7/8 April 2005, Bologna 2nd Workshop on Short Range EPS

Multimodel-Multianalysis Mesoscale Multimodel-Multianalysis Mesoscale Ensemble (MusE)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.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 PiazzaCRS4 Parco Scientifico e Tecnologico POLARIS, Edificio 1, 09010 Pula (CA), I email: marino@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

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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