Short-Range Ensemble Prediction System at INM García-Moya, J.A., Santos, C., Escribà, P.A., Santos, D., Callado, A., Simarro, J. (NWPD, INM, SPAIN) 2nd SRNWP Workshop on Short Range Ensemble, Bologna, April 2005 J.A. Garcia – Moya, Carlos Santos, Numerical Weather Prediction Department. INM. Meteorological Framework Main Weather Forecast issues are related with Short-Range extreme events. Convective precipitation is the most dangerous weather event in Spain (Some fast cyclogenesis, several cases of more than 200 mm/few hours every year). New computer Cray X1 Two main phases ( ) : 1.Cray X115 nodes (4 MSPs/node) 770 GfDetermistic Forecast. 2.Cray X1e15 nodes (8 MSPs/node) 2300 GfDeterministic + SREPS Conclusions for Multimodel Advantages Better representation of perturbations (SAMEX results) Better results Disadvantages Difficult to implement operationally (four different models should be maintained operationally) Expensive in terms of human resources No control experiment in the ensemble, use of “centroid” as control References Palmer, T. et al, 2004: Development of a European Multi-Model Ensemble System for Seasonal to Interannual Prediction (DEMETER). ECMWF, Technical Memorandum nº434. Hou, D., E. Kalnay, and K. K. Droegemeier, 2001: Objective verification of the SAMEX'98 ensemble forecasts. Mon. Wea. Rev., 129, Raftery A., Balabdaoui F., Gneiting T. and Polakowski M., 2003: Using bayesian model averaging to calibrate forecast ensembles. Technical report nº440. Department of Statistics. University of Washington. Future Verification software for multimodel ensemble (precipitations, ROC curves, …) UM model ready to use Daily run at midday Post process software (targeting clustering) Bayesian model averaging for improvement in calibration and better skill for weighted average SREPS at INM Multi-model approach Multi-boundaries: From few global deterministic models Hirlam HRM MM5 UM ECMWF GME AVN UKMO Test run & validation Hirlam, HRM and MM5. 36 hours forecast once a day (00 UTC). 5 days of comparison ( ). Four different initial and boundary conditions (EMCWF, GME from DWD, AVN from NCEP and UM from UKMO). Use ECMWF operational analysis as reference. No control experiment, then “natural” BCs will be “control” for each model (ECMWF for Hirlam, GME for HRM, AVN for MM5). Ensemble mean & Spread Maps Ensemble mean (isolines) and spread (coulours). Spatial distribution of variability. Variability comparison with meteorological pattern. MAPS SPREAD & EM TALAGRAND (RANK HISTOGRAMS) DAYLY PRE-OPERATIONAL RUN TEST RUN PERFORMANCE & VERIFICATION Stamps View of Multimodel-Multiboundaries Deterministic ECMWF as reference up-left HRM, MM5, Hirlam models in rows AVN, ECMWF, GME, UKMO BCs in columns SPREAD vs EMSDSPREAD & EMSD PLUMES BIAS & RMS OUTLINE CONCLUSIONS MULTIMODEL PROGRESS INTRANET WEB SERVER Test run area (beige) improved to Larger area (blue) HRM and UM models in migration process GME BCs not yet in large enough area, UM BCs almost running Running daily (Hirlam,MM5) models X (AVN,ECMWF) BCs EACH MODEL & BCs OUTPUTS ENSEMBLE OUTPUTS: PROBABILITY MAPS ENSEMBLE OUTPUTS: ENSEMBLE MEAN & SPREAD MAPS Monitoring in real time Deterministic outputs for each model and BCs Models X BCs tables Ensemble probabilistic outputs Probability maps: 6h accumulated precipitation, 10m wind speed, 2m temperature trends Ensemble mean & Spread maps EPSgrams Verification Deterministic scores Talagrand, Spread vs Emsd, ROC, etc.