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Titel Statistical downscaling of extreme daily precipitation using extreme value theory
VerfasserIn P. Friederichs
Konferenz EGU General Assembly 2009
Medientyp Artikel
Sprache Englisch
Digitales Dokument PDF
Erschienen In: GRA - Volume 11 (2009)
Datensatznummer 250020655
 
Zusammenfassung
Although present day weather forecast models usually cannot provide realistic descriptions of local and particularly extreme weather conditions, they provide reliable forecasts of the atmospheric circulation that encompasses the sub-scale processes leading to extremes. Hence, forecasts of extreme events can only be achieved through a combination of dynamical and statistical analysis methods, where a stable and significant statistical model based on a-priori physical reasoning establishes a-posterior a statistical-dynamical model between the local extremes and the large scale circulation. Here we present the development and application of such a statistical model calibration (downscaling) on the basis of extreme value theory, in order to derive probabilistic estimates for (extreme) local precipitation. Besides a semi-parametric approach that employs censored quantile regression we use parametric extreme value distributions to derive conditional quantile estimates. The performance of two parametric approaches is compared, which use a Poisson point process with non-stationary parameters but a constant threshold, and the non-stationary generalized Pareto distribution and a variable threshold. The downscaling applies to ERA40 reanalysis, in order to derive estimates of the conditional quantiles of daily precipitation accumulations at more than 2000 German weather stations.