nces - Online in the Cloud

PROGRAM:

NAME

nces - netCDF Ensemble Statistics

SYNTAX

nces [-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]]]
[--dbl|flt] [-F] [-G gpe_dsc] [-g grp[,...]] [-h] [--hdf] [--hdr_pad sz] [-L dfl_lvl] [-l
path] [--msa] [-N] [-n loop] [--no_tmp_fl] [--nsm_sfx grp_sfx] [-O] [-p path] [--ppc
var1[, var2[,...]]= prc]] [-R] [-r] [--ram_all] [-t thr_nbr] [--unn] [-v var[,...]] [-X
box] [-x] [-y op_typ] input-files output-file

DESCRIPTION

nces performs gridpoint averages of variables across an arbitrary number (an ensemble) of
input files, with each file receiving an equal weight in the average. Each variable in
the output-file will be the same size as the same variable in any one of the in the input-
files, and all input-files must be the same size. Whereas ncra only performs averages
over the record dimension (e.g., time), and weights each record in the record dimension
evenly, nces averages entire files, and weights each file evenly. All dimensions,
including the record dimension, are treated identically and preserved in the output-file.

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 nces is the operator to use. nces assumes
coordinate variable are properties common to all of the experiments and so does not
average them across files. Instead, nces copies the values of the coordinate variables
from the first input file to the output file.

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):
nces 85_01.nc 85_02.nc 85_03.nc 85_04.nc 85_05.nc 85.nc
nces 85_0[1-5].nc 85.nc
nces -n 5,2,1 85_01.nc 85.nc
These three commands produce identical answers. 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.,
nces -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.

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