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D. B. Stephenson, C. A. S. Coelho, and I. T. Jolliffe

Abstract

The Brier score is widely used for the verification of probability forecasts. It also forms the basis of other frequently used probability scores such as the rank probability score. By conditioning (stratifying) on the issued forecast probabilities, the Brier score can be decomposed into the sum of three components: uncertainty, reliability, and resolution. This Brier score decomposition can provide useful information to the forecast provider about how the forecasts can be improved.

Rather than stratify on all values of issued probability, it is common practice to calculate the Brier score components by first partitioning the issued probabilities into a small set of bins. This note shows that for such a procedure, an additional two within-bin components are needed in addition to the three traditional components of the Brier score. The two new components can be combined with the resolution component to make a generalized resolution component that is less sensitive to choice of bin width than is the traditional resolution component. The difference between the generalized resolution term and the conventional resolution term also quantifies how forecast skill is degraded when issuing categorized probabilities to users. The ideas are illustrated using an example of multimodel ensemble seasonal forecasts of equatorial sea surface temperatures.

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