Describe the Enhancement

This issue arose via GitHub discussion dtcenter/METplus#1742. While a probability forecast can have any value between 0 and 1, it is very often the case that probability forecasts are not actually continuous. Instead, the values are often clustered around 1/n where n is an integer number of ensemble members. This issue is to refine the configuration options in support of verifying these non-continuous, ensemble-derived probability forecasts.

Verification of probabilistic forecasts is performed in MET's Point-Stat, Grid-Stat, Stat-Analysis, and Series-Analysis tools. In general, the probability logic is enabled by the presence of the fcst.prob dictionary in the tool's configuration file. It is either defined as a boolean (true/false) or provides additional information for how to select the probability data from the input GRIB1/2 file.

Prior to computing probabilistic statistics, MET first places all the probability matched pairs into an Nx2 contingency table. The N is based on the number of probability bins between 0 and 1. And the 2 indicates whether or not the event actually occurred in the observation. The cat_thresh configuration option specified for the probabilistic input data defines the bins that should be applied in the verification step. cat_thresh can be defined using two conventions:

1. Explicitly specify thresholds spanning [0, 1], all with the same threshold type:

cat_thresh = [ >=0, >0.25, >=0.5, >=0.75, >=1.0 ];

2. Use threshold equality logic as a shorthand (the example below is equivalent to the one above):

cat_thresh = [ ==0.25 ];

Both options define the same 4 probability bins. When computing the Brier Score, MET uses the center point of these bins as the probability value for ALL points falling in that bin, as seen on this line of code. For example, any probability value between 0 and 0.249999 would be evaluated as 0.125 (the center point of that bin) in the computation of Brier Score.

The task here is to refine these options to more easily evaluate probability forecasts whose values are actually 1/n, where n is a number of ensemble members. For example, when n = 6, we'd want the values used in the Brier Score computation to be 0, 0.166, 0.333, 0.5, 0.666, 0.8333, and 1.0, corresponding to ensemble values of 0/6, 1/6, 2/6, 3/6, 4/6, 5/6, and 6/6. Defining probability bins whose center values exactly match these is confusing and tedious. The task here is to add a configuration option to make it easy and convenient.

One option for supporting this logic is by adding a 3rd variation for defining probability bins. In addition to the 2 listed above, add a 3rd option for cat_thresh = [ ==N ]; where N is some integer > 1. As of MET version 10.1.0, this setting results in a runtime error:

ERROR  : string_to_prob_thresh() -> threshold value (6) must be between 0 and 1.

We could repurpose this by letting N>1 define the number of ensemble members for which we want probability bins of size 1/n and centered on those 1/n values. The advantage to this approach is that we would not need to add/document new config options. Probability thresholds are actually defined in multiple config file contexts (cat_thresh, cov_thresh, prob_cat_thresh, prob_genesis_thresh). Supporting this in all those contexts is nice. The downside is that perhaps a new config option name with additional documentation would make the logic more clear.

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