1. Page 1© Crown copyright 2005 EuroRISK PREVIEW Windstorms Workpackage Ken Mylne, Met Office

2. © EURORISK consortium Eurorisk PREVIEW Project overview

3. © EURORISK consortium PREVIEW in some words l A project funded by the European Commission  23 millions € of eligible costs, 14 millions € granted l New information services to help risk management  Gathering users needs  Using the most advanced research and technology  Validation on operational platforms l 58 partners from 15 nations  Scientists  Operators  Industrial companies  End Users l 45 months performance schedule and yearly budget reviews

4. © EURORISK consortium  DDSC METEO France CNES EADS Astrium SERTIT MET Office INSA DPC TELESPAZIO INGV UNIFI IRPI SRSA SMHI INFOTERRA D BFG JRC ECMWF 15 countries - EU - 60 partners FMI CORE TEAM PARTNERS

5. © EURORISK consortium Cost figures

6. © EURORISK consortium FIRES Atmospheric Risks services © INSA FLOODS WINDSTORMS © Meteo France JRC

7. © EURORISK consortium Geophysical risk services Landslides Earthquake and Volcanoes

8. © EURORISK consortium Man made Risk services - Engineering - Industrial accidents General Services - Assets Mapping - Damages estimates - Damages observation

9. © EURORISK consortium Work Breakdown Structure

10. © EURORISK consortium Windstorms Workpackage

11. Page 11© Crown copyright 2005 Components of Package Risk Mapping – Return Periods/ climatology Best Practise Probability Forecasts (Day 10 – Day 1) Civil Protection Response (Demonstration) Downscaling to Local Impacts Training and Awareness Warnings related to return periods/ normal risks

12. Page 12© Crown copyright 2005 Risk Mapping  Lead:  SMHI  Task:  Map return periods  Estimate climatology

13. Page 13© Crown copyright 2005 Best Practise Probability Forecasts  Lead:  Met Office  Contributors:  ECMWF (passive)  Meteo-France  DWD  Met.no  ARPA-SIM  Task:  Co-ordinate & collate contributions at Met Office  Apply post-processing  Identify best methods  Set up operational supply

14. Page 14© Crown copyright 2005 Best Practise Probability Forecasts  Ensemble Forecast inputs:  Medium-Range (3-10 days)  ECMWF  ARPA-SIM (COSMO-LEPS) (Days 3-5)  Short-Range (1-2 days) – multi-model ensemble consisting of contributions from:  Meteo-France - PEACE  DWD - SRNWP-PEPS  Met.no - TEPS/LAM EPS  Met Office - LAMEPS/EPS  Post-processing…

15. Page 15© Crown copyright 2005 Site-specific Ensemble Forecasts  Ensemble forecasts will be collected and post- processed on a site-specific basis:  Utilise existing technology/capability  Allows bias correction and calibration  Reduced data volumes for international exchange  Aids combination of multiple forecast inputs in common format  Disadvantage:  Risk of strongest winds falling between site locations  Hi-resolution models and nowcasts add detail in short-range

16. Page 16© Crown copyright 2005 Site-specific post-processing - Kalman Filter MOS Kalman Filter MOS: Statistical model which relates model fields to observed windspeed at the site. Main features:  Corrects site biases  60-day training cycle allows rapid adjustment for model changes  Available for any site worldwide with observations  Can be set up for each model to correct its own biases

17. Page 17© Crown copyright 2005 Calibration of Probability Forecasts  Calibration forecasts of a single “event” is straightforward using a reliability diagram: 70% EPS prob  50% issued Met Office uses a more flexible approach which can adapt to the requirements of different customers, but this method could be used for Windstorms

18. Page 18© Crown copyright 2005 Calibration with rank histograms  Bin relative frequencies give probabilistic weights.  No threshold dependence (flexible)  But weights vary with:  season  parameter  forecast range/time of day  location (reduced by KF) T2m Weights at T+132, October 2001

19. Page 19© Crown copyright 2005 Calibrated probability distribution functions...  More information about ‘extremes’ plus increased reliability. T2m Weights at T+132, October 2001

20. Page 20© Crown copyright 2005 EPS Meteogram  Ensemble spread and forecast trends  Box shows 25-75% range  Whiskers show full range (or 95% after calibration)  Central bar shows median  Other models can be added Confident cold spell

21. Page 21© Crown copyright 2005 Products for the Risk Manager  Plot of ensemble spread 0% 100% Prob  Probability graph for multiple severity thresholds

22. Page 22© Crown copyright 2005 Verification  Site-specific  Greatest benefit comes from Kalman Filter (red)  Calibration adds a little, mostly for less extreme events

23. Page 23© Crown copyright 2005 Additional short-range forecasts  Met.no - hi-res downscaling (complex terrain)  Met Office – hi-res downscaling  Met Office – wind nowcasting 12 km (Part Domain)2 km

24. Page 24© Crown copyright 2005 Warnings/Downscaling  Lead:  SMHI  Contributors:  SRSA  Task:  Relate forecast probs to return periods  Downscale to identify impact hazards

25. Page 25© Crown copyright 2005 EMMA Website - for display of warnings

26. Page 26© Crown copyright 2005 Training & Awareness  Workshop topics:  Uncertainty  Best practice forecasting  Risk management  How to get best value for specific users  Lead:  SMHI  Contributors:  SRSA, Met O  Task:  Train end-users

27. Page 27© Crown copyright 2005 Civil Protection Response  Demonstrate warnings and (potential) responses  Assess impact compared to traditional methods  Lead:  SRSA  Contributors:  All  Task:  Demonstrate application of warnings in Civil Defence

28. Page 28© Crown copyright 2005 Tasks Lead Contributors Risk Mapping Probability Forecasts Civil Protection Demo Downscaling Training Warnings MetO SMHI SRSA ECMWF, M-F, met.no, DWD, SMHI SRSA, MetO

29. Page 29© Crown copyright 2005 Some more detail

30. Page 30© Crown copyright 2005 Summary Risk Mapping – Return Periods/ climatology SMHI, IMK Best Practise Probability Forecasts MetO, ECMWF, M-F, Met.no, DWD, SMHI Civil Protection Response (Demonstration) SRSA Downscaling to Local Impacts SMHI, SRSA Training and Awareness SMHI, SRSA, MetO Warnings related to return periods/ normal risks SMHI

31. Page 31© Crown copyright 2005 Issues  SRNWP – PEPS – should we have a separate KF-MOS for each member?  Downscaling to local impacts – how? SRSA?  How will risk maps be used in warning process? (Ken unclear)

32. Page 32© Crown copyright 2005 Accreditation WAFC World Area Forecast Centre

33. Page 33© Crown copyright 2005 Questions & Answers