Competing forecast verification: Using the power-divergence statistic for testing the frequency of "better"

Empirical results demonstrate that the reduction to two categories (i.e., model A is better than model B versus model B is better than A) results in a test that is reasonably robust to even severe departures from temporal independence, as well as contemporaneous correlation. The test is demonstrated on two different example verification sets: 6-h forecasts of eddy dissipation rate (m2/3 s_1) from two versions of the Graphical Turbulence Guidance model and for 12-h forecasts of 2-m temperature (8C) and 10-m wind speed (m s_1) from two versions of the High-Resolution Rapid Refresh model.

The novelty of this paper is in demonstrating the utility of the power-divergence statistic in the face of temporally dependent data, as well as the emphasis on testing for the "frequency-of-better" alongside more traditional measures.