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Meso-NH model 30 users laboratories

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1 Meso-NH model 30 users laboratories http://www.aero.obs-mip.fr/mesonh
A research model, jointly developped by Meteo-France and Laboratoire d’Aérologie (CNRS/UPS) 30 users laboratories

2 Examples of Applications of Meso-NH
General description of Meso-NH Grid nesting Clouds representation (explicit convective clouds, Sc) Cyclones Coupling with the surface Coupling with other models (hydrology, dispersion) Diagnostics Systematic validations (climatology, real time runs) Towards AROME ex

3 General description of Meso-NH
Anelastic equations with the pseudo-incompressible system of Durran Vertical coordinate following the terrain : (Gal Chen and Sommerville, 1975) Temporal discretization : Purely explicit leap-frog scheme Advection scheme : 2nd order eulerian schemes Spatial discretization : Arakawa C grid Grid nesting : One-way/Two-way Initial fields and LBC (radiative open) from ECMWF/ARPEGE/ALADIN. DYNAMICS Turbulence : 1.5 order closure Cuxart-Bougeault-Redelsperger (2000) Convection : Kain-Fritsch (1993) revised by Bechtold et al. (2001) Microphysical scheme : Bulk schemes at 1-moment or 2-moments. Up to 7 prognostic species: vapor (rv), cloud (rc), rain (rr), pristine ice (ri), snow (rs), graupel (rg), hail (rh) Radiation : ECMWF package Chemical on-line scheme : Gazeous and aerosols (Presentation C.Mari, Thursday) Externalized surface model (Presentation P.Le Moigne, this afternoon) PHYSICS

4 Types of simulations A broad range of resolution from synoptic scales (Dx~10km) to 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

5 Grid nesting technics A single constraint : an integer ratio between the resolutions and the time steps Same vertical grids. At every time step : The Coarse Model (CM) gives the lateral boundary conditions to Fine Model (FM) by interpolation One-way : the FM doesn’t influence the CM Two-way : CM fields are relaxed to the average of FM fields

6 Vaison-la-Romaine : 22 september 1992
One-way Two-way 3 nested grids : 40/10/2.5km Instantaneous precipitations 2.5km Stein et al., 2000

7 Vaison-la-Romaine : 22 september 1992
One-way Two-way 2.5 km Cumulated precipitations for 9h (Obs=300mm en 6h) Si les différences ne sont pas trop grandes à 2.5km, elles le sont beaucoup plus à 10km : cela provient du schéma de KF (activé à 10km et pas à 2.5km), qui sous-estime l’intensité de la convection. Les précip. Explicites permettent de corriger. 10km Stein et al., 2000

8 Examples of Applications of Meso-NH
General description of Meso-NH Grid nesting Clouds representation (explicit convective clouds, Sc) Cyclones Coupling with the surface Coupling with other models (hydrology, dispersion) Diagnostics Systematic validations (climatology, real time runs) Towards AROME ex

9 Mixed phase cloud representation with a bulk scheme
Ice crystals Snowflakes Graupel Hail Cloud droplets Mixed phase : 0°C 0°C Cloud droplets Raindrops Liquid phase : Cloud properties = f( , , , , , )

10 The different processes
Nucleation Autoconversion Deposition Aggregation Riming Freezing 0°C 0°C Collection Collection Melting Sedimentation

11 MESO-NH Explicit microphysical scheme :

12 Instantaneous precipitation 2.5km
2-way without ICE 2-way with ICE Stein et al., 2000

13 U W q A tropical squall line (P.Jabouille) : Idealized simulation according to a real case (COPT81) Stratiform Density Current Convective H D Lafore Moncrieff 89

14 Jabouille. Caniaux et al., 1994
Cloud droplets Rain drops Graupel Pristine ice Les hydrométéores solides (surtout glace et neige)interviennent dans la partie stratiforme. C’est la prise en compte de la glace qui permet une représentation correcte de la LG et de son enclume. Snow Jabouille. Caniaux et al., 1994

15 Three contrasted MAP cases
IOP 2A Strong Convection IOP 3 Moderate Convection IOP 8 Stratiform rain F.Lascaux and E.Richard, 2005

16 Microphysical retrievals : IOP 2A (intense convection)
12 km 100 km Tabary, 2002 (x) hail + graupel (o) hail ( ) rain 18:00 UT 19:00 UT 20:00 UT Un exemple de comparaison entre une restitution radar de paramètres microphysiques et une simulation numérique: La simulation restitue correctement l’apparition de la grêle entre 18h et 19h et l’étagement des hydrométéores: mélange grêle/graupel, grêle, mélange pluie/grêle, et pluie. Les paramètres restitués par l’algorithme radar sont: les cristaux orientés verticalement (VC) et horizontalement (HC), la neige mouillée (WS) et sèche (DS), les mélange graupel/grêle (GH) et grêle/pluie (HR), la grêle (HL), et la pluie de différentes intensités (LR,MR,HR). Z > 60 dBz

17 Hydrometeor type Radar Retrieval (S-Pol) Simulation (Meso-NH) 12 km
18:00 UT 12 km 19:00 UT (x) hail + graupel (o) hail Un exemple de comparaison entre une restitution radar de paramètres microphysiques et une simulation numérique: La simulation restitue correctement l’apparition de la grêle entre 18h et 19h et l’étagement des hydrométéores: mélange grêle/graupel, grêle, mélange pluie/grêle, et pluie. Les paramètres restitués par l’algorithme radar sont: les cristaux orientés verticalement (VC) et horizontalement (HC), la neige mouillée (WS) et sèche (DS), les mélange graupel/grêle (GH) et grêle/pluie (HR), la grêle (HL), et la pluie de différentes intensités (LR,MR,HR). 20:00 UT (x) hail + graupel graupel hail (o) hail rain rain 100 km

18 Microphysical retrievals - IOP 3 (moderate convection)
hail + graupel dry snow rain Pujol et al., 2005 18:10 UT 18:30 UT

19 Microphysical retrievals - IOP 3 (moderate convection)
S-Pol retrieval Meso-NH simulation snow snow hail + graupel rain rain

20 Microphysical retrievals - IOP 8 (stratiform rain)
S-Pol retrieval Meso-NH simulation snow rain melting snow Medina et Houze, 2003

21 Microphysical budgets : Mean vertical distribution of the hydrometeors
IOP 2A IOP 3 ice snow hail cloud rain graupel IOP 8 snow cloud Lascaux et al., 2005 rain

22 Microphysical budgets : mean vertical distribution of the different processes
ice rain IOP 2A IOP 3 IOP 8

23 Quasi-stationnary MCS 13-14 Oct. 1995
MESO-NH, x=10km OBSERVATIONS max: 31 mm MESO-NH, x=2.5km m MESO-NH, x=2.5km Initialisation Ducrocq et al (2000)’s max : 99 mm m mm max : 25 mm max : 135 mm Initial conditions: ARPEGE analysis at 18UTC Cumulated precipitation 01 UTC to 06 UTC the 14th Oct. 1995 (Ducrocq et al, 2002)

24 Gard flash-flood (8-9 Sept.2002)
Initial Conditions : Ducrocq et al (2000) Initialisation 12UTC, 08/09/02 + Sensitivity to initial conditions Gard flash-flood (8-9 Sept.2002) Ducrocq V, F.Bouttier Météo-France SRNWP/Met Office/Hirlam workshop on Variational Methods Exeter (UK) Nov 2004 Raingauges Nîmes + (Ducrocq et al, 2004) Initial Conditions : ARPEGE analysis 12UTC, 08/09/02 + MESO-NH (2.5km) Observations + Nîmes Nîmes radar 12-h accumulated précipitation from 12 UTC, 8 Sept to 00 UTC, 9 Sept 2002

25 TROCCINOX 2005 Chaboureau et al., 2005
The approach Model towards Satellite to validate the cloud coverage Convection Cirrus Méso-NH TROCCINOX Chaboureau et al., 2005 Tb 10.8 m Diff m Observation Geophysica

26 Stratocumulus : Capped BL
When the CBL is blocked by an anticyclonic subsidence FIRE 1 case of EUROCS : Forcing terms : a LS subsidence + cooling (dql/dt<0) and moistening (dqt/dt>0) under the inversion to balance the subsidence altitude (m) Cloud water mixing ratio (kg/kg) Min = g/kg Max = 0.6 g/kg 0h 12h LES simulation of the diurnal cycle (Dx=50m) Observations of the base and the top cloud layer During the day, the thinning is not sufficient to break up the cloud. Sandu et al., 2006

27 Examples of Applications of Meso-NH
General description of Meso-NH Grid nesting Clouds representation (explicit convective clouds, Sc) Cyclones Coupling with the surface Coupling with other models (hydrology, dispersion) Diagnostics Systematic validations (climatology, real time runs) Towards AROME ex

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

29 Simulations CEPMMT : trajectoires 22/01/02 00 UTC
Barbary et al.

30 Évolution en intensité
Barbary et al.

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

32 Fine scale structure (1 km)
le 22 janvier 17h10-17h20-17h30 dBZ Radar reflectivity 10-3s-1 Relative vorticity

33 Examples of Applications of Meso-NH
General description of Meso-NH Grid nesting Clouds representation (explicit convective clouds, Sc) Cyclones Coupling with the surface Coupling with other models (hydrology, dispersion) Diagnostics Systematic validations (climatology, real time runs) Towards AROME ex

34 EXTERNALIZED SURFACE :
Exchange of data flow at each time step between the 2 models Boundary conditions for turbulence and radiative schemes Méso-NH AROME Arpège / Aladin albedo emissivity radiative temperature fluxes : Momentum, heat, water vapor, CO2, chemistry Atmosphere forcing Sun position Radiative fluxes SURFACE Nature Sea Town Lake Presentation of P.Le Moigne

35 3D Meso-NH simulations (Lemonsu et al., 2004, 2005)
Mediterranean Sea Marseille veyre Massif du Puget City centre Set-up : 4 grid-nesting models from regional to city scale, with respective resolutions of 12 km, 3 km, 1 km and 250 m 500 400 300 200 100 50 (m) 600 700 France Mer Mediterranée 2000 1000 Marseille Model 1 Model 2 Model 3 Model 4 Chaine de l’Etoile Validation of simulations at urban scale Validation of simulations at regional scale N.D. de la Garde Mont St-Cyr Marseilleveyre Puget

36 Regional validation Radiosoundings St Rémy de Provence Obs St Rémy
500 1000 1500 2000 2500 Altitude (m) 21 juin 22 juin 23 juin 24 juin 25 juin 26 juin Obs Model Radiosoundings St Rémy de Provence St Rémy

37 Thermodynamic structures
Air temperature inside the streets 26 June 2001, 1400 UTC Urban network Model Lemonsu et al., 2005a

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

39 Atmospheric boundary layer
Horizontal wind field 26 June 2001, 1400 UTC z = 50 m AGL z = 400 m AGL m s-1 VAL VAL West SSB OBS OBS City centre City centre Puget Massif Puget Massif CNRS CNRS South-East DSB South SSB Marseilleveyre Marseilleveyre

40 Simulation on PARIS DAY
Realistic Without town q TKE Dx=1km Lemonsu et Masson (2001)

41 Nocturnal UBL q Without town Realistic Lemonsu et Masson (2001)

42 Formation of fog Masson (2001)

43 Meso-NH Surface CarboEurope/RE : modélisation Meso-NH/ISBA-A-gs
C.Sarrat et al., CNRM/GMME/MC2 Modelisation of the atmospheric CO2 in interaction with the surface : coupling of CO2 in Meso-NH with CO2 fluxes of ISBA-A-gs Improvement of the exchanges surface-atmosphere Improvement of water cycle/ evapotranspiration Improvement of the PBL representation Regional budget of CO2 atmosphérique Inversion of CO2 concentrations to identify sources/sinks of CO2 (Thèse T. Louvaux) ISBA-A-gs Met. forcing LE, H, Rn, W, Ts… CO2 Flux [CO2]atm Anthropogenic Sea Meso-NH Surface

44 Modélisation 3-D : Configuration
 Nesting 2 ways  Surface : ISBA-A-gs (Ecoclimap)  Vertical grid : 60 levels ( m) Domaine : Landes (320x250 km) Résolution horizontale : 2.5 km Pas de temps : T = 5 s Domaine : France (900x900 km) Résolution horizontale : 10 km Pas de temps : T = 10 s

45 Modélisation 3-D : Résultats
SFCO2 RN LE [CO2] H

46 CarboEurope/RE : modelisation Meso-NH/ISBA-A-gs and atmospheric CO2
 [CO2] simulated at 15H (june 2001) Advection + Assimilation + vertical mixing [CO2] decrease  00H : Advection + Respiration + cooling [CO2] increase

47 Coupling of Meso-NH with other models (Hydrology, Dispersion)

48 HYDROLOGY : Development of the coupling Meso-NH-ISBA-TOPMODEL
K.Chancibault et al., CNRM/GMME/MICADO Vidourle Gard Cèze Ardèche 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

49 Strategy of the coupling
t = 5 min x = 2-3 km L = 1000 km Meso-NH ou Arome flux Wmob ISBA TOPMODEL t = 5 min x = 2-3 km L = 1000 km Module de routage t = 1h x = 50 m L = 1 km

50 Dispersion with passive tracers : case of AZF
120km, Dx=2km 30km, Dx=500m Tulet et Lac (2001)

51 Vertical cross-section
the release Vertical cross-section 30min after the release

52 PERLE (Programme d’Evaluation des Rejets Locaux d’Effluents)
Modelling system for environmental emergency PERLE (Programme d’Evaluation des Rejets Locaux d’Effluents) Meso-NH 2 grids (Regional Dx=8km, L=240km/ Local Dx=2km, L=60km) 36 levels until 16km ALADIN initialization and coupling Meso-scale meteorology SPRAY Lagrangian particle model At least particles released Advection+Turbulence+random Applied to the 2 Meso-NH grids Dispersion Will be exported to AROME

53 Case of AZF Méso-NH + SPRAY Concentrations à Z=10m
Temps de réponse=25min ATC (Atmospheric Transfert Coefficient) = Trajectory of the pollutant cloud

54

55 Examples of Applications of Meso-NH
General description of Meso-NH Grid nesting Clouds representation (explicit convective clouds, Sc) Cyclones Coupling with the surface Coupling with other models (hydrology, dispersion) Diagnostics Systematic validations (climatology, real time runs) Towards AROME ex

56 Diagnostics Budget (heat, momentum, microphysics species, TKE) with masks Diagnostic fields Lagrangian trajectories (3 added prognostic fields) Passive tracers Comparison to observations (Meso-NH tools : Presentation of I.Mallet-N.Asencio) ex

57 Total water mixing ratio (vap+liq)
Dz=z-z0 after 30min Orographic convection 17km 270km Growing of a convective cell 10km Total water mixing ratio (vap+liq) Initially at z0=1500m T=14min Gheusi (2003)

58 + Trajectory/Back-trajectory
Dynamics of a thalweg Initial height z0 of particles currently at z=7000m Initial latitude y0 of particles currently at q=315K + Trajectory/Back-trajectory Gheusi (2003)

59 Exemple obs2mesonh: T2M

60 Exemple obs2mesonh: réflectivité radar Ronsard
Coupe verticale : modèle + radar Coupe Horizontale K=20 dBz Ouest Est Milan

61 Examples of Applications of Meso-NH
General description of Meso-NH Grid nesting Clouds representation (explicit convective clouds, Sc) Cyclones Coupling with the surface Coupling with other models (hydrology, dispersion) Diagnostics Systematic validations (climatology, real time runs) Towards AROME ex

62 Meso-scale modelling wind climatology
Objectif de l’exposé : présentation des produits clim qui se trouvent dans le catalogue des produits de M.F

63 Error of the climatology = Error of the model
An alternative to the measurement = the meteorological models First solution An operational numerical weather prediction, with an important record : Aladin 5 ans (resolution 0.1°) Error of the climatology = Error of the model Measurements at 10m height are used to evaluate the quality of the climatology

64 First step : Statistical selection of weather patterns
Second solution A mesoscale meteorological model (Dx=1-3 km), not yet operationnal First step : Statistical selection of weather patterns Classification of weather patterns on 700hPa geopotential of ECMWF reanalyses (résolution 1°, 15 years)  19 classes, with a weight (occurrence) Choice of the dates number  95 dates Choice of the dates : Number of the dates proportional to the frequency Second step : Simulation of the selected dates with Meso-NH 95 dates simulated (24h) with ALADIN initial and coupling fields Wind climatology build up with the weighted function of each of the 19 weather patterns Error of the climatology = Error of the model + Error of the statistical sample Methodology only for the mean wind speed (not for extreme winds, or another meteorological field)

65 Geographical area with Meso-NH wind climatology
Bourgogne 2 km Auvergne 2 km Vosges, Forêt Noire : 1.2 km Quiberon 1 km Limousin 1km Alpes du Nord 2 km Sud-Ouest 3 km Alpes du Sud 2 km Pourtour méditerranéen 3 km

66 Measurements Roses Aladin 3 ans Méso-NH 95 dates North Alps

67 France (synop) Vosges Alpes du Nord (29 stations) Alpes du Sud (26 stations) Massif Central (67 stations) Sud-Ouest (72 stations) Méditerranéen (99 stations) Aladin 3-4 ans 80 72 62 63 69 71 Obs 95 dates 88 92 91 90 87 Méso-NH 95 dates 77 75 74 80

68 Evaluation on Dry Convective boundary layer : CARBOEUROPE
Forecasts of Meso-NH (8km) in an operational mode during the experiment La Cape Sud : Comparison Meso-NH/RS of BL height (parcel method) between 6 and 17UTC Weak overestimation during the afternoon Weak underestimation during the morning

69 Examples of Applications of Meso-NH
General description of Meso-NH Grid nesting Clouds representation (explicit convective clouds, Sc) Cyclones Coupling with the surface Coupling with other models (hydrology, dispersion) Diagnostics Systematic validations (climatology, real time runs) Towards AROME ex

70 AROME : Application of Researh to Operations at MEsoscale
Future non-hydrostatic model 2.5km resolution Dynamics based on ALADIN-NH (semi-implicite, semi-lagrangian) Data assimilation ALADIN 3D-VAR Physics based on Méso-NH : microphysics ICE3, Turbulence 1D, shallow convection, externalised surface

71 Case of Gard, initial bogus
Lame d’eau Tu radar de Nîmes MésoNH 4s Arome 60s 304 mm 274 mm > 300 mm Couplage : Aladin 3h Forecasts MésoNH Dt= 4s , CPU = 24h20 AROME Dt =60s, CPU = 2h30

72 Improvement of Meso-NH physics for AROME
Depends on convective systems (anvils). Turbulence ice improves the life cycle. Improvement with tuning of microphysics. Cirrus clouds Mainly driven by dynamics. Mixed-phase microphysics Good results with AROME (no excessive W) Deep clouds Larger cloud fraction. Variety of turbulence and stability profiles - Importance of entrainment. Improvement of Mixing length -Aerosol effects - BL clouds : Sc Improvement of Meso-NH physics for AROME The CBR scheme enables to produce BL clouds. Countergradient (TOMs) insufficient. Improvement : Mass-Flux (Siebesman and Soares) Subgrid condensation with ED+MF contribution BL clouds : Cu Transition to BL clouds. Turbulent mixing dominated by large-eddy transport and entrainment at the top. Improvement : Countergradient (TOMs) versus EDMF (Siebesman and Soares) Dry CBL Stable BL and transition to neutral BL. Improvement of Mixing length. Microphysics and aerosols. Fog


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