Abstract
AbstractRank histograms are a popular way to assess the reliability of ensemble forecasting systems. If the ensemble forecasting system is reliable, the rank histogram should be flat, “up to statistical fluctuations.” There are two long‐noted challenges to this approach. Firstly, uniformity of the overall distribution is implied by but does not imply reliability; ideally the distribution of the ranks should be uniform even conditionally on different forecast scenarios. Secondly, the ranks are serially dependent in general, precluding the use of standard goodness‐of‐fit tests to assess the uniformity of rank distributions without any further precautions. The present paper deals with both these issues by drawing together the concept of stratified rank histograms, which have been developed to deal with the first issue, with ideas that exploit the reliability condition to manage the serial correlations, thus dealing with the second issue. As a result, tests for uniformity of stratified rank histograms are presented that are valid under serial correlations.
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