European extra-tropical storm damage risk from a multi-model ensemble of dynamically-downscaled global climate models

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138

Abstract. The XWS (eXtreme WindStorms) catalogue consists of storm tracks and model-generated maximum 3 s wind-gust footprints for 50 of the most extreme winter windstorms to hit Europe in the period 1979-2012. The catalogue is intended to be a valuable resource for both academia and industries such as (re)insurance, for example allowing users to characterise extreme European storms, and validate climate and catastrophe models. Several storm severity indices were investigated to find which could best represent a list of known high-loss (severe) storms. The best-performing index was S ft , which is a combination of storm area calculated from the storm footprint and maximum 925 hPa wind speed from the storm track. All the listed severe storms are included in the catalogue, and the remaining ones were selected using S ft . A comparison of the model footprint to station observations revealed that storms were generally well represented, although for some storms the highest gusts were underestimated. Possible reasons for this underestimation include the model failing to simulate strong enough pressure gradients and not representing convective gusts.A new recalibration method was developed to estimate the true distribution of gusts at each grid point and correct for this underestimation. The recalibration model allows for storm-to-storm variation which is essential given that different storms have different degrees of model bias. The catalogue is available at www.europeanwindstorms.org.

Section: Creating the Windstorm Footprintsmentioning

confidence: 99%

The XWS open access catalogue of extreme European windstorms from 1979 to 2012

Roberts

Champion

Dawkins

et al. 2014

132

138

“…For instance, (Bernardara et al, 2011) used this approach for fitting a regional surge probability distribution. Anderson-Darling (AD) goodnessof-fit test is suggested and employed by Choulakian and Stephens (2001) and Haylock (2011). The adaptation of GPD to empirical data above the threshold could also be checked via the L-moments.…”

Section: Methods For Statistical Optimization (Step 2)mentioning

confidence: 99%

A two-step framework for over-threshold modelling of environmental extremes

Bernardara

Mazas

Kergadallan

et al. 2014

Abstract. The evaluation of the probability of occurrence of extreme natural events is important for the protection of urban areas, industrial facilities and others. Traditionally, the extreme value theory (EVT) offers a valid theoretical framework on this topic. In an over-threshold modelling (OTM) approach, Pickands' theorem, (Pickands, 1975) states that, for a sample composed by independent and identically distributed (i.i.d.) values, the distribution of the data exceeding a given threshold converges through a generalized Pareto distribution (GPD). Following this theoretical result, the analysis of realizations of environmental variables exceeding a threshold spread widely in the literature. However, applying this theorem to an auto-correlated time series logically involves two successive and complementary steps: the first one is required to build a sample of i.i.d. values from the available information, as required by the EVT; the second to set the threshold for the optimal convergence toward the GPD. In the past, the same threshold was often employed both for sampling observations and for meeting the hypothesis of extreme value convergence. This confusion can lead to an erroneous understanding of methodologies and tools available in the literature. This paper aims at clarifying the conceptual framework involved in threshold selection, reviewing the available methods for the application of both steps and illustrating it with a double threshold approach.

“…Klawa and Ulbrich, 2003;Haylock, 2011;Bonazzi et al, 2012;Cusack, 2013;Roberts et al, 2014) or under future climate change conditions (e.g. Pinto et al, 2007Pinto et al, , 2012Leckebusch et al, 2008;Donat et al, 2011).…”

Section: Identifying a Damaging Footprint Characteristicmentioning

confidence: 99%

“…The 925hPa wind speed is taken from ERA-Interim reanalysis (Dee et al, 2011). A 72 h duration, commonly used in the insurance industry (Haylock, 2011), is thought to capture the storms during their most damaging phase (Roberts et al, 2014). The 3 s wind gust speed has been shown to have a robust relationship with storm damage (Klawa and Ulbrich, 2003) and is commonly used in catastrophe models for risk quantification by the re/insurance industry (Roberts et al, 2014).…”

Section: Datamentioning

confidence: 99%

“…The local 98th percentile damage threshold in L 98 is calculated from daily maximum wind gust speeds in the period October 1979-March 2012. The population density data in L 98 is taken from Haylock (2011), created based on the 2005 Gridded Population of the World version 3, regridded to the spatial resolution of the footprint data. Figure 2a shows that there is a strong positive relationship between A 20 and A 25 , indicating that the A 20 SSI is appropriate for representing insured loss, as shown by Roberts et al (2014) for the A 25 SSI.…”

Section: Identifying a Damaging Footprint Characteristicmentioning

confidence: 99%

See 1 more Smart Citation

The 21st century decline in damaging European windstorms

Dawkins

Stephenson

Lockwood

et al. 2016

Abstract. A decline in damaging European windstorms has led to a reduction in insured losses in the 21st century. This decline is explored by identifying a damaging windstorm characteristic and investigating how and why this characteristic has changed in recent years. This novel exploration is based on 6103 high-resolution model-generated historical footprints , representing the whole European domain.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. The area of the footprint exceeding 20 ms −1 over land, A 20 , is shown to be a good predictor of windstorm damage. This damaging characteristic has decreased in the 21st century, due to a statistically significant decrease in the relative frequency of windstorms exceeding 20 ms −1 in north-western Europe, although an increase is observed in southern Europe. This is explained by a decrease in the quantiles of the footprint wind gust speed distribution above approximately 18 ms −1 at locations in this region. In addition, an increased variability in the number of windstorm events is observed in the 21st century.Much of the change in A 20 is explained by the North Atlantic Oscillation (NAO). The correlation between winter total A 20 and winter-averaged mean sea-level pressure resembles the NAO pattern, shifted eastwards over Europe, and a strong positive relationship (correlation of 0.715) exists between winter total A 20 and winter-averaged NAO. The shifted correlation pattern, however, suggests that other modes of variability may also play a role in the variation in windstorm losses.