Describe the New Feature

This enhancement was proposed by @rgrumbine via METplus discussion dtcenter/METplus#2315. As of MET version 11.1.0, when verifying against point observations, all points are treated equally. When points observations are not evenly distributed across a domain, as they almost never are, the resulting statistics over-sample from the more dense locations and under-sample from the less dense locations. This issue is to develop and implement an algorithm for addressing this representativeness problem.

@rgrumbine recommends applying Voroni tessellations to this problem, using the area of the voroni cell to weight the observation it contains. There are several details to consider:

1. Weighting should applied to the computation of categorical, continuous, and ensemble statistics. While the existing grid_weight_flag option applies to continuous statistics, it is NOT applied to categorical ones. Need to address this discrepancy. Would need to change MET library code to store floating point weights rather than just integer counts. Can contingency tables with integer counts simply be replaced by sums of floating point weights?

2. Need to add a corresponding point_weight_flag to Point-Stat and Ensemble-Stat, presumably with options for None and VORONI. Should other algorithms be considered as well?

3. How do Voroni tesselations interact with masking regions? Points outside the mask are basically treated as missing data values. Do masking regions and embedded missing data impact the Voroni weight computations?

4. How to build acceptance for this proposed new algorithm?

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