Articles | Volume 4, issue 1/2
https://doi.org/10.5194/ascmo-4-65-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/ascmo-4-65-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
14 Dec 2018
| 14 Dec 2018
Hourly probabilistic snow forecasts over complex terrain: a hybrid ensemble postprocessing approach
Department of Statistics, Faculty of Economics and Statistics, Universität Innsbruck, Universitätsstraße 15, 6020 Innsbruck, Austria
Georg J. Mayr
Institute of Atmospheric and Cryospheric Sciences, Faculty of Geo- and Atmospheric Sciences, Universität Innsbruck, Innrain 52, 6020 Innsbruck, Austria
Jakob W. Messner
Department of Electrical Engineering, Technical University of Denmark, Elektrovej, Building 325, 2800 Kgs. Lyngby, Denmark
Achim Zeileis
Department of Statistics, Faculty of Economics and Statistics, Universität Innsbruck, Universitätsstraße 15, 6020 Innsbruck, Austria
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Short summary
Snowfall forecasts are important for a range of economic sectors as well as for the safety of people and infrastructure, especially in mountainous regions. This work presents a novel statistical approach to provide accurate forecasts for fresh snow amounts and the probability of snowfall combining data from various sources. The results demonstrate that the new approach is able to provide reliable high-resolution hourly snowfall forecasts for the eastern European Alps up to 3 days ahead.
Snowfall forecasts are important for a range of economic sectors as well as for the safety of...
