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

Author

Listed:
  • Reto Stauffer
  • Georg J. Mayr
  • Jakob W. Messner
  • Achim Zeileis

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.

Suggested Citation

  • Reto Stauffer & Georg J. Mayr & Jakob W. Messner & Achim Zeileis, 2018. "Hourly probabilistic snow forecasts over complex terrain: A hybrid ensemble postprocessing approach," Working Papers 2018-05, Faculty of Economics and Statistics, Universität Innsbruck.
    • Handle: RePEc:inn:wpaper:2018-05
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    References listed on IDEAS

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