Univariate and bivariate GPD methods for predicting extreme wind storm losses

Rootzén, Holger; Brodin, Erik

doi:10.1016/j.insmatheco.2008.11.002

Cited by 31 publications

(29 citation statements)

References 11 publications

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“…We subsequently checked the meteorological conditions during the identified storm episodes to establish whether the occurrence of storm losses were reasonable. This was done by examining the weather records in the Berliner Wetterkarte (since 1950;Berliner Wetterkarte, 2009) andPotsdamer Wetterkarte (before 1950;Potsdamer Wetterkarte, 1949). The most significant losses related to a storm generally occur only during one or two days; hence the calculated losses for the individual storm events are mostly similar if, for example, three-day running sums are considered instead of five-day sums.…”

Section: Identifying Loss-intensive Wind Storm Events From Daily Lossmentioning

confidence: 99%

“…To assess the occurrence of severe storms from a more integrated perspective, Della-Marta et al (2009) calculated continental-scale extreme wind indices for Europe and estimated the return periods of storm events during the second half of the 20th century. Brodin and Rootzen (2009) investigated extreme wind storm losses using data provided by the largest Swedish insurance company.…”

Section: Introductionmentioning

confidence: 99%

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High-resolution refinement of a storm loss model and estimation of return periods of loss-intensive storms over Germany

Donat

Pardowitz²,

Leckebusch

et al. 2011

Nat. Hazards Earth Syst. Sci.

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Abstract.A refined model for the calculation of storm losses is presented, making use of high-resolution insurance loss records for Germany and allowing loss estimates on a spatial level of administrative districts and for single storm events. Storm losses are calculated on the basis of wind speeds from both ERA-Interim and NCEP reanalyses. The loss model reproduces the spatial distribution of observed losses well by taking specific regional loss characteristics into account. This also permits high-accuracy estimates of total cumulated losses, though slightly underestimating the country-wide loss sums for storm "Kyrill", the most severe event in the insurance loss records from 1997 to 2007. A larger deviation, which is assigned to the relatively coarse resolution of the NCEP reanalysis, is only found for one specific rather smallscale event, not adequately captured by this dataset.The loss model is subsequently applied to the complete reanalysis period to extend the storm event catalogue to cover years when no systematic insurance records are available. This allows the consideration of loss-intensive storm events back to 1948, enlarging the event catalogue to cover the recent 60+ years, and to investigate the statistical characteristics of severe storm loss events in Germany based on a larger sample than provided by the insurance records only. Extreme value analysis is applied to the loss data to estimate the return periods of loss-intensive storms, yielding a return period for storm "Kyrill", for example, of approximately 15 to 21 years.Correspondence to: M. G. Donat

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Section: Identifying Loss-intensive Wind Storm Events From Daily Lossmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

High-resolution refinement of a storm loss model and estimation of return periods of loss-intensive storms over Germany

Donat

Pardowitz²,

Leckebusch

et al. 2011

Nat. Hazards Earth Syst. Sci.

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show abstract

“…Coles 2001) have been widely used to calculate the return period (RP) of windstorms (e.g. Brodin & Rootzen 2009, Hofherr & Kunz 2010. In particular, Della-Marta & Pinto (2009) quantified the changes in the intensity of storms over Western and Central Europe, identifying a statistically significant shortening of the RP of storms over this area when considering the Laplacian of mean sea level pressure as a measure of cyclone intensity.…”

Section: Introductionmentioning

confidence: 99%

Loss potentials associated with European windstorms under future climate conditions

Pinto¹,

Karremann²,

Born³

et al. 2012

Clim. Res.

100

104

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Possible changes in the frequency and intensity of windstorms under future climate conditions during the 21st century are investigated based on an ECHAM5 GCM multi-scenario ensemble. The intensity of a storm is quantified by the associated estimated loss derived with using an empirical model. The geographical focus is 'Core Europe', which comprises countries of Western Europe. Possible changes of losses are analysed by comparing ECHAM5 GCM data for recent (20C, 1960 to 2000) and future climate conditions (B1, A1B, A2; 2060 to 2100), each with 3 ensemble members. Changes are quantified using both rank statistics and return periods (RP) estimated by fitting an extreme value distribution using the peak over threshold method to potential storm losses. The estimated losses for ECHAM5 20C and reanalysis events show similar statistical features in terms of return periods. Under future climate conditions, all climate scenarios show an increase in both frequency and magnitude of potential losses caused by windstorms for Core Europe. Future losses that are double the highest ECHAM5 20C loss are identified for some countries. While positive changes of ranking are significant for many countries and multiple scenarios, significantly shorter RPs are mostly found under the A2 scenario for return levels correspondent to 20 yr losses or less. The emergence time of the statistically significant changes in loss varies from 2027 to 2100. These results imply an increased risk of occurrence of windstorm-associated losses, which can be largely attributed to changes in the meteorological severity of the events. Additionally, factors such as changes in the cyclone paths and in the location of the wind signatures relative to highly populated areas are also important to explain the changes in estimated losses.

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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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Univariate and bivariate GPD methods for predicting extreme wind storm losses

Cited by 31 publications

References 11 publications

High-resolution refinement of a storm loss model and estimation of return periods of loss-intensive storms over Germany

High-resolution refinement of a storm loss model and estimation of return periods of loss-intensive storms over Germany

Loss potentials associated with European windstorms under future climate conditions

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

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