TUE pm Bergot 6 06

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Published on October 24, 2007

Author: Jancis

Source: authorstream.com

Slide1:  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 Slide2:  Introduction 1) LVP conditions at Paris CdG visi<600m or ceiling <200ft (LVP conditions) : the capacity of landing / take-off is reduced by a factor 2 Current 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 –1.04 Vertical resolution : see Bergot et al. WSN05 – 1.04 Sensibility 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 Slide3:  Turbulent processes (stable cases) Radiative processes (IR+vis) Microphysical processes (condensation-evaporation, sedimentation) Exchanges between soil, vegetation and atmosphere ISBA COBEL Slide4:  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 Slide5:  Local assimilation scheme Observations ISBA offline COBEL/ISBA Local forecasting : Fog onset visibility / vertical thickness clearance forecaster guess Mesoscale NWP model (3D) Improved site-specific numerical prediction Slide6:  Results for 3 winter seasons at Paris CdG LVP Visi<600m or Ceil<200ft Hit Ratio False Alarm Rate Fog Visi<600m Hit Ratio False Alarm Rate Slide7:  Sensitivity to local assimilation LVP : visi<600m and/or ceiling<200ft Forecast time (h) Forecast time (h) Hit Ratio False Alarm Rate Slide8:  Limits 2) Accurate forecast requires : integrated approach Accurate high resolution model Dedicated 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 parameters Forecast is helpful during the first 6h Slide9:  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 conditions Future : 1h assimilation – forecast cycle (frequent update of the forecast in LVP conditions) Future : predictability - local ensemble forecast system (Roquelaure et al. WSN05 2.30) Slide10:  Conclusions / Prospective 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 scheme Test of Cobel-Isba assimilation / forecast system San Francisco : Cobel-Isba model operational in a consensus forecast system New-York : tests on Brookhaven site dedicated to observation of fog and low clouds (http://www.rap.ucar.edu/staff/tardif/fog/BNLsensors.html) Slide11:  QUESTIONS! Slide12:  Fine mesh vertical grid First level : 0.5m 20 levels below 200m (Bergot 1993 ; Bergot and Guedalia 1994 ;Guedalia and Bergot, 1994) Physical parameterizations High resolution radiation scheme (232 spectral intervals) Turbulence scheme : turbulent kinetic energy (TKE) http://www.rap.ucar.edu/staff/tardif/COBEL Slide13:  Assimilation at local scale 1) Local 1D-Var Adaptive variational assimilation scheme dedicated 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 Slide14:  Guess = previous COBEL-ISBA forecast (3h) Altitude « observations » = 3D NWP Aladin forecast Surface observations = local data (30m tower, 2m obs.) 2002-2003 Winter Bias = 0.0°C Std. Dev. = 0.3°C Temperature at 1m (observation) Temperature at 1m (CI Cobel-Isba) 1D-Var : T / q surface boundary layer Temperature at 1m (initial conditions) Slide15:  Results for the 2002-2003 winter season: 2m temperature Slide16:  Results for the 2002-2003 winter season : IR radiative fluxes Slide17:  IR fluxes when low clouds are detected Low clouds from Aladin bias=-41.9W/m2 low clouds from local assimilation bias=-1.0W/m2 3D operational NWP models are not able to give realistic forecasts of low clouds! Sensitivity to local initialisation : low clouds Slide18:  Results for 3 winter seasons at Paris CdG LVP 00UTC 03UTC 06UTC 09UTC N~=50 N~=50 N~=30 N~=15 Slide19:  Results for 3 winter seasons at Paris CdG N~=10 N~=15 N~=20 N~=20 LVP 12UTC 15UTC 18UTC 21UTC

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