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April 2005: 19 NWS/ 21 forecast products (1) Austria ALADIN-LACE (9.6 km) ARPEGE

Jean Quiby Sebastian Trepte Michael Denhard. jean.quiby@meteoswiss.ch sebastian.trepte@dwd.de michael.denhard@dwd.de. SRNWP-PEPS. a regional multi-model ensemble in Europe. April 2005: 19 NWS/ 21 forecast products (1) Austria ALADIN-LACE (9.6 km) ARPEGE

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April 2005: 19 NWS/ 21 forecast products (1) Austria ALADIN-LACE (9.6 km) ARPEGE

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  1. Jean Quiby Sebastian Trepte Michael Denhard jean.quiby@meteoswiss.ch sebastian.trepte@dwd.de michael.denhard@dwd.de SRNWP-PEPS a regional multi-model ensemble in Europe April 2005: 19 NWS/ 21 forecast products (1) Austria ALADIN-LACE (9.6 km) ARPEGE (2) Czech Repub ALADIN-LACE (9 km) ARPEGE (3) Croatia ALADIN-LACE (9 km) ARPEGE (4) Hungary ALADIN-LACE (11 km) ARPEGE (5) Slovakia ALADIN-LACE (11 km) ARPEGE (6) France ALADIN (11 km) ARPEGE (7) Belgium ALADIN (15 km) ARPEGE (8) Slovenia ALADIN (9.5 km) ARPEGE (9) UK UM-EU/LAM (20/12 km) UM-global (10) Denmark HIRLAM (16 km) ECMWF (11) Finland HIRLAM (22km) ECMWF (12) Netherlands HIRLAM (22 km) ECMWF (13) Spain HIRLAM (22 km) ECMWF (14) Ireland HIRLAM (16 km) ECMWF (15) Norway HIRLAM (22/11 km) ECMWF (16) Switzerland aLMo (7 km) ECMWF (17) Italy EuroLM (7km) EuroHRM (18) Germany LM (7 km) GME (19) PolandInstitute of Meteorology and Water Management Internet: www.dwd.de/PEPS Deutscher Wetterdienst

  2. Ensemble Generation PEPS grid with a grid spacing of 0.0625° (~7 km) covering Europe Methodology The ensemble size depends on location and every PEPS grid point has its own probability distribution Deutscher Wetterdienst

  3. Ensemble Products 1. Ensemble mean. Forecast periods +06...+30h (24 hours), +06...+18h and +18...+30h (12 hours) • Total precipitation (accumulation), sum of convective and large scale precipitation • Total snow (accumulation) ), sum of convective and large scale snow • Maximum 10 m wind speed • Maximum 10 m wind gust speed • 2 m minimum/maximum temperature 2. Probabilistic products. Forecast period +06...+30h (24 hours) • Probabilities of total precipitation Thresholds: > 20, > 50, > 100 mm • Probabilities of total snow Thresholds: > 1, > 5, > 10, > 20 cm • Probabilities of maximum wind speed Thresholds: > 10, > 15, > 20, > 25 m/s • Probabilities of maximum wind gust speed Thresholds: > 10, > 15, > 20, > 25, > 33 m/s 3. Probabilistic products. Forecast periods +06...+18h and +18...+30h (12 hours) • Probabilities of total precipitation Thresholds: > 25, > 40, > 70 mm • Probabilities of total snow Thresholds: > 1, > 5, > 10, > 20 cm • Probabilities of maximum wind speed Thresholds: > 10, > 15, > 20, > 25 m/s • Probabilities of maximum wind gust speed Thresholds: > 10, > 15, > 20, > 25, > 33 m/s 4. Ensemble size per grid point (at least two members) Deutscher Wetterdienst

  4. Maximum Ensemble Size depends on main run and on meteorological parameter Deutscher Wetterdienst

  5. Ensemble Mean 21/01/2005 00 UTC +06...30 Deutscher Wetterdienst

  6. probability forecasts 21/01/2005 00 UTC +06...30 Deutscher Wetterdienst

  7. Cut-off times SRNWP-PEPS runs operationally since December 2004(Distribution of forecasts to the contributing NWS) Deutscher Wetterdienst

  8. The SRNWP-PEPS project • SRNWP-PEPS workshop • 6th April 2005, ARPA-SIM, Italy • products • validation • further developement • rights of use Deutscher Wetterdienst

  9. Workshop products • Mask of areas without sufficient models • Wind gusts provided by COSMO and some ALADIN countries using different parametrisations statistical estimation of wind gusts within PEPS? • Statistics of availability of models • Additional products more sysoptic oriented parameters indices of convectivity • Precipitation median instead of mean lower thresholds • PEPS-Meteograms (provided by Meteoswiss) Deutscher Wetterdienst

  10. Workshop validation • Comparison with COSMO-LEPS • Scoring probabilistic forecasts • -error measures • - FBI, POD, FAR, ETS, HSS, Odds Ratio • - BS, BSS, RPS, ROC • Scale-/Object oriented techniques • - contiguous rain area method (Ebert &McBride) • Severe weather Problem • - linear error in probability space (LEPS) • Online verification WG on Verifcation to coordinate verification with high resolution observations in the contributing countries and to provide scientific expertise. Deutscher Wetterdienst

  11. Workshop Ensemble Calibration Calibrated: Intervals or events that we declare to have probability P happen a proportion P of the time Sharp: Prediction intervals are narrower on average than those obtained from climatology; the narrower the better further developement Dressing the probability distriubtion of the ensemble with observational errors and give different weights to the ensemble members Deutscher Wetterdienst

  12. Workshop is the observed value is the k th forecast further developement Using Bayesian Model Averaging (BMA) to calibrate forecast ensembles Adrian E. Raftery, Fadoua Balabdaoui, Tilmann Gneiting and Michael Polakowski Department of Statistics, University of Washington, Seattle, Washington „The model is estimated from a training set of recent data by maximum likelihood using the EM algorithm. Good results with a 25-day training period.“ Deutscher Wetterdienst

  13. Workshop further developement BMA work onprecipitationis in progress Software R package EnsembleBMAis available Source www. stat. washington. edu/ raftery www. stat. washington. edu/ MURI Deutscher Wetterdienst

  14. Workshop further developement • The SRNWP-PEPS consits of different model grids with different horizontal and vertical resolutions. • Question: • How can we account for these differences in an appropriate way ? • Statistical downscaling ? • Neighbourhood Ensemble ? Deutscher Wetterdienst

  15. Workshop spatial temporal Size of Area t Form of Area x further developement Neighbourhood Ensemble ? consider all gridpoints within a given distance of a point Neighbourhood members from different grids should not have equal weights Systematic errors (e.g. due to orography) should be corrected Deutscher Wetterdienst

  16. Workshop further developement Concerning GLOBAL PEPS: „According to most skill measures, these hybrid configurations outperform the ECMWF-EPS at short range for most variables, regions and thresholds“ from: Test of a Poor Mans Ensemble Prediction System for short range probability forecasting Arribas, A., Robertson, K.B., Mylne, K.R. • Hybrid LAM-Ensemble ? • concatenate SRNWP-PEPS with other ensemble systems • COSMO-LEPS • INM Ensemble • Meteo France PEACE Ensemble • UK-Met Office LAM • met-norway LAMEPS Deutscher Wetterdienst

  17. Workshop LMK (2.8km) "lagged average forecast" Ensemble (+18h) research projects using PEPS forecasts & products Hydrological Ensemble Forecasts for the „MULDE“ catchment Hybrid Ensemble COSMO-LEPS (+120h) SRNWP-PEPS (+48h) consistent forecast scenarios of precipitation for the Mulde catchment up to +120h International projects which use or may use SRNWP-PEPS forecasts - EURORISK Prev.I.EW windstorms workpackage - MAP D-Phase (Mesoscale Alpine Program) Deutscher Wetterdienst

  18. Workshop rights of use Scientific use products as well as individual forecasts historic as well as live data DWD distributes the request to the contributing NWS Request to DWD NWS give their permission Commercial use products only products have to be added to the ECOMET list with permission of the NWS Deutscher Wetterdienst

  19. Thank you to all contributing Weather Services ! any questions or remarks ? Deutscher Wetterdienst

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