Estimation of wind storm impacts overWestern Germany under future climate conditions using a statistical–dynamical downscaling approach

Abstract

A statistical–dynamical regionalization approach is developed to assess possible changes in wind storm impacts. The method is applied to North Rhine‐Westphalia (Western Germany) using the FOOT3DK mesoscale model for dynamical downscaling and ECHAM5/OM1 global circulation model climate projections. The method first classifies typical weather developments within the reanalysis period using K‐means cluster algorithm. Most historical wind storms are associated with four weather developments (primary storm‐clusters). Mesoscale simulations are performed for representative elements for all clusters to derive regional wind climatology. Additionally, 28 historical storms affecting Western Germany are simulated. Empirical functions are estimated to relate wind gust fields and insured losses. Transient ECHAM5/OM1 simulations show an enhanced frequency of primary storm‐clusters and storms for 2060–2100 compared to 1960–2000. Accordingly, wind gusts increase over Western Germany, reaching locally +5% for 98th wind gust percentiles (A2‐scenario). Consequently, storm losses are expected to increase substantially (+8% for A1B‐scenario, +19% for A2‐scenario). Regional patterns show larger changes over north‐eastern parts of North Rhine‐Westphalia than for western parts. For storms with return periods above 20 yr, loss expectations for Germany may increase by a factor of 2. These results document the method's functionality to assess future changes in loss potentials in regional terms.

Wind storm loss estimations in the Canton of Vaud (Western Switzerland)

“…For this study, the daily maximum wind speeds recorded at each of the 22 weather stations of the canton of Vaud during the 24 storms have been increased arbitrarily by 3 %, 5 % and 10 %. Input wind speed increase of 3 % and 5 % are in line with the future projections for Western Germany shown in Pinto et al (2010), whereas investigations with a 10 % shift considers the impact of a "worst case scenario" storm. Estimates have thus been made of losses using these enhanced wind speeds and with the former population statistics.…”

“…Estimates have thus been made of losses using these enhanced wind speeds and with the former population statistics. Moreover, the same v 98 values have been used, although these are likely to change under future climate conditions (Pinto et al, 2010). Results show that with a 3 %, 5 % and 10 % increase of wind speeds, economic costs for the canton of Vaud would shift by roughly 20 %, 35 % and 80 %, respectively (Table 1, columns 7-9).…”

“…They combine data describing a wind storm -mainly wind gusts, but also mean wind speeds (Unanwa et al, 2000) -and the spatial distribution of property values to assess economic losses related to a wind storm event. Most models consist of empirical functions that have been adjusted to historical loss data provided by insurance companies, assessing damage at different spatial scale levels, from postalcode areas (Dorland et al, 1999;Heneka et al, 2006;Pinto et al, 2010) to districts (Klawa and Ulbrich, 2003) or even countries (e.g. Leckebusch et al, 2007;Pinto et al, 2007).…”

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

“…Windstorm loss distributions are inferred from historical weather measurement data (mainly available since 1950) and also 15 increasingly from storm data simulated ab initio from numerical weather and climate prediction models (Schwierz et al 2010;Pinto et al 2010;Della-Marta et al 2010;Renggli et al 2011;Karremann et al 2014). The loss distributions are estimated by Monte Carlo simulation using ad hoc combinations of various statistical, dynamical and engineering type models: statistical models for estimating trends and correcting inhomogeneities in the historical data (Barredo 2010), either low-order parametric stochastic models (the traditional basis of many catastrophe models), or more recently, numerical 20 weather and climate models for simulating large sets of artificial hazard events, statistical models for adjusting biases in numerical model output, and stochastic models for simulating losses from the artificial windstorm events (e.g.…”

Epistemic uncertainties and natural hazard risk assessment – Part 2: Different natural hazard areas

“…Windstorm loss distributions are inferred from historical weather measurement data (mainly available since 1950) and also increasingly from storm data simulated ab initio from numerical weather and climate prediction models (Schwierz et al, 2010;Pinto et al, 2010;Della-Marta et al, 2010;Renggli et al, 2011;Karremann et al, 2014). The loss distributions are estimated by Monte Carlo simulation using ad hoc combinations 20 of various statistical, dynamical and engineering type models: statistical models for estimating trends and correcting inhomogeneities in the historical data (Barredo, 2010), either low-order parametric stochastic models (the traditional basis of many catastrophe models), or more recently, numerical weather and climate models for simulating large sets of artificial hazard events, statistical models for adjusting biases in numerical model output, and stochastic models for simulating losses from the artificial windstorm events (e.g.…”