Page 1 Crown copyright 2005 EuroRISK PREVIEW Windstorms Workpackage Ken Mylne, Met Office EURORISK consortium Eurorisk PREVIEW Project overview 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 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 EURORISK consortium Cost figures EURORISK consortium FIRES Atmospheric Risks services INSA FLOODS WINDSTORMS Meteo France JRC EURORISK consortium Geophysical risk services Landslides Earthquake and Volcanoes EURORISK consortium Man made Risk services - Engineering - Industrial accidents General Services - Assets Mapping - Damages estimates - Damages observation EURORISK consortium Work Breakdown Structure EURORISK consortium Windstorms Workpackage 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 Page 12 Crown copyright 2005 Risk Mapping Lead: SMHI Task: Map return periods Estimate climatology 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 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 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 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 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 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 Page 19 Crown copyright 2005 Calibrated probability distribution functions... More information about extremes plus increased reliability. T2m Weights at T+132, October 2001 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 Page 21 Crown copyright 2005 Products for the Risk Manager Plot of ensemble spread 0% 100% Prob Probability graph for multiple severity thresholds Page 22 Crown copyright 2005 Verification Site-specific Greatest benefit comes from Kalman Filter (red) Calibration adds a little, mostly for less extreme events 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 Page 24 Crown copyright 2005 Warnings/Downscaling Lead: SMHI Contributors: SRSA Task: Relate forecast probs to return periods Downscale to identify impact hazards Page 25 Crown copyright 2005 EMMA Website - for display of warnings 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 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 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 Page 29 Crown copyright 2005 Some more detail 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 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) Page 32 Crown copyright 2005 Accreditation WAFC World Area Forecast Centre Page 33 Crown copyright 2005 Questions & Answers