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EURO4M Météo-France motivations and contributions. Teams involved. DClim (Direction de la Climatogie) Service in charge of the climatological data including the database, quality control, monitoring of the climate, climatological products, R&D studies. P.I : Anne-Laure Gibelin
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Teams involved • DClim (Direction de la Climatogie) • Service in charge of the climatological data including the database, quality control, monitoring of the climate, climatological products, R&D studies. • P.I : Anne-Laure Gibelin • CNRM/GAME (Research department) : WP2.3 • GMAP : Numerical weather prediction group : numerical weather prediction models, assimilation for weather forecast.P.I. : Eric Bazile • GMME : Mesoscale group : Mesoscale meteorology, surface processes, mesocale analysis, surface assimilation.P.I. : Eric Martin, Jean-François Mahfouf
Background models/applications • Météo-France has a long experience in surface modelling and derived applications • Need to upgrade the current near surface variable analyses (coop. NWP consortia) • Surface Scheme : ISBA (1989), SURFEX (2003) • Snow Schemes : 4 snow shemes of varying complexity in SURFEX (2010), CROCUS (1989) to be included in SURFEX • Surface analysis : CANARI (Europe : 2009), SAFRAN (Alps : 1991, France : 2003) • Assimilation : Operational screen-level and soil analyses in the global ARPEGE model since 1998 and in the ALADIN-France LAM since 2009 • Coupling surface analysis with SVAT for monitoring : Snow model CROCUS for avalanches (1991), ISBA and the hydrological model MODCOU (SIM, 2003), Carbon Fluxes (GMES Geoland project)
The CANARI analysis system • 2D optimum interpolation scheme • Spatialisation of T2m, RH2m, SST and snow depth from surface observations (SYNOP,BUOYS) • Background : NWP short-range forecasts (ARPEGE, ALADIN,AROME) • Structure functions : homogeneous and isotropic • Advantages : robust, cheap and developed within an operational NWP software environment • Weaknesses : unrealistic features over montains and near coastlines
Zmaxi Zmini Ébauche ARPEGE 1200m k=4 900m k=3 600m k=2 300m The analysis system SAFRAN • Analysis on « homogenous » zones • Explicitely account for altidudinal gradient • All conventional observations used, radiative model for radiation terms • Structure functions rely on zoning • Advantages : altitudinal gradient, all variables to force a surface model • Weaknesses : choice of the zones, artificial limits between zones, radiation estimates
Background / reanalyses and monitoring Evolution of the number of obs./day From 1958 to 2008 • 50 years reanalyses were made over France (based on ERA40/SAFRAN). • Drought analysis Precip. Soil wetness discharge 2003 : Heat wave (soil wetness) 1976 : Drought (precipitations) Precip. Soil wetness discharge Simulated SWI of 1st of April compared to the climatology
Contribution to EURO4M • DClim (Direction de la Climatogie) : WP1.1 • Provide regional datasets for the project • Development and improvement of the control procedures to provide additional information about data quality : • Time, spatial, inter parameter consistency • focus on the control of precipitation dataset (rain gauge) by using radars and/or additional data • look at the possibility to apply the same quality control procedures for all datasets in the project • Strong experience in Data Rescue and homogenization activities • CNRM/GAME (Research department) : WP2.3 & WP2.4 • Development of a 2D surface analysis sofware based on previous software (SAFRAN, MESAN, CANARI) • coupling with a surface scheme in order to produce derived variables, in particular snow cover, soil moisture, discharge and validations • Validation in France and other areas • Validation on well-instrumented tests sites
EURO4M impacts • Synergy with other projects • GMES Geoland : land surface monitoring (carbon - coordinated by CNRM/GAME) • SAF/land : contribution of CNRM/GAME to downward solar radiation and snow albedo • SAF/Hydro : contribution of DClim and CNRM/GAME to validations • HyMeX : need 2D surface analysis at the scale of the mediterranean • Improved capability of surface monitoring at large scale • Offline applications (snow, soil wetness, fire hasard, …) • NWP (assimilation, improved surface fluxes) • …