Suchergebnisse

Abstract. A storm loss model for Germany is presented. Input data to the model are the daily maximum gust wind speeds measured at stations distributed over the country. The individual daily peak gust values are scaled with the local climatological upper 2% quantile at each station. This scaling serves to take local conditions at the stations into account, and thus permits a simple spatial interpolation of the storm field. The next step is the computation of a loss index for each storm. It is based on the excess of (scaled) wind speed over the upper 2% quantile, and on population numbers in the individual districts within Germany, with the latter serving as a proxy for the spatial distribution of values that could be affected by a storm. Using wind speeds in excess of the percentile value also serves to take spatial heterogeneity of vulnerability against storms into account. The aggregated storm index gives an estimate of the severity of an individual storm. Finally, the relation between actual loss produced by a storm and the index is estimated using published annual insurance loss due to windstorm in Germany. Index values are accumulated for each year, and the relation to actual loss is computed. The average ratio for the whole reference period is eventually used. It is shown that the interannual variability of storm-related losses can be reproduced with a correlation coefficient of r = 0.96, and even individual storm damages can be estimated. Based on these evaluations we found that only 50 storms account for about 80% of insured storm losses between 1970 and 1997.

Abstract. During the last decades, several windstorm series hit Europe leading to large aggregated losses. Such storm series are examples of serial clustering of extreme cyclones, presenting a considerable risk for the insurance industry. Clustering of events and return periods of storm series for Germany are quantified based on potential losses using empirical models. Two reanalysis data sets and observations from German weather stations are considered for 30 winters. Histograms of events exceeding selected return levels (1-, 2- and 5-year) are derived. Return periods of historical storm series are estimated based on the Poisson and the negative binomial distributions. Over 4000 years of general circulation model (GCM) simulations forced with current climate conditions are analysed to provide a better assessment of historical return periods. Estimations differ between distributions, for example 40 to 65 years for the 1990 series. For such less frequent series, estimates obtained with the Poisson distribution clearly deviate from empirical data. The negative binomial distribution provides better estimates, even though a sensitivity to return level and data set is identified. The consideration of GCM data permits a strong reduction of uncertainties. The present results support the importance of considering explicitly clustering of losses for an adequate risk assessment for economical applications.