Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/184983
Authors: 
Stauffer, Reto
Mayr, Georg J.
Messner, JakobW.
Zeileis, Achim
Year of Publication: 
2018
Series/Report no.: 
Working Papers in Economics and Statistics No. 2018-05
Abstract: 
Accurate and high-resolution snowfall and fresh snow forecasts are important for a range of economic sectors as well as for the safety of people and infrastructure, especially in mountainous regions. In this article a new hybrid statistical postprocessing method is proposed, which combines standardized anomaly model output statistics (SAMOS) with ensemble copula coupling (ECC) and a novel re-weighting scheme to produce spatially and temporally high-resolution probabilistic snow forecasts. Ensemble forecasts and hindcasts of the European Centre for Medium-Range Weather Forecasts (ECMWF) serve as input for the statistical postprocessing method, while measurements from two different networks provide the required observations. This new approach is applied to a region with very complex topography in the Eastern European Alps. The results demonstrate that the new hybrid method not only allows to provide reliable high-resolution forecasts, it also allows to combine different data sources with different temporal resolutions to create hourly probabilistic and physically consistent predictions.
Subjects: 
meteorology
ensemble postprocessing
standardized anomalies
copula coupling
high-resolution
fresh snow
snowfall
Document Type: 
Working Paper

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