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Meso-NH model 40 users laboratories A research model, jointly developped by Meteo-France and Laboratoire dAérologie (CNRS/UPS)

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Présentation au sujet: "Meso-NH model 40 users laboratories A research model, jointly developped by Meteo-France and Laboratoire dAérologie (CNRS/UPS)"— Transcription de la présentation:

1 Meso-NH model 40 users laboratories A research model, jointly developped by Meteo-France and Laboratoire dAérologie (CNRS/UPS)

2 Plan 1.General presentation of the model 2.Meso-scale simulations. 3.Large-Eddy simulations 4.Atmospheric Chemistry 5.New couplings : Electricity, Hydrology, Dispersion 6.Climatology 7.Diagnostics

3 The different meteorological model at Météo-France Global Climate Model (GCM) ( x > 100 km) : ARPEGE Climat NWP at synoptic scale : ARPEGE ( x=20-25km on France) NWP at meso- scale : ALADIN ( x=10km) NWP at meso- scale : AROME (2008) ( x=2.5km) Research model for synoptic to meso- scale : Méso-NH ( x=50km to 10m).

4 Why do we need a high resolution research model ? 1.To improve parameterizations for Large Scale models : fine resolution simulations allow to resolve the main coherent patterns and inform on fine scale variability. 2.To help the evaluation and the improvement of AROME 3.To better understand the physics (e.g. cloud processes), to characterize local effects 4.To carry out impact studies and use the model as a laboratory 5.To develop new couplings (e.g. Electricity, Hydrology …) A broad variety of developments and applications

5 A broad range of resolution from synoptic scales (Dx~10km), meso-scale (Dx~1km) to Large Eddy Simulation (Dx~10m) Real cases (from ECMWF, ARPEGE, ALADIN analyses or forecasts) Ideal cases unrealistic cases - Academic cases (validation of the dynamics) - Basic studies (Diurnal cycle …) : Cloud Resolving Model (CRM) - To reproduce an observed reality (via forcings) (intercomparison : GCSS, EUROCS …) Simulations 3D, 2D, 1D From a simple to a sophisticated physics An accurate but expensive dynamics A set of diagnostics (budgets, profilers, trajectories …) Parallelized and vectorized A broad range of hardware system for the research community : FUJITSU, NEC, CRAY, IBM, cluster of PC No operational objective. Meso-NH characteristics

6 The meso-scale simulations with Meso-NH : 1km< x<10km

7 Domaine 10-km ~ km Domaine 2.5-km Typical configuration for a real test study A father model at 10km resolution with the deep convection scheme, the subgrid condensation scheme, the ICE3 microphysics and the 1D turbulence scheme A son model at 2.5km resolution without deep convection scheme but with the shallow convection scheme, the ICE3 microphysics and the 1D turbulence scheme

8 Number of days with daily rain > 200 mm for the period [ ] on the South- East Massif Central Alpes Pyrénées 1 severe episode (+500 mm/24 h) 2002 As many other Western Mediterranean regions, Southern France is prone to devastating flash-floods during the fall season

9 Impact of the convective system on the triggering and the localization CTRL = With cooling associated to evaporation of precipitation NOC = Without cooling Cumulated precipitation during 4 hours Gard 02 CTRL = With Massif Central NOR = Without Massif Central Nuissier et Ducrocq, 2006 Cooling induced by evaporation of rain and orography forcing are 2 major factors inducing quasi-stationary convective systems

10 Pluviomètres Assimilation 10 km 6h pas 6h Without meso-scale analysis Initialisation arpège 6 UTC Without assimilation, precipitation intensity is well reproduced, but not the exact localization Jaubert and Ducrocq, 2006 Cumulated precipitation during 18 hours Impact of meso-scale assimilation

11 (Keil et Cardinali, 2003) 32km : 150x150 8km : 145x145 2km : 150x150 over 51 levels IOP8 (F<1) IOP2a (F>1) 8 km 2 km Monte Lema S Pol Ronsard ECMWF 32 km 3 Doppler radars ( ) Orographic precipitation 3D (MAP) How can dynamics modify the microphysics ? Lascaux et Richard, 2005

12 Snow Graupel Hail Cloud Rain Ice IOP2a IOP2a ( Strong convection) - Deep system (unblocked unstable case, high Fr=U/Nh) - Large amount of hail and graupel - Main process : Riming Mean vertical distribution of hydrometeors IOP8 ( Stratiform event) - Shallow system (blocked case, low Fr) -Large amount of snow - Main process : Vapor deposition on snow IOP8 Snow Lascaux et Richard, 2005 Orographic precipitation 3D (MAP)

13 Z > 60 dBz 12 km 100 km Tabary, 2002 (x) hail + graupel (o) hail ( ) rain (o) hail (x) hail + graupel ( ) rain graupel Simulation (Meso-NH) Orographic precipitation 3D (MAP) IOP2a Radar observations

14 FOG – 1D simulation – Temporal evolution on 18h from 18TU rc Without cloud droplet sedimentation With cloud droplet sedimentation With cloud droplet sedimentation but a coarser vertical resolution Rémi, S., h21h 00h 03h06h09h12h 18h21h 00h 03h06h09h12h 18h21h 00h 03h06h09h12h g/kg

15 Simulation of cyclone : case of Dina 7800 km, x=36km 1944 km, x=12km 720 km, x=4km 3600 km Automatic method of Initialization : Filtering/Bogussing Barbary et al.

16 Vertical cross-sections at x=4km K m/s K Horizontal wind S-N W-E Barbary et al.

17 Local effects : Sea breeze Urban network Model Lemonsu et al., 2005a 2m Temperature 26 June 2001, 1400 UTC Δ = 250 m

18 VAL OBS CNRS Puget Massif Marseille veyre City centre z = 400 m AGL VAL OBS CNRS m s -1 Puget Massif Marseille veyre City centre z = 50 m AGL West SSB South SSB South- East DSB Horizontal wind field 26 June 2001, 1400 UTC Lemonsu et al., 2005a Local effects : Sea breeze

19 6 m s June 2001, 1400 UTC B C D A TWL B C D A Model VDOL City center Distance (km) VDOL City center Altitude (km) ZS (m) Marseillev eyre 190 o Puget Massif CNRS (Radar) 3 km VAL (Lidar) OBS (Radar) Etoile Massif Comparison with transportable wind lidar (TWL) Lemonsu et al., 2005a

20 The Large Eddy Simulations with Meso-NH : Large eddys are resolved : TKE resolved >> TKE Subgrid

21 Impact of the pollution on the stratocumulus diurnal cycle = Aerosol indirect effect 0.7g/kg 700m r c (g/kg) Simulation LES 50m Nuage non pollué Sandu, I., TU x= y= 50m, z=10m =36h LWP (g/m²) Polluted : non precipitating Pristine : precipitating Evaporation of precipitation Cooling Limits the stratification at cloud base and the decoupling No precipitation No Cooling Maximum solar warming decoupling

22 AN OBSERVED LLJ DURING THE SABLES98 CAMPAIGN Night: September m tower Duero river basin x = 6 m, y = 4 m, z = 2m (0

23 Results (I): Mean profiles M.A. Jiménez Universitat de les Illes Balears The maximum of the wind and the height are well captured The LLJ height coincides with the inversion height The surface temperature obtained from the LES cools down much more than the observations

24 Results (II): Turbulence There is a maximum of turbulence above the Jet, mainly resolved. The layer below the jet is decoupled from the layer above In the surface layer, the LES presents more mixing than the observations Shear and buoyancy are the most important contributions to create and dissipate turbulence, respectively Total Subgrid

25 Lidar observations LES Simulations r v LES simulation g/kg P3 aircraft KA aircraft.. max (pdf) _ min (pdf) LES q v at 0.5z i Water vapor variability in convective BL : presence of dry tongues - Couvreux et al. (2005) at 12h x= y= 100m, z<50m, t=7h S(q v )<0

26 Atmospheric Chemistry

27 Meso-NH-Chemistry: Modelling of atmospheric chemistry from local (dx=1 km) to synoptic scale (dx=50 km) large-scale: MOCAGE, ECMWF,...

28 OZONE le 25 Juin UTC 9km 3km <30ppb Parc Naturel VerdonMarseille 85ppb MarseilleParc Naturel Verdon >90ppb 15 UTC >90ppb Cousin et Tulet, 2004

29 surface water percolation Deep soil Surface soil Soil (sand/clay) Aérosols scavenging Absorption/ diffusion of solar radiation Surface cooling Desertic dusts – formations, life cycle and radiative effect u* turbulence Emission Saltation

30 Desertic dusts Grini and Tulet, 2006

31 New couplings : - CO2 : coupling with SURFEX - Hydrology : coupling with SURFEX - Electricity : direct coupling with Meso-NH - Pollutant dispersion : direct coupling with Meso-NH - Duct mapping

32 Atmospheric CO 2 modelling Online coupling with the surface scheme ISBA-A-gs : CO 2 surface fluxes : - assimilation (<0) CO2 absorption by vegetation (DAY) - respiration (>0) CO2 emissions from ecosyst. depends on temperature (NIGHT) - anthropogenic emissions (>0) and ocean fluxes (<0 in our latitude) Feedback : CO 2 concentrations variations from the atmosphere to the surface ISBA-A-g s Meteorological Model LE, H, Rn, W, Ts… Atmospheric [CO 2 ] concentrations Anthropogenic Sea Meso-NH Surface Lafore et al., 98 Noilhan et al. 89, 96, Calvet et al., 98 CO 2 Fluxes

33 Sarrat et al.(2006) CO2 concentrations (ppm) may-27 14HUTC Atmospheric CO 2 modelling : May – Boundary layer heterogeneity Winter crops absorbs a large amount of CO2 creates a CO2 depletion Zi = 900m Agricultural area : low sensible heat flux Zi = 1600m Forest : high sensible heat flux Forest : high respiration

34 Atmospheric CO 2 modelling May – : comparisons obs/simu Simulated vertical cross section of CO 2 Ocean - Marmande Agricultural area Forest area Vertical cross section of observed CO 2 by aircraftoceanforestcropland forestcropland Sarrat et al., 2006 Winter crops Assimilation Forêt Respiration

35 TOPMODEL (Beven and Kirkby, 1979) distributed hydrologic model with one model by basin : 9 basins ( km²) Objectives : - Flow and rapide flood forecasts - Retroaction of the hydrology on the atmosphere - Available for AROME HYDROLOGY : Development of the coupling Meso-NH-ISBA-TOPMODEL CNRM/GMME/MICADO Crues des 5-9 septembre 2005 Débits simulés à St Martin dArdèche (~ 2500km 2 )

36 Barthe et al. [2005] Explicite electrical scheme in Meso-NH Local separation of charges Transfert and transport of charges Microphysical and dynamical processes Electric field Lightning parameterization Bidirectional leader (determinist) Vertical extension of the lightning Channel steps (probabiliste) Horizontal extension of the lightning Charge neutralization E > E trig yes no

37 Life cycle of electrical charges in a convective cell Barthe et Pinty, JGR Apparition of graupel Electrization of the cloud Apparition of electric field lightning Triggering of convection Simulation Méso-NH

38 30km, x=500m Industrial accidental release : AZF Couche résiduelle : flux de S Couche de mélange : flux de SE Max=10% de concentration initiale 30km, x=500m 10%=97 g/m 3 Max_obs=60 g/m 3 The heaviest particles have settled : strong dry deposition on Blagnac

39 SPRAY Lagrangian particle model At least particles released Advection+Turbulence+random Applied to the 2 Meso-NH grids PERLE P E R L E PERLE (Programme dEvaluation des Rejets Locaux dEffluents) Dispersion Meso-NH 2 grids (Regional x=8km, L=240km/ Local x=2km, L=60km) 36 levels until 16km ALADIN initialization and coupling Meso-scale meteorology Modelling system for environmental emergency


41 Problématique des conduits de propagation électromagnétique Problématique de détection radar offensive et défensive à bord de navires (dont porte-avion) connaissance des niveaux de vols hors de portée des RADARS, connaissance des portées RADAR Co-indice de réfraction modifié M permet dappréhender les différents mode de propagation de latmosphère. Il dépend essentiellement de lhumidité et de la température. Co-indice de réfraction modifié M Altitude Conduit de propagation Faisceau radar Propagation normale

42 Pourret, V., 2006 : PEA PREDEM Co-indice de réfraction N=(77.6/T).(P+4810.e/T)-6.e/T Sommet du conduit de propagation = Altitude de linversion de M co-indice de réfraction OG dans le sillage des îles au sommet du conduit Réfraction normale Réfraction vers le bas

43 Climatologie. Régionalisation climatique

44 Roses Aladin 3 ansMéso-NH 95 datesMeasurements Wind climatology over the North Alps


46 Climat futur : 52 cas ARPEGE Climat / OPAMED8 : modèle couplé océan- atmosphère, rés. horizontale : ~50 km Simulations ARPEGE Climat / OPAMED 8 (climat présent climat futur ) Climat présent : 51 cas méthode didentification des cas extrêmes pour sélectionner des situations représentatives CL 1CL 4CL 1CL 4 Sélection des cas les plus proches distance de corrélation spatiale Climat futur : 10 casClimat présent : 10 cas CL 1CL 4CL 1CL 4 CYPRIM : Régionalisation climatique des pluies intenses avec le modèle Meso-NH. A.-L. Beaulant

47 Simulations avec Meso-nh Configuration en 2 domaines emboités (2-way grid-nesting) Domaine 1 de résolution horizontale ~ 10 km Domaine 2 de résolution horizontale ~ 2.5 km (centré sur lévènement convectif) Les simulations débutent à 12 UTC le jour J-1 et se terminent à 06 UTC le jour suivant J+1 (42 h) ARPEGE Climat / OPAMED8 ~ km Domaine 1 : Rh ~ 10 km Domaine 2 : Rh ~ 2.5 km MESO-NH Les conditions initiales et aux limites sont fournies par les champs du modèle ARPEGE Climat / OPAMED8 (toutes les 6 heures) Rh ~ 50 km La convection est paramétrée pour le domaine à 10 km (paramétrisation de Kain et Fritsch) tandis quelle est résolue explicitement pour le domaine à 2.5 km.

48 mm mm mm mm mm mm mm mm Cumuls de pluies sur les 24 1ères heures pour les 10 cas du climat futur 16 mm mm t0 à t UTC J-1 à 12 UTC J mm

49 Diagnostics

50 Chaboureau and Pinty (2005) : Use of radiative transfer RTTOV to MSG x=30 km Amélioration des enclumes (cirrus) sur le seuil dauto-conversion

51 Réflectivités observées Réflectivités simulées avec Méso-NH (radar de Bollène le 8 sep à 21 UTC, élévation=1,2°) « Développement communautaire dun opérateur-simulateur dobservation radar » (Caumont O., V. Ducrocq, G. Delrieu, M. Gosset, J. Parent du Châtelet, J.-P. Pinty, H. Andrieu, Y. Lemaître et G. Scialom, 2006 : A radar simulator for high-resolution nonhydrostatic models. J. Atmos. Oceanic Technol.) Simulation de réflectivités radar

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