Add goodness distance calculation for performance diagram

[jvigh]

Add goodness distance calculation for performance diagram

The goal of this issue is to compute the Euclidean distance between the location of a particular set of probabilistic forecasts and the upper right-hand corner of the performance diagram. As long as the bias is within a range that is acceptable to the sponsor, this Euclidean distance represents a key aspect of the "goodness" of the forecast (high POD and high CSI/low FAR). These distances should be saved to output for later use in other performance evaluation tools such as scorecards. For additional background, see: https://docs.google.com/presentation/d/1cbPaF75E441OW_1LVahJUox6UrjlhPheEbjLfI9GOiI/edit#slide=id.p

Acceptance Testing

Prioritized in the following order (high to low):

• RI evaluation data from Jonathan’s HFIP-PREDICT project (GRIP, FHLO, etc.)
• TC genesis evaluation data from Dan Halperin’s TC genesis project (ask John Halley Gotway)
• RI data from Paul’s Ensemble RI project (lots of ensemble data)
• JTWC forecast and model data (1997-2021)

Describe tests required for new functionality.

Time Estimate

1 days

Sub-Issues

Consider breaking the new feature down into sub-issues.

• Add a checkbox for each sub-issue here.

Relevant Deadlines

Tara and Jonathan need to have a recorded presentation with results by April 30.
If we get it done by May 28, it could go into RC1.

Funding Source

Use account key 2785041 (NOAA Hur Supp 1A-3-5 METplus) for time spent on this development.

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Define Related Issue(s)

Consider the impact to the other METplus components.

• This will directly impact METcalcpy. A separate issue will be defined for related development in METplotpy. A METplus will also be developed.

• METplus, MET, METdatadb, METviewer, METexpress, METcalcpy, METplotpy

New Feature Checklist

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