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
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)
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Persistent link: https://EconPapers.repec.org/RePEc:inn:wpaper:2018-05
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