat[1]=90 lev[2]=1000 lon[3]=270 three_dmn_var[23]=23
          % ncks -C -v three_dmn_var out.nc
          lat[0]=90 lev[0]=1000 lon[0]=0 three_dmn_var[0]=20
          lat[0]=90 lev[0]=1000 lon[1]=90 three_dmn_var[1]=21
          lat[0]=90 lev[0]=1000 lon[2]=180 three_dmn_var[2]=22
          lat[0]=90 lev[0]=1000 lon[3]=270 three_dmn_var[3]=23

`-M'
     Print to screen the global metadata describing the file.  This
     includes file summary information and global attributes.  Also
     `--Mtd' and `--Metadata'.  By default `ncks' prints global
     metadata to screen if no netCDF output file and no variable
     extraction list is specified (with `-v').  Use `-M' to print
     global metadata to screen if a netCDF output is specified, or if a
     variable extraction list is specified (with `-v').

     The various combinations of printing switches can be confusing.
     In an attempt to anticipate what most users want to do, `ncks'
     uses context-sensitive defaults for printing.  Our goal is to
     minimize the use of switches required to accomplish the common
     operations.  We assume that users creating a new file or
     overwriting (e.g., with `-O') an existing file usually wish to
     copy all global and variable-specific attributes to the new file.
     In contrast, we assume that users appending (e.g., with `-A' an
     explicit variable list from one file to another usually wish to
     copy only the variable-specific attributes to the output file.
     The switches `-H', `-M', and `-m' switches are implemented as
     toggles which reverse the default behavior.  The most confusing
     aspect of this is that `-M' inhibits copying global metadata in
     overwrite mode and causes copying of global metadata in append
     mode.
          ncks -O              in.nc out.nc # Copy   VAs and GAs
          ncks -O       -v one in.nc out.nc # Copy   VAs and GAs
          ncks -O -M    -v one in.nc out.nc # Copy   VAs not GAs
          ncks -O    -m -v one in.nc out.nc # Copy   GAs not VAs
          ncks -O -M -m -v one in.nc out.nc # Copy   only data (no atts)
          ncks -A              in.nc out.nc # Append VAs and GAs
          ncks -A       -v one in.nc out.nc # Append VAs not GAs
          ncks -A -M    -v one in.nc out.nc # Append VAs and GAs
          ncks -A    -m -v one in.nc out.nc # Append only data (no atts)
          ncks -A -M -m -v one in.nc out.nc # Append GAs not VAs
     where `VAs' and `GAs' denote variable and global attributes,
     respectively.

`-m'
     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.

`--no_blank'
     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'.

`-P'
     Print data, metadata, and units to screen.  The `-P' switch is a
     convenience abbreviation for `-C -H -M -m -u'.  Also activated
     using `--print' or `--prn'.  This set of switches is useful for
     exploring file contents.

`-Q'
     Toggle printing of dimension indices and coordinate values when
     printing arrays.  Each variable's name appears flush left in the
     output.  This helps locate specific variables in lists with many
     variables and different dimensions.

`-q'
     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' (*note
     Retaining Retrieved Files::), `ncks' automatically sets `-q'.
     This allows `ncks' to retrieve remote files without automatically
     trying to print them.  Also `--quiet'.

`-s FORMAT'
     String format for text output.  Accepts C language escape
     sequences and `printf()' formats.  Also `--string'  and
     `--sng_fmt'.

`-u'
     Toggle the printing of a variable's `units' attribute, if any,
     with its values.  Also `--units'.

`--xml'
     As of NCO version 4.3.3 (July, 2013), `ncks' can print extracted
     metadata to screen (i.e., `stdout') as valid XML in NcML, the
     netCDF Markup Language.  XML is also exported by `ncdump -x'.
     Compare `ncks' "traditional" with XML printing:
          zender@roulee:~$ ncks -v one ~/nco/data/in.nc
          one: type NC_FLOAT, 0 dimensions, 1 attribute, chunked? no, compressed? no, packed? no
          one size (RAM) = 1*sizeof(NC_FLOAT) = 1*4 = 4 bytes
          one attribute 0: long_name, size = 3 NC_CHAR, value = one

          one = 1

          zender@roulee:~$ ncks --xml -v one ~/nco/data/in.nc
          <?xml version="1.0" encoding="UTF-8"?>
          <netcdf xmlns="http://www.unidata.ucar.edu/namespaces/netcdf/ncml-2.2" \
               location="/home/zender/nco/data/in.nc">
              <variable name="one" type="float">
                <attribute name="long_name" value="one" />
              </variable>
          </netcdf>
     XML-mode only prints variable metadata, not variable data.  This
     is because `ncdump -x' does the same, and we are unsure how to
     format NcML data.  Please let us know if more XML/NcML printing
     features would be useful to you.

   ---------- Footnotes ----------

   (1) Those familiar with netCDF mechanics might wish to know what is
happening here: `ncks' does not attempt to redefine the variable in
OUTPUT-FILE to match its definition in INPUT-FILE, `ncks' merely copies
the values of the variable and its coordinate dimensions, if any, from
INPUT-FILE to OUTPUT-FILE.

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 in.nc | grep -E ': type' | cut -f 1 -d ' ' | sed 's/://' | sort
   A Bash user could alias the previous filter to the shell command
`nclist' as shown below.  More complex examples could involve command
line arguments.  For example, a user may frequently be interested in
obtaining the value of an attribute, e.g., for textual file examination
or for passing to another shell command.  Say the attribute is
`purpose', the variable is `z', and the file is `in.nc'.  In this
example, `ncks -m -v z' is too verbose so a robust `grep' and `cut'
filter is desirable, such as
     ncks -M -m in.nc | grep -E -i "^z attribute [0-9]+: purpose" | cut -f 11- -d ' ' | sort
   The filters are clearly too complex to remember on-the-fly so the
entire procedure could be implemented as a shell command or function
called, say, `ncattget'
     function ncattget { ncks -M -m ${3} | grep -E -i "^${2} attribute [0-9]+: ${1}" | cut -f 11- -d ' ' | sort ; }
   The shell `ncattget' is invoked with three arugments that are, in
order, the names of the attribute, variable, and file to examine.
Global attributes are indicated by using a variable name of `global'.
This definition yields the following results
     % ncattget purpose z in.nc
     Height stored with a monotonically increasing coordinate
     % ncattget Purpose Z in.nc
     Height stored with a monotonically increasing coordinate
     % ncattget history z in.nc
     % ncattget history global in.nc
     History global attribute.
   Note that case sensitivity has been turned off for the variable and
attribute names (and could be turned on by removing the `-i' switch to
`grep').  Furthermore, extended regular expressions may be used for
both the variable and attribute names.  The next two commands
illustrate this by searching for the values of attribute `purpose' in
all variables, and then for all attributes of the variable `z':
     % ncattget purpose .+ in.nc
     1-D latitude coordinate referred to by geodesic grid variables
     1-D longitude coordinate referred to by geodesic grid variables
     ...
     % ncattget .+ Z in.nc
     Height
     Height stored with a monotonically increasing coordinate
     meter

   Extended filters are best stored as shell commands if they are used
frequently.  Shell commands may be re-used when they are defined in
shell configuration files.  These files are usually named `.bashrc',
`.cshrc', and `.profile' for the Bash, Csh, and Sh shells, respectively.
     # 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} ~/foo.nc ; ncks -H -C -v ${1} ~/foo.nc | cut -f 3- -d ' ' ; }
     # ncavg $var_nm $fl_nm : What is mean of variable?
     function ncavg { ncap2 -O -C -v -s "foo=${1}.avg();print(foo)" ${2} ~/foo.nc | cut -f 3- -d ' ' ; }
     # 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} ~/foo.nc ; ncks -H -C -v ${1} ~/foo.nc | cut -f 3- -d ' ' ; }
     # ncmax $var_nm $fl_nm : What is maximum of variable?
     function ncmax { ncap2 -O -C -v -s "foo=${1}.max();print(foo)" ${2} ~/foo.nc | cut -f 3- -d ' ' ; }
     # ncmdn $var_nm $fl_nm : What is median of variable?
     function ncmdn { ncap2 -O -C -v -s "foo=gsl_stats_median_from_sorted_data(${1}.sort());print(foo)" ${2} ~/foo.nc | cut -f 3- -d ' ' ; }
     # ncrng $var_nm $fl_nm : What is range of variable?
     function ncrng { ncap2 -O -C -v -s "foo_min=${1}.min();foo_max=${1}.max();print(foo_min,\"%f\");print(\" to \");print(foo_max,\"%f\")" ${2} ~/foo.nc ; }
     # 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 ' ' ; }

EXAMPLES

   View all data in netCDF `in.nc', printed with Fortran indexing
conventions:
     ncks -F in.nc

   Copy the netCDF file `in.nc' to file `out.nc'.
     ncks in.nc out.nc
   Now the file `out.nc' contains all the data from `in.nc'.  There
are, however, two differences between `in.nc' and `out.nc'.  First, the
`history' global attribute (*note History Attribute::) will contain the
command used to create `out.nc'.  Second, the variables in `out.nc'
will be defined in alphabetical order.  Of course the internal storage
of variable in a netCDF file should be transparent to the user, but
there are cases when alphabetizing a file is useful (see description of
`-a' switch).

   Copy all global attributes (and no variables) from `in.nc' to
`out.nc':
     ncks -A -x ~/nco/data/in.nc ~/out.nc
   The `-x' switch tells NCO to use the complement of the extraction
list (*note 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 (*note Appending
Variables::) has only to extract and copy (i.e., append) global
attributes to the output file.

   Print variable `three_dmn_var' from file `in.nc' with default
notations.  Next print `three_dmn_var' as an un-annotated text column.
Then print `three_dmn_var' signed with very high precision.  Finally,
print `three_dmn_var' as a comma-separated list.
     % ncks -C -v three_dmn_var in.nc
     lat[0]=-90 lev[0]=100 lon[0]=0 three_dmn_var[0]=0
     lat[0]=-90 lev[0]=100 lon[1]=90 three_dmn_var[1]=1
     ...
     lat[1]=90 lev[2]=1000 lon[3]=270 three_dmn_var[23]=23
     % ncks -s '%f\n' -C -v three_dmn_var in.nc
     0.000000
     1.000000
     ...
     23.000000
     % ncks -s '%+16.10f\n' -C -v three_dmn_var in.nc
        +0.0000000000
        +1.0000000000
     ...
       +23.0000000000
     % ncks -s '%f, ' -C -v three_dmn_var in.nc
     0.000000, 1.000000, ..., 23.000000,
   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.  *Note
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 in.nc
     lon[0]=0 mss_val[0]=73
     lon[1]=90 mss_val[1]=_
     lon[2]=180 mss_val[2]=73
     lon[3]=270 mss_val[3]=_
     % ncks -s '%+5.1f, ' -H -C -v mss_val in.nc
     +73.0, _, +73.0, _,

   One dimensional arrays of characters stored as netCDF variables are
automatically printed as strings, whether or not they are
NUL-terminated, e.g.,
     ncks -v fl_nm in.nc
   The `%c' formatting code is useful for printing multidimensional
arrays of characters representing fixed length strings
     ncks -s '%c' -v fl_nm_arr in.nc
   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 `out.nc' containing all variables, and any associated
coordinates, except variable `time', from netCDF `in.nc':
     ncks -x -v time in.nc out.nc

   Extract variables `time' and `pressure' from netCDF `in.nc'.  If
`out.nc' does not exist it will be created.  Otherwise the you will be
prompted whether to append to or to overwrite `out.nc':
     ncks -v time,pressure in.nc out.nc
     ncks -C -v time,pressure in.nc out.nc
   The first version of the command creates an `out.nc' which contains
`time', `pressure', and any coordinate variables associated with
PRESSURE.  The `out.nc' from the second version is guaranteed to
contain only two variables `time' and `pressure'.

   Create netCDF `out.nc' containing all variables from file `in.nc'.
Restrict the dimensions of these variables to a hyperslab.  Print (with
`-H') the hyperslabs to the screen for good measure.  The specified
hyperslab is: the fifth value in dimension `time'; the half-open range
LAT > 0. in coordinate `lat'; the half-open range LON < 330. in
coordinate `lon'; the closed interval 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 *Note Hyperslabs::.
     ncks -H -d time,5 -d lat,,0.0 -d lon,330.0, -d band,0.3,0.5
     -d lev,1000.0 in.nc out.nc

   Assume the domain of the monotonically increasing longitude
coordinate `lon' is 0 < LON < 360.  Here, `lon' is an example of a
wrapped coordinate.  `ncks' will extract a hyperslab which crosses the
Greenwich meridian simply by specifying the westernmost longitude as
MIN and the easternmost longitude as MAX, as follows:
     ncks -d lon,260.0,45.0 in.nc out.nc
   For more details *Note Wrapped Coordinates::.

4.8 `ncpdq' netCDF Permute Dimensions Quickly
=============================================

SYNTAX
     ncpdq [-3] [-4] [-6] [-A] [-a [-]DIM[,...]] [-C] [-c]
     [--cnk_dmn nm,sz] [--cnk_map map] [--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] [--no_tmp_fl]
     [-O] [-o OUTPUT-FILE] [-P PCK_PLC] [-p PATH]
     [-R] [-r] [--ram_all] [-t THR_NBR] [-U] [--unn] [-v VAR[,...]] [-X ...] [-x]
     INPUT-FILE [OUTPUT-FILE]

DESCRIPTION

   `ncpdq' performs one of two distinct functions, packing or dimension
permutation, but not both, when invoked.  `ncpdq' is optimized to
perform these actions in a parallel fashion with a minimum of time and
memory.  The "pdq" may stand for "Permute Dimensions Quickly", "Pack
Data Quietly", "Pillory Dan Quayle", or other silly uses.

Packing and Unpacking Functions
-------------------------------

The `ncpdq' packing (and unpacking) algorithms are described in *note
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'
"Unpacking"
     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
*note 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 (*note 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'
     Map: Pack [`NC_DOUBLE',`NC_FLOAT'] to `NC_SHORT'
     Types copied instead of packed:
     [`NC_INT64',`NC_UINT64',`NC_INT',`NC_UINT',`NC_SHORT',`NC_USHORT',`NC_CHAR',`NC_BYTE',`NC_UBYTE']
     PCK_MAP key values: `flt_sht', `pck_map_flt_sht'
"Pack Floating Precisions to `NC_BYTE'"
     Definition: Pack floating precision types to `NC_BYTE'
     Map: Pack [`NC_DOUBLE',`NC_FLOAT'] to `NC_BYTE'
     Types copied instead of packed:
     [`NC_INT64',`NC_UINT64',`NC_INT',`NC_UINT',`NC_SHORT',`NC_USHORT',`NC_CHAR',`NC_BYTE',`NC_UBYTE']
     PCK_MAP key values: `flt_byt', `pck_map_flt_byt'
"Pack Higher Precisions to `NC_SHORT'"
     Definition: Pack higher precision types to `NC_SHORT'
     Map: Pack
     [`NC_DOUBLE',`NC_FLOAT',`NC_INT64',`NC_UINT64',`NC_INT',`NC_UINT']
     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'
     Map: Pack
     [`NC_DOUBLE',`NC_FLOAT',`NC_INT64',`NC_UINT64',`NC_INT',`NC_UINT',`NC_SHORT',`NC_USHORT']
     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
     Map: Pack [`NC_DOUBLE',`NC_INT64',`NC_UINT64'], to `NC_INT'.  Pack
     [`NC_FLOAT',`NC_INT',`NC_UINT'] to `NC_SHORT'.  Pack
     [`NC_SHORT',`NC_USHORT'] to `NC_BYTE'.
     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 (*note 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 *note Concatenation:: for a detailed example.  The record dimension
is always the most slowly varying dimension in a record variable (*note
C and Fortran Index Conventions::).  The specified re-ordering fails if
it requires creating more than one record dimension amongst all the
output variables (1).

   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 (*note C and Fortran Index Conventions::).  To do
this, include all the variable's dimensions in the dimension re-order
list in reversed order.  Explicit examples of these two techniques
appear below.

EXAMPLES

   Pack and unpack all variables in file `in.nc' and store the results
in `out.nc':
     ncpdq in.nc out.nc # Same as ncpack in.nc out.nc
     ncpdq -P all_new -M flt_sht in.nc out.nc # Defaults
     ncpdq -P all_xst in.nc out.nc
     ncpdq -P upk in.nc out.nc # Same as ncunpack in.nc out.nc
     ncpdq -U in.nc out.nc # Same as ncunpack in.nc out.nc
   The first two commands pack any unpacked variable in the input file.
They also unpack and then re-pack every packed variable.  The third
command only packs unpacked variables in the input file.  If a variable
is already packed, the third command copies it unchanged to the output
file.  The fourth and fifth commands unpack any packed variables.  If a
variable is not packed, the third command copies it unchanged.

   The previous examples all utilized the default packing map.  Suppose
you wish to archive all data that are currently unpacked into a form
which only preserves 256 distinct values.  Then you could specify the
packing map PCK_MAP as `hgh_byt' and the packing policy PCK_PLC as
`all_xst':
     ncpdq -P all_xst -M hgh_byt in.nc out.nc
   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' (*note 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.  
     # Convert HDF file to netCDF file
     ncl_convert2nc modis.hdf
     # Unpack using HDF convention and repack using netCDF convention
     ncpdq --hdf_upk -P xst_new modis.nc modis.nc
   The `ncl_convert2nc' command distributed with NCL is a commonly used
tool for converting HDF files to netCDF.  In this case it produces an
output file `modis.nc' which preserves the HDF packing used in the
input file.  The `ncpdq' command first unpacks all packed variables
using the HDF unpacking algorithm (as specified by `--hdf_upk'), and
then repacks those same variables using the netCDF algorithm (because
that is the only algorithm NCO packs with).  As described above the
`--P xst_new' packing policy only repacks variables that are already
packed.  Not-packed variables are copied directly without loss of
precision (2).

   Re-order file `in.nc' so that the dimension `lon' always precedes
the dimension `lat' and store the results in `out.nc':
     ncpdq -a lon,lat in.nc out.nc
     ncpdq -v three_dmn_var -a lon,lat in.nc out.nc
   The first command re-orders every variable in the input file.  The
second command extracts and re-orders only the variable `three_dmn_var'.

   Suppose the dimension `lat' represents latitude and monotonically
increases increases from south to north.  Reversing the `lat' dimension
means re-ordering the data so that latitude values decrease
monotonically from north to south.  Accomplish this with
     % ncpdq -a -lat in.nc out.nc
     % ncks -C -v lat in.nc
     lat[0]=-90
     lat[1]=90
     % ncks -C -v lat out.nc
     lat[0]=90
     lat[1]=-90
   This operation reversed the latitude dimension of all variables.
Whitespace immediately preceding the negative sign that specifies
dimension reversal may be dangerous.  Quotes and long options can help
protect negative signs that should indicate dimension reversal from
being interpreted by the shell as dashes that indicate new command line
switches.
     ncpdq -a -lat in.nc out.nc # Dangerous? Whitespace before "-lat"
     ncpdq -a '-lat' in.nc out.nc # OK. Quotes protect "-" in "-lat"
     ncpdq -a lon,-lat in.nc out.nc # OK. No whitespace before "-"
     ncpdq --rdr=-lat in.nc out.nc # Preferred. Uses "=" not whitespace

   To create the mathematical transpose of a variable, place all its
dimensions in the dimension re-order list in reversed order.  This
example creates the transpose of `three_dmn_var':
     % ncpdq -a lon,lev,lat -v three_dmn_var in.nc out.nc
     % ncks -C -v three_dmn_var in.nc
     lat[0]=-90 lev[0]=100 lon[0]=0 three_dmn_var[0]=0
     lat[0]=-90 lev[0]=100 lon[1]=90 three_dmn_var[1]=1
     lat[0]=-90 lev[0]=100 lon[2]=180 three_dmn_var[2]=2
     ...
     lat[1]=90 lev[2]=1000 lon[1]=90 three_dmn_var[21]=21
     lat[1]=90 lev[2]=1000 lon[2]=180 three_dmn_var[22]=22
     lat[1]=90 lev[2]=1000 lon[3]=270 three_dmn_var[23]=23
     % ncks -C -v three_dmn_var out.nc
     lon[0]=0 lev[0]=100 lat[0]=-90 three_dmn_var[0]=0
     lon[0]=0 lev[0]=100 lat[1]=90 three_dmn_var[1]=12
     lon[0]=0 lev[1]=500 lat[0]=-90 three_dmn_var[2]=4
     ...
     lon[3]=270 lev[1]=500 lat[1]=90 three_dmn_var[21]=19
     lon[3]=270 lev[2]=1000 lat[0]=-90 three_dmn_var[22]=11
     lon[3]=270 lev[2]=1000 lat[1]=90 three_dmn_var[23]=23

   To completely reverse the storage order of a variable, include all
its dimensions in the re-order list, each prefixed by a negative sign.
This example reverses the storage order of `three_dmn_var':
     % ncpdq -a -lat,-lev,-lon -v three_dmn_var in.nc out.nc
     % ncks -C -v three_dmn_var in.nc
     lat[0]=-90 lev[0]=100 lon[0]=0 three_dmn_var[0]=0
     lat[0]=-90 lev[0]=100 lon[1]=90 three_dmn_var[1]=1
     lat[0]=-90 lev[0]=100 lon[2]=180 three_dmn_var[2]=2
     ...
     lat[1]=90 lev[2]=1000 lon[1]=90 three_dmn_var[21]=21
     lat[1]=90 lev[2]=1000 lon[2]=180 three_dmn_var[22]=22
     lat[1]=90 lev[2]=1000 lon[3]=270 three_dmn_var[23]=23
     % ncks -C -v three_dmn_var out.nc
     lat[0]=90 lev[0]=1000 lon[0]=270 three_dmn_var[0]=23
     lat[0]=90 lev[0]=1000 lon[1]=180 three_dmn_var[1]=22
     lat[0]=90 lev[0]=1000 lon[2]=90 three_dmn_var[2]=21
     ...
     lat[1]=-90 lev[2]=100 lon[1]=180 three_dmn_var[21]=2
     lat[1]=-90 lev[2]=100 lon[2]=90 three_dmn_var[22]=1
     lat[1]=-90 lev[2]=100 lon[3]=0 three_dmn_var[23]=0

   Creating a record dimension named, e.g., `time', in a file which has
no existing record dimension is simple with `ncecat':
     ncecat -O -u time in.nc out.nc # Create degenerate record dimension named "time"

   Now consider a file with all dimensions, including `time', fixed
(non-record).  Suppose the user wishes to convert `time' from a fixed
dimension to a record dimension.  This may be useful, for example, when
the user wishes to append additional time slices to the data.  As of
NCO version 4.0.1 (April, 2010) the preferred method for doing this is
with `ncks':
     ncks -O --mk_rec_dmn time in.nc out.nc # Change "time" to record dimension

   Prior to 4.0.1, the procedure to change an existing fixed dimension
into a record dimension required three separate commands, `ncecat'
followed by `ncpdq', and then `ncwa'.  The recommended method is now to
use `ncks --fix_rec_dmn', yet it is still instructive to present the
original procedure, as it shows how multiple operators can achieve the
same ends by different means: 
     ncecat -O in.nc out.nc # Add degenerate record dimension named "record"
     ncpdq -O -a time,record out.nc out.nc # Switch "record" and "time"
     ncwa -O -a record out.nc out.nc # Remove (degenerate) "record"
   The first step creates a degenerate (size equals one) record
dimension named (by default) `record'.  The second step swaps the
ordering of the dimensions named `time' and `record'.  Since `time' now
occupies the position of the first (least rapidly varying) dimension,
it becomes the record dimension.  The dimension named `record' is no
longer a record dimension.  The third step averages over this
degenerate `record' dimension.  Averaging over a degenerate dimension
does not alter the data.  The ordering of other dimensions in the file
(`lat', `lon', etc.) is immaterial to this procedure.  See *note ncecat
netCDF Ensemble Concatenator:: and *note ncks netCDF Kitchen Sink:: for
other methods of changing variable dimensionality, including the record
dimension.

   ---------- Footnotes ----------

   (1) This limitation, imposed by the netCDF storage layer, may be
relaxed in the future with netCDF4.

   (2) `ncpdq' does not support packing data using the HDF convention.
Although it is now straightforward to support this, we think it might
sow more confusion than it reaps.  Let us know if you disagree and
would like NCO to support packing data with HDF algorithm.

4.9 `ncra' netCDF Record Averager
=================================

SYNTAX
     ncra [-3] [-4] [-6] [-A] [-C] [-c]
     [--cnk_dmn nm,sz] [--cnk_map map] [--cnk_plc plc] [--cnk_scl sz]
     [-D DBG] [-d DIM,[MIN][,[MAX][,[STRIDE][,[DURATION]]]] [-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] [-R] [-r] [--ram_all] [--rec_apn] [--rth_dbl|flt]
     [-t THR_NBR] [--unn] [-v VAR[,...]] [-X ...] [-x] [-y OP_TYP]
     [INPUT-FILES] [OUTPUT-FILE]

DESCRIPTION

   `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.  *Note Averaging
vs. Concatenating::, for a description of the distinctions between the
various averagers 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 (*note 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 (*note 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.  (*note Operation Types::).

EXAMPLES

   Average files `85.nc', `86.nc', ... `89.nc' along the record
dimension, and store the results in `8589.nc': 
     ncra 85.nc 86.nc 87.nc 88.nc 89.nc 8589.nc
     ncra 8[56789].nc 8589.nc
     ncra -n 5,2,1 85.nc 8589.nc
   These three methods produce identical answers.  *Note Specifying
Input Files::, for an explanation of the distinctions between these
methods.

   Assume the files `85.nc', `86.nc', ... `89.nc' each contain a record
coordinate TIME of length 12 defined such that the third record in
`86.nc' contains data from March 1986, etc.  NCO knows how to hyperslab
the record dimension across files.  Thus, to average data from
December, 1985 through February, 1986:
     ncra -d time,11,13 85.nc 86.nc 87.nc 8512_8602.nc
     ncra -F -d time,12,14 85.nc 86.nc 87.nc 8512_8602.nc
   The file `87.nc' is superfluous, but does not cause an error.  The
`-F' turns on the Fortran (1-based) indexing convention.  The following
uses the STRIDE option to average all the March temperature data from
multiple input files into a single output file
     ncra -F -d time,3,,12 -v temperature 85.nc 86.nc 87.nc 858687_03.nc
   *Note Stride::, for a description of the STRIDE argument.

   Assume the TIME coordinate is incrementally numbered such that
January, 1985 = 1 and December, 1989 = 60.  Assuming `??' only expands
to the five desired files, the following averages June, 1985-June, 1989:
     ncra -d time,6.,54. ??.nc 8506_8906.nc

4.10 `ncrcat' netCDF Record Concatenator
========================================

SYNTAX
     ncrcat [-3] [-4] [-6] [-A] [-C] [-c]
     [--cnk_dmn nm,sz] [--cnk_map map] [--cnk_plc plc] [--cnk_scl sz]
     [-D DBG] [-d DIM,[MIN][,[MAX][,[STRIDE][,[DURATION]]]] [-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] [-R] [-r] [--ram_all] [--rec_apn]
     [-t THR_NBR] [--unn] [-v VAR[,...]] [-X ...] [-x]
     [INPUT-FILES] [OUTPUT-FILE]

DESCRIPTION

   `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.  *Note Averaging
vs. Concatenating::, for a description of the distinctions between the
various averagers 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 (*note 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 (*note 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 *note ARM Conventions:: for a complete description.

EXAMPLES

   Concatenate files `85.nc', `86.nc', ... `89.nc' along the record
dimension, and store the results in `8589.nc': 
     ncrcat 85.nc 86.nc 87.nc 88.nc 89.nc 8589.nc
     ncrcat 8[56789].nc 8589.nc
     ncrcat -n 5,2,1 85.nc 8589.nc
   These three methods produce identical answers.  *Note Specifying
Input Files::, for an explanation of the distinctions between these
methods.

   Assume the files `85.nc', `86.nc', ... `89.nc' each contain a record
coordinate TIME of length 12 defined such that the third record in
`86.nc' contains data from March 1986, etc.  NCO knows how to hyperslab
the record dimension across files.  Thus, to concatenate data from
December, 1985-February, 1986:
     ncrcat -d time,11,13 85.nc 86.nc 87.nc 8512_8602.nc
     ncrcat -F -d time,12,14 85.nc 86.nc 87.nc 8512_8602.nc
   The file `87.nc' is superfluous, but does not cause an error.  When
`ncra' and `ncrcat' encounter a file which does contain any records
that meet the specified hyperslab criteria, they disregard the file and
proceed to the next file without failing.  The `-F' turns on the
Fortran (1-based) indexing convention.  

   The following uses the STRIDE option to concatenate all the March
temperature data from multiple input files into a single output file
     ncrcat -F -d time,3,,12 -v temperature 85.nc 86.nc 87.nc 858687_03.nc
   *Note Stride::, for a description of the STRIDE argument.

   Assume the TIME coordinate is incrementally numbered such that
January, 1985 = 1 and December, 1989 = 60.  Assuming `??' only expands
to the five desired files, the following concatenates June, 1985-June,
1989:
     ncrcat -d time,6.,54. ??.nc 8506_8906.nc

4.11 `ncrename' netCDF Renamer
==============================

v SYNTAX
     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]]

DESCRIPTION

   `ncrename' renames netCDF dimensions, variables, attributes, and
groups.  Each object that has a name in the list of old names is
renamed using the corresponding name in the list of new names.  All the
new names must be unique.  Every old name must exist in the input file,
unless the old name is preceded by the period (or "dot") character `.'.
The validity of OLD_NAME is not checked prior to the renaming.  Thus,
if OLD_NAME is specified without the the `.' prefix and is not present
in INPUT-FILE, `ncrename' will abort.  The NEW_NAME should never be
prefixed by a `.' (or else the period will be included as part of the
new name).  The OPTIONS and EXAMPLES show how to select specific
variables whose attributes are to be renamed.

   `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 the names of
the INPUT-FILE in place without prompting and without creating a
temporary copy of `input-file'.  This is because the renaming operation
is considered reversible if the user makes a mistake.  The NEW_NAME can
easily be changed back to OLD_NAME by using `ncrename' one more time.

   Note that renaming a dimension to the name of a dependent variable
can be used to invert the relationship between an independent coordinate
variable and a dependent variable.  In this case, the named dependent
variable must be one-dimensional and should have no missing values.
Such a variable will become a coordinate variable.

   According to the `netCDF User Guide', renaming properties in netCDF
files does not incur the penalty of recopying the entire file when the
NEW_NAME is shorter than the OLD_NAME.

OPTIONS

`-a OLD_NAME,NEW_NAME'
     Attribute renaming.  The old and new names of the attribute are
     specified with `-a' (or `--attribute') by the associated OLD_NAME
     and NEW_NAME values.  Global 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 VAR_NM
     `global' has special significance--it indicates that ATT_NM refers
     to a global or group attribute, and not to a variable named
     `global'.  In other words, a VAR_NM of `global' is syntactically
     equivalent to a VAR_NM that is empty.  The VAR_NAME@ATT_NAME
     syntax is accepted, but not required, for the NEW_NAME.

`-d OLD_NAME,NEW_NAME'
     Dimension renaming.  The old and new names of the dimension are
     specified with `-d' (or `--dmn', `--dimension') by the associated
     OLD_NAME and NEW_NAME values.  This option may be specified more
     than once.

`-g OLD_NAME,NEW_NAME'
     Group renaming.  The old and new names of the dimension are
     specified with `-g' (or `--grp', `--group') by the associated
     OLD_NAME and NEW_NAME values.  This option may be specified more
     than once.

`-v OLD_NAME,NEW_NAME'
     Variable renaming.  The old and new names of the variable are
     specified with `-v' (or `--variable') by the associated OLD_NAME
     and NEW_NAME values.  This option may be specified more than once.

EXAMPLES

   Rename the variable `p' to `pressure' and `t' to `temperature' in
netCDF `in.nc'.  In this case `p' must exist in the input file (or
`ncrename' will abort), but the presence of `t' is optional:
     ncrename -v p,pressure -v .t,temperature in.nc

   Rename the attribute `long_name' to `largo_nombre' in the variable
`u', and no other variables in netCDF `in.nc'.
     ncrename -a u@long_name,largo_nombre in.nc

   Rename group `g8' to `g20' in netCDF `in.nc'.
     ncrename -g g8,g20 in_grp.nc

   `ncrename' does not automatically attach dimensions to variables of
the same name.  If you want to rename a coordinate variable so that it
remains a coordinate variable, you must separately rename both the
dimension and the variable:
     ncrename -d lon,longitude -v lon,longitude in.nc

   Create netCDF `out.nc' identical to `in.nc' except the attribute
`_FillValue' is changed to `missing_value', the attribute `units' is
changed to `CGS_units' (but only in those variables which possess it),
the attribute `hieght' is changed to `height' in the variable `tpt',
and in the variable `prs_sfc', if it exists.
     ncrename -a _FillValue,missing_value -a .units,CGS_units \
       -a tpt@hieght,height -a prs_sfc@.hieght,height in.nc out.nc
   The presence and absence of the `.' and `@' features cause this
command to execute successfully only if a number of conditions are met.
All variables _must_ have a `_FillValue' attribute _and_ `_FillValue'
must also be a global attribute.  The `units' attribute, on the other
hand, will be renamed to `CGS_units' wherever it is found but need not
be present in the file at all (either as a global or a variable
attribute).  The variable `tpt' must contain the `hieght' attribute.
The variable `prs_sfc' need not exist, and need not contain the
`hieght' attribute.

   Rename the global or group attribute `Convention' to `Conventions'
     ncrename -a Convention,Conventions  in.nc # Variable and global atts.
     ncrename -a .Convention,Conventions in.nc # Variable and global atts.
     ncrename -a @Convention,Conventions  in.nc # Global atts. only
     ncrename -a @.Convention,Conventions in.nc # Global atts. only
     ncrename -a global@Convention,Conventions   in.nc # Global atts. only
     ncrename -a .global@.Convention,Conventions in.nc # Global atts. only
   The examples without the `@' 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.

4.12 `ncwa' netCDF Weighted Averager
====================================

SYNTAX
     ncwa [-3] [-4] [-6] [-A] [-a DIM[,...]] [-B MASK_COND] [-b] [-C] [-c]
     [--cnk_dmn nm,sz] [--cnk_map map] [--cnk_plc plc] [--cnk_scl sz]
     [-D DBG] [-d DIM,[MIN][,[MAX][,[STRIDE]]] [-F]
     [-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] [-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]

DESCRIPTION

   `ncwa' averages variables in a single file over arbitrary
dimensions, with options to specify weights, masks, and normalization.
*Note Averaging vs. Concatenating::, for a description of the
distinctions between the various averagers and concatenators.  The
default behavior of `ncwa' is to arithmetically average every numerical
variable over all dimensions and to produce a scalar result for each.

   Averaged dimensions are, by default, eliminated as dimensions.
Their corresponding coordinates, if any, are output as scalars.  The
`-b' switch (and its long option equivalents `--rdd' and
`--retain-degenerate-dimensions') causes `ncwa' to retain averaged
dimensions as degenerate (size 1) dimensions.  This maintains the
association between a dimension (or coordinate) and variables after
averaging and simplifies, for instance, later concatenation along the
degenerate dimension.

   To average variables over only a subset of their dimensions, specify
these dimensions in a comma-separated list following `-a', e.g., `-a
time,lat,lon'.  As with all arithmetic operators, the operation may be
restricted to an arbitrary hypserslab by employing the `-d' option
(*note 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 (1).  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.
(2).

   As explained in *Note 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 (*note Operation Types::).

   ---------- Footnotes ----------

   (1) The default behavior of (`-I') changed on 1998/12/01--before
this date the default was not to weight or mask coordinate variables.

   (2) If `lat_wgt' contains Gaussian weights then the value of
`latitude' in the OUTPUT-FILE will be the area-weighted centroid of the
hyperslab.  For the example given, this is about 30 degrees.

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.  (1).  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.

   ---------- Footnotes ----------

   (1) The three switches `-m', `-T', and `-M' are maintained for
backward compatibility and may be deprecated in the future.  It is
safest to write scripts using `--mask_condition'.

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 (*note 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
*note 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 in.nc foo.nc;ncks foo.nc

EXAMPLES

   Given file `85_0112.nc':
     netcdf 85_0112 {
     dimensions:
             lat = 64 ;
             lev = 18 ;
             lon = 128 ;
             time = UNLIMITED ; // (12 currently)
     variables:
             float lat(lat) ;
             float lev(lev) ;
             float lon(lon) ;
             float time(time) ;
             float scalar_var ;
             float three_dmn_var(lat, lev, lon) ;
             float two_dmn_var(lat, lev) ;
             float mask(lat, lon) ;
             float gw(lat) ;
     }

   Average all variables in `in.nc' over all dimensions and store
results in `out.nc':
     ncwa in.nc out.nc
   All variables in `in.nc' are reduced to scalars in `out.nc' since
`ncwa' averages over all dimensions unless otherwise specified (with
`-a').

   Store the zonal (longitudinal) mean of `in.nc' in `out.nc':
     ncwa -a lon in.nc out1.nc
     ncwa -a lon -b in.nc out2.nc
   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 out1.nc
     lon = 135
     % ncks -C -H -v lon out2.nc
     lon[0] = 135
   In either case the tally is simply the size of `lon', i.e., 180 for
the `85_0112.nc' file described by the sample header above.

   Compute the meridional (latitudinal) mean, with values weighted by
the corresponding element of GW (1):
     ncwa -w gw -a lat in.nc out.nc
   Here the tally is simply the size of `lat', or 64.  The sum of the
Gaussian weights is 2.0.

   Compute the area mean over the tropical Pacific:
     ncwa -w gw -a lat,lon -d lat,-20.,20. -d lon,120.,270. in.nc out.nc
   Here the tally is

   64 times 128 = 8192.

   Compute the area-mean over the globe using only points for which

   ORO < 0.5

   (2):
     ncwa -B 'ORO < 0.5'      -w gw -a lat,lon in.nc out.nc
     ncwa -m ORO -M 0.5 -T lt -w gw -a lat,lon in.nc out.nc
   It is considerably simpler to specify the complete MASK_COND with
the single string argument to `-B' than with the three separate
switches `-m', `-T', and `-M' (3).  If in doubt, enclose the MASK_COND
within quotes since some of the comparators have special meanings to
the shell.

   Assuming 70% of the gridpoints are maritime, then here the tally is

   0.70 times 8192 = 5734.

   Compute the global annual mean over the maritime tropical Pacific:
     ncwa -B 'ORO < 0.5'      -w gw -a lat,lon,time \
       -d lat,-20.0,20.0 -d lon,120.0,270.0 in.nc out.nc
     ncwa -m ORO -M 0.5 -T lt -w gw -a lat,lon,time \
       -d lat,-20.0,20.0 -d lon,120.0,270.0 in.nc out.nc
   Further examples will use the one-switch specification of MASK_COND.

   Determine the total area of the maritime tropical Pacific, assuming
the variable AREA contains the area of each gridcell
     ncwa -N -v area -B 'ORO < 0.5' -a lat,lon \
       -d lat,-20.0,20.0 -d lon,120.0,270.0 in.nc out.nc
   Weighting AREA (e.g., by GW) is not appropriate because AREA is
_already_ area-weighted by definition.  Thus the `-N' switch, or,
equivalently, the `-y ttl' switch, correctly integrate the cell areas
into a total regional area.

   Mask a file to contain _FILLVALUE everywhere except where THR_MIN <=
MSK_VAR <= THR_MAX:
     # Set masking variable and its scalar thresholds
     export msk_var='three_dmn_var_dbl' # Masking variable
     export thr_max='20' # Maximum allowed value
     export thr_min='10' # Minimum allowed value
     ncecat -O in.nc out.nc # Wrap out.nc in degenerate "record" dimension
     ncwa -O -a record -B "${msk_var} <= ${thr_max}" out.nc out.nc
     ncecat -O out.nc out.nc # Wrap out.nc in degenerate "record" dimension
     ncwa -O -a record -B "${msk_var} >= ${thr_min}" out.nc out.nc
   After the first use of `ncwa', `out.nc' contains _FILLVALUE where
`${msk_var} >= ${thr_max}'.  The process is then repeated on the
remaining data to filter out points where `${msk_var} <= ${thr_min}'.
The resulting `out.nc' contains valid data only where THR_MIN <=
MSK_VAR <= THR_MAX.

   ---------- Footnotes ----------

   (1) `gw' stands for "Gaussian weight" in many climate models.

   (2) `ORO' stands for "Orography" in some climate models and in those
models ORO < 0.5 selects ocean gridpoints.

   (3) Unfortunately the `-B' and `--mask_condition' options are
unsupported on Windows (with the MVS compiler), which lacks a free,
standard parser and lexer.

5 Contributing
**************

We welcome contributions from anyone.  The project homepage at
`https://sf.net/projects/nco' contains more information on how to
contribute.

   Financial contributions to NCO development may be made through
PayPal
(https://www.paypal.com/xclick/business=zender%40uci.edu&item_name=NCO+development&item_number=nco_dnt_dvl&no_note=1&tax=0&currency_code=USD).
NCO has been shared for over 10 years yet only two users have
contributed any money to the developers (1).  So you could be the third!

   ---------- Footnotes ----------

   (1) Happy users have sent me a few gifts, though.  This includes a
box of imported chocolate.  Mmm.  Appreciation and gifts are definitely
better than money.  Naturally, I'm too lazy to split and send gifts to
the other developers.  However, unlike some NCO developers, I have a
steady "real job".  My intent is to split monetary donations among the
active developers and to send them their shares via PayPal.

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
     Concept, design and implementation of operators from 1995-2000.
     Since then autotools, bug-squashing, chunking, documentation,
     packing, NCO library redesign, `ncap2' features, `ncbo', `ncpdq',
     SMP threading and MPI parallelization, netCDF4 integration,
     external funding, project management, science research, releases.  

Henry Butowsky
     Non-linear operations and `min()', `max()', `total()' support in
     `ncra' and `ncwa'.  Type conversion for arithmetic.  Migration to
     netCDF3 API.  `ncap2' parser, lexer, and I/O.  Multislabbing
     algorithm.  Variable wildcarding.  Various hacks.  `ncap2'
     language.  

Rorik Peterson
     Original autotool build support.  Long command-line options.
     Original UDUnits support.  Debianization.  Numerous bug-fixes.  

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, Gary Strand, Andrew Wittenberg, Remik Ziemlinski
     Excellent bug reports and feature requests.  

Daniel Baumann, Nick Bower, Luk Claebs, Barry deFreese, 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!

5.2 Proposals for Institutional Funding
=======================================

NSF has funded a project (http://nco.sf.net#prp_sei) 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.

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.

6.1 Daily data in one file
==========================

Suppose we have daily data from Jan 1st, 1990 to Dec. 31, 2005 in the
file of `in.nc' with the record dimension as `time'.

*Monthly average:* 
     for yyyy in {1990..2005}; do      # Loop over years
       for moy in {1..12}; do          # Loop over months
         mm=$( printf "%02d" ${moy} )  # Change to 2-digit format

         # Average specific month yyyy-mm
         ncra -O -d time,"${yyyy}-${mm}-01","${yyyy}-${mm}-31" \
              in.nc in_${yyyy}${mm}.nc
       done
     done

     # Concatenate monthly files together
     ncrcat -O in_??????.nc out.nc

*Annual average:* 
     for yyyy in {1990..2005}; do      # Loop over years
       ncra -O -d time,"${yyyy}-01-01","${yyyy}-12-31" in.nc in_${yyyy}.nc
     done

     # Concatenate annual files together
     ncrcat -O in_????.nc out.nc
   The `-O' switch means to overwrite the pre-existing files (*note
Batch Mode::).  The `-d' option is to specify the range of hyperslabs
(*note Hyperslabs::).  There are detailed instructions on `ncra' (*note
ncra netCDF Record Averager:: and `ncrcat' (*note ncrcat netCDF Record
Concatenator::).  NCO supports UDUnits so that we can use readable
dates as time dimension (*note UDUnits Support::).

6.2 Monthly data in one file
============================

Inside the input file `in.nc', 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 in.nc out.nc

*Annual average:* 
     ncra -O --mro -d time,,,12,12 in.nc out.nc
   Here we used the duration 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
(*note Duration::).  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 in.nc out_mro.nc
     ncra -v snd -d time,"1990-12-01",,12,3 in.nc out_sro.nc
   In the first output file, `out_mro.nc', SND is still a 3D array with
dimensions `time' * `latitude' * `longitude', but the length of `time'
now is 16, meaning 16 winters.  In the second output file,
`out_sro.nc', the length of `time' is only 1.  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.

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 `snd_19900101.nc',
`snd_19900102.nc', `snd_19900103.nc' ...  If you want to know the
monthly average of Jan 1990, you can write like,
     ncra -O snd_199001??.nc out.nc
   You might want to use loop if you need the average of each month.
     for moy in {1..12}; do          # Loop over months
       mm=$( printf "%02d" ${moy} )  # Change to 2-digit format

       ncra -O snd_????${mm}??.nc out_${mm}.nc
     done

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.

7 CMIP5 Example
***************

The fifth phase of the Coupled Model Intercomparison Project (CMIP5
(http://cmip-pcmdi.llnl.gov/cmip5/index.html?submenuheader=0) 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 repetitive process which impedes new research and
hypothesis testing.  Our NASA ACCESS project is designed to simplify and
accelerate this process.  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.  Over the summer of 2013, we will add scripts
which make use of new NCO features that combine all the loops in the
analysis into single commands by exploiting NCO's new group aggregation
and arithmetic features.

7.1 Combine Files
=================

Sometimes, the data of one ensemble member will be stored in several
files to reduce single file size.  This script illustrates how to
concatenate these into one file.  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' (*note 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/snc_LImon_bcc-csm1-1_historical_r1i1p1_185001-190012.nc
     # /data/cmip5/snc_LImon_bcc-csm1-1_historical_r1i1p1_190101-200512.nc
     #
     # Output files like:
     # /data/cmip5/snc_LImon_bcc-csm1-1_historical_r1i1p1_185001-200512.nc
     #
     # Online: http://nco.sourceforge.net/nco.html#Combine-Files
     #
     # Execute this script: bash cmb_fl.sh
     #===========================================================================

     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 ]
           then
           	echo "There is only 1 file in" ${nsm}.
           	continue
           fi

           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 \
             ${drc_in}${var[var_id]}_${rlm}_${mdl}_${xpt[0]}_\
             ${nsm}_${yyyymm_str}-${yyyymm_end}

           # Remove useless files
           rm ${drc_in}${var[var_id]}_${rlm}_${mdl}_${xpt[0]}_${nsm}_\
             !(${yyyymm_str}-${yyyymm_end})
         done
       done
     done

7.2 Global Distribution of Long-term Average
============================================

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 (*note Figure 7.1: fgr:glb.).  Key steps include:
  1. Average ensemble members of each model using `ncea' (*note ncea
     netCDF Ensemble Averager::)

  2. Average the record dimension using `ncra' (*note ncra netCDF
     Record Averager::)

  3. Store results of each model as a distinct group in a single output
     file using `ncecat' (*note ncrcat netCDF Record Concatenator::)
     with the `--gag' option

     #!/bin/bash

     #===========================================================================
     # After cmb_fl.sh
     # Example: Long-term average of each model globally
     #
     # Input files like:
     # /data/cmip5/snc_LImon_bcc-csm1-1_historical_r1i1p1_185001-200512.nc
     #
     # Output files like:
     # /data/cmip5/output/snc/snc_LImon_all-mdl_historical_all-nsm_clm.nc
     #
     # Online:
     #  http://nco.sourceforge.net/nco.html#Global-Distribution-of-Long_002dterm-Average
     #
     # Execute this script: bash glb_avg.sh
     #===========================================================================

     #---------------------------------------------------------------------------
     # 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
       	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 \
           ${drc_out}${fld_out[var_id]}${var[var_id]}_${rlm}_${mdl}_${xpt[0]}\
           _all-nsm_195601-200512.nc

       	# Average along time
       	ncra -O ${drc_out}${fld_out[var_id]}${var[var_id]}_${rlm}_${mdl}_${xpt[0]}\
           _all-nsm_195601-200512.nc \
           ${drc_out}${fld_out[var_id]}${var[var_id]}_${mdl}.nc

       	echo Model ${mdl} done!
       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 \
         ${drc_out}${fld_out[var_id]}${var[var_id]}_${rlm}_\
         all-mdl_${xpt[0]}_all-nsm_clm.nc

     	echo Var ${var[var_id]} done!
     done

7.3 Annual Average over Regions
===============================

Annual Average over Regions.

Figure 7.2: Annual Average over Regions.

   This section illustrates how to calculate the annual average over
specific regions (*note Figure 7.2: fgr:anl.).  Key steps include:
  1. Spatial average using `ncap2' (*note ncap2 netCDF Arithmetic
     Processor::) and `ncwa' (*note ncwa netCDF Weighted Averager::);

  2. Change dimension order using `ncpdq' (*note ncpdq netCDF Permute
     Dimensions Quickly::);

  3. Annual average using `ncra' (*note ncra netCDF Record Averager::);

  4. Anomaly from long-term average using `ncbo' (*note ncbo netCDF
     Binary Operator::);

  5. Standard deviation using `ncbo' (*note ncbo netCDF Binary
     Operator::) and `ncea' (*note ncea netCDF Ensemble Averager::);

  6. Rename variables using `ncrename' (*note ncrename netCDF
     Renamer::);

  7. Edit attributions using `ncatted' (*note ncatted netCDF Attribute
     Editor::);

  8. Linear regression using `ncap2' (*note ncap2 netCDF Arithmetic
     Processor::);

  9. Use `ncap2' (*note ncap2 netCDF Arithmetic Processor::) with nco
     script file (i.e., `.nco' file);

 10. Move variables around using `ncks' (*note ncks netCDF Kitchen
     Sink::).
        *Main Script*
     #!/bin/bash
     # includes gsl_rgr.nco

     #===========================================================================
     # After cmb_fl.sh
     # Example: Annual trend of each model over Greenland and Tibet
     #   ( time- and spatial-average, standard deviation,
     #   anomaly and linear regression)
     #
     # Input files:
     # /data/cmip5/snc_LImon_bcc-csm1-1_historical_r1i1p1_185001-200512.nc
     #
     # Output files:
     # /data/cmip5/outout/snc/snc_LImon_all-mdl_historical_all-nsm_annual.nc
     #
     # Online: http://nco.sourceforge.net/nco.html#Annual-Average-over-Regions
     #
     # Execute this script: bash ann_avg.sh
     #===========================================================================

     #---------------------------------------------------------------------------
     # 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}_gw.nc
           # 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}_gw.nc \
             ${drc_out}${fld_out[var_id]}${pfx}_gw_1.nc
           # 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}_gw.nc \
             ${drc_out}${fld_out[var_id]}${pfx}_gw_2.nc

           # Concatenate 2 regions together
           ncecat -O -u rgn ${drc_out}${fld_out[var_id]}${pfx}_gw_?.nc \
             ${drc_out}${fld_out[var_id]}${pfx}_gw_rgn4.nc

           # Change the order of the dimensions
           ncpdq -O -a time,rgn ${drc_out}${fld_out[var_id]}${pfx}_gw_rgn4.nc \
             ${drc_out}${fld_out[var_id]}${pfx}_gw_rgn4.nc

           # Remove the temporary files (optional)
           rm ${drc_out}${fld_out[var_id]}${pfx}_gw_?.nc \
             ${drc_out}${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}_gw_rgn4.nc \
             ${drc_out}${fld_out[var_id]}${pfx}_yrly.nc

           # Anomaly
           # Long-term average
           ncwa -O -a time ${drc_out}${fld_out[var_id]}${pfx}_yrly.nc \
             ${drc_out}${fld_out[var_id]}${pfx}_clm.nc
           # Subtract long-term average
           ncbo -O --op_typ=- ${drc_out}${fld_out[var_id]}${pfx}_yrly.nc \
             ${drc_out}${fld_out[var_id]}${pfx}_clm.nc \
             ${drc_out}${fld_out[var_id]}${pfx}_anm.nc
         done

         rm ${drc_out}${fld_out[var_id]}${var[var_id]}_${rlm}_${mdl}_${xpt[0]}_*_yrly.nc

         # Average over all the ensemble members
         ncea -O -4 ${drc_out}${fld_out[var_id]}${var[var_id]}_\
           ${rlm}_${mdl}_${xpt[0]}_*_anm.nc ${drc_out}${fld_out[var_id]}\
           ${var[var_id]}_${rlm}_${mdl}_${xpt[0]}_all-nsm_anm.nc

         # Standard deviation ------------------------------
         for fn in $( ls ${drc_out}${fld_out[var_id]}${var[var_id]}_${rlm}_${mdl}_\
           ${xpt[0]}_*_anm.nc ); do
           pfx=$( 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} \
             ${drc_out}${fld_out[var_id]}${var[var_id]}_\
             ${rlm}_${mdl}_${xpt[0]}_all-nsm_anm.nc \
             ${drc_out}${fld_out[var_id]}${pfx}_dlt.nc
         done

         # RMS
         ncea -O -y rmssdn ${drc_out}${fld_out[var_id]}${var[var_id]}_${rlm}_\
           ${mdl}_${xpt[0]}_*_dlt.nc \
           ${drc_out}${fld_out[var_id]}${var[var_id]}_${rlm}_\
           ${mdl}_${xpt[0]}_all-nsm_sdv.nc
         # Rename variables
         ncrename -v ${var[var_id]},sdv \
           ${drc_out}${fld_out[var_id]}${var[var_id]}_${rlm}_\
           ${mdl}_${xpt[0]}_all-nsm_sdv.nc
         # 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" \
           ${drc_out}${fld_out[var_id]}${var[var_id]}_${rlm}_\
           ${mdl}_${xpt[0]}_all-nsm_sdv.nc
         #------------------------------------------------------------

         # 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 \
           ${drc_out}${fld_out[var_id]}${var[var_id]}_${rlm}_\
           ${mdl}_${xpt[0]}_all-nsm_anm.nc ${drc_out}${fld_out[var_id]}${var[var_id]}\
           _${rlm}_${mdl}_${xpt[0]}_all-nsm_anm_rgr.nc
         #!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!

         # Get rid of temporary variables
         ncks -O -v c0,c1,pval,${var[var_id]},gw \
           ${drc_out}${fld_out[var_id]}${var[var_id]}_${rlm}_${mdl}_\
           ${xpt[0]}_all-nsm_anm_rgr.nc \
           ${drc_out}${fld_out[var_id]}${var[var_id]}_${mdl}.nc
         #------------------------------------------------------------

         # Move the variable 'sdv' into the anomaly files (i.e., *anm.nc files)
         ncks -A -v sdv \
           ${drc_out}${fld_out[var_id]}${var[var_id]}_${rlm}_\
           ${mdl}_${xpt[0]}_all-nsm_sdv.nc \
           ${drc_out}${fld_out[var_id]}${var[var_id]}_${mdl}.nc
         rm ${drc_out}${fld_out[var_id]}${var[var_id]}_*historical*

         echo Model ${mdl} done!
       done

       # Store models as groups in the output file
       ncecat -O --gag ${drc_out}${fld_out[var_id]}${var[var_id]}_*.nc
       ${drc_out}${fld_out[var_id]}${var[var_id]}_\
         ${rlm}_all-mdl_${xpt[0]}_all-nsm_annual.nc

       echo Var ${var[var_id]} done!
     done
   *gsl_rgr.nco*
     // Linear Regression
     // Called by ann_avg.sh
     // Caution: make sure the variable name is
     //  in agreement with the main script (now is 'snd')
     // Online: http://nco.sourceforge.net/nco.html#Annual-Average-over-Regions

     // 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()
     //------------------------------------------------------------
     ram_write(c0);
     ram_write(c1);

7.4 Monthly Cycle
=================

Monthly Cycle.

Figure 7.3: Monthly Cycle.

   This script illustrates how to calculate the monthly anomaly from the
annual average (*note Figure 7.3: fgr:mon.).
     #!/bin/bash

     #============================================================
     # After cmb_fl.sh
     # Example: Monthly cycle of each model in Greenland
     #
     # Input files:
     # /data/cmip5/snc_LImon_bcc-csm1-1_historical_r1i1p1_185001-200512.nc
     #
     # Output files:
     # /data/cmip5/snc/snc_LImon__all-mdl_historical_all-nsm_GN_mthly-anm.nc
     #
     # Online: http://nco.sourceforge.net/nco.html#Monthly-Cycle
     #
     # Execute this script: bash mcc.sh
     #============================================================

     #------------------------------------------------------------
     # 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 \
           ${drc_out}${fld_out[var_id]}${var[var_id]}_${rlm}_${mdl}_${xpt[0]}_all-nsm.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]}_all-nsm.nc \
           ${drc_out}${fld_out[var_id]}${var[var_id]}_${rlm}_${mdl}_${xpt[0]}_all-nsm.nc
         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]}_all-nsm.nc \
           ${drc_out}${fld_out[var_id]}${var[var_id]}_${rlm}_${mdl}_${xpt[0]}_all-nsm_GN.nc

         # 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" \
                 ${drc_out}${fld_out[var_id]}${var[var_id]}_${rlm}_${mdl}_\
                 ${xpt[0]}_all-nsm_GN.nc ${drc_out}${fld_out[var_id]}${var[var_id]}_\
                 ${rlm}_${mdl}_${xpt[0]}_all-nsm_GN_${yr}.nc
             fi

             # The specific month
             ncks -O -d time,"${yr}-${mm}-01 00:00:0.0","${yr}-${mm}-31 23:59:9.9" \
               ${drc_out}${fld_out[var_id]}${var[var_id]}_\
               ${rlm}_${mdl}_${xpt[0]}_all-nsm_GN.nc \
               ${drc_out}${fld_out[var_id]}${var[var_id]}_${rlm}_${mdl}_${xpt[0]}_\
               all-nsm_GN_${yr}${mm}.nc
             # 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 \
               ${drc_out}${fld_out[var_id]}${var[var_id]}_${rlm}_${mdl}_${xpt[0]}_\
               all-nsm_GN_${yr}.nc ${drc_out}${fld_out[var_id]}${var[var_id]}_${rlm}_\
               ${mdl}_${xpt[0]}_all-nsm_GN_${yr}${mm}_anm.nc
           done

           # Average over years
           ncra -O ${drc_out}${fld_out[var_id]}${var[var_id]}_${rlm}_${mdl}_\
             ${xpt[0]}_all-nsm_GN_????${mm}_anm.nc \
             ${drc_out}${fld_out[var_id]}${var[var_id]}_${rlm}_${mdl}_\
             ${xpt[0]}_all-nsm_GN_${mm}_anm.nc
         done
         #--------------------------------------------------

         # Concatenate months together
         ncrcat -O ${drc_out}${fld_out[var_id]}${var[var_id]}_${rlm}_${mdl}_\
           ${xpt[0]}_all-nsm_GN_??_anm.nc \
           ${drc_out}${fld_out[var_id]}${var[var_id]}_${mdl}.nc

         echo Model ${mdl} done!
       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 \
         ${drc_out}${fld_out[var_id]}${var[var_id]}_${rlm}_all-mdl_\
         ${xpt[0]}_all-nsm_GN_mthly-anm.nc

       echo Var ${var[var_id]} done!
     done

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 (*note Bilinear
     interpolation::)

  2. Rename variables, dimensions and attributions using `ncrename'
     (*note ncrename netCDF Renamer::).
        *Main Script*
     #!/bin/bash
     # 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/MCD43C3.A2000049.005.2006271205532.nc
     #
     # Output files:
     # /modis/mcd43c3/cesm-grid/MCD43C3.2000049.regrid.nc
     #
     # Online: http://nco.sourceforge.net/nco.html#Regrid-MODIS-Data
     #
     # Execute this script: bash rgr.sh
     #===========================================================================

     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}.regrid.nc
       # Keep only the new variables
       ncks -O -v wsa_sw_less,bsa_sw_less ${drc_in}${fld_out}${var}.${sfx}.regrid.nc \
         ${drc_in}${fld_out}${var}.${sfx}.regrid.nc
       # 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 \
         ${drc_in}${fld_out}${var}.${sfx}.regrid.nc

       echo $sfx done.
     done
   *bi_interp.nco*
     // Bilinear interpolation
     // Included by rgr.sh
     // Online: http://nco.sourceforge.net/nco.html#Regrid-MODIS-Data

     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,\
       -1.41361,-2.35602,-3.29843,-4.24084,-5.18325,-6.12565,-7.06806,\
       -8.01047,-8.95288,-9.89529,-10.8377,-11.7801,-12.7225,-13.6649,\
       -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,\
       -27.8011,-28.7435,-29.6859,-30.6283,-31.5707,-32.5131,-33.4555,\
       -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,\
       -54.1885,-55.1309,-56.0733,-57.0157,-57.9581,-58.9005,-59.8429,\
       -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,\
       -73.9791,-74.9215,-75.8639,-76.8063,-77.7487,-78.6911,-79.6335,\
       -80.5759,-81.5183,-82.4607,-83.4031,-84.3456,-85.288,-86.2304,\
       -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,\
       -170,-168.75,-167.5,-166.25,-165,-163.75,-162.5,-161.25,-160,\
       -158.75,-157.5,-156.25,-155,-153.75,-152.5,-151.25,-150,-148.75,\
       -147.5,-146.25,-145,-143.75,-142.5,-141.25,-140,-138.75,-137.5,\
       -136.25,-135,-133.75,-132.5,-131.25,-130,-128.75,-127.5,-126.25,\
       -125,-123.75,-122.5,-121.25,-120,-118.75,-117.5,-116.25,-115,\
       -113.75,-112.5,-111.25,-110,-108.75,-107.5,-106.25,-105,-103.75,\
       -102.5,-101.25,-100,-98.75,-97.5,-96.25,-95,-93.75,-92.5,-91.25,\
       -90,-88.75,-87.5,-86.25,-85,-83.75,-82.5,-81.25,-80,-78.75,-77.5,\
       -76.25,-75,-73.75,-72.5,-71.25,-70,-68.75,-67.5,-66.25,-65,-63.75,\
       -62.5,-61.25,-60,-58.75,-57.5,-56.25,-55,-53.75,-52.5,-51.25,-50,\
       -48.75,-47.5,-46.25,-45,-43.75,-42.5,-41.25,-40,-38.75,-37.5,\
       -36.25,-35,-33.75,-32.5,-31.25,-30,-28.75,-27.5,-26.25,-25,-23.75,\
       -22.5,-21.25,-20,-18.75,-17.5,-16.25,-15,-13.75,-12.5,-11.25,-10,\
       -8.75,-7.5,-6.25,-5,-3.75,-2.5,-1.25,0,1.25,2.5,3.75,5,6.25,7.5,\
       8.75,10,11.25,12.5,13.75,15,16.25,17.5,18.75,20,21.25,22.5,23.75,\
       25,26.25,27.5,28.75,30,31.25,32.5,33.75,35,36.25,37.5,38.75,40,\
       41.25,42.5,43.75,45,46.25,47.5,48.75,50,51.25,52.5,53.75,55,56.25,\
       57.5,58.75,60,61.25,62.5,63.75,65,66.25,67.5,68.75,70,71.25,72.5,\
       73.75,75,76.25,77.5,78.75,80,81.25,82.5,83.75,85,86.25,87.5,88.75,\
       90,91.25,92.5,93.75,95,96.25,97.5,98.75,100,101.25,102.5,103.75,\
       105,106.25,107.5,108.75,110,111.25,112.5,113.75,115,116.25,117.5,\
       118.75,120,121.25,122.5,123.75,125,126.25,127.5,128.75,130,131.25,\
       132.5,133.75,135,136.25,137.5,138.75,140,141.25,142.5,143.75,145,\
       146.25,147.5,148.75,150,151.25,152.5,153.75,155,156.25,157.5,\
       158.75,160,161.25,162.5,163.75,165,166.25,167.5,168.75,170,171.25,\
       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
     bsa_sw_less=bilinear_interp_wrap(bsa_sw,out,latn,lonn,lat,lon);
     wsa_sw_less=bilinear_interp_wrap(wsa_sw,out,latn,lonn,lat,lon);

     // 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";

7.6 Add Coordinates to MODIS Data
=================================

*Main Script*
     #!/bin/bash

     #============================================================
     # 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/MCD43C3.2000049.regrid.nc
     #
     # Output files:
     # /modis/mcd43c3/cesm-grid/MCD43C3.2000049.regrid.nc
     #
     # Online: http://nco.sourceforge.net/nco.html#Add-Coordinates-to-MODIS-Data
     #
     # Execute this script: bash add_crd.sh
     #============================================================

     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 \
         ${drc_in}${fld_in}${var}.${sfx}.nc
     done
   *crd.nco*
     // Add coordinates to MODIS HDF data
     // Included by add_crd.sh
     // Online: http://nco.sourceforge.net/nco.html#Add-Coordinates-to-MODIS-Data

     lon = array(0.f, 0.05, $lon) - 180;
     lat = 90.f- array(0.f, 0.05, $lat);

7.7 Permute MODIS Coordinates
=============================

MODIS orders latitude data from 90oN to -90oN, and longitude from
-180oE to 180oE.  However, CMIP5 orders latitude from -90oN to 90oN,
and longitude from 0oE to 360oE.  This script changes the MODIS
coordinates to follow the CMIP5 convention.
     #!/bin/bash

     ##===========================================================================
     ## 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/MCD43C3.2000049.regrid.nc
     ##
     ## Output files:
     ## /modis/mcd43c3/cesm-grid/MCD43C3.2000049.regrid.nc
     ##
     ## Online: http://nco.sourceforge.net/nco.html#Permute-MODIS-Coordinates
     ##
     ## Execute this script: bash pmt_crd.sh
     ##===========================================================================

     ##---------------------------------------------------------------------------
     ## 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}
     done

7.8 Hierarchical Data Files
===========================

Hierarchical Data Files support arbitrarily nested groups.  The
following NCO operators now can work recursively through all groups:
   * `ncbo' (*note ncbo netCDF Binary Operator::)

   * `ncecat' (*note ncecat netCDF Ensemble Concatenator::)

   * `ncks' (*note ncks netCDF Kitchen Sink::)

   * `ncpdq' (*note ncpdq netCDF Permute Dimensions Quickly::)

   * `ncwa' (*note ncwa netCDF Weighted Averager::)
   Here is an example showing:
  1. How to create a hierarchical data file from multiple files using
     `ncecat' (*note ncecat netCDF Ensemble Concatenator::) or `ncks'
     (*note ncks netCDF Kitchen Sink::);

  2. Hyperslabs using `ncks' (*note ncks netCDF Kitchen Sink::);

  3. Spatial average and time average using `ncwa' (*note ncwa netCDF
     Weighted Averager::);

  4. Anomaly from average using `ncbo' (*note ncbo netCDF Binary
     Operator::).

     #!/bin/bash
     #
     #============================================================
     # Process netCDF-4 files with nested groups
     #  - Create groups
     #    - Input files as top level groups
     #    - And/Or append variables or groups from other files
     #  - Hyperslab
     #  - Spatial average
     #  - Time average
     #  - Anomaly
     #============================================================
     #- Directory of input files
     drc_in='/media/grele_data/wenshan/cesm/historical-exp/nco_grp/'

     #------------------------------------------------------------
     # Create groups
     #------------------------------------------------------------
     # Input files as top level groups; group name = file name
     #   - The input files are:
     #       CCSM4_his.nc
     #       CESM1-BGC_esmHis.nc
     #       CESM1-BGC_his.nc
     #       CESM1-CAM5_his.nc
     #       CESM1-FASTCHEM_his.nc
     #       CESM1-WACCM_his.nc
     #   - One file looks like:
     #       dimensions:
     #         lat = 192
     #         lon = 288
     #         time = 192  // unlimited
     #       variables:
     #         double lat ( lat )
     #           units :	degrees_north
     #           axis :	Y
     #           long_name :	latitude
     #         double lon ( lon )
     #           units : degrees_east
     #           axis :  X
     #           long_name : longitude
     #         float snc ( time, lat, lon )
     #           long_name : Snow Area Cover
     #           units : %
     #           _FillValue : 1e+20
     #       ...
     #------------------------------------------------------------
     ncecat --gag ${drc_in}C*.nc ${drc_in}snc_LImon_r1i1p1_199001-200512.nc
     # Same for snow depth data (ie, snd)
     # Pay attention to the files you list using *; here I changed the files manually
     ncecat --gag ${drc_in}C*.nc ${drc_in}snd_LImon_r1i1p1_199001-200512.nc

     # Append variables
     # - Append variable snc in each group in file snc_LImon_r1i1p1_199001-200512.nc
     #   to the coresponding group in file snd_LImon_r1i1p1_199001-200512.nc
     sfx='_LImon_r1i1p1_199001-200512'
     ncks -A -v snc ${drc_in}snc${sfx}.nc ${drc_in}snd${sfx}.nc
     # - Rename output file
     mv ${drc_in}snd${sfx}.nc ${drc_in}snow${sfx}.nc

     # Append groups
     # - Multiple group levels
     #   - Input files as top level
     ncecat --gag ${drc_in}r?i1p1.nc ${drc_in} \
       snc_LImon_CCSM4_historical_all-nsm_199001-200512.nc
     #   - /r?i1p1 --> /CCSM4_his/r?i1p1
     ncks -G CCSM4_his -O \
       ${drc_in}snc_LImon_CCSM4_historical_all-nsm_199001-200512.nc \
       ${drc_in}snc_LImon_CCSM4_historical_all-nsm_199001-200512.nc
     # - Append group
     ncks -A -g CCSM4_his \
       ${drc_in}snc_LImon_CCSM4_historical_all-nsm_199001-200512.nc \
       ${drc_in}snc_LImon_CESM1-CAM5_historical_all-nsm_199001-200512.nc
     # Rename output file
     mv ${drc_in}snc_LImon_CESM1-CAM5_historical_all-nsm_199001-200512.nc \
       ${drc_in}snc_LImon_all-mdl_historical_all-nsm_199001-200512.nc

     #------------------------------------------------------------
     # Hyperslab
     #------------------------------------------------------------
     # Greenland
     ncks -O -d lat,60.0,75.0 -d lon,300.0,340.0 ${drc_in}snow${sfx}.nc \
       ${drc_in}snow${sfx}_GrIS.nc

     #------------------------------------------------------------
     # Spatial Average
     #------------------------------------------------------------
     ncwa -O -a lat,lon ${drc_in}snow${sfx}_GrIS.nc \
       ${drc_in}snow${sfx}_GrIS-avg.nc

     #------------------------------------------------------------
     # Hyperslab and spatial average in one step
     #------------------------------------------------------------
     ncwa -O -d lat,60.0,75.0 -d lon,300.0,340.0 -a lat,lon \
       ${drc_in}snow${sfx}.nc ${drc_in}${sfx}_GrIS-avg.nc

     #------------------------------------------------------------
     # Time Average (eg, monthly)
     #------------------------------------------------------------
     for moy in {1..12}; do  # Loop over months
       mm=$( printf "%02d" ${moy} ) # Change to 2-digit format

       ncwa -O -d time,"1990-${mm}-01 00:00:0.0","2005-${mm}-21 23:59:9.9",12 \
         -a time ${drc_in}snow${sfx}_GrIS.nc ${drc_in}snow${sfx}_GrIS_${mm}.nc
     done
     ncecat -O -u mon ${drc_in}snow${sfx}_GrIS_??.nc \
       ${drc_in}snow${sfx}_GrIS_mon-avg.nc

     #------------------------------------------------------------
     # Anomaly
     #------------------------------------------------------------
     ncbo -O --op_typ=- ${drc_in}snow${sfx}_GrIS.nc ${drc_in}snow${sfx}_GrIS-avg.nc \
       ${drc_in}snow${sfx}_GrIS-anomaly.nc

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/{}

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:

     /ptmp/zender/archive/T42x1_40

     Each component model has its own subdirectory, e.g.,

     /ptmp/zender/archive/T42x1_40/atm
     /ptmp/zender/archive/T42x1_40/cpl
     /ptmp/zender/archive/T42x1_40/ice
     /ptmp/zender/archive/T42x1_40/lnd
     /ptmp/zender/archive/T42x1_40/ocn

     within which model output is tagged with the particular model name

     /ptmp/zender/archive/T42x1_40/atm/T42x1_40.cam2.h0.0001-01.nc
     /ptmp/zender/archive/T42x1_40/atm/T42x1_40.cam2.h0.0001-02.nc
     /ptmp/zender/archive/T42x1_40/atm/T42x1_40.cam2.h0.0001-03.nc
     ...
     /ptmp/zender/archive/T42x1_40/atm/T42x1_40.cam2.h0.0001-12.nc
     /ptmp/zender/archive/T42x1_40/atm/T42x1_40.cam2.h0.0002-01.nc
     /ptmp/zender/archive/T42x1_40/atm/T42x1_40.cam2.h0.0002-02.nc
     ...

     or

     /ptmp/zender/archive/T42x1_40/lnd/T42x1_40.clm2.h0.0001-01.nc
     /ptmp/zender/archive/T42x1_40/lnd/T42x1_40.clm2.h0.0001-02.nc
     /ptmp/zender/archive/T42x1_40/lnd/T42x1_40.clm2.h0.0001-03.nc
     ...

     ************************************************************************
     Task 1: Regional processing
     ************************************************************************
     The first task in data processing is often creating seasonal cycles.
     Imagine a 100-year simulation with its 1200 monthly mean files.
     Our goal is to create a single file containing 12 months of data.
     Each month in the output file is the mean of 100 input files.

     Normally, we store the "reduced" data in a smaller, local directory.

     caseid='T42x1_40'
     #drc_in="${DATA}/archive/${caseid}/atm"
     drc_in="${DATA}/${caseid}"
     drc_out="${DATA}/${caseid}"
     mkdir -p ${drc_out}
     cd ${drc_out}

     Method 1: Assume all data in directory applies
     for mth in {1..12}; do
       mm=`printf "%02d" $mth`
       ncra -O -D 1 -o ${drc_out}/${caseid}_clm${mm}.nc \
         ${drc_in}/${caseid}.cam2.h0.*-${mm}.nc
     done # end loop over mth

     Method 2: Use shell 'globbing' to construct input filenames
     for mth in {1..12}; do
       mm=`printf "%02d" $mth`
       ncra -O -D 1 -o ${drc_out}/${caseid}_clm${mm}.nc \
         ${drc_in}/${caseid}.cam2.h0.00??-${mm}.nc \
         ${drc_in}/${caseid}.cam2.h0.0100-${mm}.nc
     done # end loop over mth

     Method 3: Construct input filename list explicitly
     for mth in {1..12}; do
       mm=`printf "%02d" $mth`
       fl_lst_in=''
       for yr in {1..100}; do
         yyyy=`printf "%04d" $yr`
         fl_in=${caseid}.cam2.h0.${yyyy}-${mm}.nc
         fl_lst_in="${fl_lst_in} ${caseid}.cam2.h0.${yyyy}-${mm}.nc"
       done # end loop over yr
       ncra -O -D 1 -o ${drc_out}/${caseid}_clm${mm}.nc -p ${drc_in} \
         ${fl_lst_in}
     done # end loop over mth

     Make sure the output file averages correct input files!
     ncks -M prints global metadata:

       ncks -M ${drc_out}/${caseid}_clm01.nc

     The input files ncra used to create the climatological monthly mean
     will appear in the global attribute named 'history'.

     Use ncrcat to aggregate the climatological monthly means

       ncrcat -O -D 1 \
         ${drc_out}/${caseid}_clm??.nc ${drc_out}/${caseid}_clm_0112.nc

     Finally, create climatological means for reference.
     The climatological time-mean:

       ncra -O -D 1 \
         ${drc_out}/${caseid}_clm_0112.nc ${drc_out}/${caseid}_clm.nc

     The climatological zonal-mean:

       ncwa -O -D 1 -a lon \
         ${drc_out}/${caseid}_clm.nc ${drc_out}/${caseid}_clm_x.nc

     The climatological time- and spatial-mean:

       ncwa -O -D 1 -a lon,lat,time -w gw \
         ${drc_out}/${caseid}_clm.nc ${drc_out}/${caseid}_clm_xyt.nc

     This file contains only scalars, e.g., "global mean temperature",
     used for summarizing global results of a climate experiment.

     Climatological monthly anomalies = Annual Cycle:
     Subtract climatological mean from climatological monthly means.
     Result is annual cycle, i.e., climate-mean has been removed.

       ncbo -O -D 1 -o ${drc_out}/${caseid}_clm_0112_anm.nc \
         ${drc_out}/${caseid}_clm_0112.nc ${drc_out}/${caseid}_clm_xyt.nc

     ************************************************************************
     Task 2: Correcting monthly averages
     ************************************************************************
     The previous step appoximates all months as being equal, so, e.g.,
     February weighs slightly too much in the climatological mean.
     This approximation can be removed by weighting months appropriately.
     We must add the number of days per month to the monthly mean files.
     First, create a shell variable dpm:

     unset dpm # Days per month
     declare -a dpm
     dpm=(0 31 28.25 31 30 31 30 31 31 30 31 30 31) # Allows 1-based indexing

     Method 1: Create dpm directly in climatological monthly means
     for mth in {1..12}; do
       mm=`printf "%02d" ${mth}`
       ncap2 -O -s "dpm=0.0*date+${dpm[${mth}]}" \
         ${drc_out}/${caseid}_clm${mm}.nc ${drc_out}/${caseid}_clm${mm}.nc
     done # end loop over mth

     Method 2: Create dpm by aggregating small files
     for mth in {1..12}; do
       mm=`printf "%02d" ${mth}`
       ncap2 -O -v -s "dpm=${dpm[${mth}]}" ~/nco/data/in.nc \
         ${drc_out}/foo_${mm}.nc
     done # end loop over mth
     ncecat -O -D 1 -p ${drc_out} -n 12,2,2 foo_${mm}.nc foo.nc
     ncrename -O -D 1 -d record,time ${drc_out}/foo.nc
     ncatted -O -h \
       -a long_name,dpm,o,c,"Days per month" \
       -a units,dpm,o,c,"days" \
       ${drc_out}/${caseid}_clm_0112.nc
     ncks -A -v dpm ${drc_out}/foo.nc ${drc_out}/${caseid}_clm_0112.nc

     Method 3: Create small netCDF file using ncgen
     cat > foo.cdl << 'EOF'
     netcdf foo {
     dimensions:
     	time=unlimited;
     variables:
     	float dpm(time);
     	dpm:long_name="Days per month";
     	dpm:units="days";
     data:
     	dpm=31,28.25,31,30,31,30,31,31,30,31,30,31;
     }
     EOF
     ncgen -b -o foo.nc foo.cdl
     ncks -A -v dpm ${drc_out}/foo.nc ${drc_out}/${caseid}_clm_0112.nc

     Another way to get correct monthly weighting is to average daily
     output files, if available.

     ************************************************************************
     Task 3: Regional processing
     ************************************************************************
     Let's say you are interested in examining the California region.
     Hyperslab your dataset to isolate the appropriate latitude/longitudes.

     ncks -O -D 1 -d lat,30.0,37.0 -d lon,240.0,270.0 \
         ${drc_out}/${caseid}_clm_0112.nc \
         ${drc_out}/${caseid}_clm_0112_Cal.nc

     The dataset is now much smaller!
     To examine particular metrics.

     ************************************************************************
     Task 4: Accessing data stored remotely
     ************************************************************************
     OPeNDAP server examples:

     UCI DAP servers:
     ncks -M -p http://dust.ess.uci.edu/cgi-bin/dods/nph-dods/dodsdata in.nc
     ncrcat -O -C -D 3 \
       -p http://dust.ess.uci.edu/cgi-bin/dods/nph-dods/dodsdata \
       -l /tmp in.nc in.nc ~/foo.nc

     Unidata DAP servers:
     ncks -M -p http://motherlode.ucar.edu:8080/thredds/dodsC/testdods in.nc
     ncrcat -O -C -D 3 \
       -p http://motherlode.ucar.edu:8080/thredds/dodsC/testdods \
       -l /tmp in.nc in.nc ~/foo.nc

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

     LLNL PCMDI IPCC OPeNDAP Data Portal:
     ncks -M -p http://username:password@esgcet.llnl.gov/cgi-bin/dap-cgi.py/ipcc4/sresa1b/ncar_ccsm3_0 pcmdi.ipcc4.ncar_ccsm3_0.sresa1b.run1.atm.mo.xml

     Earth System Grid (ESG): http://www.earthsystemgrid.org

     caseid='b30.025.ES01'
     CCSM3.0 1% increasing CO2 run, T42_gx1v3, 200 years starting in year 400
     Atmospheric post-processed data, monthly averages, e.g.,
     /data/zender/tmp/b30.025.ES01.cam2.h0.TREFHT.0400-01_cat_0449-12.nc
     /data/zender/tmp/b30.025.ES01.cam2.h0.TREFHT.0400-01_cat_0599-12.nc

     ESG supports password-protected FTP access by registered users
     NCO uses the .netrc file, if present, for password-protected FTP access
     Syntax for accessing single file is, e.g.,
     ncks -O -D 3 \
       -p ftp://climate.llnl.gov/sresa1b/atm/mo/tas/ncar_ccsm3_0/run1 \
       -l /tmp tas_A1.SRESA1B_1.CCSM.atmm.2000-01_cat_2099-12.nc ~/foo.nc

     # Average surface air temperature tas for SRESA1B scenario
     # This loop is illustrative and will not work until NCO correctly
     # translates '*' to FTP 'mget' all remote files
     for var in 'tas'; do
     for scn in 'sresa1b'; do
     for mdl in 'cccma_cgcm3_1 cccma_cgcm3_1_t63 cnrm_cm3 csiro_mk3_0 \
     gfdl_cm2_0 gfdl_cm2_1 giss_aom giss_model_e_h giss_model_e_r \
     iap_fgoals1_0_g inmcm3_0 ipsl_cm4 miroc3_2_hires miroc3_2_medres \
     miub_echo_g mpi_echam5 mri_cgcm2_3_2a ncar_ccsm3_0 ncar_pcm1 \
     ukmo_hadcm3 ukmo_hadgem1'; do
     for run in '1'; do
             ncks -R -O -D 3 -p ftp://climate.llnl.gov/${scn}/atm/mo/${var}/${mdl}/run${run} -l ${DATA}/${scn}/atm/mo/${var}/${mdl}/run${run} '*' ${scn}_${mdl}_${run}_${var}_${yyyymm}_${yyyymm}.nc
     done # end loop over run
     done # end loop over mdl
     done # end loop over scn
     done # end loop over var

     cd sresa1b/atm/mo/tas/ukmo_hadcm3/run1/
     ncks -H -m -v lat,lon,lat_bnds,lon_bnds -M tas_A1.nc | m
     bds -x 096 -y 073 -m 33 -o ${DATA}/data/dst_3.75x2.5.nc # ukmo_hadcm3
     ncview ${DATA}/data/dst_3.75x2.5.nc

     # msk_rgn is California mask on ukmo_hadcm3 grid
     # area is correct area weight on ukmo_hadcm3 grid
     ncks -A -v area,msk_rgn ${DATA}/data/dst_3.75x2.5.nc \
     ${DATA}/sresa1b/atm/mo/tas/ukmo_hadcm3/run1/area_msk_ukmo_hadcm3.nc

     Template for standardized data:
     ${scn}_${mdl}_${run}_${var}_${yyyymm}_${yyyymm}.nc

     e.g., raw data
     ${DATA}/sresa1b/atm/mo/tas/ukmo_hadcm3/run1/tas_A1.nc
     becomes standardized data

     Level 0: raw from IPCC site--no changes except for name
              Make symbolic link name match raw data
     Template: ${scn}_${mdl}_${run}_${var}_${yyyymm}_${yyyymm}.nc

     ln -s -f tas_A1.nc sresa1b_ukmo_hadcm3_run1_tas_200101_209911.nc
     area_msk_ukmo_hadcm3.nc

     Level I: Add all variables (but not standardized in time)
              to file containing msk_rgn and area
     Template: ${scn}_${mdl}_${run}_${yyyymm}_${yyyymm}.nc

     /bin/cp area_msk_ukmo_hadcm3.nc sresa1b_ukmo_hadcm3_run1_200101_209911.nc
     ncks -A -v tas sresa1b_ukmo_hadcm3_run1_tas_200101_209911.nc \
                    sresa1b_ukmo_hadcm3_run1_200101_209911.nc
     ncks -A -v pr  sresa1b_ukmo_hadcm3_run1_pr_200101_209911.nc \
                    sresa1b_ukmo_hadcm3_run1_200101_209911.nc

     If already have file then:
     mv sresa1b_ukmo_hadcm3_run1_200101_209911.nc foo.nc
     /bin/cp area_msk_ukmo_hadcm3.nc sresa1b_ukmo_hadcm3_run1_200101_209911.nc
     ncks -A -v tas,pr foo.nc sresa1b_ukmo_hadcm3_run1_200101_209911.nc

     Level II: Correct # years, months
     Template: ${scn}_${mdl}_${run}_${var}_${yyyymm}_${yyyymm}.nc

     ncks -d time,....... file1.nc file2.nc
     ncrcat file2.nc file3.nc sresa1b_ukmo_hadcm3_run1_200001_209912.nc

     Level III: Many derived products from level II, e.g.,

           A. Global mean timeseries
           ncwa -w area -a lat,lon \
                sresa1b_ukmo_hadcm3_run1_200001_209912.nc \
     	   sresa1b_ukmo_hadcm3_run1_200001_209912_xy.nc

           B. Califoria average timeseries
           ncwa -m msk_rgn -w area -a lat,lon \
                sresa1b_ukmo_hadcm3_run1_200001_209912.nc \
     	   sresa1b_ukmo_hadcm3_run1_200001_209912_xy_Cal.nc

10 References
*************

   * [ZeM07]  Zender, C. S., and H. J. Mangalam (2007), Scaling
     Properties of Common Statistical Operators for Gridded Datasets,
     Int. J. High Perform. Comput. Appl., 21(4), 485-498,
     doi:10.1177/1094342007083802.

   * [Zen08]  Zender, C. S. (2008), Analysis of Self-describing Gridded
     Geoscience Data with netCDF Operators (NCO), Environ. Modell.
     Softw., 23(10), 1338-1342, doi:10.1016/j.envsoft.2008.03.004.

   * [WZJ07]  Wang, D. L., C. S. Zender, and S. F. Jenks (2007),
     DAP-enabled Server-side Data Reduction and Analysis, Proceedings
     of the 23rd AMS Conference on Interactive Information and
     Processing Systems (IIPS) for Meteorology, Oceanography, and
     Hydrology, Paper 3B.2, January 14-18, San Antonio, TX. American
     Meteorological Society, AMS Press, Boston, MA.

   * [ZMW06]  Zender, C. S., H. Mangalam, and D. L. Wang (2006),
     Improving Scaling Properties of Common Statistical Operators for
     Gridded Geoscience Datasets, Eos Trans. AGU, 87(52), Fall Meet.
     Suppl., Abstract IN53B-0827.

   * [ZeW07]  Zender, C. S., and D. L. Wang (2007), High performance
     distributed data reduction and analysis with the netCDF Operators
     (NCO), Proceedings of the 23rd AMS Conference on Interactive
     Information and Processing Systems (IIPS) for Meteorology,
     Oceanography, and Hydrology, Paper 3B.4, January 14-18, San
     Antonio, TX. American Meteorological Society, AMS Press, Boston,
     MA.

   * [WZJ06]  Wang, D. L., C. S. Zender, and S. F. Jenks (2006),
     Server-side netCDF Data Reduction and Analysis, Eos Trans. AGU,
     87(52), Fall Meet. Suppl., Abstract IN53B-0826.

   * [WZJ073]  Wang, D. L., C. S. Zender, and S. F. Jenks (2007),
     Server-side parallel data reduction and analysis, in Advances in
     Grid and Pervasive Computing, Second International Conference, GPC
     2007, Paris, France, May 2-4, 2007, Proceedings. IEEE Lecture
     Notes in Computer Science, vol. 4459, edited by C. Cerin and K.-C.
     Li, pp. 744-750, Springer-Verlag, Berlin/Heidelberg,
     doi:10.1007/978-3-540-72360-8_67.

   * [WZJ074]  Wang, D. L., C. S. Zender and S. F. Jenks (2007), A
     System for Scripted Data Analysis at Remote Data Centers, Eos
     Trans. AGU, 88(52), Fall Meet. Suppl., Abstract IN11B-0469.

   * [WZJ081]  Wang, D. L., C. S. Zender and S. F. Jenks (2008),
     Cluster Workflow Execution of Retargeted Data Analysis Scripts,
     Proceedings of the 8th IEEE Int'l Symposium on Cluster Computing
     and the Grid (IEEE CCGRID '08), pp. 449-458, Lyon, France, May
     2008.

   * [WZJ091]  Wang, D. L., C. S. Zender, and S. F. Jenks (2009),
     Efficient Clustered Server-side Data Analysis Workflows using
     SWAMP, Earth Sci. Inform., 2(3), 141-155,
     doi:10.1007/s12145-009-0021-z.

   * [PFT88]  Press, Flannery, Teukolsky, and Vetterling (1988),
     Numerical Recipes in C, Cambridge Univ. Press, New York, NY.

General Index
*************

" (double quote):                              Voir 4.2.   (ligne  7761)
#include:                                      Voir 4.1.1. (ligne  4927)
$ (wildcard character):                        Voir 3.11.  (ligne  2239)
% (modulus):                                   Voir 4.1.25.
                                                           (ligne  7299)
' (end quote):                                 Voir 4.2.   (ligne  7761)
*:                                             Voir 4.3.   (ligne  7866)
* (filename expansion):                        Voir 3.11.  (ligne  2239)
* (multiplication):                            Voir 4.1.25.
                                                           (ligne  7299)
* (wildcard character):                        Voir 3.11.  (ligne  2248)
+:                                             Voir 4.3.   (ligne  7866)
+ (addition):                                  Voir 4.1.25.
                                                           (ligne  7299)
+ (wildcard character):                        Voir 3.11.  (ligne  2248)
-:                                             Voir 4.3.   (ligne  7866)
- (subtraction):                               Voir 4.1.25.
                                                           (ligne  7299)
--3:                                           Voir 3.9.   (ligne  1914)
--4:                                           Voir 3.9.   (ligne  1914)
--64bit:                                       Voir 3.9.   (ligne  1914)
--abc:                                         Voir 4.7.   (ligne  8633)
--alphabetize:                                 Voir 4.7.   (ligne  8633)
--apn <1>:                                     Voir 4.7.1. (ligne  8972)
--apn <2>:                                     Voir 3.33.  (ligne  4604)
--apn:                                         Voir 2.3.   (ligne   848)
--append <1>:                                  Voir 4.7.1. (ligne  8972)
--append <2>:                                  Voir 3.33.  (ligne  4604)
--append:                                      Voir 2.3.   (ligne   848)
--auxiliary:                                   Voir 3.21.  (ligne  3011)
--auxiliary LON_MIN,LON_MAX,LAT_MIN,LAT_MAX:   Voir 3.21.  (ligne  3011)
--bfr_sz_hnt:                                  Voir 3.28.  (ligne  3699)
--binary:                                      Voir 4.7.   (ligne  8643)
--bnr:                                         Voir 4.7.   (ligne  8643)
--cdl:                                         Voir 4.7.   (ligne  8663)
--chunk_dimension:                             Voir 3.25.  (ligne  3444)
--chunk_map:                                   Voir 3.25.  (ligne  3444)
--chunk_policy:                                Voir 3.25.  (ligne  3444)
--chunk_scalar:                                Voir 3.25.  (ligne  3444)
--cnk_dmn:                                     Voir 3.25.  (ligne  3444)
--cnk_map:                                     Voir 3.25.  (ligne  3444)
--cnk_map CNK_MAP:                             Voir 3.25.  (ligne  3506)
--cnk_plc:                                     Voir 3.25.  (ligne  3444)
--cnk_scl:                                     Voir 3.25.  (ligne  3444)
--coords <1>:                                  Voir 3.36.  (ligne  4748)
--coords:                                      Voir 3.12.  (ligne  2321)
--crd <1>:                                     Voir 3.36.  (ligne  4748)
--crd:                                         Voir 3.12.  (ligne  2321)
--create_ram <1>:                              Voir 3.29.  (ligne  3729)
--create_ram:                                  Voir 2.3.   (ligne   805)
--data:                                        Voir 4.7.   (ligne  8703)
--dbg_lvl DEBUG-LEVEL <1>:                     Voir 3.4.   (ligne  1456)
--dbg_lvl DEBUG-LEVEL <2>:                     Voir 2.8.   (ligne  1180)
--dbg_lvl DEBUG-LEVEL:                         Voir 1.6.   (ligne   719)
--dbl:                                         Voir 3.32.2.
                                                           (ligne  4245)
--debug-level DEBUG-LEVEL <1>:                 Voir 2.8.   (ligne  1180)
--debug-level DEBUG-LEVEL:                     Voir 1.6.   (ligne   719)
--deflate:                                     Voir 3.26.  (ligne  3592)
--dfl_lvl:                                     Voir 3.26.  (ligne  3592)
--dimension DIM,[MIN],[MAX],[STRIDE],[DURATION]:Voir 3.18. (ligne  2755)
--dimension DIM,[MIN],[MAX],STRIDE:            Voir 3.16.  (ligne  2650)
--dimension DIM,[MIN][,[MAX][,[STRIDE]]] <1>:  Voir 3.22.  (ligne  3120)
--dimension DIM,[MIN][,[MAX][,[STRIDE]]] <2>:  Voir 3.20.  (ligne  2963)
--dimension DIM,[MIN][,[MAX][,[STRIDE]]] <3>:  Voir 3.19.  (ligne  2821)
--dimension DIM,[MIN][,[MAX][,[STRIDE]]]:      Voir 3.15.  (ligne  2547)
--diskless_all:                                Voir 3.29.  (ligne  3729)
--dmn DIM,[MIN],[MAX],[STRIDE],DURATION]:      Voir 3.18.  (ligne  2755)
--dmn DIM,[MIN],[MAX],STRIDE:                  Voir 3.16.  (ligne  2650)
--dmn DIM,[MIN][,[MAX][,[STRIDE]]] <1>:        Voir 3.22.  (ligne  3120)
--dmn DIM,[MIN][,[MAX][,[STRIDE]]] <2>:        Voir 3.20.  (ligne  2963)
--dmn DIM,[MIN][,[MAX][,[STRIDE]]] <3>:        Voir 3.19.  (ligne  2821)
--dmn DIM,[MIN][,[MAX][,[STRIDE]]]:            Voir 3.15.  (ligne  2547)
--exclude <1>:                                 Voir 4.7.1. (ligne  8966)
--exclude:                                     Voir 3.11.  (ligne  2073)
--file_format:                                 Voir 3.9.   (ligne  1914)
--file_list:                                   Voir 3.35.  (ligne  4652)
--fix_rec_dmn DIM:                             Voir 4.7.   (ligne  8648)
--fl_bnr:                                      Voir 4.7.   (ligne  8643)
--fl_fmt:                                      Voir 3.9.   (ligne  1914)
--fl_lst_in:                                   Voir 3.35.  (ligne  4652)
--fl_out FL_OUT:                               Voir 3.6.   (ligne  1614)
--fl_spt:                                      Voir 4.1.   (ligne  4860)
--flt:                                         Voir 3.32.2.
                                                           (ligne  4245)
--fnc_tbl:                                     Voir 4.1.25.
                                                           (ligne  7434)
--fortran:                                     Voir 3.14.  (ligne  2512)
--glb_mtd_spr:                                 Voir 4.5.   (ligne  8291)
--gpe GPE_DSC:                                 Voir 3.13.  (ligne  2346)
--group GRP:                                   Voir 3.11.  (ligne  2073)
--grp GRP:                                     Voir 3.11.  (ligne  2073)
--hdf_unpack:                                  Voir 3.30.  (ligne  3806)
--hdf_upk:                                     Voir 3.30.  (ligne  3806)
--hdr_pad HDR_PAD:                             Voir 3.2.   (ligne  1353)
--header_pad HDR_PAD:                          Voir 3.2.   (ligne  1353)
--hieronymus:                                  Voir 4.7.   (ligne  8703)
--history:                                     Voir 3.34.  (ligne  4624)
--hst:                                         Voir 3.34.  (ligne  4624)
--intersection:                                Voir 3.11.  (ligne  2164)
--lcl OUTPUT-PATH:                             Voir 3.7.   (ligne  1646)
--local OUTPUT-PATH:                           Voir 3.7.   (ligne  1646)
--map CNK_MAP:                                 Voir 3.25.  (ligne  3506)
--map PCK_MAP:                                 Voir 4.8.   (ligne  9154)
--mask-value MASK_VAL:                         Voir 4.12.1.
                                                           (ligne  9806)
--mask-variable MASK_VAR:                      Voir 4.12.  (ligne  9734)
--mask_comparator MASK_COMP:                   Voir 4.12.1.
                                                           (ligne  9789)
--mask_condition MASK_COND <1>:                Voir 4.12.1.
                                                           (ligne  9789)
--mask_condition MASK_COND:                    Voir 4.12.  (ligne  9734)
--mask_value MASK_VAL:                         Voir 4.12.1.
                                                           (ligne  9806)
--mask_variable MASK_VAR:                      Voir 4.12.  (ligne  9734)
--md5_dgs:                                     Voir 3.27.  (ligne  3629)
--md5_digest:                                  Voir 3.27.  (ligne  3629)
--md5_write_attribute:                         Voir 3.27.  (ligne  3629)
--md5_wrt_att:                                 Voir 3.27.  (ligne  3629)
--metadata:                                    Voir 4.7.   (ligne  8784)
--Metadata:                                    Voir 4.7.   (ligne  8747)
--mk_rec_dmn DIM <1>:                          Voir 4.7.   (ligne  8695)
--mk_rec_dmn DIM:                              Voir 4.5.   (ligne  8349)
--mro:                                         Voir 3.18.  (ligne  2755)
--msa:                                         Voir 3.19.  (ligne  2821)
--msa_user_order:                              Voir 3.19.  (ligne  2821)
--msa_usr_rdr:                                 Voir 3.19.  (ligne  2821)
--msk_cmp_typ MASK_COMP:                       Voir 4.12.1.
                                                           (ligne  9789)
--msk_cnd MASK_COND:                           Voir 4.12.  (ligne  9734)
--msk_cnd_sng MASK_COND:                       Voir 4.12.1.
                                                           (ligne  9789)
--msk_nm MASK_VAR:                             Voir 4.12.  (ligne  9734)
--msk_val MASK_VAL:                            Voir 4.12.1.
                                                           (ligne  9806)
--msk_var MASK_VAR:                            Voir 4.12.  (ligne  9734)
--mtd:                                         Voir 4.7.   (ligne  8784)
--Mtd:                                         Voir 4.7.   (ligne  8747)
--netcdf4:                                     Voir 3.9.   (ligne  1914)
--nintap LOOP:                                 Voir 3.5.   (ligne  1512)
--no-blank:                                    Voir 4.7.   (ligne  8794)
--no-coords <1>:                               Voir 3.36.  (ligne  4748)
--no-coords:                                   Voir 3.12.  (ligne  2321)
--no-crd <1>:                                  Voir 3.36.  (ligne  4748)
--no-crd:                                      Voir 3.12.  (ligne  2321)
--no_blank:                                    Voir 4.7.   (ligne  8794)
--no_rec_dmn DIM:                              Voir 4.7.   (ligne  8648)
--no_tmp_fl <1>:                               Voir 3.29.  (ligne  3799)
--no_tmp_fl:                                   Voir 2.3.   (ligne   805)
--noblank:                                     Voir 4.7.   (ligne  8794)
--nsx:                                         Voir 3.11.  (ligne  2164)
--omp_num_threads THR_NBR:                     Voir 3.3.   (ligne  1374)
--op_rlt MASK_COMP:                            Voir 4.12.1.
                                                           (ligne  9789)
--op_typ OP_TYP <1>:                           Voir 4.3.   (ligne  7866)
--op_typ OP_TYP:                               Voir 3.31.  (ligne  3943)
--open_ram <1>:                                Voir 3.29.  (ligne  3729)
--open_ram:                                    Voir 2.3.   (ligne   805)
--operation OP_TYP <1>:                        Voir 4.3.   (ligne  7866)
--operation OP_TYP:                            Voir 3.31.  (ligne  3943)
--output FL_OUT:                               Voir 3.6.   (ligne  1614)
--overwrite <1>:                               Voir 3.33.  (ligne  4604)
--overwrite:                                   Voir 2.3.   (ligne   848)
--ovr <1>:                                     Voir 3.33.  (ligne  4604)
--ovr:                                         Voir 2.3.   (ligne   848)
--pack_policy PCK_PLC:                         Voir 4.8.   (ligne  9087)
--path INPUT-PATH <1>:                         Voir 3.7.   (ligne  1646)
--path INPUT-PATH:                             Voir 3.5.   (ligne  1512)
--pck_map PCK_MAP:                             Voir 4.8.   (ligne  9154)
--pck_plc PCK_PLC:                             Voir 4.8.   (ligne  9087)
--print:                                       Voir 4.7.   (ligne  8802)
--prn:                                         Voir 4.7.   (ligne  8802)
--prn_fnc_tbl:                                 Voir 4.1.25.
                                                           (ligne  7434)
--pseudonym:                                   Voir 1.3.   (ligne   490)
--pth INPUT-PATH <1>:                          Voir 3.7.   (ligne  1646)
--pth INPUT-PATH:                              Voir 3.5.   (ligne  1512)
--quiet:                                       Voir 4.7.   (ligne  8814)
--ram_all:                                     Voir 3.29.  (ligne  3729)
--rcd_nm ULM_NM:                               Voir 4.5.   (ligne  8240)
--rec_apn:                                     Voir 3.17.  (ligne  2710)
--record_append:                               Voir 3.17.  (ligne  2710)
--retain:                                      Voir 3.8.   (ligne  1873)
--revision <1>:                                Voir 3.38.  (ligne  4811)
--revision:                                    Voir 1.6.   (ligne   719)
--rth_dbl:                                     Voir 3.32.2.
                                                           (ligne  4245)
--rth_flt:                                     Voir 3.32.2.
                                                           (ligne  4245)
--rtn:                                         Voir 3.8.   (ligne  1873)
--script:                                      Voir 4.1.   (ligne  4860)
--script-file:                                 Voir 4.1.   (ligne  4860)
--sng_fmt:                                     Voir 4.7.   (ligne  8822)
--spt:                                         Voir 4.1.   (ligne  4860)
--string:                                      Voir 4.7.   (ligne  8822)
--thr_nbr THR_NBR:                             Voir 3.3.   (ligne  1374)
--threads THR_NBR:                             Voir 3.3.   (ligne  1374)
--ulm_nm ULM_NM:                               Voir 4.5.   (ligne  8240)
--union:                                       Voir 3.11.  (ligne  2073)
--units:                                       Voir 4.7.   (ligne  8827)
--unn:                                         Voir 3.11.  (ligne  2073)
--unpack:                                      Voir 4.8.   (ligne  9138)
--upk:                                         Voir 4.8.   (ligne  9138)
--variable VAR <1>:                            Voir 4.7.1. (ligne  8966)
--variable VAR:                                Voir 3.11.  (ligne  2073)
--version <1>:                                 Voir 3.38.  (ligne  4811)
--version:                                     Voir 1.6.   (ligne   719)
--vrs <1>:                                     Voir 3.38.  (ligne  4811)
--vrs:                                         Voir 1.6.   (ligne   719)
--weight WEIGHT:                               Voir 4.12.  (ligne  9734)
--weight WGT1[,WGT2]:                          Voir 4.6.   (ligne  8392)
--wgt_var WEIGHT:                              Voir 4.12.  (ligne  9734)
--wgt_var WGT1[,WGT2]:                         Voir 4.6.   (ligne  8392)
--write_tmp_fl:                                Voir 2.3.   (ligne   805)
--wrt_tmp_fl:                                  Voir 2.3.   (ligne   805)
--xcl <1>:                                     Voir 4.7.1. (ligne  8966)
--xcl:                                         Voir 3.11.  (ligne  2073)
--xml:                                         Voir 4.7.   (ligne  8831)
-3 <1>:                                        Voir 3.9.   (ligne  1914)
-3:                                            Voir 1.5.   (ligne   624)
-4 <1>:                                        Voir 3.9.   (ligne  1914)
-4:                                            Voir 1.5.   (ligne   624)
-5:                                            Voir 4.7.   (ligne  8626)
-A <1>:                                        Voir 4.8.   (ligne  9269)
-A:                                            Voir 4.7.1. (ligne  8972)
-a <1>:                                        Voir 4.7.1. (ligne  8960)
-a:                                            Voir 4.7.   (ligne  8633)
-A <1>:                                        Voir 3.33.  (ligne  4604)
-A:                                            Voir 2.3.   (ligne   848)
-b <1>:                                        Voir 4.7.   (ligne  8643)
-b <2>:                                        Voir 4.3.   (ligne  7967)
-b:                                            Voir 4.1.24.
                                                           (ligne  7277)
-B MASK_COND <1>:                              Voir 4.12.1.
                                                           (ligne  9789)
-B MASK_COND:                                  Voir 4.12.  (ligne  9734)
-C:                                            Voir 4.1.24.
                                                           (ligne  7289)
-c:                                            Voir 3.36.  (ligne  4748)
-C:                                            Voir 3.36.  (ligne  4748)
-c:                                            Voir 3.12.  (ligne  2321)
-C:                                            Voir 3.12.  (ligne  2321)
-D:                                            Voir 1.6.   (ligne   704)
-D DEBUG-LEVEL <1>:                            Voir 3.4.   (ligne  1456)
-D DEBUG-LEVEL <2>:                            Voir 2.8.   (ligne  1180)
-D DEBUG-LEVEL:                                Voir 1.6.   (ligne   719)
-d DIM,[MIN],[MAX],[STRIDE],[DURATION]:        Voir 3.18.  (ligne  2755)
-d DIM,[MIN],[MAX],STRIDE:                     Voir 3.16.  (ligne  2650)
-d DIM,[MIN][,[MAX][,[STRIDE]]] <1>:           Voir 3.22.  (ligne  3120)
-d DIM,[MIN][,[MAX][,[STRIDE]]] <2>:           Voir 3.20.  (ligne  2963)
-d DIM,[MIN][,[MAX][,[STRIDE]]] <3>:           Voir 3.19.  (ligne  2821)
-d DIM,[MIN][,[MAX][,[STRIDE]]]:               Voir 3.15.  (ligne  2547)
-d DIM,[MIN][,[MAX]]:                          Voir 4.12.  (ligne  9726)
-f:                                            Voir 4.1.25.
                                                           (ligne  7434)
-F:                                            Voir 3.14.  (ligne  2512)
-G GPE_DSC:                                    Voir 3.13.  (ligne  2346)
-g GRP:                                        Voir 3.11.  (ligne  2073)
-H:                                            Voir 4.7.   (ligne  8703)
-h:                                            Voir 4.2.   (ligne  7581)
-H:                                            Voir 3.35.  (ligne  4652)
-h:                                            Voir 3.34.  (ligne  4624)
-I:                                            Voir 4.12.  (ligne  9762)
-L:                                            Voir 3.26.  (ligne  3592)
-l OUTPUT-PATH:                                Voir 3.7.   (ligne  1646)
-m:                                            Voir 4.7.   (ligne  8784)
-M <1>:                                        Voir 4.7.   (ligne  8747)
-M <2>:                                        Voir 4.5.   (ligne  8291)
-M:                                            Voir 3.9.   (ligne  1996)
-M CNK_MAP:                                    Voir 3.25.  (ligne  3506)
-m MASK_VAR:                                   Voir 4.12.  (ligne  9734)
-M PCK_MAP:                                    Voir 4.8.   (ligne  9154)
-N:                                            Voir 4.12.2.
                                                           (ligne  9822)
-n LOOP <1>:                                   Voir 3.5.   (ligne  1512)
-n LOOP:                                       Voir 2.7.   (ligne  1050)
-O <1>:                                        Voir 3.33.  (ligne  4604)
-O:                                            Voir 2.3.   (ligne   848)
-o FL_OUT <1>:                                 Voir 3.6.   (ligne  1614)
-o FL_OUT:                                     Voir 2.7.   (ligne  1104)
-P:                                            Voir 4.7.   (ligne  8802)
-p INPUT-PATH <1>:                             Voir 3.7.   (ligne  1707)
-p INPUT-PATH:                                 Voir 3.5.   (ligne  1512)
-P PCK_PLC:                                    Voir 4.8.   (ligne  9087)
-q:                                            Voir 4.7.   (ligne  8814)
-Q:                                            Voir 4.7.   (ligne  8808)
-r:                                            Voir 3.38.  (ligne  4811)
-R:                                            Voir 3.8.   (ligne  1873)
-r:                                            Voir 1.6.   (ligne   704)
-s:                                            Voir 4.7.   (ligne  8822)
-t THR_NBR <1>:                                Voir 3.3.   (ligne  1374)
-t THR_NBR:                                    Voir 2.9.1. (ligne  1265)
-U:                                            Voir 4.8.   (ligne  9138)
-u:                                            Voir 4.7.   (ligne  8827)
-u ULM_NM:                                     Voir 4.5.   (ligne  8240)
-v:                                            Voir 4.8.   (ligne  9269)
-v VAR <1>:                                    Voir 4.7.1. (ligne  8966)
-v VAR:                                        Voir 3.11.  (ligne  2073)
-w WEIGHT:                                     Voir 4.12.  (ligne  9734)
-w WGT1[,WGT2]:                                Voir 4.6.   (ligne  8392)
-x:                                            Voir 4.7.1. (ligne  8966)
-X:                                            Voir 3.21.  (ligne  3011)
-x:                                            Voir 3.11.  (ligne  2073)
-X LON_MIN,LON_MAX,LAT_MIN,LAT_MAX:            Voir 3.21.  (ligne  3011)
-y OP_TYP <1>:                                 Voir 4.3.   (ligne  7866)
-y OP_TYP:                                     Voir 3.31.  (ligne  3943)
.:                                             Voir 4.11.  (ligne  9569)
. (wildcard character):                        Voir 3.11.  (ligne  2239)
.bashrc:                                       Voir 4.7.1. (ligne  8867)
.netrc:                                        Voir 3.7.   (ligne  1646)
.rhosts:                                       Voir 3.7.   (ligne  1646)
/:                                             Voir 4.3.   (ligne  7866)
/ (division):                                  Voir 4.1.25.
                                                           (ligne  7299)
/*...*/ (comment):                             Voir 4.1.1. (ligne  4923)
// (comment):                                  Voir 4.1.1. (ligne  4923)
0 (NUL):                                       Voir 4.2.   (ligne  7770)
32-bit offset file format:                     Voir 3.9.   (ligne  1996)
64-bit offset file format:                     Voir 3.9.   (ligne  1996)
64BIT files:                                   Voir 3.9.   (ligne  1914)
: (separator character):                       Voir 3.13.  (ligne  2386)
; (end of statement):                          Voir 4.1.1. (ligne  4920)
<arpa/nameser.h>:                              Voir 1.2.1. (ligne   459)
<resolv.h>:                                    Voir 1.2.1. (ligne   459)
? (filename expansion):                        Voir 3.11.  (ligne  2239)
? (question mark):                             Voir 4.2.   (ligne  7761)
? (wildcard character):                        Voir 3.11.  (ligne  2248)
@ (attribute):                                 Voir 4.1.1. (ligne  4933)
@ (separator character):                       Voir 3.13.  (ligne  2386)
[] (array delimiters):                         Voir 4.1.1. (ligne  4911)
\ (backslash):                                 Voir 4.2.   (ligne  7761)
\" (protected double quote):                   Voir 4.2.   (ligne  7761)
\' (protected end quote):                      Voir 4.2.   (ligne  7761)
\? (protected question mark):                  Voir 4.2.   (ligne  7761)
\\ (ASCII \, backslash):                       Voir 4.2.   (ligne  7756)
\\ (protected backslash):                      Voir 4.2.   (ligne  7761)
\a (ASCII BEL, bell):                          Voir 4.2.   (ligne  7756)
\b (ASCII BS, backspace):                      Voir 4.2.   (ligne  7756)
\f (ASCII FF, formfeed):                       Voir 4.2.   (ligne  7756)
\n (ASCII LF, linefeed):                       Voir 4.2.   (ligne  7746)
\n (linefeed):                                 Voir 4.7.1. (ligne  8977)
\r (ASCII CR, carriage return):                Voir 4.2.   (ligne  7756)
\t (ASCII HT, horizontal tab):                 Voir 4.2.   (ligne  7746)
\t (horizontal tab):                           Voir 4.7.1. (ligne  8977)
\v (ASCII VT, vertical tab):                   Voir 4.2.   (ligne  7756)
^ (power):                                     Voir 4.1.25.
                                                           (ligne  7299)
^ (wildcard character):                        Voir 3.11.  (ligne  2239)
_FillValue <1>:                                Voir 4.11.  (ligne  9658)
_FillValue:                                    Voir 4.8.   (ligne  9198)
_FILLVALUE:                                    Voir 4.8.   (ligne  9198)
_FillValue <1>:                                Voir 4.6.   (ligne  8442)
_FillValue <2>:                                Voir 4.2.   (ligne  7586)
_FillValue <3>:                                Voir 3.30.  (ligne  3806)
_FillValue:                                    Voir 3.24.  (ligne  3329)
`NCO User Guide':                              Voir 1.1.   (ligne   304)
`User Guide':                                  Voir 1.1.   (ligne   304)
ABS:                                           Voir 4.1.25.
                                                           (ligne  7302)
absolute value:                                Voir 4.1.25.
                                                           (ligne  7302)
ACOS:                                          Voir 4.1.25.
                                                           (ligne  7302)
ACOSH:                                         Voir 4.1.25.
                                                           (ligne  7302)
add:                                           Voir 4.3.   (ligne  7866)
add_offset <1>:                                Voir 4.10.  (ligne  9506)
add_offset <2>:                                Voir 4.8.   (ligne  9080)
add_offset <3>:                                Voir 4.5.   (ligne  8312)
add_offset:                                    Voir 3.30.  (ligne  3806)
ADD_OFFSET:                                    Voir 2.10.  (ligne  1319)
adding data <1>:                               Voir 4.6.   (ligne  8365)
adding data:                                   Voir 4.3.   (ligne  7848)
addition <1>:                                  Voir 4.6.   (ligne  8365)
addition <2>:                                  Voir 4.3.   (ligne  7848)
addition:                                      Voir 4.1.25.
                                                           (ligne  7299)
Alexander Hansen:                              Voir 5.1.   (ligne 10064)
alias <1>:                                     Voir 4.8.   (ligne  9148)
alias <2>:                                     Voir 4.7.1. (ligne  8867)
alias:                                         Voir 4.3.   (ligne  7910)
all:                                           Voir 3.25.  (ligne  3474)
alphabetization:                               Voir 4.7.   (ligne  8633)
alphabetize output:                            Voir 4.7.1. (ligne  8960)
alternate invocations:                         Voir 4.3.   (ligne  7866)
anchor:                                        Voir 3.11.  (ligne  2115)
anchoring:                                     Voir 3.11.  (ligne  2115)
Andrea Cimatoribus:                            Voir 5.1.   (ligne 10073)
Andrew Wittenberg:                             Voir 5.1.   (ligne 10049)
annual average:                                Voir 7.3.   (ligne 10415)
annual average from daily data:                Voir 6.1.   (ligne 10133)
annual average from monthly data:              Voir 6.2.   (ligne 10156)
anomalies <1>:                                 Voir 7.4.   (ligne 10650)
anomalies <2>:                                 Voir 7.3.   (ligne 10415)
anomalies:                                     Voir 4.3.   (ligne  7951)
ANSI:                                          Voir 1.2.   (ligne   374)
ANSI C:                                        Voir 4.1.25.
                                                           (ligne  7433)
appending data <1>:                            Voir 4.7.   (ligne  8573)
appending data:                                Voir 4.1.24.
                                                           (ligne  7277)
appending to files <1>:                        Voir 4.7.1. (ligne  8972)
appending to files <2>:                        Voir 3.33.  (ligne  4604)
appending to files:                            Voir 2.3.   (ligne   848)
appending variables <1>:                       Voir 4.8.   (ligne  9269)
appending variables <2>:                       Voir 4.1.   (ligne  4875)
appending variables <3>:                       Voir 2.4.   (ligne   873)
appending variables:                           Voir 2.3.   (ligne   848)
AR4:                                           Voir 4.4.   (ligne  8144)
arccosine function:                            Voir 4.1.25.
                                                           (ligne  7302)
arcsine function:                              Voir 4.1.25.
                                                           (ligne  7302)
arctangent function:                           Voir 4.1.25.
                                                           (ligne  7302)
area:                                          Voir 3.36.  (ligne  4683)
area-averaging:                                Voir 7.3.   (ligne 10415)
arithmetic operators <1>:                      Voir 4.12.  (ligne  9726)
arithmetic operators:                          Voir 3.24.  (ligne  3335)
arithmetic processor:                          Voir 4.1.   (ligne  4842)
ARM conventions <1>:                           Voir 4.10.  (ligne  9519)
ARM conventions:                               Voir 3.37.  (ligne  4780)
array:                                         Voir 4.1.5. (ligne  5216)
array function:                                Voir 4.1.5. (ligne  5216)
array indexing:                                Voir 4.1.1. (ligne  4914)
array storage:                                 Voir 4.1.1. (ligne  4917)
array syntax:                                  Voir 4.1.1. (ligne  4911)
arrays:                                        Voir 4.1.5. (ligne  5216)
arrival value:                                 Voir 4.6.   (ligne  8406)
ASCII:                                         Voir 4.2.   (ligne  7744)
ASIN:                                          Voir 4.1.25.
                                                           (ligne  7302)
ASINH:                                         Voir 4.1.25.
                                                           (ligne  7302)
asort:                                         Voir 4.1.16.
                                                           (ligne  5978)
assignment statement:                          Voir 4.1.1. (ligne  4920)
asynchronous file access:                      Voir 3.7.   (ligne  1646)
ATAN:                                          Voir 4.1.25.
                                                           (ligne  7302)
ATANH:                                         Voir 4.1.25.
                                                           (ligne  7302)
attribute inheritance:                         Voir 4.1.6. (ligne  5380)
attribute names <1>:                           Voir 4.11.  (ligne  9560)
attribute names:                               Voir 4.2.   (ligne  7563)
attribute propagation:                         Voir 4.1.6. (ligne  5380)
attribute syntax:                              Voir 4.1.1. (ligne  4933)
attribute, units:                              Voir 3.22.  (ligne  3120)
attributes:                                    Voir 4.2.   (ligne  7563)
attributes, appending:                         Voir 4.2.   (ligne  7646)
attributes, creating:                          Voir 4.2.   (ligne  7646)
attributes, deleting:                          Voir 4.2.   (ligne  7646)
attributes, editing <1>:                       Voir 7.5.   (ligne 10766)
attributes, editing <2>:                       Voir 7.3.   (ligne 10415)
attributes, editing:                           Voir 4.2.   (ligne  7646)
attributes, global <1>:                        Voir 4.11.  (ligne  9605)
attributes, global <2>:                        Voir 4.7.1. (ligne  8966)
attributes, global <3>:                        Voir 4.7.   (ligne  8559)
attributes, global <4>:                        Voir 4.2.   (ligne  7630)
attributes, global <5>:                        Voir 3.37.  (ligne  4804)
attributes, global <6>:                        Voir 3.35.  (ligne  4652)
attributes, global <7>:                        Voir 3.34.  (ligne  4624)
attributes, global:                            Voir 2.7.   (ligne  1078)
attributes, modifying <1>:                     Voir 7.5.   (ligne 10766)
attributes, modifying <2>:                     Voir 7.3.   (ligne 10415)
attributes, modifying:                         Voir 4.2.   (ligne  7646)
attributes, overwriting <1>:                   Voir 7.5.   (ligne 10766)
attributes, overwriting <2>:                   Voir 7.3.   (ligne 10415)
attributes, overwriting:                       Voir 4.2.   (ligne  7646)
attributesncap2:                               Voir 4.1.6. (ligne  5347)
autoconf:                                      Voir 1.6.   (ligne   727)
automagic <1>:                                 Voir 2.7.   (ligne  1051)
automagic:                                     Voir 1.2.   (ligne   413)
automatic type conversion <1>:                 Voir 4.1.25.
                                                           (ligne  7392)
automatic type conversion:                     Voir 3.32.  (ligne  4105)
auxiliary coordinates:                         Voir 3.36.  (ligne  4748)
average <1>:                                   Voir 7.4.   (ligne 10650)
average <2>:                                   Voir 7.3.   (ligne 10415)
average <3>:                                   Voir 7.2.   (ligne 10331)
average <4>:                                   Voir 6.3.   (ligne 10182)
average <5>:                                   Voir 6.2.   (ligne 10153)
average <6>:                                   Voir 6.1.   (ligne 10119)
average <7>:                                   Voir 4.12.  (ligne  9771)
average:                                       Voir 3.31.  (ligne  3943)
averaging data <1>:                            Voir 4.12.  (ligne  9695)
averaging data <2>:                            Voir 4.9.   (ligne  9409)
averaging data <3>:                            Voir 4.4.   (ligne  8118)
averaging data:                                Voir 3.24.  (ligne  3329)
avg:                                           Voir 3.31.  (ligne  3943)
avg():                                         Voir 4.1.11.
                                                           (ligne  5665)
avgsqr:                                        Voir 3.31.  (ligne  3943)
Barry deFreese:                                Voir 5.1.   (ligne 10052)
base_time:                                     Voir 3.37.  (ligne  4780)
bash <1>:                                      Voir 4.7.1. (ligne  8867)
bash:                                          Voir 3.11.  (ligne  2296)
Bash shell:                                    Voir 4.7.1. (ligne  8867)
Bash Shell:                                    Voir 4.3.   (ligne  8067)
Bash shell:                                    Voir 4.3.   (ligne  7895)
batch mode:                                    Voir 3.33.  (ligne  4604)
beer:                                          Voir 3.32.2.
                                                           (ligne  4502)
benchmarks:                                    Voir 3.3.   (ligne  1433)
Bessel function:                               Voir 4.1.19.
                                                           (ligne  6470)
bilinear interpolation:                        Voir 7.5.   (ligne 10766)
Bill Kocik:                                    Voir 5.1.   (ligne 10028)
binary format:                                 Voir 4.7.   (ligne  8643)
binary operations <1>:                         Voir 4.3.   (ligne  7848)
binary operations:                             Voir 2.9.2. (ligne  1271)
binary Operators:                              Voir 4.1.2. (ligne  5025)
blank:                                         Voir 4.7.   (ligne  8794)
bounds:                                        Voir 3.36.  (ligne  4737)
bounds convention:                             Voir 3.36.  (ligne  4737)
Bourne Shell <1>:                              Voir 4.3.   (ligne  8067)
Bourne Shell:                                  Voir 3.16.  (ligne  2687)
Brian Mays:                                    Voir 5.1.   (ligne 10022)
broadcasting:                                  Voir 4.3.   (ligne  8005)
broadcasting groups <1>:                       Voir 4.3.   (ligne  7984)
broadcasting groups:                           Voir 1.5.   (ligne   618)
broadcasting variables <1>:                    Voir 4.12.  (ligne  9695)
broadcasting variables <2>:                    Voir 4.6.   (ligne  8510)
broadcasting variables <3>:                    Voir 4.3.   (ligne  7938)
broadcasting variables:                        Voir 4.1.2. (ligne  4971)
BSD:                                           Voir 3.4.   (ligne  1453)
Buffer sizes:                                  Voir 3.28.  (ligne  3699)
buffering:                                     Voir 2.10.  (ligne  1309)
bugs, reporting:                               Voir 1.6.   (ligne   664)
byte():                                        Voir 4.1.11.
                                                           (ligne  5777)
C index convention:                            Voir 3.14.  (ligne  2512)
C language <1>:                                Voir 4.7.   (ligne  8822)
C language <2>:                                Voir 4.2.   (ligne  7770)
C language <3>:                                Voir 4.1.2. (ligne  4940)
C language <4>:                                Voir 4.1.1. (ligne  4907)
C language <5>:                                Voir 3.32.2.
                                                           (ligne  4257)
C language <6>:                                Voir 3.32.1.
                                                           (ligne  4238)
C language <7>:                                Voir 3.24.  (ligne  3377)
C language:                                    Voir 1.2.   (ligne   395)
C Shell <1>:                                   Voir 4.3.   (ligne  8067)
C Shell:                                       Voir 3.16.  (ligne  2687)
C++:                                           Voir 1.2.   (ligne   365)
c++:                                           Voir 1.2.   (ligne   360)
C89:                                           Voir 1.2.   (ligne   374)
C99 <1>:                                       Voir 1.2.1. (ligne   447)
C99:                                           Voir 1.2.   (ligne   378)
C_FORMAT:                                      Voir 2.10.  (ligne  1319)
CAM3:                                          Voir 3.32.2.
                                                           (ligne  4398)
cc:                                            Voir 1.2.   (ligne   360)
CC:                                            Voir 1.2.   (ligne   360)
CCM Processor <1>:                             Voir 4.10.  (ligne  9525)
CCM Processor <2>:                             Voir 4.9.   (ligne  9448)
CCM Processor:                                 Voir 3.5.   (ligne  1512)
CCSM <1>:                                      Voir 9.     (ligne 11146)
CCSM:                                          Voir 5.2.   (ligne 10088)
CCSM conventions:                              Voir 3.36.  (ligne  4683)
CDL:                                           Voir 4.7.   (ligne  8663)
CEIL:                                          Voir 4.1.25.
                                                           (ligne  7302)
ceiling function:                              Voir 4.1.25.
                                                           (ligne  7302)
cell-based grids:                              Voir 3.21.  (ligne  3028)
CF conventions <1>:                            Voir 4.3.   (ligne  7976)
CF conventions <2>:                            Voir 3.36.  (ligne  4683)
CF conventions <3>:                            Voir 3.22.  (ligne  3217)
CF conventions <4>:                            Voir 3.21.  (ligne  3011)
CF conventions:                                Voir 3.12.  (ligne  2337)
change_miss():                                 Voir 4.1.10.
                                                           (ligne  5599)
char():                                        Voir 4.1.11.
                                                           (ligne  5780)
characters, special:                           Voir 4.2.   (ligne  7746)
Charlie Zender <1>:                            Voir 5.1.   (ligne  9994)
Charlie Zender:
          Voir ``Foreword''.                               (ligne   203)
chocolate:                                     Voir 5.     (ligne  9973)
chunking <1>:                                  Voir 4.7.   (ligne  8787)
chunking <2>:                                  Voir 3.25.  (ligne  3444)
chunking:                                      Voir 1.5.   (ligne   607)
chunking map:                                  Voir 3.25.  (ligne  3459)
chunking policy:                               Voir 3.25.  (ligne  3459)
chunksize:                                     Voir 3.25.  (ligne  3459)
clang:                                         Voir 1.2.   (ligne   360)
CLASSIC files:                                 Voir 3.9.   (ligne  1914)
client-server:                                 Voir 3.7.1. (ligne  1759)
Climate and Forecast Metadata Convention:      Voir 3.22.  (ligne  3217)
climate model <1>:                             Voir 4.12.2.
                                                           (ligne  9886)
climate model <2>:                             Voir 4.5.   (ligne  8302)
climate model <3>:                             Voir 3.5.   (ligne  1575)
climate model <4>:                             Voir 2.6.1. (ligne   973)
climate model <5>:                             Voir 2.2.   (ligne   772)
climate model:                                 Voir 2.1.   (ligne   746)
clipping operators:                            Voir 4.1.2. (ligne  5097)
CMIP <1>:                                      Voir 4.5.   (ligne  8246)
CMIP:                                          Voir 4.4.   (ligne  8144)
CMIP5:                                         Voir 7.     (ligne 10208)
cnk_all:                                       Voir 3.25.  (ligne  3474)
cnk_dmn:                                       Voir 3.25.  (ligne  3512)
cnk_g2d:                                       Voir 3.25.  (ligne  3474)
cnk_g3d:                                       Voir 3.25.  (ligne  3474)
CNK_MAP:                                       Voir 3.25.  (ligne  3506)
cnk_prd:                                       Voir 3.25.  (ligne  3512)
cnk_rd1:                                       Voir 3.25.  (ligne  3512)
cnk_scl:                                       Voir 3.25.  (ligne  3512)
cnk_xpl:                                       Voir 3.25.  (ligne  3474)
Comeau:                                        Voir 1.2.   (ligne   344)
command line options:                          Voir 3.4.   (ligne  1438)
command line switches <1>:                     Voir 4.     (ligne  4832)
command line switches <2>:                     Voir 3.6.   (ligne  1614)
command line switches <3>:                     Voir 3.     (ligne  1337)
command line switches:                         Voir 2.1.   (ligne   762)
comments:                                      Voir 4.1.1. (ligne  4923)
como:                                          Voir 1.2.   (ligne   360)
Compaq:                                        Voir 1.2.   (ligne   344)
comparator:                                    Voir 4.12.1.
                                                           (ligne  9797)
compatability:                                 Voir 1.2.   (ligne   344)
compilers:                                     Voir 3.6.   (ligne  1636)
complementary error function:                  Voir 4.1.25.
                                                           (ligne  7302)
compression <1>:                               Voir 4.7.   (ligne  8787)
compression:                                   Voir 3.26.  (ligne  3592)
concatenation <1>:                             Voir 4.10.  (ligne  9478)
concatenation <2>:                             Voir 4.8.   (ligne  9228)
concatenation <3>:                             Voir 4.5.   (ligne  8206)
concatenation:                                 Voir 2.4.   (ligne   873)
conditional Operator:                          Voir 4.1.2. (ligne  5087)
config.guess:                                  Voir 1.6.   (ligne   727)
configure.eg:                                  Voir 1.6.   (ligne   727)
constraint expressions:                        Voir 3.7.1. (ligne  1864)
contributing:                                  Voir 5.     (ligne  9961)
contributors:                                  Voir 5.1.   (ligne  9993)
coordinate limits:                             Voir 3.15.  (ligne  2547)
coordinate variable <1>:                       Voir 4.12.  (ligne  9762)
coordinate variable <2>:                       Voir 4.3.   (ligne  7973)
coordinate variable <3>:                       Voir 3.36.  (ligne  4748)
coordinate variable <4>:                       Voir 3.31.  (ligne  3975)
coordinate variable:                           Voir 3.22.  (ligne  3155)
coordinate variables:                          Voir 4.11.  (ligne  9652)
coordinates <1>:                               Voir 7.6.   (ligne 10905)
coordinates <2>:                               Voir 3.36.  (ligne  4748)
coordinates:                                   Voir 3.21.  (ligne  3011)
coordinates convention:                        Voir 3.36.  (ligne  4748)
coordinates, modifying:                        Voir 7.7.   (ligne 10953)
core dump <1>:                                 Voir 4.7.1. (ligne  9021)
core dump <2>:                                 Voir 2.8.   (ligne  1170)
core dump:                                     Voir 1.6.   (ligne   664)
COS:                                           Voir 4.1.25.
                                                           (ligne  7302)
COSH:                                          Voir 4.1.25.
                                                           (ligne  7302)
cosine function:                               Voir 4.1.25.
                                                           (ligne  7302)
covariance:                                    Voir 4.1.24.
                                                           (ligne  7226)
cp:                                            Voir 3.11.  (ligne  2115)
Cray <1>:                                      Voir 2.8.   (ligne  1166)
Cray:                                          Voir 1.2.   (ligne   344)
csh:                                           Voir 3.11.  (ligne  2296)
Csh shell:                                     Voir 4.7.1. (ligne  8867)
cxx:                                           Voir 1.2.   (ligne   360)
Cygwin <1>:                                    Voir 1.3.   (ligne   490)
Cygwin:                                        Voir 1.2.1. (ligne   455)
daily data <1>:                                Voir 6.4.   (ligne 10200)
daily data <2>:                                Voir 6.3.   (ligne 10182)
daily data:                                    Voir 6.1.   (ligne 10116)
Daniel Baumann:                                Voir 5.1.   (ligne 10052)
Daniel Wang:                                   Voir 5.1.   (ligne 10010)
DAP:                                           Voir 3.7.1. (ligne  1759)
data access protocol:                          Voir 3.7.1. (ligne  1759)
data safety <1>:                               Voir 4.11.  (ligne  9581)
data safety:                                   Voir 2.3.   (ligne   783)
data, missing <1>:                             Voir 4.2.   (ligne  7586)
data, missing:                                 Voir 3.24.  (ligne  3329)
date:                                          Voir 3.36.  (ligne  4683)
datesec:                                       Voir 3.36.  (ligne  4683)
DBG_LVL <1>:                                   Voir 3.3.   (ligne  1422)
DBG_LVL <2>:                                   Voir 2.8.   (ligne  1180)
DBG_LVL:                                       Voir 1.6.   (ligne   719)
DDRA:                                          Voir 5.2.   (ligne 10088)
Debian:                                        Voir 1.5.   (ligne   631)
DEBUG-LEVEL <1>:                               Voir 2.8.   (ligne  1180)
DEBUG-LEVEL:                                   Voir 1.6.   (ligne   719)
debugging <1>:                                 Voir 3.3.   (ligne  1422)
debugging <2>:                                 Voir 2.8.   (ligne  1180)
debugging:                                     Voir 1.6.   (ligne   704)
DEC:                                           Voir 1.2.   (ligne   344)
defdim():                                      Voir 4.1.3. (ligne  5119)
defining dimensions in ncap2:                  Voir 4.1.3. (ligne  5119)
deflation <1>:                                 Voir 4.7.   (ligne  8787)
deflation <2>:                                 Voir 3.26.  (ligne  3592)
deflation:                                     Voir 1.5.   (ligne   603)
degenerate dimension <1>:                      Voir 4.12.2.
                                                           (ligne  9877)
degenerate dimension <2>:                      Voir 4.12.  (ligne  9715)
degenerate dimension <3>:                      Voir 4.9.   (ligne  9422)
degenerate dimension <4>:                      Voir 4.8.   (ligne  9379)
degenerate dimension <5>:                      Voir 4.6.   (ligne  8420)
degenerate dimension <6>:                      Voir 4.5.   (ligne  8354)
degenerate dimension <7>:                      Voir 4.3.   (ligne  7967)
degenerate dimension <8>:                      Voir 4.1.24.
                                                           (ligne  7277)
degenerate dimension <9>:                      Voir 3.31.  (ligne  4020)
degenerate dimension:                          Voir 3.25.  (ligne  3506)
delete_miss():                                 Voir 4.1.10.
                                                           (ligne  5611)
deletion (groups):                             Voir 3.13.  (ligne  2386)
demotion:                                      Voir 3.32.  (ligne  4105)
Dennis Heimbigner:                             Voir 5.1.   (ligne  9985)
derived fields:                                Voir 4.1.   (ligne  4867)
digest:                                        Voir 3.27.  (ligne  3629)
Digital:                                       Voir 1.2.   (ligne   344)
dimension limits:                              Voir 3.15.  (ligne  2547)
dimension names:                               Voir 4.11.  (ligne  9560)
dimension order:                               Voir 7.3.   (ligne 10415)
dimensions, growing:                           Voir 4.1.24.
                                                           (ligne  7196)
disjoint files:                                Voir 2.4.   (ligne   896)
diskless files:                                Voir 3.29.  (ligne  3729)
Distributed Data Reduction & Analysis:         Voir 5.2.   (ligne 10088)
Distributed Oceanographic Data System:         Voir 3.7.1. (ligne  1759)
divide:                                        Voir 4.3.   (ligne  7866)
dividing data:                                 Voir 4.3.   (ligne  7848)
division:                                      Voir 4.1.25.
                                                           (ligne  7299)
dmn:                                           Voir 3.25.  (ligne  3512)
documentation:                                 Voir 1.1.   (ligne   304)
DODS <1>:                                      Voir 3.8.   (ligne  1902)
DODS:                                          Voir 3.7.1. (ligne  1759)
DODS_ROOT:                                     Voir 3.7.1. (ligne  1759)
dot product:                                   Voir 4.12.2.
                                                           (ligne  9822)
double():                                      Voir 4.1.11.
                                                           (ligne  5793)
double-precision:                              Voir 4.1.25.
                                                           (ligne  7433)
dsort:                                         Voir 4.1.16.
                                                           (ligne  5978)
duration:                                      Voir 3.18.  (ligne  2755)
dynamic linking:                               Voir 1.4.   (ligne   516)
Ed Hartnett:                                   Voir 5.1.   (ligne  9985)
Ed Hill:                                       Voir 5.1.   (ligne 10055)
eddy covariance:                               Voir 4.1.24.
                                                           (ligne  7235)
editing attributes:                            Voir 4.2.   (ligne  7563)
egrep:                                         Voir 3.11.  (ligne  2216)
Elliptic integrals:                            Voir 4.1.19.
                                                           (ligne  6490)
ensemble <1>:                                  Voir 4.4.   (ligne  8132)
ensemble:                                      Voir 2.6.1. (ligne   973)
ensemble average:                              Voir 4.4.   (ligne  8118)
ensemble concatenation:                        Voir 4.5.   (ligne  8206)
ERF:                                           Voir 4.1.25.
                                                           (ligne  7302)
ERFC:                                          Voir 4.1.25.
                                                           (ligne  7302)
Eric Blake:                                    Voir 5.1.   (ligne 10067)
error function:                                Voir 4.1.25.
                                                           (ligne  7302)
error tolerance:                               Voir 2.3.   (ligne   783)
Etienne Tourigny:                              Voir 5.1.   (ligne 10076)
exclusion <1>:                                 Voir 4.7.1. (ligne  8966)
exclusion:                                     Voir 3.11.  (ligne  2073)
execution time <1>:                            Voir 4.11.  (ligne  9596)
execution time <2>:                            Voir 3.24.  (ligne  3387)
execution time <3>:                            Voir 3.2.   (ligne  1353)
execution time <4>:                            Voir 2.10.  (ligne  1310)
execution time <5>:                            Voir 2.3.   (ligne   799)
execution time:                                Voir 1.4.   (ligne   517)
EXP:                                           Voir 4.1.25.
                                                           (ligne  7302)
exponent:                                      Voir 3.32.2.
                                                           (ligne  4480)
exponentiation:                                Voir 4.1.25.
                                                           (ligne  7299)
exponentiation function:                       Voir 4.1.25.
                                                           (ligne  7302)
expressions:                                   Voir 4.1.1. (ligne  4936)
extended regular expressions <1>:              Voir 4.2.   (ligne  7607)
extended regular expressions <2>:              Voir 4.1.24.
                                                           (ligne  7254)
extended regular expressions <3>:              Voir 3.11.  (ligne  2216)
extended regular expressions:                  Voir 2.7.   (ligne  1086)
extraction <1>:                                Voir 4.7.1. (ligne  8966)
extraction:                                    Voir 3.11.  (ligne  2073)
f90:                                           Voir 1.2.1. (ligne   455)
features, requesting:                          Voir 1.6.   (ligne   664)
File buffers:                                  Voir 3.28.  (ligne  3699)
file combination:                              Voir 7.1.   (ligne 10231)
file deletion:                                 Voir 3.8.   (ligne  1873)
file multiplication:                           Voir 4.6.   (ligne  8499)
file removal:                                  Voir 3.8.   (ligne  1873)
file retention:                                Voir 3.8.   (ligne  1873)
files, multiple:                               Voir 3.5.   (ligne  1546)
files, numerous input:                         Voir 2.7.   (ligne  1050)
Filipe Fernandes:                              Voir 5.1.   (ligne 10061)
filters:                                       Voir 4.7.1. (ligne  8867)
findgen-equivalent:                            Voir 4.1.5. (ligne  5216)
fix record dimension <1>:                      Voir 4.7.   (ligne  8695)
fix record dimension:                          Voir 4.5.   (ligne  8250)
fixed dimension <1>:                           Voir 4.7.   (ligne  8648)
fixed dimension:                               Voir 4.5.   (ligne  8250)
flags:                                         Voir 4.1.24.
                                                           (ligne  7211)
flattening (groups):                           Voir 3.13.  (ligne  2386)
float:                                         Voir 4.1.25.
                                                           (ligne  7433)
float():                                       Voir 4.1.11.
                                                           (ligne  5789)
FLOOR:                                         Voir 4.1.25.
                                                           (ligne  7302)
floor function:                                Voir 4.1.25.
                                                           (ligne  7302)
flt_byt:                                       Voir 4.8.   (ligne  9160)
flt_sht:                                       Voir 4.8.   (ligne  9160)
for():                                         Voir 4.1.14.
                                                           (ligne  5923)
force append:                                  Voir 3.33.  (ligne  4604)
force overwrite:                               Voir 3.33.  (ligne  4604)
foreword:
          Voir ``Foreword''.                               (ligne   203)
Fortran <1>:                                   Voir 4.10.  (ligne  9533)
Fortran <2>:                                   Voir 4.9.   (ligne  9456)
Fortran:                                       Voir 3.32.2.
                                                           (ligne  4257)
Fortran index convention:                      Voir 3.14.  (ligne  2512)
FORTRAN_FORMAT:                                Voir 2.10.  (ligne  1319)
Francesco Lovergine:                           Voir 5.1.   (ligne 10052)
FTP:                                           Voir 3.8.   (ligne  1891)
ftp <1>:                                       Voir 3.7.   (ligne  1646)
ftp:                                           Voir 1.2.1. (ligne   461)
funding:                                       Voir 5.2.   (ligne 10088)
g++ <1>:                                       Voir 1.2.1. (ligne   474)
g++:                                           Voir 1.2.   (ligne   360)
g2d:                                           Voir 3.25.  (ligne  3474)
g3d:                                           Voir 3.25.  (ligne  3474)
GAG:                                           Voir 4.5.   (ligne  8258)
GAMMA <1>:                                     Voir 4.1.25.
                                                           (ligne  7302)
GAMMA:                                         Voir 1.2.   (ligne   411)
gamma function <1>:                            Voir 4.1.25.
                                                           (ligne  7302)
gamma function:                                Voir 4.1.19.
                                                           (ligne  6456)
Gary Strand:                                   Voir 5.1.   (ligne 10049)
Gaussian weights:                              Voir 4.12.2.
                                                           (ligne  9886)
Gavin Burris:                                  Voir 5.1.   (ligne 10070)
Gayathri Venkitachalam:                        Voir 5.1.   (ligne 10043)
gcc <1>:                                       Voir 1.2.1. (ligne   474)
gcc:                                           Voir 1.2.   (ligne   360)
GCM <1>:                                       Voir 3.32.2.
                                                           (ligne  4398)
GCM:                                           Voir 2.2.   (ligne   772)
geographical weight:                           Voir 7.4.   (ligne 10650)
George Shapavalov:                             Voir 5.1.   (ligne 10058)
George Shapovalov:                             Voir 5.1.   (ligne 10025)
get_miss():                                    Voir 4.1.10.
                                                           (ligne  5605)
gethostname:                                   Voir 1.2.1. (ligne   459)
getopt:                                        Voir 3.4.   (ligne  1453)
getopt.h:                                      Voir 3.4.   (ligne  1453)
getopt_long:                                   Voir 3.4.   (ligne  1453)
getuid:                                        Voir 1.2.1. (ligne   459)
Glenn Davis:                                   Voir 5.1.   (ligne  9985)
global attribute <1>:                          Voir 4.11.  (ligne  9605)
global attribute:                              Voir 4.2.   (ligne  7811)
global attributes <1>:                         Voir 4.11.  (ligne  9605)
global attributes <2>:                         Voir 4.7.1. (ligne  8966)
global attributes <3>:                         Voir 4.7.   (ligne  8559)
global attributes <4>:                         Voir 4.2.   (ligne  7630)
global attributes <5>:                         Voir 3.37.  (ligne  4804)
global attributes <6>:                         Voir 3.35.  (ligne  4652)
global attributes <7>:                         Voir 3.34.  (ligne  4624)
global attributes:                             Voir 2.7.   (ligne  1078)
globbing <1>:                                  Voir 4.10.  (ligne  9525)
globbing <2>:                                  Voir 4.9.   (ligne  9448)
globbing <3>:                                  Voir 4.3.   (ligne  7896)
globbing <4>:                                  Voir 4.1.24.
                                                           (ligne  7254)
globbing <5>:                                  Voir 3.11.  (ligne  2296)
globbing <6>:                                  Voir 3.5.   (ligne  1512)
globbing:                                      Voir 2.7.   (ligne  1086)
GNU <1>:                                       Voir 3.11.  (ligne  2216)
GNU:                                           Voir 3.4.   (ligne  1439)
gnu-win32:                                     Voir 1.2.1. (ligne   455)
GNU/Linux:                                     Voir 2.8.   (ligne  1170)
GNUmakefile:                                   Voir 1.2.1. (ligne   455)
God:                                           Voir 3.22.  (ligne  3265)
grep -E:                                       Voir 3.11.  (ligne  2216)
group aggregation:                             Voir 4.5.   (ligne  8258)
group names:                                   Voir 4.11.  (ligne  9560)
group path:                                    Voir 3.13.  (ligne  2366)
groups <1>:                                    Voir 7.8.   (ligne 10999)
groups:                                        Voir 1.5.   (ligne   612)
groups, moving:                                Voir 3.13.  (ligne  2474)
groups, renaming:                              Voir 3.13.  (ligne  2474)
growing dimensions:                            Voir 4.1.24.
                                                           (ligne  7196)
GSL <1>:                                       Voir 4.1.20.
                                                           (ligne  6832)
GSL <2>:                                       Voir 4.1.19.
                                                           (ligne  6441)
GSL:                                           Voir 1.2.   (ligne   404)
GSL_SF_BESSEL_JN:                              Voir 4.1.19.
                                                           (ligne  6470)
GSL_SF_GAMMA:                                  Voir 4.1.19.
                                                           (ligne  6456)
gsl_sf_legendre_Pl:                            Voir 4.1.19.
                                                           (ligne  6526)
gw <1>:                                        Voir 4.12.2.
                                                           (ligne  9886)
gw:                                            Voir 3.36.  (ligne  4683)
Harry Mangalam:                                Voir 5.1.   (ligne 10013)
hash:                                          Voir 3.27.  (ligne  3629)
HDF <1>:                                       Voir 5.2.   (ligne 10095)
HDF <2>:                                       Voir 4.7.   (ligne  8663)
HDF <3>:                                       Voir 3.9.   (ligne  1914)
HDF:                                           Voir 1.5.   (ligne   551)
HDF unpacking:                                 Voir 3.30.  (ligne  3874)
HDF5:                                          Voir 1.5.   (ligne   573)
hdp:                                           Voir 4.7.   (ligne  8663)
help:                                          Voir 1.6.   (ligne   664)
Henry Butowsky:                                Voir 5.1.   (ligne  9999)
hgh_byt:                                       Voir 4.8.   (ligne  9160)
hgh_sht:                                       Voir 4.8.   (ligne  9160)
hierarchical data:                             Voir 7.8.   (ligne 10999)
Hierarchical Data Format:                      Voir 1.5.   (ligne   551)
history <1>:                                   Voir 4.7.1. (ligne  8958)
history <2>:                                   Voir 4.2.   (ligne  7581)
history <3>:                                   Voir 3.37.  (ligne  4804)
history <4>:                                   Voir 3.34.  (ligne  4624)
history <5>:                                   Voir 3.7.   (ligne  1646)
history:                                       Voir 2.7.   (ligne  1131)
HP:                                            Voir 1.2.   (ligne   344)
HPSS:                                          Voir 3.7.   (ligne  1683)
hsi:                                           Voir 3.7.   (ligne  1683)
HTML:                                          Voir 1.1.   (ligne   304)
HTTP protocol:                                 Voir 3.7.1. (ligne  1759)
hyai:                                          Voir 3.36.  (ligne  4683)
hyam:                                          Voir 3.36.  (ligne  4683)
hybi:                                          Voir 3.36.  (ligne  4683)
hybm:                                          Voir 3.36.  (ligne  4683)
hybrid coordinate system:                      Voir 4.1.4. (ligne  5170)
hyperbolic arccosine function:                 Voir 4.1.25.
                                                           (ligne  7302)
hyperbolic arcsine function:                   Voir 4.1.25.
                                                           (ligne  7302)
hyperbolic arctangent function:                Voir 4.1.25.
                                                           (ligne  7302)
hyperbolic cosine function:                    Voir 4.1.25.
                                                           (ligne  7302)
hyperbolic sine function:                      Voir 4.1.25.
                                                           (ligne  7302)
hyperbolic tangent:                            Voir 4.1.25.
                                                           (ligne  7302)
hyperslab <1>:                                 Voir 4.12.  (ligne  9726)
hyperslab <2>:                                 Voir 4.10.  (ligne  9501)
hyperslab <3>:                                 Voir 4.9.   (ligne  9432)
hyperslab <4>:                                 Voir 4.5.   (ligne  8244)
hyperslab <5>:                                 Voir 4.4.   (ligne  8138)
hyperslab <6>:                                 Voir 3.25.  (ligne  3463)
hyperslab:                                     Voir 3.15.  (ligne  2547)
hyperslabs:                                    Voir 4.1.5. (ligne  5232)
I/O <1>:                                       Voir 3.19.  (ligne  2870)
I/O <2>:                                       Voir 3.14.  (ligne  2516)
I/O:                                           Voir 3.7.1. (ligne  1830)
I/O block size:                                Voir 3.28.  (ligne  3699)
I18N:                                          Voir 3.1.   (ligne  1345)
IBM:                                           Voir 1.2.   (ligne   344)
icc:                                           Voir 1.2.   (ligne   360)
ID Quoting:                                    Voir 4.1.27.
                                                           (ligne  7501)
IDL:                                           Voir 2.1.   (ligne   755)
IEEE:                                          Voir 3.32.1.
                                                           (ligne  4212)
IEEE NaN:                                      Voir 4.2.   (ligne  7792)
if():                                          Voir 4.1.8. (ligne  5465)
ilimit:                                        Voir 2.8.   (ligne  1169)
implicit conversion:                           Voir 3.32.2.
                                                           (ligne  4245)
include:                                       Voir 4.1.15.
                                                           (ligne  5963)
including files:                               Voir 4.1.1. (ligne  4927)
index convention:                              Voir 3.14.  (ligne  2512)
indgen-equivalent:                             Voir 4.1.5. (ligne  5216)
inexact conversion:                            Voir 4.1.25.
                                                           (ligne  7388)
Info:                                          Voir 1.1.   (ligne   304)
input files <1>:                               Voir 3.6.   (ligne  1614)
input files <2>:                               Voir 3.5.   (ligne  1512)
input files:                                   Voir 2.7.   (ligne  1104)
INPUT-PATH <1>:                                Voir 3.7.   (ligne  1707)
INPUT-PATH:                                    Voir 3.5.   (ligne  1512)
installation <1>:                              Voir 1.6.   (ligne   727)
installation:                                  Voir 1.2.   (ligne   344)
int():                                         Voir 4.1.11.
                                                           (ligne  5786)
int64():                                       Voir 4.1.11.
                                                           (ligne  5808)
integration:                                   Voir 4.12.2.
                                                           (ligne  9822)
integrity:                                     Voir 3.27.  (ligne  3629)
Intel:                                         Voir 1.2.   (ligne   344)
Internationalization:                          Voir 3.1.   (ligne  1345)
interoperability:                              Voir 3.30.  (ligne  3874)
interpolation <1>:                             Voir 7.5.   (ligne 10766)
interpolation:                                 Voir 4.6.   (ligne  8365)
intersection:                                  Voir 3.11.  (ligne  2073)
introduction:                                  Voir 1.     (ligne   293)
invert_map:                                    Voir 4.1.16.
                                                           (ligne  5978)
IPCC <1>:                                      Voir 5.2.   (ligne 10088)
IPCC:                                          Voir 4.4.   (ligne  8144)
irregular grids:                               Voir 4.1.17.
                                                           (ligne  6224)
ISO:                                           Voir 1.2.   (ligne   365)
James Gallagher:                               Voir 5.1.   (ligne  9985)
Jim Edwards:                                   Voir 5.1.   (ligne 10034)
John Caron:                                    Voir 5.1.   (ligne  9985)
Juliana Rew:                                   Voir 5.1.   (ligne 10037)
Karen Schuchardt:                              Voir 5.1.   (ligne 10040)
Keith Lindsay:                                 Voir 5.1.   (ligne 10049)
kitchen sink:                                  Voir 4.7.   (ligne  8525)
Kyle Wilcox:                                   Voir 5.1.   (ligne 10070)
L10N:                                          Voir 3.1.   (ligne  1346)
large datasets <1>:                            Voir 3.3.   (ligne  1400)
large datasets:                                Voir 2.8.   (ligne  1149)
Large File Support <1>:                        Voir 3.10.  (ligne  2041)
Large File Support:                            Voir 2.8.   (ligne  1149)
lat_bnds:                                      Voir 3.36.  (ligne  4683)
LD_LIBRARY_PATH:                               Voir 1.4.   (ligne   516)
left hand casting <1>:                         Voir 4.1.4. (ligne  5170)
left hand casting:                             Voir 2.9.2. (ligne  1271)
Legendre polynomial:                           Voir 4.1.19.
                                                           (ligne  6526)
Lempel-Ziv deflation:                          Voir 3.26.  (ligne  3592)
Len Makin:                                     Voir 5.1.   (ligne 10031)
lexer:                                         Voir 4.1.   (ligne  4842)
LFS <1>:                                       Voir 3.10.  (ligne  2041)
LFS:                                           Voir 2.8.   (ligne  1149)
LHS:                                           Voir 4.1.4. (ligne  5170)
libnco:                                        Voir 1.2.   (ligne   365)
libraries:                                     Voir 1.4.   (ligne   516)
linkers:                                       Voir 3.6.   (ligne  1636)
Linux:                                         Voir 4.1.25.
                                                           (ligne  7435)
LLVM:                                          Voir 1.2.   (ligne   363)
LN:                                            Voir 4.1.25.
                                                           (ligne  7302)
ln -s <1>:                                     Voir 4.8.   (ligne  9148)
ln -s:                                         Voir 4.3.   (ligne  7910)
LOG:                                           Voir 4.1.25.
                                                           (ligne  7302)
LOG10:                                         Voir 4.1.25.
                                                           (ligne  7302)
logarithm, base 10:                            Voir 4.1.25.
                                                           (ligne  7302)
logarithm, natural:                            Voir 4.1.25.
                                                           (ligne  7302)
lon_bnds:                                      Voir 3.36.  (ligne  4683)
long double:                                   Voir 4.1.25.
                                                           (ligne  7433)
long options <1>:                              Voir 4.8.   (ligne  9311)
long options:                                  Voir 3.4.   (ligne  1452)
long-term average:                             Voir 7.2.   (ligne 10331)
longitude:                                     Voir 3.20.  (ligne  2963)
Lori Sentman:                                  Voir 5.1.   (ligne 10049)
lrint().:                                      Voir 3.32.1.
                                                           (ligne  4212)
lround().:                                     Voir 3.32.1.
                                                           (ligne  4205)
Luk Claes:                                     Voir 5.1.   (ligne 10052)
Macintosh:                                     Voir 1.2.   (ligne   344)
Makefile <1>:                                  Voir 3.7.1. (ligne  1772)
Makefile <2>:                                  Voir 1.5.   (ligne   566)
Makefile <3>:                                  Voir 1.2.1. (ligne   455)
Makefile:                                      Voir 1.2.   (ligne   370)
malloc():                                      Voir 2.9.2. (ligne  1280)
mantissa:                                      Voir 3.32.2.
                                                           (ligne  4480)
manual type conversion:                        Voir 3.32.  (ligne  4105)
map_dmn:                                       Voir 3.25.  (ligne  3512)
map_prd:                                       Voir 3.25.  (ligne  3512)
map_rd1:                                       Voir 3.25.  (ligne  3512)
map_scl:                                       Voir 3.25.  (ligne  3512)
Mark Flanner:                                  Voir 5.1.   (ligne 10049)
Markus Liebig:                                 Voir 5.1.   (ligne 10049)
Martin Dix:                                    Voir 5.1.   (ligne 10049)
Martin Otte:                                   Voir 5.1.   (ligne 10076)
Martin Schmidt:                                Voir 5.1.   (ligne 10049)
mask <1>:                                      Voir 4.1.24.
                                                           (ligne  7219)
mask:                                          Voir 4.1.17.
                                                           (ligne  6224)
mask condition <1>:                            Voir 4.12.2.
                                                           (ligne  9931)
mask condition:                                Voir 4.12.1.
                                                           (ligne  9789)
masked average:                                Voir 4.12.  (ligne  9695)
Mass Store System:                             Voir 3.7.   (ligne  1646)
Matej Vela:                                    Voir 5.1.   (ligne 10052)
mathematical functions:                        Voir 4.1.25.
                                                           (ligne  7302)
Matlab:                                        Voir 2.1.   (ligne   755)
max:                                           Voir 3.31.  (ligne  3943)
max():                                         Voir 4.1.11.
                                                           (ligne  5674)
maximum:                                       Voir 3.31.  (ligne  3943)
MD5 digest:                                    Voir 3.27.  (ligne  3629)
mean:                                          Voir 3.31.  (ligne  3943)
memory available:                              Voir 2.9.   (ligne  1191)
memory leaks:                                  Voir 2.9.2. (ligne  1271)
memory requirements <1>:                       Voir 3.11.  (ligne  2197)
memory requirements:                           Voir 2.9.   (ligne  1191)
merging files <1>:                             Voir 4.7.   (ligne  8573)
merging files:                                 Voir 2.4.   (ligne   873)
metadata:                                      Voir 4.7.   (ligne  8784)
metadata optimization:                         Voir 3.2.   (ligne  1353)
metadata, global <1>:                          Voir 4.7.   (ligne  8747)
metadata, global:                              Voir 4.5.   (ligne  8291)
Michael Prather:                               Voir 3.32.2.
                                                           (ligne  4589)
Michael Schulz:                                Voir 5.1.   (ligne 10049)
Microsoft <1>:                                 Voir 1.2.1. (ligne   440)
Microsoft:                                     Voir 1.2.   (ligne   344)
Microsoft Visual Studio:                       Voir 1.2.1. (ligne   440)
Mike Folk:                                     Voir 1.5.   (ligne   551)
Mike Page:                                     Voir 5.1.   (ligne 10049)
min:                                           Voir 3.31.  (ligne  3943)
min():                                         Voir 4.1.11.
                                                           (ligne  5677)
minimum:                                       Voir 3.31.  (ligne  3943)
missing values <1>:                            Voir 4.7.   (ligne  8794)
missing values <2>:                            Voir 4.6.   (ligne  8442)
missing values <3>:                            Voir 4.2.   (ligne  7586)
missing values:                                Voir 3.24.  (ligne  3329)
missing values ncap2:                          Voir 4.1.10.
                                                           (ligne  5560)
missing_value <1>:                             Voir 4.11.  (ligne  9658)
missing_value <2>:                             Voir 3.30.  (ligne  3806)
missing_value:                                 Voir 3.24.  (ligne  3329)
MKS units:                                     Voir 3.22.  (ligne  3134)
MODIS <1>:                                     Voir 7.6.   (ligne 10905)
MODIS:                                         Voir 7.5.   (ligne 10766)
modulus:                                       Voir 4.1.25.
                                                           (ligne  7299)
monotonic coordinates:                         Voir 2.10.  (ligne  1314)
monthly average <1>:                           Voir 7.4.   (ligne 10650)
monthly average:                               Voir 6.1.   (ligne 10119)
monthly data <1>:                              Voir 6.4.   (ligne 10200)
monthly data <2>:                              Voir 6.3.   (ligne 10182)
monthly data:                                  Voir 6.2.   (ligne 10150)
move groups:                                   Voir 3.13.  (ligne  2474)
MRO:                                           Voir 3.18.  (ligne  2755)
MSA:                                           Voir 3.19.  (ligne  2821)
msk_*:                                         Voir 3.36.  (ligne  4683)
msrcp <1>:                                     Voir 3.8.   (ligne  1891)
msrcp:                                         Voir 3.7.   (ligne  1683)
msread:                                        Voir 3.7.   (ligne  1683)
MSS:                                           Voir 3.7.   (ligne  1646)
multi-file operators <1>:                      Voir 4.10.  (ligne  9494)
multi-file operators <2>:                      Voir 4.9.   (ligne  9425)
multi-file operators <3>:                      Voir 4.5.   (ligne  8286)
multi-file operators <4>:                      Voir 4.4.   (ligne  8153)
multi-file operators <5>:                      Voir 3.6.   (ligne  1626)
multi-file operators <6>:                      Voir 3.5.   (ligne  1546)
multi-file operators:                          Voir 2.9.1. (ligne  1209)
multi-hyperslab:                               Voir 3.19.  (ligne  2821)
Multi-Record Operator:                         Voir 3.18.  (ligne  2755)
multiplication <1>:                            Voir 4.6.   (ligne  8499)
multiplication <2>:                            Voir 4.3.   (ligne  7848)
multiplication:                                Voir 4.1.25.
                                                           (ligne  7299)
multiply:                                      Voir 4.3.   (ligne  7866)
multiplying data <1>:                          Voir 4.6.   (ligne  8365)
multiplying data:                              Voir 4.3.   (ligne  7848)
multislab:                                     Voir 3.19.  (ligne  2821)
mv:                                            Voir 3.11.  (ligne  2115)
MVS <1>:                                       Voir 1.2.1. (ligne   440)
MVS:                                           Voir 1.2.   (ligne   360)
naked characters:                              Voir 4.3.   (ligne  7895)
NaN:                                           Voir 4.2.   (ligne  7792)
NASA:                                          Voir 5.2.   (ligne 10095)
NASA EOSDIS:                                   Voir 2.7.   (ligne  1051)
National Virtual Ocean Data System:            Voir 3.7.1. (ligne  1840)
nc__enddef():                                  Voir 3.2.   (ligne  1353)
NC_BYTE <1>:                                   Voir 4.8.   (ligne  9160)
NC_BYTE <2>:                                   Voir 4.3.   (ligne  7973)
NC_BYTE:                                       Voir 3.15.  (ligne  2617)
NC_CHAR <1>:                                   Voir 4.8.   (ligne  9160)
NC_CHAR <2>:                                   Voir 4.3.   (ligne  7973)
NC_CHAR:                                       Voir 3.15.  (ligne  2617)
NC_DISKLESS:                                   Voir 3.29.  (ligne  3729)
NC_DOUBLE <1>:                                 Voir 4.8.   (ligne  9160)
NC_DOUBLE:                                     Voir 4.1.25.
                                                           (ligne  7433)
NC_FLOAT:                                      Voir 4.8.   (ligne  9160)
NC_INT:                                        Voir 4.8.   (ligne  9160)
NC_INT64 <1>:                                  Voir 4.8.   (ligne  9160)
NC_INT64:                                      Voir 1.5.   (ligne   594)
NC_SHORT:                                      Voir 4.8.   (ligne  9160)
NC_UBYTE <1>:                                  Voir 4.8.   (ligne  9160)
NC_UBYTE:                                      Voir 1.5.   (ligne   594)
NC_UINT <1>:                                   Voir 4.8.   (ligne  9160)
NC_UINT:                                       Voir 1.5.   (ligne   594)
NC_UINT64 <1>:                                 Voir 4.8.   (ligne  9160)
NC_UINT64:                                     Voir 1.5.   (ligne   594)
NC_USHORT <1>:                                 Voir 4.8.   (ligne  9160)
NC_USHORT:                                     Voir 1.5.   (ligne   594)
ncadd:                                         Voir 4.3.   (ligne  7848)
ncap:                                          Voir 4.1.   (ligne  4842)
ncap2 <1>:                                     Voir 4.8.   (ligne  9080)
ncap2 <2>:                                     Voir 4.1.   (ligne  4842)
ncap2 <3>:                                     Voir 3.32.3.
                                                           (ligne  4594)
ncap2 <4>:                                     Voir 3.3.   (ligne  1400)
ncap2 <5>:                                     Voir 2.9.2. (ligne  1271)
ncap2:                                         Voir 1.2.   (ligne   404)
NCAR:                                          Voir 2.2.   (ligne   772)
NCAR MSS:                                      Voir 3.7.   (ligne  1646)
ncatted <1>:                                   Voir 4.2.   (ligne  7563)
ncatted <2>:                                   Voir 3.34.  (ligne  4642)
ncatted <3>:                                   Voir 3.24.  (ligne  3356)
ncatted:                                       Voir 3.11.  (ligne  2216)
ncattget <1>:                                  Voir 4.7.1. (ligne  8867)
ncattget:                                      Voir 4.2.   (ligne  7570)
ncavg:                                         Voir 4.7.1. (ligne  8867)
ncbo <1>:                                      Voir 4.3.   (ligne  7848)
ncbo:                                          Voir 3.24.  (ligne  3404)
ncdiff:                                        Voir 4.3.   (ligne  7848)
ncdivide:                                      Voir 4.3.   (ligne  7848)
ncdmnsz:                                       Voir 4.7.1. (ligne  8867)
ncdump <1>:                                    Voir 4.7.   (ligne  8663)
ncdump:                                        Voir 3.9.   (ligne  2012)
ncea <1>:                                      Voir 4.4.   (ligne  8118)
ncea <2>:                                      Voir 3.24.  (ligne  3404)
ncea:                                          Voir 2.6.2. (ligne  1018)
ncecat <1>:                                    Voir 4.5.   (ligne  8206)
ncecat:                                        Voir 2.6.1. (ligne   960)
ncextr:                                        Voir 4.7.   (ligne  8538)
ncflint <1>:                                   Voir 4.6.   (ligne  8365)
ncflint <2>:                                   Voir 3.24.  (ligne  3404)
ncflint:                                       Voir 2.6.3. (ligne  1040)
ncgen:                                         Voir 4.7.   (ligne  8663)
ncks <1>:                                      Voir 4.7.   (ligne  8525)
ncks <2>:                                      Voir 4.1.24.
                                                           (ligne  7277)
ncks <3>:                                      Voir 3.26.  (ligne  3623)
ncks:                                          Voir 3.9.   (ligne  1996)
NCL <1>:                                       Voir 4.8.   (ligne  9275)
NCL:                                           Voir 2.1.   (ligne   755)
ncl_convert2nc:                                Voir 4.8.   (ligne  9275)
nclist:                                        Voir 4.7.1. (ligne  8867)
ncmax:                                         Voir 4.7.1. (ligne  8867)
ncmdn:                                         Voir 4.7.1. (ligne  8867)
ncmin:                                         Voir 4.7.1. (ligne  8867)
NcML:                                          Voir 4.7.   (ligne  8831)
ncmult:                                        Voir 4.3.   (ligne  7848)
ncmultiply:                                    Voir 4.3.   (ligne  7848)
NCO availability:                              Voir 1.1.   (ligne   296)
NCO homepage:                                  Voir 1.1.   (ligne   320)
nco script file:                               Voir 7.3.   (ligne 10415)
nco.config.log.${GNU_TRP}.foo:                 Voir 1.6.   (ligne   727)
nco.configure.${GNU_TRP}.foo:                  Voir 1.6.   (ligne   727)
nco.make.${GNU_TRP}.foo:                       Voir 1.6.   (ligne   727)
nco_input_file_list <1>:                       Voir 3.35.  (ligne  4652)
nco_input_file_list:                           Voir 2.7.   (ligne  1078)
nco_input_file_number <1>:                     Voir 3.35.  (ligne  4652)
nco_input_file_number:                         Voir 2.7.   (ligne  1078)
nco_openmp_thread_number:                      Voir 3.3.   (ligne  1374)
ncpack:                                        Voir 4.8.   (ligne  9062)
ncpdq <1>:                                     Voir 4.10.  (ligne  9506)
ncpdq <2>:                                     Voir 4.8.   (ligne  9062)
ncpdq <3>:                                     Voir 4.5.   (ligne  8312)
ncpdq <4>:                                     Voir 3.25.  (ligne  3463)
ncpdq <5>:                                     Voir 3.3.   (ligne  1400)
ncpdq:                                         Voir 2.6.1. (ligne   993)
ncra <1>:                                      Voir 4.9.   (ligne  9409)
ncra <2>:                                      Voir 4.1.24.
                                                           (ligne  7277)
ncra <3>:                                      Voir 3.24.  (ligne  3404)
ncra:                                          Voir 2.6.2. (ligne  1018)
ncrcat <1>:                                    Voir 4.10.  (ligne  9478)
ncrcat <2>:                                    Voir 3.3.   (ligne  1400)
ncrcat:                                        Voir 2.6.1. (ligne   960)
ncrecsz:                                       Voir 4.7.1. (ligne  8867)
ncrename <1>:                                  Voir 4.11.  (ligne  9560)
ncrename:                                      Voir 3.24.  (ligne  3356)
ncrng:                                         Voir 4.7.1. (ligne  8867)
NCSA:                                          Voir 1.5.   (ligne   573)
ncsub:                                         Voir 4.3.   (ligne  7848)
ncsubtract:                                    Voir 4.3.   (ligne  7848)
ncunits:                                       Voir 4.7.1. (ligne  8867)
ncunpack:                                      Voir 4.8.   (ligne  9062)
ncwa <1>:                                      Voir 4.12.  (ligne  9695)
ncwa <2>:                                      Voir 4.1.24.
                                                           (ligne  7277)
ncwa <3>:                                      Voir 3.24.  (ligne  3404)
ncwa <4>:                                      Voir 3.3.   (ligne  1400)
ncwa:                                          Voir 2.6.2. (ligne  1018)
ndims():                                       Voir 4.1.11.
                                                           (ligne  5719)
NEARBYINT:                                     Voir 4.1.25.
                                                           (ligne  7302)
nearest integer function (exact):              Voir 4.1.25.
                                                           (ligne  7302)
nearest integer function (inexact):            Voir 4.1.25.
                                                           (ligne  7302)
NEC:                                           Voir 1.2.   (ligne   344)
Neil Davis:                                    Voir 3.32.1.
                                                           (ligne  4199)
nesting:                                       Voir 4.1.1. (ligne  4927)
netCDF:                                        Voir 1.1.   (ligne   324)
netCDF2 <1>:                                   Voir 3.9.   (ligne  1914)
netCDF2:                                       Voir 1.5.   (ligne   541)
NETCDF2_ONLY:                                  Voir 1.5.   (ligne   558)
netCDF3 <1>:                                   Voir 3.9.   (ligne  1914)
netCDF3:                                       Voir 1.5.   (ligne   541)
netCDF3 classic file format:                   Voir 3.9.   (ligne  1996)
netCDF4 <1>:                                   Voir 3.9.   (ligne  1914)
netCDF4:                                       Voir 1.5.   (ligne   573)
netCDF4 classic file format:                   Voir 3.9.   (ligne  1996)
netCDF4 file format:                           Voir 3.9.   (ligne  1996)
NETCDF4 files:                                 Voir 3.9.   (ligne  1914)
NETCDF4_CLASSIC files:                         Voir 3.9.   (ligne  1914)
NETCDF4_ROOT:                                  Voir 1.5.   (ligne   636)
Nick Bower:                                    Voir 5.1.   (ligne 10052)
NINTAP <1>:                                    Voir 4.10.  (ligne  9525)
NINTAP <2>:                                    Voir 4.9.   (ligne  9448)
NINTAP:                                        Voir 3.5.   (ligne  1512)
NO_NETCDF_2:                                   Voir 1.5.   (ligne   549)
non-coordinate grid properties:                Voir 3.36.  (ligne  4713)
non-rectangular grids:                         Voir 4.1.17.
                                                           (ligne  6224)
non-standard grids:                            Voir 4.1.17.
                                                           (ligne  6224)
normalization:                                 Voir 4.12.2.
                                                           (ligne  9822)
Not-a-Number:                                  Voir 4.2.   (ligne  7792)
NRA:                                           Voir 5.2.   (ligne 10095)
nrnet:                                         Voir 3.7.   (ligne  1683)
NSF:                                           Voir 5.2.   (ligne 10088)
NT (Microsoft operating system):               Voir 1.2.1. (ligne   440)
NUL <1>:                                       Voir 4.8.   (ligne  9198)
NUL:                                           Voir 4.2.   (ligne  7770)
NUL-termination:                               Voir 4.2.   (ligne  7770)
null operation:                                Voir 4.6.   (ligne  8496)
number literals ncap2:                         Voir 4.1.7. (ligne  5403)
number_miss():                                 Voir 4.1.10.
                                                           (ligne  5614)
numerator:                                     Voir 4.12.2.
                                                           (ligne  9822)
NVODS:                                         Voir 3.7.1. (ligne  1840)
nxt_lsr:                                       Voir 4.8.   (ligne  9160)
oceanography:                                  Voir 3.7.1. (ligne  1759)
octal dump:                                    Voir 3.9.   (ligne  2024)
od:                                            Voir 3.9.   (ligne  2024)
OMP_NUM_THREADS:                               Voir 3.3.   (ligne  1400)
on-line documentation:                         Voir 1.1.   (ligne   304)
open source <1>:                               Voir 3.7.1. (ligne  1840)
open source:
          Voir ``Foreword''.                               (ligne   221)
Open-source Project for a Network Data Access Protocol:Voir 3.7.1.
                                                           (ligne  1759)
OPeNDAP.:                                      Voir 3.7.1. (ligne  1759)
OpenMP <1>:                                    Voir 3.3.   (ligne  1374)
OpenMP <2>:                                    Voir 2.9.1. (ligne  1264)
OpenMP:                                        Voir 2.9.   (ligne  1198)
operation types <1>:                           Voir 4.12.  (ligne  9771)
operation types <2>:                           Voir 4.9.   (ligne  9441)
operation types <3>:                           Voir 4.4.   (ligne  8147)
operation types:                               Voir 3.31.  (ligne  3943)
operator speed <1>:                            Voir 4.11.  (ligne  9596)
operator speed <2>:                            Voir 3.24.  (ligne  3387)
operator speed <3>:                            Voir 3.2.   (ligne  1353)
operator speed <4>:                            Voir 2.10.  (ligne  1310)
operator speed <5>:                            Voir 2.3.   (ligne   799)
operator speed:                                Voir 1.4.   (ligne   517)
operators:
          Voir ``Summary''.                                (ligne   279)
OptIPuter:                                     Voir 5.2.   (ligne 10088)
Orion Powlawski:                               Voir 5.1.   (ligne 10055)
ORO <1>:                                       Voir 4.12.2.
                                                           (ligne  9898)
ORO:                                           Voir 3.36.  (ligne  4683)
OS:                                            Voir 1.2.   (ligne   344)
output file <1>:                               Voir 3.6.   (ligne  1614)
output file:                                   Voir 2.7.   (ligne  1104)
OUTPUT-PATH:                                   Voir 3.7.   (ligne  1707)
overview:                                      Voir 2.10.  (ligne  1295)
overwriting files <1>:                         Voir 3.33.  (ligne  4604)
overwriting files:                             Voir 2.3.   (ligne   848)
pack():                                        Voir 4.1.11.
                                                           (ligne  5696)
pack(x):                                       Voir 3.30.  (ligne  3806)
pack_byte():                                   Voir 4.1.11.
                                                           (ligne  5700)
pack_int():                                    Voir 4.1.11.
                                                           (ligne  5706)
pack_short():                                  Voir 4.1.11.
                                                           (ligne  5703)
packing <1>:                                   Voir 4.10.  (ligne  9506)
packing <2>:                                   Voir 4.8.   (ligne  9062)
packing <3>:                                   Voir 4.5.   (ligne  8312)
packing <4>:                                   Voir 3.30.  (ligne  3806)
packing <5>:                                   Voir 3.25.  (ligne  3463)
packing:                                       Voir 3.7.1. (ligne  1822)
packing map:                                   Voir 4.8.   (ligne  9154)
packing policy:                                Voir 4.8.   (ligne  9080)
papers:                                        Voir 2.10.  (ligne  1295)
parallel:                                      Voir 8.     (ligne 11139)
parallelism <1>:                               Voir 5.2.   (ligne 10090)
parallelism:                                   Voir 3.3.   (ligne  1374)
parser:                                        Voir 4.1.   (ligne  4842)
pasting variables:                             Voir 2.4.   (ligne   873)
pathCC:                                        Voir 1.2.   (ligne   360)
pathcc:                                        Voir 1.2.   (ligne   360)
PathScale:                                     Voir 1.2.   (ligne   344)
Patrice Dumas:                                 Voir 5.1.   (ligne 10055)
Patrick Kursawe:                               Voir 5.1.   (ligne 10058)
pattern matching <1>:                          Voir 4.2.   (ligne  7607)
pattern matching <2>:                          Voir 3.11.  (ligne  2216)
pattern matching:                              Voir 2.7.   (ligne  1086)
PayPal:                                        Voir 5.     (ligne  9965)
PCK_MAP:                                       Voir 4.8.   (ligne  9154)
PCK_PLC:                                       Voir 4.8.   (ligne  9087)
peak memory usage:                             Voir 2.9.   (ligne  1191)
Pedro Vicente:                                 Voir 5.1.   (ligne 10016)
performance <1>:                               Voir 4.11.  (ligne  9596)
performance <2>:                               Voir 3.24.  (ligne  3387)
performance <3>:                               Voir 3.2.   (ligne  1353)
performance <4>:                               Voir 2.10.  (ligne  1301)
performance <5>:                               Voir 2.3.   (ligne   799)
performance:                                   Voir 1.4.   (ligne   517)
Perl <1>:                                      Voir 4.2.   (ligne  7744)
Perl <2>:                                      Voir 2.7.   (ligne  1118)
Perl:                                          Voir 2.1.   (ligne   755)
permute dimensions:                            Voir 4.8.   (ligne  9062)
permute():                                     Voir 4.1.5. (ligne  5324)
Peter Campbell:                                Voir 5.1.   (ligne 10049)
pgCC:                                          Voir 1.2.   (ligne   360)
pgcc:                                          Voir 1.2.   (ligne   360)
PGI:                                           Voir 1.2.   (ligne   344)
philosophy:                                    Voir 2.1.   (ligne   746)
pipes:                                         Voir 2.7.   (ligne  1099)
plc_all:                                       Voir 3.25.  (ligne  3474)
plc_g2d:                                       Voir 3.25.  (ligne  3474)
plc_g3d:                                       Voir 3.25.  (ligne  3474)
plc_xpl:                                       Voir 3.25.  (ligne  3474)
portability:                                   Voir 1.2.   (ligne   344)
positional arguments:                          Voir 3.6.   (ligne  1614)
POSIX <1>:                                     Voir 3.11.  (ligne  2234)
POSIX:                                         Voir 3.4.   (ligne  1439)
POW:                                           Voir 4.1.25.
                                                           (ligne  7302)
power:                                         Voir 4.1.25.
                                                           (ligne  7299)
power function:                                Voir 4.1.25.
                                                           (ligne  7302)
prd:                                           Voir 3.25.  (ligne  3512)
precision:                                     Voir 4.1.25.
                                                           (ligne  7433)
preprocessor tokens:                           Voir 1.2.1. (ligne   455)
presentations:                                 Voir 1.1.   (ligne   314)
print() ncap2:                                 Voir 4.1.9. (ligne  5531)
printf:                                        Voir 1.2.   (ligne   374)
printf() <1>:                                  Voir 4.7.1. (ligne  8977)
printf() <2>:                                  Voir 4.7.   (ligne  8822)
printf():                                      Voir 4.2.   (ligne  7746)
printing files contents:                       Voir 4.7.   (ligne  8525)
printing variables:                            Voir 4.7.   (ligne  8525)
Processor <1>:                                 Voir 4.10.  (ligne  9525)
Processor:                                     Voir 4.9.   (ligne  9448)
Processor, CCM:                                Voir 3.5.   (ligne  1512)
promotion <1>:                                 Voir 4.1.25.
                                                           (ligne  7392)
promotion <2>:                                 Voir 3.32.2.
                                                           (ligne  4245)
promotion:                                     Voir 3.32.  (ligne  4105)
proposals:                                     Voir 5.2.   (ligne 10088)
pseudonym:                                     Voir 1.3.   (ligne   490)
publications:                                  Voir 1.1.   (ligne   314)
QLogic:                                        Voir 1.2.   (ligne   344)
Qt:                                            Voir 1.2.1. (ligne   447)
quadruple-precision:                           Voir 4.1.25.
                                                           (ligne  7433)
Quick Start:                                   Voir 6.     (ligne 10108)
quiet:                                         Voir 4.7.   (ligne  8814)
quotes <1>:                                    Voir 4.8.   (ligne  9311)
quotes <2>:                                    Voir 4.3.   (ligne  7896)
quotes <3>:                                    Voir 4.1.24.
                                                           (ligne  7254)
quotes:                                        Voir 3.11.  (ligne  2296)
RAG:                                           Voir 4.5.   (ligne  8230)
RAM:                                           Voir 2.9.   (ligne  1191)
RAM disks <1>:                                 Voir 3.29.  (ligne  3729)
RAM disks:                                     Voir 2.3.   (ligne   805)
RAM files <1>:                                 Voir 3.29.  (ligne  3729)
RAM files:                                     Voir 2.3.   (ligne   805)
RAM variables <1>:                             Voir 4.1.11.
                                                           (ligne  5827)
RAM variables:                                 Voir 3.29.  (ligne  3759)
ram_delete():                                  Voir 4.1.12.
                                                           (ligne  5838)
ram_write():                                   Voir 4.1.12.
                                                           (ligne  5838)
random walk:                                   Voir 3.32.2.
                                                           (ligne  4468)
rank <1>:                                      Voir 4.12.  (ligne  9735)
rank <2>:                                      Voir 4.3.   (ligne  7938)
rank:                                          Voir 4.1.2. (ligne  4971)
rcp <1>:                                       Voir 3.7.   (ligne  1646)
rcp:                                           Voir 1.2.1. (ligne   461)
RCS:                                           Voir 3.38.  (ligne  4811)
rd1:                                           Voir 3.25.  (ligne  3512)
re-dimension:                                  Voir 4.8.   (ligne  9062)
re-order dimensions:                           Voir 4.8.   (ligne  9062)
record aggregation:                            Voir 4.5.   (ligne  8230)
record append:                                 Voir 3.17.  (ligne  2710)
record average:                                Voir 4.9.   (ligne  9409)
record concatenation:                          Voir 4.10.  (ligne  9478)
record dimension <1>:                          Voir 4.10.  (ligne  9491)
record dimension <2>:                          Voir 4.9.   (ligne  9409)
record dimension <3>:                          Voir 4.8.   (ligne  9226)
record dimension <4>:                          Voir 4.7.   (ligne  8648)
record dimension <5>:                          Voir 4.5.   (ligne  8240)
record dimension <6>:                          Voir 4.4.   (ligne  8138)
record dimension <7>:                          Voir 3.25.  (ligne  3577)
record dimension <8>:                          Voir 3.14.  (ligne  2528)
record dimension:                              Voir 2.4.   (ligne   876)
record variable <1>:                           Voir 4.8.   (ligne  9230)
record variable:                               Voir 3.14.  (ligne  2523)
rectangular grids:                             Voir 4.1.17.
                                                           (ligne  6224)
recursion:                                     Voir 3.11.  (ligne  2115)
recursive:                                     Voir 3.11.  (ligne  2115)
regex:                                         Voir 3.11.  (ligne  2234)
regression:                                    Voir 7.3.   (ligne 10415)
regressions archive:                           Voir 1.6.   (ligne   734)
regrid:                                        Voir 7.5.   (ligne 10766)
regular expressions <1>:                       Voir 4.2.   (ligne  7607)
regular expressions <2>:                       Voir 4.1.24.
                                                           (ligne  7254)
regular expressions <3>:                       Voir 3.11.  (ligne  2216)
regular expressions <4>:                       Voir 3.5.   (ligne  1512)
regular expressions:                           Voir 2.7.   (ligne  1086)
remap:                                         Voir 4.1.16.
                                                           (ligne  5978)
Remik Ziemlinski:                              Voir 5.1.   (ligne 10049)
remote files <1>:                              Voir 3.7.   (ligne  1646)
remote files:                                  Voir 1.2.1. (ligne   461)
rename groups:                                 Voir 3.13.  (ligne  2474)
renaming attributes <1>:                       Voir 7.5.   (ligne 10766)
renaming attributes:                           Voir 4.11.  (ligne  9560)
renaming dimensions <1>:                       Voir 7.5.   (ligne 10766)
renaming dimensions:                           Voir 4.11.  (ligne  9560)
renaming groups:                               Voir 4.11.  (ligne  9560)
renaming variables <1>:                        Voir 7.5.   (ligne 10766)
renaming variables <2>:                        Voir 7.3.   (ligne 10415)
renaming variables:                            Voir 4.11.  (ligne  9560)
reporting bugs:                                Voir 1.6.   (ligne   664)
reshape variables:                             Voir 4.8.   (ligne  9062)
restrict:                                      Voir 1.2.   (ligne   397)
reverse data:                                  Voir 4.8.   (ligne  9341)
reverse dimensions:                            Voir 4.8.   (ligne  9062)
reverse():                                     Voir 4.1.5. (ligne  5317)
RINT:                                          Voir 4.1.25.
                                                           (ligne  7302)
rms:                                           Voir 3.31.  (ligne  3943)
rmssdn:                                        Voir 3.31.  (ligne  3943)
rmssdn():                                      Voir 4.1.11.
                                                           (ligne  5683)
root-mean-square:                              Voir 3.31.  (ligne  3943)
Rorik Peterson:                                Voir 5.1.   (ligne 10006)
ROUND:                                         Voir 4.1.25.
                                                           (ligne  7302)
rounding:                                      Voir 3.32.2.
                                                           (ligne  4468)
rounding functions:                            Voir 4.1.25.
                                                           (ligne  7302)
RPM:                                           Voir 1.5.   (ligne   631)
running average:                               Voir 4.9.   (ligne  9409)
Russ Rew:                                      Voir 5.1.   (ligne  9985)
safeguards <1>:                                Voir 4.11.  (ligne  9581)
safeguards:                                    Voir 2.3.   (ligne   783)
scale_factor <1>:                              Voir 4.10.  (ligne  9506)
scale_factor <2>:                              Voir 4.8.   (ligne  9080)
scale_factor <3>:                              Voir 4.5.   (ligne  8312)
scale_factor:                                  Voir 3.30.  (ligne  3806)
SCALE_FORMAT:                                  Voir 2.10.  (ligne  1319)
scaling <1>:                                   Voir 4.6.   (ligne  8499)
scaling:                                       Voir 2.10.  (ligne  1301)
Scientific Data Operators:                     Voir 5.2.   (ligne 10088)
scl:                                           Voir 3.25.  (ligne  3512)
Scott Capps:                                   Voir 5.1.   (ligne 10046)
scp <1>:                                       Voir 3.7.   (ligne  1646)
scp:                                           Voir 1.2.1. (ligne   461)
script file:                                   Voir 4.1.   (ligne  4860)
SDO:                                           Voir 5.2.   (ligne 10088)
seasonal average:                              Voir 6.2.   (ligne 10153)
security:                                      Voir 3.27.  (ligne  3629)
SEIII:                                         Voir 5.2.   (ligne 10088)
semi-colon:                                    Voir 4.1.1. (ligne  4920)
server <1>:                                    Voir 3.8.   (ligne  1894)
server <2>:                                    Voir 3.7.1. (ligne  1759)
server:                                        Voir 2.8.   (ligne  1166)
Server-Side Distributed Data Reduction & Analysis:Voir 5.2.
                                                           (ligne 10088)
server-side processing <1>:                    Voir 5.2.   (ligne 10088)
server-side processing:                        Voir 3.7.1. (ligne  1864)
set_miss():                                    Voir 4.1.10.
                                                           (ligne  5588)
sftp <1>:                                      Voir 3.7.   (ligne  1646)
sftp:                                          Voir 1.2.1. (ligne   461)
SGI:                                           Voir 1.2.   (ligne   344)
Sh shell:                                      Voir 4.7.1. (ligne  8867)
shared memory machines:                        Voir 2.9.   (ligne  1198)
shared memory parallelism:                     Voir 3.3.   (ligne  1374)
shell <1>:                                     Voir 4.7.1. (ligne  8867)
shell <2>:                                     Voir 4.3.   (ligne  7896)
shell <3>:                                     Voir 4.1.24.
                                                           (ligne  7254)
shell <4>:                                     Voir 3.22.  (ligne  3190)
shell <5>:                                     Voir 3.11.  (ligne  2296)
shell:                                         Voir 2.7.   (ligne  1086)
SIGNEDNESS:                                    Voir 2.10.  (ligne  1319)
SIN:                                           Voir 4.1.25.
                                                           (ligne  7302)
sine function:                                 Voir 4.1.25.
                                                           (ligne  7302)
single-precision:                              Voir 4.1.25.
                                                           (ligne  7433)
SINH:                                          Voir 4.1.25.
                                                           (ligne  7302)
size():                                        Voir 4.1.11.
                                                           (ligne  5716)
SMP:                                           Voir 3.3.   (ligne  1374)
sort:                                          Voir 4.1.16.
                                                           (ligne  5978)
sort alphabetically <1>:                       Voir 4.7.1. (ligne  8960)
sort alphabetically:                           Voir 4.7.   (ligne  8633)
source code:                                   Voir 1.1.   (ligne   296)
spatial distribution:                          Voir 7.2.   (ligne 10331)
special characters:                            Voir 4.2.   (ligne  7761)
speed <1>:                                     Voir 4.11.  (ligne  9596)
speed <2>:                                     Voir 3.24.  (ligne  3387)
speed <3>:                                     Voir 3.2.   (ligne  1353)
speed <4>:                                     Voir 2.10.  (ligne  1310)
speed <5>:                                     Voir 2.8.   (ligne  1175)
speed <6>:                                     Voir 2.3.   (ligne   799)
speed:                                         Voir 1.4.   (ligne   517)
sqravg:                                        Voir 3.31.  (ligne  3943)
sqravg():                                      Voir 4.1.11.
                                                           (ligne  5668)
SQRT:                                          Voir 4.1.25.
                                                           (ligne  7302)
sqrt:                                          Voir 3.31.  (ligne  3943)
square root function:                          Voir 4.1.25.
                                                           (ligne  7302)
SSDDRA:                                        Voir 5.2.   (ligne 10088)
SSH <1>:                                       Voir 3.8.   (ligne  1891)
SSH:                                           Voir 1.2.1. (ligne   461)
sshort():                                      Voir 4.1.11.
                                                           (ligne  5783)
standard deviation <1>:                        Voir 7.3.   (ligne 10415)
standard deviation:                            Voir 3.31.  (ligne  3943)
standard input <1>:                            Voir 4.10.  (ligne  9494)
standard input <2>:                            Voir 4.9.   (ligne  9425)
standard input <3>:                            Voir 4.5.   (ligne  8286)
standard input <4>:                            Voir 4.4.   (ligne  8153)
standard input:                                Voir 2.7.   (ligne  1069)
standard_name:                                 Voir 3.21.  (ligne  3011)
stat() system call:                            Voir 3.28.  (ligne  3699)
statement:                                     Voir 4.1.1. (ligne  4905)
static linking:                                Voir 1.4.   (ligne   516)
stdin <1>:                                     Voir 4.10.  (ligne  9494)
stdin <2>:                                     Voir 4.9.   (ligne  9425)
stdin <3>:                                     Voir 4.5.   (ligne  8286)
stdin <4>:                                     Voir 4.4.   (ligne  8153)
stdin <5>:                                     Voir 3.35.  (ligne  4652)
stdin:                                         Voir 2.7.   (ligne  1069)
Steve Emmerson:                                Voir 5.1.   (ligne  9985)
stride <1>:                                    Voir 4.10.  (ligne  9502)
stride <2>:                                    Voir 4.9.   (ligne  9433)
stride <3>:                                    Voir 3.22.  (ligne  3181)
stride <4>:                                    Voir 3.19.  (ligne  2859)
stride <5>:                                    Voir 3.16.  (ligne  2650)
stride:                                        Voir 3.15.  (ligne  2571)
strings:                                       Voir 4.2.   (ligne  7770)
Stu Muller:                                    Voir 5.1.   (ligne 10049)
stub:                                          Voir 3.7.   (ligne  1716)
sub-cycle:                                     Voir 3.18.  (ligne  2755)
subsetting <1>:                                Voir 4.7.1. (ligne  8966)
subsetting <2>:                                Voir 3.36.  (ligne  4748)
subsetting <3>:                                Voir 3.12.  (ligne  2321)
subsetting:                                    Voir 3.11.  (ligne  2073)
subtract:                                      Voir 4.3.   (ligne  7866)
subtracting data:                              Voir 4.3.   (ligne  7848)
subtraction <1>:                               Voir 4.3.   (ligne  7848)
subtraction:                                   Voir 4.1.25.
                                                           (ligne  7299)
summary:
          Voir ``Summary''.                                (ligne   279)
Sun:                                           Voir 1.2.   (ligne   344)
swap space <1>:                                Voir 2.9.   (ligne  1191)
swap space:                                    Voir 2.8.   (ligne  1154)
switches:                                      Voir 3.4.   (ligne  1439)
symbolic links <1>:                            Voir 4.8.   (ligne  9148)
symbolic links <2>:                            Voir 4.3.   (ligne  7910)
symbolic links <3>:                            Voir 2.7.   (ligne  1112)
symbolic links <4>:                            Voir 2.6.   (ligne   930)
symbolic links:                                Voir 1.3.   (ligne   480)
synchronous file access:                       Voir 3.7.   (ligne  1646)
synonym:                                       Voir 1.3.   (ligne   490)
syntax:                                        Voir 4.1.1. (ligne  4905)
System calls:                                  Voir 3.28.  (ligne  3699)
Takeshi Enomoto:                               Voir 5.1.   (ligne 10064)
TAN:                                           Voir 4.1.25.
                                                           (ligne  7302)
TANH:                                          Voir 4.1.25.
                                                           (ligne  7302)
temporary files <1>:                           Voir 3.29.  (ligne  3799)
temporary files:                               Voir 2.3.   (ligne   783)
temporary output files <1>:                    Voir 4.11.  (ligne  9581)
temporary output files <2>:                    Voir 3.29.  (ligne  3799)
temporary output files:                        Voir 2.3.   (ligne   783)
TeXinfo:                                       Voir 1.1.   (ligne   304)
THR_NBR:                                       Voir 3.3.   (ligne  1400)
threads <1>:                                   Voir 3.3.   (ligne  1374)
threads <2>:                                   Voir 2.9.1. (ligne  1264)
threads:                                       Voir 2.9.   (ligne  1198)
time <1>:                                      Voir 3.37.  (ligne  4780)
time:                                          Voir 3.22.  (ligne  3173)
time-averaging <1>:                            Voir 7.4.   (ligne 10650)
time-averaging <2>:                            Voir 7.3.   (ligne 10415)
time-averaging <3>:                            Voir 7.2.   (ligne 10331)
time-averaging <4>:                            Voir 6.3.   (ligne 10182)
time-averaging <5>:                            Voir 6.2.   (ligne 10153)
time-averaging <6>:                            Voir 6.1.   (ligne 10119)
time-averaging:                                Voir 4.1.24.
                                                           (ligne  7277)
time_offset:                                   Voir 3.37.  (ligne  4780)
timestamp:                                     Voir 3.34.  (ligne  4624)
total:                                         Voir 3.31.  (ligne  3943)
transpose <1>:                                 Voir 4.8.   (ligne  9240)
transpose:                                     Voir 3.14.  (ligne  2521)
TREFHT:                                        Voir 3.32.2.
                                                           (ligne  4398)
TRUNC:                                         Voir 4.1.25.
                                                           (ligne  7302)
trunc():                                       Voir 3.32.1.
                                                           (ligne  4187)
truncation (groups):                           Voir 3.13.  (ligne  2386)
truncation function:                           Voir 4.1.25.
                                                           (ligne  7302)
truth condition <1>:                           Voir 4.12.2.
                                                           (ligne  9931)
truth condition:                               Voir 4.12.1.
                                                           (ligne  9789)
ttl:                                           Voir 3.31.  (ligne  3943)
ttl():                                         Voir 4.1.11.
                                                           (ligne  5686)
type conversion:                               Voir 3.32.  (ligne  4099)
type():                                        Voir 4.1.11.
                                                           (ligne  5722)
ubyte():                                       Voir 4.1.11.
                                                           (ligne  5799)
UDUnits <1>:                                   Voir 3.36.  (ligne  4683)
UDUnits <2>:                                   Voir 3.22.  (ligne  3120)
UDUnits:                                       Voir 1.2.   (ligne   352)
uint():                                        Voir 4.1.11.
                                                           (ligne  5805)
ulimit:                                        Voir 2.8.   (ligne  1170)
unary operations:                              Voir 2.9.2. (ligne  1271)
UNICOS:                                        Voir 2.8.   (ligne  1166)
Unidata <1>:                                   Voir 3.22.  (ligne  3120)
Unidata <2>:                                   Voir 1.5.   (ligne   573)
Unidata:                                       Voir 1.2.   (ligne   352)
union:                                         Voir 3.11.  (ligne  2073)
union of files:                                Voir 2.4.   (ligne   896)
unit64():                                      Voir 4.1.11.
                                                           (ligne  5811)
units <1>:                                     Voir 4.6.   (ligne  8517)
units <2>:                                     Voir 4.2.   (ligne  7818)
units:                                         Voir 3.22.  (ligne  3120)
UNIX <1>:                                      Voir 4.7.1. (ligne  8867)
UNIX <2>:                                      Voir 3.5.   (ligne  1518)
UNIX <3>:                                      Voir 3.4.   (ligne  1439)
UNIX <4>:                                      Voir 2.7.   (ligne  1086)
UNIX <5>:                                      Voir 1.2.1. (ligne   459)
UNIX:                                          Voir 1.2.   (ligne   352)
unlimited dimension:                           Voir 4.5.   (ligne  8240)
unmap:                                         Voir 4.1.16.
                                                           (ligne  5978)
unpack():                                      Voir 4.1.11.
                                                           (ligne  5709)
unpack(x):                                     Voir 3.30.  (ligne  3806)
unpacking <1>:                                 Voir 4.10.  (ligne  9506)
unpacking <2>:                                 Voir 4.8.   (ligne  9062)
unpacking <3>:                                 Voir 4.5.   (ligne  8312)
unpacking <4>:                                 Voir 3.30.  (ligne  3806)
unpacking:                                     Voir 3.7.1. (ligne  1822)
URL:                                           Voir 3.7.   (ligne  1646)
ushort():                                      Voir 4.1.11.
                                                           (ligne  5802)
value list:                                    Voir 4.1.6. (ligne  5359)
variable names:                                Voir 4.11.  (ligne  9560)
variables, appending:                          Voir 7.3.   (ligne 10415)
variance:                                      Voir 3.31.  (ligne  3943)
version:                                       Voir 3.38.  (ligne  4811)
Vista (Microsoft operating system):            Voir 1.2.1. (ligne   440)
weighted average <1>:                          Voir 7.4.   (ligne 10650)
weighted average:                              Voir 4.12.  (ligne  9695)
Wenshan Wang:                                  Voir 5.1.   (ligne 10079)
wget:                                          Voir 3.7.   (ligne  1646)
where():                                       Voir 4.1.13.
                                                           (ligne  5853)
while():                                       Voir 4.1.14.
                                                           (ligne  5923)
whitespace:                                    Voir 3.22.  (ligne  3181)
wildcards <1>:                                 Voir 4.2.   (ligne  7607)
wildcards <2>:                                 Voir 3.11.  (ligne  2216)
wildcards:                                     Voir 3.5.   (ligne  1512)
WIN32:                                         Voir 1.2.1. (ligne   455)
Windows <1>:                                   Voir 1.2.1. (ligne   440)
Windows:                                       Voir 1.2.   (ligne   344)
wrapped coordinates <1>:                       Voir 4.7.1. (ligne  9051)
wrapped coordinates <2>:                       Voir 4.1.17.
                                                           (ligne  6251)
wrapped coordinates <3>:                       Voir 3.20.  (ligne  2963)
wrapped coordinates:                           Voir 3.15.  (ligne  2622)
wrapped filenames:                             Voir 3.5.   (ligne  1575)
WWW documentation:                             Voir 1.1.   (ligne   304)
xargs <1>:                                     Voir 3.6.   (ligne  1631)
xargs:                                         Voir 2.7.   (ligne  1086)
xlc:                                           Voir 1.2.   (ligne   360)
xlC:                                           Voir 1.2.   (ligne   360)
XML:                                           Voir 4.7.   (ligne  8831)
XP (Microsoft operating system):               Voir 1.2.1. (ligne   440)
xpl:                                           Voir 3.25.  (ligne  3474)
Yorick <1>:                                    Voir 2.10.  (ligne  1322)
Yorick:                                        Voir 2.1.   (ligne   755)
| (wildcard character):                        Voir 3.11.  (ligne  2248)
