Testing competing precipitation forecasts accurately and efficiently: The spatial prediction comparison test

The SPCT yields a statistical test that solves one important issue for verifying forecasts spatially by accounting for spatial correlation. Important for precipitation forecasts is that the test requires no distributional assumptions, is easy to perform, and can be applied efficiently to either gridded or nongridded spatial fields. The test compares loss functions between two competing forecasts, where any such function can be used, but most still suffer from the limitations of traditional gridpoint-by-gridpoint assessment techniques.

Therefore, two new loss functions to the SPCT are introduced here that address these concerns. The first is based on distance maps and the second on image warping. Results are consistent with other spatial assessment methods, but provide a relatively straightforward mechanism for comparing forecasts with a statistically powerful test.

The SPCT combined with these loss functions provides a new mechanism for appropriately testing which of two competing precipitation models is best, and whether the result is statistically significant or not.