Abstract
Verification of ensemble forecasts for extreme events remains a challenging
question. The general public as well as the media naturely pay particular
attention on extreme events and conclude about the global predictive
performance of ensembles, which are often unskillful when they are needed.
Ashing classical verification tools to focus on such events can lead to
unexpected behaviors. To square up these effects, thresholded and weighted
scoring rules have been developed. Most of them use derivations of the
Continuous Ranked Probability Score (CRPS). However, some properties of the
CRPS for extreme events generate undesirable effects on the quality of
verification. Using theoretical arguments and simulation examples, we
illustrate some pitfalls of conventional verification tools and propose a
different direction to assess ensemble forecasts using extreme value theory,
considering proper scores as random variables.
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