Generating synthetic rainfall fields by R‐vine copulas applied to seamless probabilistic predictions
Peter Schaumann,
Martin Rempel,
Ulrich Blahak
et al.
Abstract:Many post‐processing methods improve forecasts at individual locations but remove their correlation structure. However, this information is essential for forecasting larger‐scale events, such as the total precipitation amount over areas like river catchments, which are relevant for weather warnings and flood predictions. We propose a method to reintroduce spatial correlation into a post‐processed forecast using an R‐vine copula fitted to historical observations. The method rearranges predictions at individual … Show more
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