1. Federal Department of Home Affairs FDHA Federal Office of Meteorology and Climatology MeteoSwiss European wind storms and reinsurance loss: New estimates of the risk ACRE Meeting, Zurich, 24.06.2008 Paul Della-Marta, Mark Liniger, Christof Appenzeller, David Bresch, Pamela Köllner-Heck, Veruska Muccione

2. 2 Wind Storm Risk Paul Della-Marta - ACRE Meeting Zurich Outline Overview of wind storm risk assessment European wind storm climate from 1880 (EMULATE) The PreWiStoR project Improved estimates of European wind storm climate Improved estimates of wind storm loss Conclusions How can ACRE improve wind storm risk assessment?

3. 3 Wind Storm Risk Paul Della-Marta - ACRE Meeting Zurich Wind Storms in Europe: What is the risk?

4. 4 Wind Storm Risk Paul Della-Marta - ACRE Meeting Zurich How can we estimate the Risk? : Data DataWind Gust QualityLength (yrs) Spatial scale Temp. scale(hr) 10m winddirectLow~10-50small6 Pressure/Ge opot. derivedMedium+100large6-24 Satellite/Profi ler param?~10small1-24 ReanalysisparamHigh~50medium6 RCMparam?~50small<6 GCM/S2Dparam?~1000large24 In-Situ Model

5. 5 Wind Storm Risk Paul Della-Marta - ACRE Meeting Zurich Data example: Daria, 26.01.1990 m/s Maximum wind speed at each gridpoint over the duration of the storm SwissRe: MSLP In-situ wind Dynamics ERA-40 Geostr. wind @850hPa Assimilate d obs. *not wind Dynamics

6. 6 Wind Storm Risk Paul Della-Marta - ACRE Meeting Zurich How can we estimate the Risk? Extreme Value Theory Method of Estimation e.g. Maximum likelihood, L-Moments Quantification of Uncertainty Model parameters, return periods Model diagnostics Goodness-of-fit Maximal use of information Peak Over Threshold instead of Block Maxima See Coles (2001)

7. 7 Wind Storm Risk Paul Della-Marta - ACRE Meeting Zurich The wind climate of Europe 1880-2003 Geostrophic Wind Speed derived from daily MSLP EMULATE (Ansell et al 2006) (over land only, ONDJFMA)

8. 8 Wind Storm Risk Paul Della-Marta - ACRE Meeting Zurich Storm Selection Method Scalar wind statistic Winter 1999/2000 95% threshold

9. 9 Wind Storm Risk Paul Della-Marta - ACRE Meeting Zurich The storm climate of Europe 1880-2001 Derived from daily MSLP EMULATE data 1957-2002 1880-2002

10. 10 Wind Storm Risk Paul Della-Marta - ACRE Meeting Zurich Change in return period and confidence using EMULATE data Land areas only 1957-20021880-2002

11. 11 Wind Storm Risk Paul Della-Marta - ACRE Meeting Zurich See van den Brink et al. IJC (2005) PreWiStoR: Prediction of winter Wind Storm Risk Problem: Observed records of wind storms are not long enough Solution: ~150 storms based on observations. Use probabilistic modelling to generate synthetic storms based on perturbed statistics Calculate losses New approach to use ENSEMBLE prediction systems (seasonal to decadal, s2d) Replace statistical perturbation with physics Utilise around ~500 seasons of s2d data Obtain a better estimate of wind storm risk and losses

12. 12 Wind Storm Risk Paul Della-Marta - ACRE Meeting Zurich The concept of the PreWiStoR project Let Lorenz attractor represent the possible trajectories of extreme wind related weather in the current climate The envelope (climate) of trajectories of the system observed in ERA-40 (45years) The climate of trajectories sampled in s2d data (300+ years) Source: Wikipedia

13. 13 Wind Storm Risk Paul Della-Marta - ACRE Meeting Zurich PreWiStoR: Data Seasonal to decadal (s2d) climate prediction models Using the seasonal forecasting model of the ECMWF A coupled ocean-atmosphere Global Circulation Model 6-7 month forecast, T159, 26 years hindcast 11 (41) member ensemble First month removed to ensure independence Separate ocean analysis system to initiate the seasonal forecasts ENSEMBLE prediction system: Model is run many times  Initial conditions are perturbed  Probabilistic Forecasts

14. 14 Wind Storm Risk Paul Della-Marta - ACRE Meeting Zurich Comparison of wind storm climatologies from s2d Wind storm climatologies are different in magnitude and shape and frequency All s2d models seem to have a less negative shape than ERA-40

15. 15 Wind Storm Risk Paul Della-Marta - ACRE Meeting Zurich Percentile calibration and sampling 45 year periods SYS3 has a more extreme wind climate than ERA-40 SYS2 has a similar extreme wind climate to ERA-40 After application of calibration Which one is closer to the true climate? Can we tell which one is correct?

16. 16 Wind Storm Risk Paul Della-Marta - ACRE Meeting Zurich Sampling experiments with s2d climatologies Randomly order the 315 years of s2d winter seasons Plot the estimated shape parameter as a function of the number of seasons used in the calc. Repeat many times to simulate chaotic inter-annual variability See Vannitsem Tellus (2007) Shape (& Scale) parameter has not converged at most (~70%) 45 year length climatologies  ERA-40 underestimates European storm climate

17. 17 Wind Storm Risk Paul Della-Marta - ACRE Meeting Zurich Sampling experiments with s2d climatologies Randomly order the 315 years of s2d winter seasons Plot the estimated shape parameter as a function of the number of seasons used in the calc. Repeat many times to simulate chaotic inter-annual variability See Vannitsem Tellus (2007) Shape (& Scale) parameter has not converged at most (~70%) 45 year length climatologies  ERA-40 underestimates European storm climate

18. 18 Wind Storm Risk Paul Della-Marta - ACRE Meeting Zurich Swiss Re Wind Storm Loss Model ( catXos ) Vulnerability curve shows a cubic relation which is capped Portfolio value distribution is inhomogeous

19. 19 Wind Storm Risk Paul Della-Marta - ACRE Meeting Zurich Range of loss uncertainty due to sampling 45 year periods Swiss Re estimates of expected loss- frequency fit within the range of sampling uncertainty SYS3 shows the added value of using a longer dataset

20. 20 Wind Storm Risk Paul Della-Marta - ACRE Meeting Zurich Conclusions Using EMULATE data we can improve our estimates of wind storm risk  longer climatology provides greater confidence EMULATE data represents a 24hr mean MSLP  not ideal for wind storms  Geostrophic approximations Ensemble dynamical forecasts also improve storm risk estimates  Evidence that ERA-40 underestimates the risk Dynamical models have biases and other deficientcies Use of s2d data has replaced statistical perturbation of storms (SwissRE) with dynamical perturbations (s2d) Sampling experiments lead to greater insight to risk of storms and loss uncertainty

21. 21 Wind Storm Risk Paul Della-Marta - ACRE Meeting Zurich How can ACRE improve wind storm risk assessment? Longer reanalyses  sample more of the dynamics Longer ensemble based reanalyses  sample more of the dynamics + account for observation errors Longer reanalyses can be used to provide a greater range of initial conditions S2d data only have a hindcast of ~ 30 years  limited sample of initial conditions But to be most useful we need: High temporal resolution output variables and/or integrated quantities e.g. Max. Wind Gust No just event-based but a full climatology else frequencies are hard to define

22. 22 Wind Storm Risk Paul Della-Marta - ACRE Meeting Zurich A bivariate extreme value peak over threshold model for wind storm intensity and loss Using the methodology in Coles (2001) and the evd R - package Fitted to ERA40 wind storm Q95 and the transformed %TIV Could be used to define the vulnerability with real loss data

23. 23 Wind Storm Risk Paul Della-Marta - ACRE Meeting Zurich