Statistical modelling of European windstorm footprints to explore hazard characteristics and insured loss

Abstract

This thesis uses statistical modelling to better understand the relationship between insured losses and hazard footprint characteristics for European windstorms (extra- tropical cyclones). The footprint of a windstorm is defined as the maximum wind gust speed to occur at a set of spatial locations over the duration of the storm. A better understanding of this relationship is required because the most damaging historical windstorms have had footprints with differing characteristics. Some have a large area of relatively low wind gust speeds, while others have a smaller area of higher wind gust speeds. In addition, this insight will help to explain the surprising, sharp decline in European wind related losses in the mid 1990’s. This novel exploration is based on 5730 high resolution model generated historical footprints (1979-2012) representing the whole European domain.

Functions of extreme footprint wind gust speeds, known as storm severity measures, are developed to represent footprint characteristics. Exploratory data analysis is used to compare which storm severity measures are most successful at classifying 23 extreme windstorms, known to have caused large insured losses. Summarising the footprint using these scalar severity measures, however, fails to capture different combinations of spatial scale and local intensity characteristics. To overcome this, a novel statistical model for windstorm footprints is developed, initially for pairs of locations using a bivariate Gaussian copula model; subsequently extended to represent the whole European domain using a geostatistical spatial model. Throughout, the distribution of wind gust speeds at each location is modelled using a left-truncated Generalised Extreme Value (GEV) distribution. Synthetic footprints, simulated from the geostatistical model, are then used in a sensitivity study to explore whether the local intensity or spatial dependence structure of a footprint has the most influence on insured loss. This contributes a novel example of sensitivity analysis applied to a stochastic natural hazards model.

The area of the footprint exceeding 25ms−1 over land is the most successful storm severity measure at classifying extreme loss windstorms, ranking all 23 within the top 18% of events. Marginally transformed wind gust speeds are identified as being asymptotically independent and second-order stationary, allowing for the spatial dependence to be represented by a geostatistical covariance function. The geostatistical windstorm footprint model is able to quickly (∼3 seconds) simulate synthetic footprints which realistically represent joint losses throughout Europe. The sensitivity study identifies that the left-truncated GEV parameters have a greater influence on insured loss than the geostatistical spatial dependence parameters. The observed decline in wind related losses in the 1990’s can therefore be attributed to a change in the local intensity rather than the spatial structure of footprint wind gust speeds.