The spatial structure of  European wind storms as characterized by bivariate extreme-value Copulas

Bonazzi, A.; Cusack, Stephen; Mitas, C.; Jewson, Stephen

doi:10.5194/nhess-12-1769-2012

Cited by 32 publications

(35 citation statements)

References 29 publications

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“…This is slightly lower than the 25 ms −1 threshold used by Roberts et al (2014), in order to reduce the sampling uncertainty in locations where the wind gust speeds are weaker; therefore they rarely exceed 25 ms −1 . This threshold choice is consistent with the damage threshold used by Bonazzi et al (2012) and the damage threshold used with insurance companies in Germany as identified by Klawa and Ulbrich (2003). The SSI for windstorm event i is defined as…”

Section: Identifying a Damaging Footprint Characteristicmentioning

confidence: 68%

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The 21st century decline in damaging European windstorms

Dawkins

Stephenson

Lockwood

et al. 2016

Nat. Hazards Earth Syst. Sci.

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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.

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Section: Identifying a Damaging Footprint Characteristicmentioning

confidence: 68%

“…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 21st century decline in damaging European windstorms

Dawkins

Stephenson

Lockwood

et al. 2016

Nat. Hazards Earth Syst. Sci.

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“…The set of 135 storms we consider contains famous European windstorm events such as 87J, Daria, Vivian, Anatol, Lothar, Martin and Kyrill. For details related to the observational data set, the storm reconstruction method and how the 135 most severe storms were selected, the reader is referred to Bonazzi et al (2012). Using the time series of the number of storms per year over the 39-year data record, we find a sample overdispersion of 1.39, strongly suggesting the time series exhibits clustering.…”

Section: A Simple Clustered Model Of European Windstorm Datamentioning

confidence: 96%

Modelling clustering of natural hazard phenomena and the effect on re/insurance loss perspectives

Khare

Bonazzi²,

Mitas

et al. 2015

Nat. Hazards Earth Syst. Sci.

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Abstract. In this paper, we present a conceptual framework for modelling clustered natural hazards that makes use of historical event data as a starting point. We review a methodology for modelling clustered natural hazard processes called Poisson mixtures. This methodology is suited to the application we have in mind as it naturally models processes that yield cross-event correlation (unlike homogeneous Poisson models), has a high degree of tunability to the problem at hand and is analytically tractable. Using European windstorm data as an example, we provide evidence that the historical data show strong evidence of clustering. We then develop Poisson and Clustered simulation models for the data, demonstrating clearly the superiority of the Clustered model which we have implemented using the Poisson mixture approach. We then discuss the implications of including clustering in models of prices of catXL contracts, one of the most commonly used mechanisms for transferring risk between primary insurers and reinsurers. This paper provides a number of unique insights into the impact clustering has on modelled catXL contract prices. The simple modelling example in this paper provides a clear and insightful starting point for practitioners tackling more complex natural hazard risk problems.

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“…extreme value models (Brodin and Rootzén 2009;Della-30 Marta et al 2009), assumptions about model-dependence of simulated storms (Sansom et al 2013), and assumptions about dependency in space-time and between events (Bonazzi et al 2012;Economou et al 2014 • Numerical weather and climate models show biases in storm properties that have resisted model improvements over the past 40 years e.g. too zonal storm tracks over W. Europe , poor representation of small horizontal scale processes even at very high resolution e.g.…”

Section: Uncertainty Quantification In Windstorm Hazard Estimationmentioning

confidence: 99%

Epistemic uncertainties and natural hazard risk assessment. 1. A review of different natural hazard areas

Beven¹,

Almeida²,

Aspinall³

et al. 2017

Preprint

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This paper discusses how epistemic uncertainties are considered in a number of different natural hazard areas including floods, landslides and debris flows, dam safety, droughts, earthquakes, tsunamis, volcanic ash clouds and pyroclastic flows, and wind storms. In each case it is common practice to treat most uncertainties in the form of aleatory 20 probability distributions but this may lead to an underestimation of the resulting uncertainties in assessing the hazard, consequences and risk. It is suggested that such analyses might be usefully extended by looking at different scenarios of assumptions about sources of epistemic uncertainty, with a view to reducing the element of surprise in future hazard occurrences. Since every analysis is necessarily conditional on the assumptions made about the nature of sources of epistemic uncertainty it is also important to follow the guidelines for good practice suggested in the companion Part 2. 25

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The spatial structure of European wind storms as characterized by bivariate extreme-value Copulas

Abstract: Abstract.The winds associated with extra-tropical cyclones are amongst the costliest natural perils in Europe.

Cited by 32 publications

References 29 publications

The 21st century decline in damaging European windstorms

The 21st century decline in damaging European windstorms

Modelling clustering of natural hazard phenomena and the effect on re/insurance loss perspectives

Epistemic uncertainties and natural hazard risk assessment. 1. A review of different natural hazard areas

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