The Discrete Brier and Ranked Probability Skill Scores

Abstract

Abstract The Brier skill score (BSS) and the ranked probability skill score (RPSS) are widely used measures to describe the quality of categorical probabilistic forecasts. They quantify the extent to which a forecast strategy improves predictions with respect to a (usually climatological) reference forecast. The BSS can thereby be regarded as the special case of an RPSS with two forecast categories. From the work of Müller et al., it is known that the RPSS is negatively biased for ensemble prediction systems with small ensemble sizes, and that a debiased version, the RPSSD, can be obtained quasi empirically by random resampling from the reference forecast. In this paper, an analytical formula is derived to directly calculate the RPSS bias correction for any ensemble size and combination of probability categories, thus allowing an easy implementation of the RPSSD. The correction term itself is identified as the “intrinsic unreliability” of the ensemble prediction system. The performance of this new formulation of the RPSSD is illustrated in two examples. First, it is applied to a synthetic random white noise climate, and then, using the ECMWF Seasonal Forecast System 2, to seasonal predictions of near-surface temperature in several regions of different predictability. In both examples, the skill score is independent of ensemble size while the associated confidence thresholds decrease as the number of ensemble members and forecast/observation pairs increase.