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URN: urn:nbn:de:swb:90-483944
DOI: 10.5194/hess-19-1787-2015
Zitationen: 13
Web of Science
Zitationen: 13

Stochastic bias correction of dynamically downscaled precipitation fields for Germany through Copula-based integration of gridded observation data

Mao, G.; Vogl, S.; Laux, P.; Wagner, S.; Kunstmann, H.

Dynamically downscaled precipitation fields from regional climate models (RCMs) often cannot be used directly for regional climate studies. Due to their inherent biases, i.e., systematic over- or underestimations compared to observations, several correction approaches have been developed. Most of the bias correction procedures such as the quantile mapping approach employ a transfer function that is based on the statistical differences between RCM output and observations. Apart from such transfer function-based statistical correction algorithms, a stochastic bias correction technique, based on the concept of Copula theory, is developed here and applied to correct precipitation fields from the Weather Research and Forecasting (WRF) model. For dynamically downscaled precipitation fields we used high-resolution (7 km, daily) WRF simulations for Germany driven by ERA40 reanalysis data for 1971-2000. The REGNIE (REGionalisierung der NIEderschlagshöhen) data set from the German Weather Service (DWD) is used as gridded observation data (1 km, daily) and aggregated to 7 km for this application. The 30-year time series are split into a calibr ... mehr

Zugehörige Institution(en) am KIT Fakultät für Physik (PHYSIK)
Institut für Meteorologie und Klimaforschung - Atmosphärische Umweltforschung (IMK-IFU)
Publikationstyp Zeitschriftenaufsatz
Jahr 2015
Sprache Englisch
Identifikator ISSN: 1027-5606
KITopen ID: 1000048394
HGF-Programm 12.02.03; LK 01
Erschienen in Hydrology and Earth System Sciences
Band 19
Heft 4
Seiten 1787-1806
Bemerkung zur Veröffentlichung Gefördert durch den KIT-Publikationsfonds
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