Describe the Enhancement

MET development version 11.0.0-beta4 made several changes to the "stat" output. This issue is to enhance METplotpy to plot the newly added statistics and enable them to be included in scorecards.

I don't see a need to plot the N_GE_OBS and N_LT_OBS columns that were also added, but they are needed in the aggregation of the other columns.

• Issue Add the Mean Absolute Difference (SPREAD_MD) to the ECNT line type MET#2332 adds 1 new column (SPREAD_MD) to the ECNT line type generated by Ensemble-Stat and the HiRA method in Point-Stat. See Table 13.2. The SPREAD_MD is the ensemble mean absolute difference and is an alternative measure of ensemble spread. This column should be aggregated over multiple cases using a weighted average approach, where the weight is defined by the number of matched pairs in the TOTAL column.

• Issue Add MAE to the ECNT line type from Ensemble-Stat and for HiRA MET#2325 adds 2 new columns (MAE and MAE_OERR) to the ECNT line type generated by Ensemble-Stat and the HiRA method in Point-Stat. See Table 13.2. The MAE is the mean absolute error. These columns should be aggregated together over multiple cases using a weighted average approach, where the weight is defined by the number of matched pairs in the TOTAL column.

These MAE's should be added to the list of statistics where 0 is the perfect score.

• Issue Add new bias ratio statistic to the ECNT line type from Ensemble-Stat and for HiRA MET#2058 adds 5 new columns (BIAS_RATIO, N_GE_OBS, ME_GE_OBS, N_LT_OBS, ME_LT_OBS) to the ECNT line type generated by Ensemble-Stat and the HiRA method in Point-Stat. See Table 13.2. All 5 columns are added so that the BIAS_RATIO can be aggregated over multiple runs, as described below.
  • N_GE_OBS is an integer and is aggregated over multiple runs by summing the values.
  • ME_GE_OBS is a float and is aggregated as a weighted average where the weights are defined by the N_GE_OBS column.
  • N_LT_OBS is an integer and is aggregated over multiple runs by summing the values.
  • ME_LT_OBS is a float and is aggregated as a weighted average where the weights are defined by the N_LT_OBS column.
  • BIAS_RATIO is a float >0. It aggregated by first aggregating ME_GE_OBS and ME_LT_OBS and then computing BIAS_RATIO = ME_GE_OBS / absolute value of ME_LT_OBS.

The ME_GE_OBS and ME_LT_OBS columns are mean errors and should added to list of statistics where 0 is the perfect score.
The BIAS_RATIO is the ratio of those 2 ME values, and I suppose a perfect score would be 0/0 which is undefined. This probably should go in the list of statistics where 1 is the perfect score... where the amount of high bias equal the amount of low bias. In this way, its similar to the frequency bias (FBIAS) statistic.

Sample data for this issue can be found in the nightly build output in seneca:/d1/projects/MET/MET_regression/develop/NB20221101/MET-develop/test_output (or a more recent date).
And here's a tarfile of that output:
ftp://ftp.rap.ucar.edu/incoming/irap/met_help/NB20221101_develop_test_output.tar.gz

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Split 2799991 and 2773542

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