Scoring Rules for Forecast Verification

Abstract The problem of probabilistic forecast verification is approached from a theoretical point of view starting from three basic desiderata: additivity, exclusive dependence on physical observations (“locality”), and strictly proper behavior. By imposing such requirements and only using elementary mathematics, a univocal measure of forecast goodness is demonstrated to exist. This measure is the logarithmic score, based on the relative entropy between the observed occurrence frequencies and the predicted probabilities for the forecast events. Information theory is then used as a guide to choose the scoring-scale offset for obtaining meaningful and fair skill scores. Finally the Brier score is assessed and, for single-event forecasts, its equivalence to the second-order approximation of the logarithmic score is shown. The large part of the presented results are far from being new or original, nevertheless their use still meets with some resistance in the weather forecast community. This paper aims at providing a clear presentation of the main arguments for using the logarithmic score.