Articles | Volume 16, issue 11
https://doi.org/10.5194/nhess-16-2391-2016
https://doi.org/10.5194/nhess-16-2391-2016
Research article
 | 
21 Nov 2016
Research article |  | 21 Nov 2016

An analysis of uncertainties and skill in forecasts of winter storm losses

Tobias Pardowitz, Robert Osinski, Tim Kruschke, and Uwe Ulbrich

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Cited articles

Bröcker, J. and Smith, L.: From ensemble forecasts to predictive distribution functions, Tellus A, 60, 663–678, https://doi.org/10.1111/j.1600-0870.2008.00333.x, 2008.
Buizza, R., Miller, M., and Palmer, T. N.: Stochastic representation of model uncertainties in the ECMWF ensemble prediction system, Q. J. Roy. Meteorol. Soc., 125, 2887–2908, https://doi.org/10.1002/qj.49712556006, 1999.
Doms, G.: A Description of the Nonhydrostatic Regional COSMO-Model – Part I: Dynamics and Numerics Consortium for Small-Scale Modelling, Deutscher Wetterdienst, Offenbach, Germany, 2011.
Donat, M. G., Pardowitz, T., Leckebusch, G. C., Ulbrich, U., and Burghoff, O.: High-resolution refinement of a storm loss model and estimation of return periods of loss-intensive storms over Germany, Nat. Hazards Earth Syst. Sci., 11, 2821–2833, https://doi.org/10.5194/nhess-11-2821-2011, 2011.
ECMWF: Operational archive for the Ensemble prediction system, available at: http://apps.ecmwf.int/archive-catalogue/?class=od&stream=enfo&expver=1 (last access: 1 April 2016), 2016.
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This paper describes an approach to derive probabilistic predictions of local winter storm damage occurrences. Such predictions are subject to large uncertainty due to meteorological forecast uncertainty and uncertainties in modelling weather impacts. The paper aims to quantify these uncertainties and demonstrate that valuable predictions can be made on the district level several days ahead.
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