1. 7/8 April 2005, Bologna 2nd Workshop on Short Range EPS Multimodel-Multianalysis Mesoscale Ensemble (MusE) P. A. Chessa, C. Dessy, G. Ficca, C. Castiglia Servizio Agrometeorologico della Sardegna, Viale Porto Torres 119, 07100, Sassari, I email: chessa@sar.sardegna.itchessa@sar.sardegna.it Pre-operational setup  Models: BOLAM - MM5 - RAMS  I.C. and B.C.: AVN 12Z - ECMWF 12Z  Area:13.5W-34N/24.5E-54.5N  Spatial Resol.:0.25°  Fct. time range:+72h (step:6h)  Test period: 15/10/2002 15/04/2003  Implementation: Cluster Linux with 16 nodes bi-processor, Intel Xeon 3.06 Ghz – 1MB Cache L3 RATIONALE  Support the operational activities of SAR (the Sardinian MetService)  Provide early warning for unusual/severe weather events  Study the predictability of important local phenomena  Test the possibility to set up an operational ensemble “on the cheap” using Linux Clusters and developing a suitable GRID computing system M. Marrocu, I. Di Piazza CRS4 Parco Scientifico e Tecnologico POLARIS, Edificio 1, 09010 Pula (CA), I email: marino@crs4.itmarino@crs4.it The system is under-dispersive although the spread-skill correlation is acceptable: needs calibration. Bayesian Model Averaging and methods based on non parametric distribution are under study for the operational setup. Calibration of precipitation almost impossible over the typical learning period (60 to 90 days): needs a different approach (perhaps reforcasting can be one) Deterministic approach Superensemble (SE): The coefficients found through a multilinear regression Bias removed mean (EM): All the model are unbiased before the calculation of SE and EM Pre-operational tests Example for 2m Temperature  21 ground station over Sardinia  Test of several training periods of different lengths and variable position in the available sample  Test period of 60 days with variable position  Analysed 2m T over all domain Results  Superensemble and bias removed ensemble mean appear to work for parameters as temperature, geopotential height, wind intensity and mslp. For variables like rainfall a different approach has to be used.  Both SE and EM are better, on average, than the best model and in the worst cases (i. e. very high spread) tend to be very close to it. This make them the natural candidates as control forecasts in a Multimodel context. Impossible to say which is the best ! Minimization of scores different from RMSE may be needed.  Better results may be obtained using an iterative approach to calculate the regression coefficients in order to ignore (for each grid point, variable time step, etc) the model outputs associate to negative values. This could also help for a possible probabilistic interpretation. Learning period Best trade off: about 90 days Clearly flow dependent Better results can be obtained not considering the model outputs associated to negative coefficients. Future plans  Design and assessment of probabilistic forecasts products (under way)  Set up (6 to 9 months) of the operational ensemble :  Models: BOLAM – MM5 – RAMS  I.C. and B.C. : 06Z and 18Z AVN; +12h forecast of the 12Z ECMWF run;  Area: 16W-30N / 32E-58N;  Spatial resolution: 0.18° (nesting at higher resolution for specific needs) ;  Forecast time range: 96h.  Extension of the ensemble size and increase of spatial resolution (12-18 months)  Specific application:  Ship routing (Project WERMED);  Grid computing test case (Project GRIDA3):  Flash Flood Warning System (Project CEDRINO BASIN). Impossible to say which is best between SE and EM. 2m T over all domain (analyzed data): an example of the regression coefficient fields