METplus Practical Session Guide (Version 4.0) | MET Tool: Point-Stat > Reconfigure

Point-Stat Tool: Reconfigure

Now we'll reconfigure and rerun Point-Stat.

Start by making a copy of the configuration file we just used:
cp PointStatConfig_tutorial_run1 PointStatConfig_tutorial_run2

This time, we'll use two dictionary entries to specify the forecast field in order to set different thresholds for each vertical level. Point-Stat may be configured to verify as many or as few model variables and vertical levels as you desire.

Edit the PointStatConfig_tutorial_run2 file as follows:
vi PointStatConfig_tutorial_run2
  • Set:
    fcst = {
       field = [
        {
          name       = "TMP";
          level      = [ "Z2" ];
          cat_thresh = [ >273, >278, >283, >288 ];
        },
        {
          name       = "TMP";
          level      = [ "P750-850" ];
          cat_thresh = [ >278 ];
        }
      ];
    }
    obs = fcst;

    to verify 2-meter temperature and temperature fields between 750hPa and 850hPa, using the thresholds specified.

  • Set:
     message_type = ["ADPUPA","ADPSFC"]
     sid_inc = [];
     sid_exc = [];
     obs_quality = [];
     duplicate_flag = NONE;
     obs_summary = NONE;
     obs_perc_value = 50;

    to include the Upper Air (UPA) and Surface (SFC) observations in the evaluation

  • Set:
    mask = {
       grid  = [ "G212" ];
       poly  = [ "MET_BASE/poly/EAST.poly",
                 "MET_BASE/poly/WEST.poly" ];
       sid   = [];
       llpnt = [];
    }

    to compute statistics over the NCEP Grid 212 region and over the Eastern and Western United States, as defined by the polylines specified.

  • Set:
    interp = {
       vld_thresh = 1.0;
       shape       = SQUARE;
       type = [
          {
             method = NEAREST;
             width  = 1;
          },
          {
             method = DW_MEAN;
             width  = 5;
          }
       ];
    }

    to indicate that the forecast values should be interpolated to the observation locations using the nearest neighbor method and by computing a distance-weighted average of the forecast values over the 5 by 5 box surrounding the observation location.

  • Set:
    output_flag = {
       fho    = BOTH;
       ctc    = BOTH;
       cts    = BOTH;
       mctc   = NONE;
       mcts   = NONE;
       cnt    = BOTH;
       sl1l2  = BOTH;
       sal1l2 = NONE;
       vl1l2  = NONE;
       val1l2 = NONE;
       pct    = NONE;
       pstd   = NONE;
       pjc    = NONE;
       prc    = NONE;
       ecnt   = NONE;
       eclv   = BOTH;
       mpr    = BOTH;
    }

    to switch the SL1L2 and CTS output to BOTH and generate the optional ASCII output files for them.

  • Set:
    output_prefix = "run2";

    to customize the output file names for this run.

Let's look at our configuration selections and figure out the number of verification tasks Point-Stat will perform:
  • 2 fields: TMP/Z2 and TMP/P750-850
  • 2 observing message types: ADPUPA and ADPSFC
  • 3 masking regions: G212, EAST.poly, and WEST.poly
  • 2 interpolations: UW_MEAN width 1 (nearest-neighbor) and DW_MEAN width 5

Multiplying 2 * 2 * 3 * 2 = 24. So in this example, Point-Stat will accumulate matched forecast/observation pairs into 24 groups. However, some of these groups will result in 0 matched pairs being found. To each non-zero group, the specified threshold(s) will be applied to compute contingency tables.

Can you diagnose why some of these verification tasks resulted in zero matched pairs? (Hint: Reread the tip two pages back!)