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Remember the past: a comparison of time-adaptive training schemes for non-homogeneous regression

Lang, Moritz N.; Lerch, Sebastian; Mayr, Georg J.; Simon, Thorsten; Stauffer, Reto; Zeileis, Achim

Non-homogeneous regression is a frequently used post-processing method for increasing the predictive skill of probabilistic ensemble weather forecasts. To adjust for seasonally varying error characteristics between ensemble forecasts and corresponding observations, different timeadaptive training schemes, including the classical sliding training window, have been developed for non-homogeneous regression. This study compares three such training approaches with the sliding-window approach for the application of post-processing near-surface air temperature forecasts across central Europe. The predictive performance is evaluated conditional on three different groups of stations located in plains, in mountain foreland, and within mountainous terrain, as well as on a specific change in the ensemble forecast system of the European Centre for Medium-Range Weather Forecasts (ECMWF) used as input for the post-processing. The results show that time-adaptive training schemes using data over multiple years stabilize the temporal evolution of the coefficient estimates, yielding an increased predictive performance for all station types tested compared to the classical sliding-window approach based on the most recent days only. ... mehr

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Verlagsausgabe §
DOI: 10.5445/IR/1000117421
Veröffentlicht am 26.02.2020
DOI: 10.5194/npg-27-23-2020
Zitationen: 4
Web of Science
Zitationen: 3
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Stochastik (STOCH)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2020
Sprache Englisch
Identifikator ISSN: 1607-7946
KITopen-ID: 1000117421
Erschienen in Nonlinear processes in geophysics
Band 27
Heft 1
Seiten 23–34
Vorab online veröffentlicht am 05.02.2020
Nachgewiesen in Scopus
Web of Science
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