520 mm/24 h

1. SREPS at INM • Multi-model approach • Multi-boundaries: • From few global • deterministic models Hirlam HRM MM5 UM 520 mm/24 h ECMWF GME AVN UKMO Pmsl & Z500 300Wind Pmsl & 6hAccPrec BIAS & RMS 10mWind T2m Z500 Pmsl T500 PLUMES Pmsl Z500 SPREAD & EMSD Pmsl Z500 2mT MAPS SPREAD & EM • 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 EMSD Pmsl TALAGRAND (RANK HISTOGRAMS) Z500 6hAccPrec>=1mm >=5mm >=10mm >=20mm Pmsl H+24 Pmsl H+36 Z500 H+24 Z500 H+36 Pmsl H+24 Z500 H+24 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 • New computer Cray X1 • Two main phases (2002-2005) : • Cray X1 15 nodes (4 MSPs/node) 770 GfDetermistic Forecast. • Cray X1e 15 nodes (8 MSPs/node) 2300 Gf Deterministic + SREPS OUTLINE • 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). TEST RUN PERFORMANCE & VERIFICATION Test run & validation Hirlam, HRM and MM5. 36 hours forecast once a day (00 UTC). 5 days of comparison (20040103-20040107). 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). DAYLY PRE-OPERATIONAL RUN MULTIMODEL PROGRESS EACH MODEL & BCs OUTPUTS • 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 ENSEMBLE OUTPUTS: PROBABILITY MAPS INTRANET WEB SERVER • 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. ENSEMBLE OUTPUTS: ENSEMBLE MEAN & SPREAD MAPS Z500 HH+24 Z500 HH+48 • Ensemble mean & Spread Maps • Ensemble mean (isolines) and spread (coulours). • Spatial distribution of variability. • Variability comparison with meteorological pattern. CONCLUSIONS • 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 • 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 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, 73-91. 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. J.A. Garcia – Moya, j.garciamoya@inm.es Carlos Santos, csantos@inm.es Numerical Weather Prediction Department. INM.