Short-Range Ensemble Prediction System at INMShort-Range Ensemble Prediction System at INMGarcía-Moya, J.A., Santos, C., Escribà, P.A., Santos, D., Callado, A., Simarro, J. (NWPD, INM, SPAIN)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,2nd SRNWP Workshop on Short Range Ensemble, Bologna, April 2005Bologna, April 2005

J.A. Garcia – Moya, j.garciamoya@inm.es Carlos Santos, csantos@inm.es 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 X1Two main phases (2002-2005) :1. Cray X1 15 nodes (4 MSPs/node) 770

Gf Determistic Forecast.2. Cray X1e 15 nodes (8 MSPs/node)

2300 Gf Deterministic + SREPS

Conclusions for MultimodelAdvantages

• 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

ReferencesPalmer, 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.

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

HirlamHRMMM5UM

ECMWFGMEAVNUKMO

Test run & validationHirlam, 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).

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)

DAYL

Y PR

E-OP

ERAT

IONA

L RU

NTE

ST R

UN P

ERFO

RMAN

CE &

VER

IFIC

ATIO

N

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

OUTL

INE

CONC

LUSI

ONS

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 timeDeterministic 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.