Next, run Ensemble-Stat on the command line using the following command, using wildcards to list the 6 input ensemble member files:
ensemble_stat \
6 ${METPLUS_DATA}/met_test/data/sample_fcst/2009123112/*gep*/d01_2009123112_02400.grib \
EnsembleStatConfig_tutorial \
-grid_obs ${METPLUS_DATA}/met_test/data/sample_obs/ST4/ST4.2010010112.24h \
-point_obs ${METPLUS_DATA}/met_test/out/ascii2nc/precip24_2010010112.nc \
-outdir . \
-v 2
6 ${METPLUS_DATA}/met_test/data/sample_fcst/2009123112/*gep*/d01_2009123112_02400.grib \
EnsembleStatConfig_tutorial \
-grid_obs ${METPLUS_DATA}/met_test/data/sample_obs/ST4/ST4.2010010112.24h \
-point_obs ${METPLUS_DATA}/met_test/out/ascii2nc/precip24_2010010112.nc \
-outdir . \
-v 2
Ensemble-Stat is now performing the tasks we requested in the configuration file. Note that we've passed the input ensemble data directly on the command line by specifying the number of ensemble members (6) followed by their names using wildcards. We've also specified one gridded StageIV analysis field (-grid_obs) and one file containing point rain gauge observations (-point_obs) to be used in computing rank histograms. This tool should run pretty quickly.
When Ensemble-Stat is finished, it will have created 8 output files in the current directory: 7 ASCII statistics files (.stat, _ecnt.txt, _rhist.txt, _phist.txt, _orank.txt, _ssvar.txt , and _relp.txt ), and a NetCDF matched pairs file (_orank.nc).
