Update on model developments: Update on model developments: Meteo-France NWP modelsMeteo-France NWP modelsUpdate on model developments: Update on model developments: Meteo-France NWP modelsMeteo-France NWP models

CLOUDNET Workshop / Paris 4-5 April 2005

Jean-Marcel PiriouCentre National de Recherches MétéorologiquesGroupe de Modélisation pour l’Assimilation et la Prévision

Summary:Summary:

• Update on model developmentsUpdate on model developments

• Work done:Work done: Validating models within Validating models within

CLOUDNET: BLH, surface fluxesCLOUDNET: BLH, surface fluxes

• Ongoing work:Ongoing work: comparing radar vs SYNOP comparing radar vs SYNOP

cloudiness scores cloudiness scores

• Now available:Now available: Model output on the new Model output on the new

sites sites

• Perspectives:Perspectives: reading the CLOUDNET reading the CLOUDNET

database in Toulousedatabase in Toulouse

Update on model developmentsUpdate on model developments

Update on model developmentsUpdate on model developments• 2004-012004-01 Sea ice masks from SSMI, relax Sea ice masks from SSMI, relax

towards NESDIS 0.5° SSTs, reduce snow towards NESDIS 0.5° SSTs, reduce snow

evaporation rates, …evaporation rates, …

• 2004-032004-03 Use AQUA radiances in data Use AQUA radiances in data

assimilation, interactive mixing length, …assimilation, interactive mixing length, …

• 2004-052004-05 Cloudiness (more cirrus clouds, more Cloudiness (more cirrus clouds, more

cloudiness intermediate values), FMR radiation cloudiness intermediate values), FMR radiation

scheme (3h ARPEGE predictions, 1h scheme (3h ARPEGE predictions, 1h

assimilation)assimilation)

• 2004-102004-10 Use AMSU-B data, Seawind Quickscat, Use AMSU-B data, Seawind Quickscat,

……

Global ARPEGE, stretched & regular grids

Limited area ALADIN

Cloud Resolving Model AROME

NWP GCMClimate GCM25-70kmoperations

Mesoscale modelling10kmoperations

Precipitating convective clouds explicitly taken into account

2.5kmoperations 2008

« Unifying » SGS physical schemes:

Radiation Turbulence SGS convection

Validating models within Validating models within

CLOUDNETCLOUDNET

Selection of dry or cloudy convective boundary layer

Selection of days between April and August 2003Cabauw 95 daysChilbolton 81daysSIRTA 75 days

Models : ARPEGEIFS Met-Office model : turbulent fluxes are not availableRACMO : results are strange – more test are needed

Comparisons between models and observations done on an hourly basis

Validating models within CLOUDNET: Anne MathieuValidating models within CLOUDNET: Anne Mathieu

Frequency distributions of CLBH observed and diagnosed (LCL)

Slightly better agreement than with the CLBH predicted Essentially same flaws than the predicted CLBH.

Validating models within CLOUDNET: Anne MathieuValidating models within CLOUDNET: Anne Mathieu

Conclusions

For selected days of cloudy convective boundary layer on the CLOUDNET stations

Boundary layer cloud base height predicted within more than 300m 40% of the hours for IFS55% of the hours for ARPEGE.

Same behavior in the different stations.

ARPEGE : Under-estimation of the CLBH due to warm and humid biases at the surface

Essential condition to have a good prediction of dry and cloudy boundary layer diurnal cycle : right surface field prediction.

• Soil scheme• Surface layer scheme• Precipitations (convection)

Validating models within CLOUDNET: Anne MathieuValidating models within CLOUDNET: Anne Mathieu

Comparing radar vs SYNOP Comparing radar vs SYNOP

cloudiness scorescloudiness scores

Comparing radar vs SYNOP cloudiness scoresComparing radar vs SYNOP cloudiness scores• The ARPEGE (Météo-France global model) The ARPEGE (Météo-France global model)

cloudiness scores against CLOUDNET radars cloudiness scores against CLOUDNET radars

improved, as the scores against SYNOP improved, as the scores against SYNOP

became less goodbecame less good

• The validation team has made a more The validation team has made a more

extensive comparison CLOUDNET radars vs extensive comparison CLOUDNET radars vs

SYNOP total cloudinessSYNOP total cloudiness

• How to compute a good model equivalent to How to compute a good model equivalent to

the SYNOP total, low, medium and high the SYNOP total, low, medium and high

cloudiness?cloudiness?

• Validating cloudiness: more confident in Validating cloudiness: more confident in

radar/lidar validations than to SYNOP radar/lidar validations than to SYNOP

observationsobservations

Model output on the new sitesModel output on the new sites

Model output on the new sitesModel output on the new sites

• Since 1st september 2002:Since 1st september 2002: sites Chibolton, sites Chibolton,

Cabauw, Palaiseau Cabauw, Palaiseau

• Since 16 March 2005:Since 16 March 2005: sites Lindenberg and sites Lindenberg and

Potenza, plus the 5 ARM sites: Darwin, Manaus, Potenza, plus the 5 ARM sites: Darwin, Manaus,

Nauru, North Slope of Alaska, Southern Great Nauru, North Slope of Alaska, Southern Great

Plains (10 sites daily, cron) Work done by Plains (10 sites daily, cron) Work done by

François Vinit.François Vinit.

PerspectivesPerspectives

• Reading in 2005 the CLOUDNET 10 sites Reading in 2005 the CLOUDNET 10 sites

database in Toulouse (François Vinit).database in Toulouse (François Vinit).

• AROME (2.5km) model dataAROME (2.5km) model data

Summary:Summary:

• Update on model developmentsUpdate on model developments

• Work done:Work done: Validating models within Validating models within

CLOUDNET: BLH, surface fluxesCLOUDNET: BLH, surface fluxes

• Ongoing work:Ongoing work: comparing radar vs SYNOP comparing radar vs SYNOP

cloudiness scores cloudiness scores

• Now available:Now available: Model output on the new Model output on the new

sites sites

• Perspectives:Perspectives: reading the CLOUDNET reading the CLOUDNET

database in Toulousedatabase in Toulouse

PHYSICS

Global ARPEGEAquaplanet mode

SCM ARPEGE (EUROCS, GATE, TOGA,BOMEX, ARM, …)

LAM ALADIN / coupled / 10 km

Global stretched ARPEGE / 4DVAR-ass. / 20 to 200 km

Global regular ARPEGE / 4DVAR-ass. / 66 km

Present operational schemes / modified in 2003

Under progress / Done in 2003

Radiation Geleyn and Hollingsworth (1979), Ritter and Geleyn (1992)

More accurate infra-red exchanges between surface and layers

Cloudiness New scheme after Xu & Randall 1996

Grid-scale cloud scheme Diagnostic in ql/i, all supersaturation removed, liquid/ice condensation T, melting/ freezing/ evaporation/ Kessler (1979), Clough and Franks (1991)

Prognostic ql/i, qr/s

Subgrid-scale cloud scheme (convection)

mass-flux scheme, CISK-type closure and triggering, water

vapour budget using a Kuo-type closure, downdrafts, momentum flux

Modified trigger functions (TKE, CIN) and cloud entrainment rates

Turbulence 1st order closure scheme after Louis (1979), Louis and al. (1981), using a flux-gradient K-theory with Ri dependency, variable roughness lengths over sea (Charnock

Reduced turb. in st. cond.

PrognosticTKE scheme, mixing « Betts » conservative variables thetal and qt instead of theta and qv

Description of the large-scale Description of the large-scale

cloud and precipitation cloud and precipitation

schemescheme

Cloud schemeCloud scheme

Developed by P. Lopez (QJRMS, 2002)

Designed for variational assimilation of cloud and RR obs

Prognostic var : Qc (cloud condensates) & Qp (precip water)

Semi-lagrangian treatment of the fall of precipitation

(Lopez,2002)