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Meso-NH model

A research model, jointly developped by Meteo-France and Laboratoire d’Aérologie (CNRS/UPS). Meso-NH model. 40 users laboratories. http://mesonh.aero.obs-mip.fr/mesonh/. Plan. General presentation of the model Meso-scale simulations. Large-Eddy simulations Atmospheric Chemistry

hermione-holloway
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Meso-NH model

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  1. A research model, jointly developped by Meteo-France and Laboratoire d’Aérologie (CNRS/UPS) Meso-NH model 40 users laboratories http://mesonh.aero.obs-mip.fr/mesonh/

  2. Plan • General presentation of the model • Meso-scale simulations. • Large-Eddy simulations • Atmospheric Chemistry • New couplings: Electricity, Hydrology, Dispersion • Climatology • Diagnostics

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

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

  5. Meso-NH characteristics • 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.

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

  7. ~500-600 km Typical configuration for a real test study Domaine 10-km • 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 Domaine 2.5-km

  8. As many other Western Mediterranean regions, Southern France is prone to devastating flash-floods during the fall season Number of days with daily rain > 200 mm for the period [1958-2000] on the South-East Massif Central Alpes 2002 1 severe episode (+500 mm/24 h) Pyrénées

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

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

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

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

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

  14. 18h 18h 18h 21h 21h 21h 03h 03h 03h 06h 06h 06h 09h 09h 09h 12h 12h 12h 00h 00h 00h FOG – 1D simulation – Temporal evolution on 18h from 18TU rc rc Without cloud droplet sedimentation With cloud droplet sedimentation g/kg g/kg rc With cloud droplet sedimentation but a coarser vertical resolution g/kg Rémi, S., 2006

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

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

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

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

  19. ZS (m) 3 km Etoile Massif 500 VAL (Lidar) 400 190o 300 OBS (Radar) 200 Puget Massif CNRS (Radar) 100 -6 -4 -2 0 2 4 6 m s-1 Marseilleveyre 50 Comparison with transportable wind lidar (TWL) 26 June 2001, 1400 UTC 2.5 Model TWL D D 2.0 C C 1.5 Altitude (km) B 1.0 B A 0.5 City center City center A VDOL VDOL 0 2 4 6 0 2 4 6 Distance (km) Distance (km) Lemonsu et al., 2005a

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

  21. LWP (g/m²) Polluted : non precipitating Evaporation of precipitation  Cooling  Limits the stratification at cloud base and the decoupling Pristine : precipitating No precipitation  No Cooling  Maximum solar warming  decoupling Impact of the pollution on the stratocumulus diurnal cycle = Aerosol indirect effect 0.7g/kg 700m rc(g/kg) Simulation LES 50m Nuage non pollué Dx=Dy= 50m, Dz=10m T=36h 0TU 6 12 18 24 30 36 Sandu, I., 2007

  22. AN OBSERVED LLJ DURING THE SABLES98 CAMPAIGN Objective: study the mixing processes across the maximum of the wind of an observed Low-Level Jet (LLJ) using LES • x = 6 m, y = 4 m, z = 2m (0 <z<100 m) and stretched above (z = 5 m at about 400 m) 100m tower Duero river basin • Night: 20-21 September 1998 M.A. Jiménez Universitat de les Illes Balears

  23. Results (I): Mean profiles • 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 M.A. Jiménez Universitat de les Illes Balears

  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 moremixing than the observations • Shear and buoyancy are the most important contributions to create and dissipate turbulence, respectively Total Subgrid

  25. Lidar observations at 12h LES simulation LES Simulations 5.0 5.5 6.0 6.5 7.0 7.5 8.0 8.5 9.0 9.5 10. 5.0 5.5 6.0 6.5 7.0 7.5 8.0 8.5 9.0 9.5 10. 5.0 5.5 6.0 6.5 7.0 7.5 8.0 8.5 9.0 9.5 10. 5.0 5.5 6.0 6.5 7.0 7.5 8.0 8.5 9.0 9.5 10. g/kg g/kg rv’ P3 aircraft qv’ at 0.5zi KA aircraft LES Water vapor variability in convective BL : presence of dry tongues - Couvreux et al. (2005) S(qv)<0 . .max(pdf) _ min(pdf) Dx=Dy= 100m, Dz<50m, Dt=7h

  26. Atmospheric Chemistry

  27. Meso-NH-Chemistry: Modelling of atmospheric chemistry from local (dx=1 km) to synoptic scale (dx=50 km) http://mesonh.aero.obs-mip.fr large-scale: MOCAGE, ECMWF, ...

  28. 3km 9km 9 UTC 85ppb <30ppb 15 UTC >90ppb >90ppb Parc Naturel Verdon Marseille Marseille Parc Naturel Verdon OZONE le 25 Juin 2001 Cousin et Tulet, 2004

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

  30. Grini and Tulet, 2006 Desertic dusts

  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 CO2 modelling Online coupling with the surface scheme ISBA-A-gs :  CO2 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 : CO2 concentrations variations from the atmosphere to the surface Meso-NH Surface Meteorological Model LE, H, Rn, W, Ts… ISBA-A-gs CO2 Fluxes Atmospheric [CO2]concentrations Anthropogenic Sea Noilhan et al. 89, 96, Calvet et al., 98 Lafore et al., 98

  33. Zi = 1600m Forest : high sensible heat flux Zi = 900m Agricultural area : low sensible heat flux Atmospheric CO2 modelling : May – 27 2005 Boundary layer heterogeneity CO2 concentrations (ppm) may-27 14HUTC Forest : high respiration Winter crops absorbs a large amount of CO2creates a CO2 depletion Sarrat et al.(2006)

  34. Vertical cross section of observed CO2 by aircraft Forest area Agricultural area Atmospheric CO2 modellingMay – 27 2005 : comparisons obs/simu Simulated vertical cross section of CO2 Ocean - Marmande ocean forest cropland forest cropland Winter crops  Assimilation Forêt  Respiration Sarrat et al., 2006

  35. Débits simulés à St Martin d’Ardèche (~ 2500km2) Crues des 5-9 septembre 2005 HYDROLOGY : Development of the coupling Meso-NH-ISBA-TOPMODEL CNRM/GMME/MICADO • TOPMODEL (Beven and Kirkby, 1979) distributed hydrologic model with one model by basin : 9 basins (200-2200 km²) • Objectives : - Flow and rapide flood forecasts - Retroaction of the hydrology on the atmosphere - Available for AROME

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

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

  38. Industrial accidental release : AZF Couche de mélange : flux de SE Max=10% de concentration initiale Couche résiduelle : flux de S 10%=97mg/m3 Max_obs=60mg/m3 The heaviest particles have settled : strong dry deposition on Blagnac 30km, Dx=500m 30km, Dx=500m

  39. Meso-scale meteorology Meso-NH • 2 grids (Regional Dx=8km, L=240km/ Local Dx=2km, L=60km) • 36 levels until 16km • ALADIN initialization and coupling • Lagrangian particle model • At least 10000 particles released • Advection+Turbulence+random • Applied to the 2 Meso-NH grids SPRAY Modelling system for environmental emergency • PERLE(Programmed’Evaluationdes Rejets Locauxd’Effluents) Dispersion

  40. Altitude Faisceau radar Propagation normale Conduit de propagation Co-indice de réfraction modifié M 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 d’appréhender les différents mode de propagation de l’atmosphère. Il dépend essentiellement de l’humidité et de la température.

  41. Co-indice de réfraction N=(77.6/T).(P+4810.e/T)-6.e/T Sommet du conduit de propagation = Altitude de l’inversion de M co-indice de réfraction Réfraction vers le bas Réfraction normale OG dans le sillage des îles au sommet du conduit Pourret, V., 2006 : PEA PREDEM

  42. Climatologie . Régionalisation climatique

  43. Measurements Roses Aladin 3 ans Méso-NH 95 dates Wind climatology over the North Alps

  44. OBS MESO-NH 80% ALADIN 76% HYERES

  45. méthode d’identification des cas extrêmes pour sélectionner des situations représentatives CYPRIM :Régionalisation climatique des pluies intenses avec le modèle Meso-NH . A.-L. Beaulant Simulations ARPEGE Climat / OPAMED 8 (climat présent 1960-2000 + climat futur 2070-2099) ARPEGE Climat / OPAMED8 : modèle couplé océan-atmosphère, rés. horizontale : ~50 km Climat présent : 51 cas Climat futur : 52 cas CL 1 CL 4 CL 1 CL 4 Sélection des cas les plus proches distance de corrélation spatiale Climat présent : 10 cas Climat futur : 10 cas CL 1 CL 4 CL 1 CL 4

  46. ~500-600 km 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 l’évènement convectif) Domaine 1 : Rh ~ 10 km • La convection est paramétrée pour le domaine à 10 km (paramétrisation de Kain et Fritsch) tandis qu’elle est résolue explicitement pour le domaine à 2.5 km. Rh ~ 50 km ARPEGE Climat / OPAMED8 Domaine 2 : Rh ~ 2.5 km • Les conditions initiales et aux limites sont fournies par les champs du modèle ARPEGE Climat / OPAMED8 (toutes les 6 heures) • Les simulations débutent à 12 UTC le jour J-1 et se terminent à 06 UTC le jour suivant J+1 (42 h) MESO-NH

  47. Cumuls de pluies sur les 24 1ères heures pour les 10 cas du climat futur t0 à t0+24 12 UTC J-1 à 12 UTC J 20711011 20791004 20831018 20891020 20891021 431 mm 185 mm 260 mm 298 mm 460 mm 20901102 20901112 20911010 20951007 20981110 378 mm 16 mm 137 mm 291 mm 291 mm 5 25 50 100 150 200 250 300 350 400 450 mm

  48. Diagnostics

  49. Amélioration des enclumes (cirrus) sur le seuil d’auto-conversion Chaboureau and Pinty (2005) : Use of radiative transfer RTTOV to MSG Dx=30 km

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