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Meso-NH model
40 users laboratories
A research model, jointly developped by Meteo-France and Laboratoire d’Aérologie (CNRS/UPS)
http://mesonh.aero.obs-mip.fr/mesonh/
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
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).
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
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
Domaine 10-km
~500-600 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
Number of days with daily rain > 200 mm for the period [1958-2000] on the South-East
Mas
sif
Cent
ral
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
Impact of the convective system on the triggering and the localization
CTRL = With cooling associated to evaporation of precipitationNOC = Without cooling
Cumulated precipitation during
4 hours
Gard ‘02
CTRL = With Massif CentralNOR = 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
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
(Keil et Cardinali, 2003)32km : 150x1508km : 145x1452km : 150x150over 51 levels
IOP8 (F<1)
IOP2a(F>1)
8 km
2 km
Monte Lema
S Pol
Ronsard
ECMWF32 km
3 Dopplerradars ( )
Orographic precipitation 3D (MAP)Orographic precipitation 3D (MAP)
How can dynamics modify the microphysics ?
Lascaux et Richard, 2005
SnowGraupel
Hail
Cloud Rain
IceIOP2a
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)Orographic precipitation 3D (MAP)
Z > 60 dBz
12
km
100 kmTabary, 2002
(x) hail + graupel
(o) hail
( ) rain
(o) hail
(x) hail + graupel
( ) rain
graupel
Simulation (Meso-NH)Orographic precipitation 3D (MAP) IOP2aOrographic precipitation 3D (MAP) IOP2a
Radar observations
FOG – 1D simulation – Temporal evolution on 18h from 18TU
rc rc
rc
Without cloud droplet sedimentation
With cloud droplet sedimentation
With cloud droplet sedimentation but a coarser vertical resolution
Rémi, S., 2006
18h 21h 00h 03h 06h 09h 12h 18h 21h 00h 03h 06h 09h 12h
18h 21h 00h 03h 06h 09h 12h
g/kg g/kg
g/kg
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.
Local effects : Sea breeze
Urban network
Model
Lemonsu et al., 2005a
2m Temperature 26 June 2001, 1400 UTC
Δ = 250 m
VAL
OBS
CNRSPuget Massif
Marseilleveyre
City centre
z = 400 m AGL
VAL
OBS
CNRS
m s-1
Puget Massif
Marseilleveyre
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
6 m s-1420-2-4-6
26 June 2001, 1400 UTC
B
C
D
A
TWL
B
C
D
A
Model
VDOLCity
center0 2 4 6 0 2 4 6Distance (km) Distance (km)
VDOLCity
center
0.5
1.0
1.5
2.0
2.5
Alt
itude (
km
)500
400
300
200
100
50
ZS (m)
Marseilleveyre
190o
Puget MassifCNRS
(Radar)
3 km
VAL (Lidar)
OBS (Radar)
Etoile Massif
Comparison with transportable wind lidar (TWL)
Lemonsu et al., 2005a
Impact of the pollution on the stratocumulus diurnal cycle = Aerosol indirect effect
0.7g/kg700mrc(g/kg)
Simulation LES 50mNuage non pollué
Sandu, I., 2007
0TU 6 12 18 24 30 36
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
AN OBSERVED LLJ DURING THE SABLES98 CAMPAIGN
Night: 20-21 September 1998100m tower
Duero river basin
x = 6 m, y = 4 m, z = 2m (0 <z<100 m) and stretched above (z = 5 m at about 400 m)
M.A. JiménezUniversitat de les Illes Balears
Objective: study the mixing processes across the maximum of the wind of an observed Low-Level Jet (LLJ) using LES
Results (I): Mean profiles
M.A. JiménezUniversitat de les Illes Balears
The maximum of the wind and the height are well capturedThe LLJ height coincides with theinversion height
The surface temperature obtained fromthe LES cools down much more thanthe observations
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
TotalSubgrid
Lidar observationsLES Simulations
rv’
LES simulation5.05.56.06.57.07.58.08.59.09.510.
5.05.56.06.57.07.58.08.59.09.510.
5.05.56.06.57.07.58.08.59.09.510.
5.05.56.06.57.07.58.08.59.09.510.
g/kg g/kg
P3 aircraftKA aircraft. . max (pdf)_ min (pdf)LES
qv’ at 0.5zi
Water vapor variability in convective BL : presence of dry tongues - Couvreux et al. (2005)
at 12h
x=y= 100m, z<50m, t=7h
S(qv)<0
Meso-NH-Chemistry: Modelling of atmospheric chemistry from local (dx=1 km) to synoptic scale (dx=50 km)
http://mesonh.aero.obs-mip.fr
larg
e-sc
ale:
MO
CAGE,
ECM
WF,
...
OZONE le 25 Juin 2001
9 UTC
9km 3km
<30ppb
Parc Naturel VerdonMarseille
85ppb
Marseille Parc Naturel Verdon
>90ppb15 UTC >90ppb
Cousin et Tulet, 2004
surfacewater percolation
Deep soil
Surface soil
Soil (sand/clay)
Aérosols scavenging
Absorption/ diffusionof solar radiation
Surface cooling
Desertic dusts – formations, life cycle and radiative effect
u*turbulence
Emission
Saltation
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
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
ISBA-A-gs
Meteorological Model LE, H, Rn, W, Ts…
Atmospheric [CO2]
concentrations
Anthropogenic
Sea
Meso-NHMeso-NHSurfaceSurface
Lafore et al., 98
Noilhan et al. 89, 96, Calvet et al., 98
CO2 Fluxes
Sarrat et al.(2006)
CO2 concentrations (ppm) may-27 14HUTC
Atmospheric CO2 modelling : May – 27 2005 Boundary layer heterogeneity
Winter crops absorbs a large amount of CO2creates a CO2 depletion
Zi = 900mAgricultural area : low sensible heat flux
Zi = 1600mForest : high sensible heat flux
Forest : high respiration
Atmospheric CO2 modellingMay – 27 2005 : comparisons obs/simu
Simulated vertical cross section of CO2 Ocean - Marmande
Agricultural areaForest area
Vertical cross section of observed CO2 by aircraft
oceanocean forestforest croplandcroplandforestforest croplandcropland
Sarrat et al., 2006
Winter crops AssimilationForêt Respiration
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
HYDROLOGY : Development of the coupling Meso-NH-ISBA-TOPMODELCNRM/GMME/MICADO
Crues des 5-9 septembre 2005
Débits simulés à St Martin d’Ardèche (~ 2500km2)
Barthe et al. [2005]
+
+
-
Explicite electrical scheme in Meso-NH
Local separation of charges
Transfert and transport of chargesMicrophysical and dynamical processes
Electric field
Lightning parameterizationBidirectional leader (determinist)
Vertical extension of the lightningChannel steps (probabiliste)
Horizontal extension of the lightning
Charge neutralization
E > Etrig
yes
no
Life cycle of electrical charges in a convective cell
Barthe et Pinty, JGR
Apparition of graupel
Electrization of the cloud
Apparition of electric fieldlightning
Triggering of convectionSimulation Méso-NH
30km, x=500m
Industrial accidental release : AZF
Couche résiduelle : flux de SCouche de mélange : flux de SE
Max=10% de concentration initiale
30km, x=500m
COLOMIERS
8:45
0
10
20
30
40
50
60
70
0:1
5
1:0
0
1:4
5
2:3
0
3:1
5
4:0
0
4:4
5
5:3
0
6:1
5
7:0
0
7:4
5
8:3
0
9:1
5
10
:00
10
:45
11:3
0
12
:15
13
:00
13
:45
14
:30
15
:15
16
:00
16
:45
17
:30
18
:15
19
:00
19
:45
20
:30
21
:15
22
:00
22
:45
23
:30
10%=97g/m3
Max_obs=60g/m3
The heaviest particles have settled : strong dry deposition on Blagnac
SPRAY• Lagrangian particle model•At least 10000 particles released •Advection+Turbulence+random• Applied to the 2 Meso-NH grids
PERLEPERLE (PProgramme d’EEvaluation des RRejets L Locaux d’EEffluents)
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
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.
Co-indice de réfraction modifié M
Altitude
Conduit de propagation
Faisceau radar
Propagation normale
)4810
(6.77
106
T
eP
TN
a
hNM
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 l’inversion de M co-indice de réfraction
OG dans le sillage des îles au sommet du conduit
Réfraction normale
Réfraction vers le bas
Climat futur : 52 cas
ARPEGE Climat / OPAMED8 : modèle couplé océan-atmosphère, rés. horizontale : ~50 km
Simulations ARPEGE Climat / OPAMED 8
(climat présent 1960-2000 + climat futur 2070-2099)
Climat présent : 51 cas
méthode d’identification des cas extrêmes pour sélectionner des situations
représentatives
CL 1 CL 4 CL 1 CL 4
Sélection des cas les plus prochesdistance de corrélation spatiale
Climat futur : 10 casClimat présent : 10 cas
CL 1 CL 4 CL 1 CL 4
CYPRIM : Régionalisation climatique des pluies intenses avec le modèle Meso-NH . A.-L. Beaulant
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)
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
~500-600 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 qu’elle est résolue explicitement pour le domaine à 2.5 km.
20711011
185 mm
20791004
298 mm
20911010
378 mm
20891020
460 mm
20891021
260 mm
20901102
137 mm
20831018
431 mm
20901112
291 mm
Cumuls de pluies sur les 24 1ères heures pour les 10 cas du climat futur
16 mm
20981110
291 mm
20951007
t0 à t0+24 12 UTC J-1 à 12 UTC J
5 25 50 100 150 200 250 300 350 400 450 mm
Chaboureau and Pinty (2005) : Use of radiative transfer RTTOV to MSG
x=30 km
Amélioration des enclumes (cirrus) sur le seuil d’auto-conversion
)10,10.2min( 5.3)16.273(06.05* Tir
Réflectivités observées Réflectivités simulées avec Méso-NH
(radar de Bollène le 8 sep. 2002 à 21 UTC, élévation=1,2°)
« Développement communautaire d’un opérateur-simulateur d’observation 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 radarSimulation de réflectivités radar