METplus Practical Session Guide (July 2019) | MET Tool: Ensemble-Stat > Ensemble-Stat Tool: Configure

Start by making an output directory for Ensemble-Stat and changing directories:

mkdir -p ${METPLUS_TUTORIAL_DIR}/output/met_output/ensemble_stat
cd ${METPLUS_TUTORIAL_DIR}/output/met_output/ensemble_stat

The behavior of Ensemble-Stat is controlled by the contents of the configuration file passed to it on the command line. The default Ensemble-Stat configuration file may be found in the data/config/EnsembleStatConfig_default file. The configurations used by the test script may be found in the scripts/config/EnsembleStatConfig* files. Prior to modifying the configuration file, users are advised to make a copy of the default:

cp ${MET_BUILD_BASE}/share/met/config/EnsembleStatConfig_default EnsembleStatConfig_tutorial

Open up the EnsembleStatConfig_tutorial file for editing with your preferred text editor.

vi EnsembleStatConfig_tutorial

The configurable items for Ensemble-Stat are broken out into two sections. The first section specifies how the ensemble should be processed to derive summary fields, such as the ensemble mean and spread. The second section specifies how the ensemble should be verified directly, such as the computation of rank histograms and spread/skill. The configurable items include specifications for the following:

  • Section 1: Ensemble Processing (ens dictionary)
    • The ensemble fields to be summarized at the specified vertical level or accumulation interval.
    • The threshold values to be applied in computing ensemble relative frequencies (e.g. the percent of ensemble members exceeding some threshold at each point).
    • Thresholds to specify how many of the ensemble members must actually be present with valid data.
  • Section 2: Verification (fcst and obs dictionaries)
    • The forecast and observation fields to be verified at the specified vertical level or accumulation interval.
    • The matching time window for point observations.
    • The type of point observations to be matched to the forecasts.
    • The areas over which to aggregate statistics - as predefined grids or configurable lat/lon polylines.
    • The interpolation or smoothing methods to be used.

You may find a complete description of the configurable items in section 9.3.2 of the MET User's Guide. Please take some time to review them.

For this tutorial, we'll configure Ensemble-Stat to summarize and verify 24-hour accumulated precipitation. While we'll run Ensemble-Stat on a single field, please note that it may be configured to operate on multiple fields. The ensemble we're verifying consists of 6 members defined over the west coast of the United States. Edit the EnsembleStatConfig_tutorial file as follows:

  • In the ens dictionary, set
       field = [
         {
           name       = "APCP";
           level      = [ "A24" ];
           cat_thresh = [ >0, >=5.0, >=10.0 ];
         }
       ];

    To read 24-hour accumulated precipitation from the input GRIB files and compute ensemble relative frequencies for the thresolds listed.

  • In the fcst dictionary, set
       field = [
         {
           name     = "APCP";
           level      = [ "A24" ];
         }
       ];

    To also verify the 24-hour accumulated precipitation fields.

  • In the fcst dictionary, set:

       message_type = [ "ADPSFC" ];

    To verify against surface observations.

     

  • In the mask dictionary, set:
       grid = [ "FULL" ]; 

    To accumulate statistics over the full model domain.

  • In the mask dictionary, set
       poly = [ "MET_BASE/poly/NWC.poly",
                "MET_BASE/poly/SWC.poly" ];

    To also verify over the northwest coast (NWC) and southwest coast (SWC) subregions.

  • Set:
    output_flag = {
       ecnt  = BOTH;
       rhist = BOTH;
       phist = BOTH;
       orank = BOTH;
       ssvar = BOTH;
       relp  = BOTH;
    }

    To compute continuous ensemble statistics (ECNT), ranked histogram (RHIST), probability integral transform histogram (PHIST), observation ranks (ORANK), spread-skill variance (SSVAR), and relative position (RELP).

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