On the clustering of winter storm loss events over Germany
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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2014-08-01
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| Series: | Natural Hazards and Earth System Sciences |
| Online Access: | http://www.nat-hazards-earth-syst-sci.net/14/2041/2014/nhess-14-2041-2014.pdf |
| Summary: | 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. |
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| ISSN: | 1561-8633 1684-9981 |