Hourly probabilistic snow forecasts over complex terrain: A hybrid ensemble postprocessing approach

Reto Stauffer (), Georg J. Mayr (), Jakob W. Messner () and Achim Zeileis ()

Working Papers from Faculty of Economics and Statistics, Universität Innsbruck

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.

Keywords: meteorology; ensemble postprocessing; standardized anomalies; copula coupling; high-resolution; fresh snow; snowfall (search for similar items in EconPapers)
Pages: 38
Date: 2018-05
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Persistent link: https://EconPapers.repec.org/RePEc:inn:wpaper:2018-05

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