A probabilistic verification score for contours: Methodology and application to Arctic ice‐edge forecasts. (28th August 2018) - Goessling, H. F.; Jung, T.
Abstract : We introduce a verification score for probabilistic forecasts of contours – the Spatial Probability Score (SPS). Defined as the spatial integral of local (Half) Brier Scores, the SPS can be considered the spatial analogue of the Continuous Ranked Probability Score (CRPS). Applying the SPS to idealized ensemble forecasts of the Arctic sea‐ice edge in a global coupled climate model, we demonstrate that the metric responds in a meaningful way to ensemble size, spread, and bias. When applied to individual forecasts or ensemble means (or quantiles), the SPS is reduced to the 'volume' of mismatch, which in the case of the ice edge corresponds to the Integrated Ice Edge Error (IIEE). By comparing initialized forecasts with climatological and persistence forecasts, we confirm earlier findings on the potential predictability of the Arctic sea‐ice edge from a probabilistic viewpoint. We conclude that the SPS is a promising probabilistic verification metric, for contour forecasts in general and for ice‐edge forecasts in particular. Abstract : We introduce a verification score for probabilistic forecasts of contours – the Spatial Probability Score (SPS) – and apply it to idealized ensemble forecasts of the Arctic sea‐ice edge in a global coupled climate model. We demonstrate that the metric responds properly to ensemble size, spread, and bias, and conclude that the SPS is a promising probabilistic verification metric, for contour forecasts in general and for ice‐edge forecasts in particular.