ncbo - Online in the Cloud

PROGRAM:

NAME

ncbo - netCDF Binary Operator

SYNTAX

ncbo [-3] [-4] [-6] [-7] [-A] [--bfr sz][-C][-c] [--cnk_byt sz][--cnk_dmn nm,sz]
[--cnk_map map] [--cnk_min sz] [--cnk_plc plc] [--cnk_scl sz][-D dbg_lvl] [-d dim,[
min][,[ max]]] [-F] [-G gpe_dsc] [-g grp[,...]] [--glb att_name= att_val]] [-h] [--hdf]
[--hdr_pad sz] [-L dfl_lvl] [-l path] [--msa] [--no_tmp_fl] [-O] [-p path] [-R] [-r]
[--ram_all] [-t thr_nbr] [--unn] [-v var[,...]] [-X box] [-x] file_1 file_2 file_3

DESCRIPTION

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).

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

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:
ncbo -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 xavg_85.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 xavg_85.nc
ncbo 85_0112.nc xavg_85.nc tx_anm_85_0112.nc
Assuming 85_0112.nc has dimensions time and lon, this example only works if xavg_85.nc has
no time or lon dimension.

As a final example, say we have five years of monthly data (i.e., 60 months) stored in
8501_8912.nc and we wish to create a file which contains the twelve month seasonal cycle
of the average monthly anomaly from the five-year mean of this data. The following method
is just one permutation of many which will accomplish the same result. First use ncwa to
create the file containing 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:
foreach idx (01 02 03 04 05 06 07 08 09 10 11 12)
ncks -F -d time,,,12 t_anm_8501_8912.nc foo.
ncra foo. t_anm_8589_.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,,,12 t_anm_8501_8912.nc 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 t_anm_8589_0112.nc

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