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Recent Experiences with the INM Multi-model EPS scheme

Recent Experiences with the INM Multi-model EPS scheme. García-Moya, J.A., Callado, A., Santos, C., Santos, D., Simarro, J., B Orfila. Modelling Area – Spanish Met Service INM EWGLAN/SREPS meeting Federal Office of Meteorology and Climatology MeteoSwiss 9-12 October 2006. Outline.

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Recent Experiences with the INM Multi-model EPS scheme

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  1. Recent Experiences with the INM Multi-model EPS scheme García-Moya, J.A., Callado, A., Santos, C., Santos, D., Simarro, J., B Orfila. Modelling Area – Spanish Met Service INM EWGLAN/SREPS meeting Federal Office of Meteorology and Climatology MeteoSwiss 9-12 October 2006

  2. Outline • Introduction • SREPS system at INM • Monitoring and postprocessing • Verification against observation vs Verification against analysis • Further Work and Future of SREPS • Conclusions EWGLAM/SRNWP Meetings

  3. Introduction Triggering and Consolidating INM SREPS • NWP plan, (April 1999) • SAMEX. (Summer 2000) • Global boundaries; LAMs. 2000-2004 • Cray X1E. (2001-2005) • Gathering the team EWGLAM/SRNWP Meetings

  4. Ensemble for Short Range • Surface parameters are the most important ones for weather forecast. • Forecast of extreme events (convective precip, gales,…) is probabilistic. • Short Range Ensemble prediction can help to forecast these events. • Forecast risk (Palmer, ECMWF Seminar 2002) is the goal for both Medium- and, also, Short-Range Prediction. EWGLAM/SRNWP Meetings

  5. What do we need? • Enough computer power. • Research • Following recommendations of the workshops. • Technical difficulties. • Large storage system. • Database software (MARS like ECMWF). • Post-processing and graphics software. • Enough staff for maintenance and monitoring. • Verification software. EWGLAM/SRNWP Meetings

  6. SREPS at INM • 72 hours forecast four times a day (00, 06, 12 y 18 UTC). • Characteristics: • 5 models. • 4 boundary conditions. • 4 last ensembles (HH, HH-6, HH-12, HH-18). • 20 member ensemble every 6 hours • Time-lagged Super-Ensemble of 80 members every 6 hours. EWGLAM/SRNWP Meetings

  7. Multi-model • Hirlam (http://hirlam.org). • HRM from DWD (German Weather Service). • MM5 (http://box.mmm.ucar.edu/mm5/). • UM from UKMO (Great Britain Weather Service). • LM (Lokal Model) from COSMO consortium. EWGLAM/SRNWP Meetings

  8. Multi-Boundaries From different global deterministic models: • ECMWF • UM from UKMO (Great Britain Weather Service) • AVN from NCEP • GME from DWD (German Weather Service) EWGLAM/SRNWP Meetings

  9. The team • José A. García-Moya. • Carlos Santos (Hirlam, verification & graphics, web server). • Daniel Santos (MM5, Bayesian Model Average). • Alfons Callado (UM & grib software). • Juan Simarro (HRM, LM and Vertical interpolation software). EWGLAM/SRNWP Meetings

  10. Thanks to… • MetOffice • Ken Mylne, Jorge Bornemann • DWD • Detlev Majewski, Michael Gertz • ECMWF • Metview Team • COSMO • Chiara Marsigli, Ulrich Schättler EWGLAM/SRNWP Meetings

  11. Current Ensemble • 72 hours forecast twice a day (00 & 12 UTC). • Characteristics: • 5 models. • 4 boundary conditions. • 20 member ensemble every 12 hours EWGLAM/SRNWP Meetings

  12. EWGLAM/SRNWP Meetings

  13. HP Computer Cray X1e • 16 nodes, 8 MSP’s each ( ~2.4 Tf peak perf.) • Deterministic Forecast • SREPS • Climatic runs EWGLAM/SRNWP Meetings

  14. Post-processing • Integration areas 0.25 latxlon, 40 levels • Interpolation to a common area • ~ North Atlantic + Europe • Grid 380x184, 0.25º • Software • Enhanced PC + Linux • ECMWF Metview + Local developments • Outputs • Deterministic • Ensemble probabilistic EWGLAM/SRNWP Meetings

  15. Monitoring in real time • Intranet web server • Deterministic outputs • Models X BCs tables • Maps for each couple (model,BCs) • Ensemble probabilistic outputs • Probability maps: 6h accumulated precipitation, 10m wind speed, 24h 2m temperature trend • Ensemble mean & Spread maps • EPSgrams (work in progress) • Verification: Deterministic & Probabilistic • Against ECMWF analysis • Against observations EWGLAM/SRNWP Meetings

  16. Monit 2: all models X bcs EWGLAM/SRNWP Meetings

  17. Monit 3: All Prob 24h 2m T trend EWGLAM/SRNWP Meetings

  18. Monit 4: Spread - Emean maps EWGLAM/SRNWP Meetings

  19. Case Study 2006061000 >=1mm • More than 15 mm/6 hours >=5mm >=10mm >=20mm EWGLAM/SRNWP Meetings

  20. Verification • The 2006 first half (6months) verification results against both references observations and ECMWF analysis are available. • Calibration: with synoptic variables Z500, T500, Pmsl • Response to binary events: reliability and resolution of surface variables: 10m surface wind, 6h and 24h accumulated precipitation EWGLAM/SRNWP Meetings

  21. Verification exercise • Interpolation to a common area • ~ North Atlantic + North Africa + Europe • Lat-lon Grid 380x184, 0.25º • ~180 days (Jan1 to Jun30 2006). • Two different references: • Analysis: ECMWF (6h and 24h det fc for Acc. Prec.) • Observations: TEMP & SYNOP • Verification software • ~ ECMWF Metview + Local developments • Deterministic scores • Synoptic variables: Bias & RMSE for each member & Ens Mean • Probabilistic ensemble scores • Synoptic variables: Calibration • Surface variables: Response to binary events EWGLAM/SRNWP Meetings

  22. Probabilistic ensemble scores • Ensemble calibration: • Synoptic variables: • Z500, T500, Pmsl • Scores: • Rank histograms • Spread-skill EWGLAM/SRNWP Meetings

  23. Rank histograms: examples Large spread Small spread Over prediction Under prediction Well calibrated EWGLAM/SRNWP Meetings

  24. Z500 H+24 Observations Z500 H+24 Analysis Z500 • Verification exercise: • ~ North Atlantic + North Africa + Europe, Lat-lon Grid 380x184, 0.25º • ~180 days (Jan1 to Jun30 2006). • Analysis: ECMWF (6h and 24h det fc for Acc. Prec.) • Observations: TEMP & SYNOP • Synoptic variables (here Z500) spread-skill & rank histograms against observations, show the ensemble is under-dispersive, a bit under-forecasting • The same against ECMWF analysis is very good EWGLAM/SRNWP Meetings

  25. Probabilistic ensemble scores • Response to binary events: • Surface variables: • 10m surface wind (10,15,20m/s thresholds) • 6h accumulated precipitation (1,5,10,20mm thresholds) • 24h accumulated precipitation (1,5,10,20mm thresholds) • Scores: • Reliability, sharpness (H+24, H+48) • ROC, Relative Value (H+24, H+48) • BSS, ROCA with forecast length EWGLAM/SRNWP Meetings

  26. 24hAccPrec ROC & ROCA >=10mm • Surface variables against observations show medium/quite good reliability and good resolution, degrading with threshold (clearly) and forecast length • Here is shown 24h Accumulated precipitation performance in HH+30 forecasts: reliability with sharpness, ROC and ROCA, Brier Skill Score, Relative economic value. • Verification against ECMWF analysis is much better 24h Acc Prec H+30 Reliability & Sharpness 24h Acc Prec H+30 Roc & ROCA 24h Acc Prec Brier Skill Score 24h Acc Prec H+30 Relative value EWGLAM/SRNWP Meetings

  27. Further work • The ensemble performance could be improved with some post-processing, today under development (Flattery method): • Bias correction • Calibration using Bayesian Model Averaging (BMA) EWGLAM/SRNWP Meetings

  28. ROAD MAP EWGLAM/SRNWP Meetings

  29. FUTURE ISSUES • Aladin and WRF as additional forecasting models • Multi analysis from HIRLAM 3DVAR model and first guess from global model forecasts • Alternative methods for multiple initial conditions • Verification against observations (high resolution precipitation network over Europe) • More post-process software (clustering) • Statistical downscaling applied to SREPS outputs • Convergence with GLAMEPS and regional THORPEX • Data policy aspects EWGLAM/SRNWP Meetings

  30. Conclusions • A Multi-model-Multi-boundaries Short Range Ensemble Prediction System (MMSREPS), is preoperational at INM • Verification results (2006 first half), against both observations and ECMWF analysis have been obtained • These first results look promising: • Verification against ECMWF analysis shows very good results • Verification against observations shows quite good results • Ensemble is under-dispersive • Good response to binary events • Future of INM SREPS is still open EWGLAM/SRNWP Meetings

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