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.

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

Introduction

1) LVP conditions at Paris CdGvisi<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 CdG

LVPVisi<600m or

Ceil<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

ioF

alse

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 quality

1D 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 airport

dedicated 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 : 0.5m

20 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 (

CI

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 CdG

LVP00UTC 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