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Langue: en

Version: 336052 (ubuntu - 24/10/10)

Section: 1 (Commandes utilisateur)


ncbo - netCDF Binary Operator


ncbo [-3] [-4] [-6] [-A] [-C] [-c] [-D dbg] [-d dim,[ min][,[ max]]] [-F] [-h] [-L dfl_lvl] [-l path] [-O] [-p path] [-R] [-r] [-t thr_nbr] [-v var[,...]] [-X box] [-x] file_1 file_2 file_3


ncbo subtracts variables in file_2 from the corresponding variables (those with the same name) in file_1 and stores the results in file_3. Variables in file_2 are broadcast to conform to the corresponding variable in file_1 if necessary. Broadcasting a variable means creating data in non-existing dimensions from the data in existing dimensions. For example, a two dimensional variable in file_2 can be subtracted from a four, three, or two (but not one or zero) dimensional variable (of the same name) in file_1. This functionality allows the user to compute anomalies from the mean. Note that variables in file_1 are not broadcast to conform to the dimensions in file_2. Thus, ncbo, the number of dimensions, or rank, of any processed variable in file_1 must be greater than or equal to the rank of the same variable in file_2. Furthermore, the size of all dimensions common to both file_1 and file_2 must be equal.

When computing anomalies from the mean it is often the case that file_2 was created by applying an averaging operator to a file with the same dimensions as file_1, if not 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 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. An example is given below.

ncbo will never difference 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 NCAR CSM fields (e.g., ORO). See NCAR CSM Conventions for a complete description. Finally, we note that ncflint (ncflint netCDF File Interpolator) can be also perform file subtraction (as well as addition, multiplication and interpolation).


Say files and each contain 12 months of data. Compute the change in the monthly averages from 1985 to 1986:


The following examples demonstrate the broadcasting feature of ncbo. Say we wish to compute the monthly anomalies of T from the yearly average of T for the year 1985. First we create the 1985 average from the monthly data, which is stored with the record dimension time.

ncwa -O -a time
The second command, ncwa, gets rid of the time dimension of size 1 that ncra left in Now none of the variables in has a time dimension. A quicker way to accomplish this is to use ncwa from the beginning:
ncwa -a time
We are now ready to use ncbo to compute the anomalies for 1985:
ncbo -v T
Each of the 12 records in now contains the monthly deviation of T from the annual mean of T for each gridpoint.

Say we wish to compute the monthly gridpoint anomalies from the zonal annual mean. A zonal mean is a quantity that has been averaged over the longitudinal (or x) direction. First we use ncwa to average over longitudinal direction lon, creating, the zonal mean of Then we use ncbo to subtract the zonal annual means from the monthly gridpoint data:

ncwa -a lon
Assuming has dimensions time and lon, this example only works if has no time or lon dimension.

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

ncwa -a time
Next use ncbo to create a file containing the difference of each month's data from the five-year mean:
Now use ncks to group 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:
foreach idx (01 02 03 04 05 06 07 08 09 10 11 12)
ncks -F -d time,,,12 foo.
ncra foo.
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,,,12 foo.
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


NCO manual pages written by Charlie Zender and Brian Mays.


Report bugs to <>. Copyright © 1995-2010 Charlie Zender
This is free software; see the source for copying conditions. There is NO warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.


The full documentation for NCO is maintained as a Texinfo manual called the NCO User's Guide. Because NCO is mathematical in nature, the documentation includes TeX-intensive portions not viewable on character-based displays. Hence the only complete and authoritative versions of the NCO User's Guide are the PDF (recommended), DVI, and Postscript versions at <>, <>, and <>, respectively. HTML and XML versions are available at <> and <>, respectively.

If the info and NCO programs are properly installed at your site, the command

info nco

should give you access to the complete manual, except for the TeX-intensive portions.


The NCO homepage at <> contains more information.
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