1Deutscher WetterdienstMärz 2005 April 2005: 19 NWS/ 21 forecast products (1) AustriaALADIN-LACE (9.6 km) ARPEGE (2) Czech Repub ALADIN-LACE (9 km) ARPEGE (3) CroatiaALADIN-LACE (9 km) ARPEGE (4) HungaryALADIN-LACE (11 km) ARPEGE (5) SlovakiaALADIN-LACE (11 km) ARPEGE (6) FranceALADIN (11 km) ARPEGE (7) BelgiumALADIN (15 km) ARPEGE (8) SloveniaALADIN (9.5 km) ARPEGE (9) UKUM-EU/LAM (20/12 km) UM-global (10) Denmark HIRLAM (16 km) ECMWF (11) FinlandHIRLAM (22km) ECMWF (12) NetherlandsHIRLAM (22 km) ECMWF (13) SpainHIRLAM (22 km) ECMWF (14) IrelandHIRLAM (16 km) ECMWF (15) NorwayHIRLAM (22/11 km) ECMWF (16) SwitzerlandaLMo (7 km) ECMWF (17) ItalyEuroLM (7km) EuroHRM (18) Germany LM (7 km) GME (19) Poland Institute of Meteorology and Water Management SRNWP-PEPS a regional multi-model ensemble in Europe Internet: Jean Quiby Sebastian Trepte Michael Denhard

2Deutscher WetterdienstMärz 2005 Generation PEPS grid with a grid spacing of ° (~7 km) covering Europe The ensemble size depends on location and every PEPS grid point has its own probability distribution Methodology Ensemble

3Deutscher WetterdienstMärz 2005 Ensemble Products 4. Ensemble size per grid point (at least two members) 1. Ensemble mean. Forecast periods h (24 hours), h and h (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 h (24 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 3. Probabilistic products. Forecast periods h and h (12 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 speedThresholds: > 10, > 15, > 20, > 25, > 33 m/s

4Deutscher WetterdienstMärz 2005 Maximum Ensemble Size depends on main run and on meteorological parameter

5Deutscher WetterdienstMärz 2005 Ensemble Mean 21/01/ UTC

6Deutscher WetterdienstMärz /01/ UTC probability forecasts

7Deutscher WetterdienstMärz 2005 Cut-off times SRNWP-PEPS runs operationally since December 2004 (Distribution of forecasts to the contributing NWS)

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

9Deutscher WetterdienstMärz 2005 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)

10Deutscher WetterdienstMärz 2005 validation Workshop 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.

11Deutscher WetterdienstMärz 2005 further developement 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 Dressing the probability distriubtion of the ensemble with observational errors and give different weights to the ensemble members

12Deutscher WetterdienstMärz 2005 further developement Workshop Using Bayesian Model Averaging (BMA) to calibrate forecast ensembles „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.“ Adrian E. Raftery, Fadoua Balabdaoui, Tilmann Gneiting and Michael Polakowski Department of Statistics, University of Washington, Seattle, Washington is the observed value is the k th forecast

13Deutscher WetterdienstMärz 2005 further developement Workshop BMA work on precipitation is in progress Software R package EnsembleBMA is available Source www. stat. washington. edu/ raftery www. stat. washington. edu/ MURI

14Deutscher WetterdienstMärz 2005 further developement Workshop 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 ?

15Deutscher WetterdienstMärz 2005 further developement Workshop Neighbourhood Ensemble ? consider all gridpoints within a given distance of a point Size of Area Form of Area spatial temporal x t Neighbourhood members from different grids should not have equal weights Systematic errors (e.g. due to orography) should be corrected

16Deutscher WetterdienstMärz 2005 further developement Workshop 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 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.

17Deutscher WetterdienstMärz 2005 research projects using PEPS forecasts & products Workshop Hydrological Ensemble Forecasts for the „MULDE“ catchment Hybrid Ensemble COSMO-LEPS (+120h) SRNWP-PEPS (+48h) LMK (2.8km) "lagged average forecast" Ensemble (+18h) 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)

18Deutscher WetterdienstMärz 2005 rights of use Workshop Scientific use products as well as individual forecasts historic as well as live data Commercial use products only Request to DWD DWD distributes the request to the contributing NWS NWS give their permission products have to be added to the ECOMET list with permission of the NWS

19Deutscher WetterdienstMärz 2005 any questions or remarks ? Thank you to all contributing Weather Services !