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Improved site-specific numerical model of fog and low clouds. T. Bergot - Météo-France CNRM/GMME. 1) Methodology. -dedicated observations -Cobel-Isba 1D model -adaptative local assimilation scheme. 2) Results for Paris-CdG airport. - PowerPoint PPT Presentation
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T. Bergot - Météo-France CNRM/GMME
1) Methodology
2) Results for Paris-CdG airport
Improved site-specific numerical model of fog and low clouds
-dedicated observations-Cobel-Isba 1D model-adaptative local assimilation scheme
-results for 2002-2003, 2003-2004 and 2004-2005 winter seasons-applications / limits
3) Conclusions / prospectives
Introduction1) LVP conditions at Paris CdG
visi<600m or ceiling <200ft (LVP conditions) : the capacity of landing / take-off is reduced by a factor 2Current operational NWP models are not able to provide valuable information to forecast LVP conditions
2) Why?Physical processes associated to fog (e.g. turbulence in stable layer) : see Bergot et al. WSN05 –1.04Vertical resolution : see Bergot et al. WSN05 – 1.04Sensibility to initial conditions : high density observing network + adaptive mesoscale assimilation scheme
“local” integrated forecast system : •High resolution Cobel-Isba model•Dedicated observations + local assimilation scheme
Mesoscale terms : ALADIN
•Advections•Geostrophic wind•clouds
Turbulent processes (stable cases)
Radiative processes (IR+vis)
Microphysical processes (condensation-evaporation, sedimentation)
Exchanges between soil, vegetation and atmosphereISBA
COBEL
Meteorological tower of 30m : T / Hu%
Ground measurements : T / W inside the soil (between ground and –50cm) short- and long-wave radiations
Airport terminal 1:T / H%
Radiation fluxes
Since december 2002
International Paris CdG airport
Local assimilation Local assimilation schemescheme
ObservationsObservations ISBA offlineISBA offline
COBEL/ISBACOBEL/ISBA
Local forecasting :Local forecasting :Fog onsetFog onsetvisibility / vertical thickness visibility / vertical thickness clearanceclearance
forecasterforecaster
guess
Mesoscale NWP Mesoscale NWP model (3D)model (3D)
Improved site-specific numerical prediction
Results for 3 winter seasons at Paris CdGLVP
Visi<600m orCeil<200ft
Hit Ratio
False Alarm Rate
FogVisi<600m
Hit Ratio
False Alarm Rate
Sensitivity to local assimilation
LVP : visi<600m and/or ceiling<200ft
Forecast time (h)
Forecast time (h)
Hit
Rat
ioFa
lse
Ala
rm R
ate
Limits
2) Accurate forecast requires : integrated approach
Accurate high resolution modelDedicated measurements inside surface boundary layer (nocturnal inversion)Adaptive assimilation scheme at local scale
1) Forecast quality1D model can be an alternative tool to forecast local parametersForecast is helpful during the first 6h
Conclusions / perspectives
1) Operational forecast : Paris CdG
2) Other sites in France : Paris-Orly, Lyon-St Exupery
Operational since 2004-2005 winter season : improvement of the forecast of LVP conditionsFuture : 1h assimilation – forecast cycle (frequent update of the forecast in LVP conditions)Future : predictability - local ensemble forecast system (Roquelaure et al. WSN05 2.30)
Conclusions / Prospective
1) Collaboration : US C&V(http://www.ll.mit.edu/AviationWeather/cvp.html)
2) Collaboration : Morocco – Casablanca airportdedicated observations = sounding + SYNOP/METAR Optimization of local assimilation schemeTest of Cobel-Isba assimilation / forecast system
San Francisco : Cobel-Isba model operational in a consensus forecast systemNew-York : tests on Brookhaven site dedicated to observation of fog and low clouds (http://www.rap.ucar.edu/staff/tardif/fog/BNLsensors.html)
QUESTIONS!
Fine mesh vertical gridFine mesh vertical gridFirst level : 0.5m20 levels below 200m
(Bergot 1993 ; Bergot and Guedalia 1994 ;Guedalia and Bergot, 1994)
Physical parameterizationsPhysical parameterizationsHigh resolution radiation scheme (232 spectral intervals)Turbulence scheme : turbulent kinetic energy (TKE)
http://www.rap.ucar.edu/staff/tardif/COBEL
Assimilation at local scale
1) Local 1D-VarAdaptive variational assimilation schemededicated observations
2) Initialisation of fog / low clouds
3) Initialisation of soil parameters
Define the depth of the cloudy area (minimization of the model errors on the radiative fluxes divergence)Correction of the atmospheric profiles below and inside the cloudy area (dry / moist mixed area)
Soil temperature and moisture : linear interpolation of measurements
Guess = previous COBEL-ISBA forecast (3h)Altitude « observations » = 3D NWP Aladin forecastSurface observations = local data (30m tower, 2m obs.)
2002-2003 WinterBias = 0.0°CStd. Dev. = 0.3°C
Temperature at 1m (observation)
Tem
pera
ture
at 1
m (C
I Cob
el-I
sba)
1D-Var : T / q surface boundary layer
Temperature at 1m(initial conditions)
Results for the 2002-2003 winter season:2m temperature
Results for the 2002-2003 winter season :IR radiative fluxes
IR fluxes when low clouds are detected
Low clouds from Aladinbias=-41.9W/m2
low clouds from local assimilationbias=-1.0W/m2
3D operational NWP models are not able to give realistic forecasts of low clouds!
Sensitivity to local initialisation : low clouds
Results for 3 winter seasons at Paris CdGLVP00UTC 03UTC
06UTC 09UTC
N~=50 N~=50
N~=30 N~=15
Results for 3 winter seasons at Paris CdG
N~=10 N~=15
N~=20 N~=20
LVP12UTC 15UTC
18UTC 21UTC