ALBERT

Description: The winds associated with extra-tropical cyclones are amongst the costliest natural perils in Europe. Re/insurance companies typically have insured exposure at multiple locations and hence the losses they incur from any individual storm crucially depend on that storm's spatial structure. Motivated by this, this study investigates the spatial structure of the most extreme windstorms in Europe. The data consists of a carefully constructed set of 135 of the most damaging storms in the period 1972–2010. Extreme value copulas are applied to this data to investigate the spatial dependencies of gusts. The copula method is used to investigate three aspects of windstorms. First, spatial maps of expected hazard damage between large cities and their surrounding areas are presented. Second, we demonstrate a practical application of the copula method to benchmark catalogues of artificial storms for use in the re/insurance sector. Third, the copula-based method is used to investigate the sensitivity of spatially aggregated damage to climate variability. The copula method allows changes to be expressed in terms of storm frequency, local intensity, and storm spatial structure and gives a more detailed view of how climate variability may affect multi-location risk in Europe.

Description: The clustering of severe European windstorms on annual timescales has substantial impacts on the re/insurance industry. Management of the risk is impaired by large uncertainties in estimates of clustering from historical storm datasets typically covering the past few decades. The uncertainties are unusually large because clustering depends on the variance of storm counts. Eight storm datasets are gathered for analysis in this study in order to reduce these uncertainties. Six of the datasets contain more than 100~years of severe storm information to reduce sampling errors, and the diversity of information sources and analysis methods between datasets sample observational errors. All storm severity measures used in this study reflect damage, to suit re/insurance applications. It is found that the shortest storm dataset of 42 years in length provides estimates of clustering with very large sampling and observational errors. The dataset does provide some useful information: indications of stronger clustering for more severe storms, particularly for southern countries off the main storm track. However, substantially different results are produced by removal of one stormy season, 1989/1990, which illustrates the large uncertainties from a 42-year dataset. The extended storm records place 1989/1990 into a much longer historical context to produce more robust estimates of clustering. All the extended storm datasets show a greater degree of clustering with increasing storm severity and suggest clustering of severe storms is much more material than weaker storms. Further, they contain signs of stronger clustering in areas off the main storm track, and weaker clustering for smaller-sized areas, though these signals are smaller than uncertainties in actual values. Both the improvement of existing storm records and development of new historical storm datasets would help to improve management of this risk.