An analysis of uncertainties and skill in forecasts of winter storm losses
- 1Hans Ertel Centre for Weather Research, Optimal Use of Weather Forecast Branch, Berlin, Germany
- 2Freie Universität Berlin, Institute of Meteorology, Carl-Heinrich-Becker Weg 6–10, 12165 Berlin, Germany
- 3CNRM UMR 3589, Météo-France/CNRS, 42 avenue Gustave Coriolis, 31057 Toulouse, France
- 4GEOMAR Helmholtz Centre for Ocean Research Kiel, Düsternbrooker Weg 20, 24105 Kiel, Germany
Abstract. This paper describes an approach to derive probabilistic predictions of local winter storm damage occurrences from a global medium-range ensemble prediction system (EPS). Predictions of storm damage occurrences are subject to large uncertainty due to meteorological forecast uncertainty (typically addressed by means of ensemble predictions) and uncertainties in modelling weather impacts. The latter uncertainty arises from the fact that local vulnerabilities are not known in sufficient detail to allow for a deterministic prediction of damages, even if the forecasted gust wind speed contains no uncertainty. Thus, to estimate the damage model uncertainty, a statistical model based on logistic regression analysis is employed, relating meteorological analyses to historical damage records. A quantification of the two individual contributions (meteorological and damage model uncertainty) to the total forecast uncertainty is achieved by neglecting individual uncertainty sources and analysing resulting predictions. Results show an increase in forecast skill measured by means of a reduced Brier score if both meteorological and damage model uncertainties are taken into account. It is demonstrated that skilful predictions on district level (dividing the area of Germany into 439 administrative districts) are possible on lead times of several days. Skill is increased through the application of a proper ensemble calibration method, extending the range of lead times for which skilful damage predictions can be made.