A probabilistic verification score for contours: Methodology and application to Arctic ice-edge forecasts


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helge.goessling [ at ] awi.de

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 analog of the Continuous Ranked Probability Score (CRPS). Applying the SPS to idealised 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 case of the ice edge corresponds to the Integrated Ice Edge Error (IIEE). By comparing initialised 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.



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ISI/Scopus peer-reviewed
Publication Status
Published
Eprint ID
46738
DOI 10.1002/qj.3242

Cite as
Goessling, H. and Jung, T. (2018): A probabilistic verification score for contours: Methodology and application to Arctic ice-edge forecasts , Quarterly Journal of the Royal Meteorological Society . doi: 10.1002/qj.3242


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