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T. Bergot - Météo-France CNRM/GMME

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.

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T. Bergot - Météo-France CNRM/GMME

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  1. 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 -results for 2002-2003, 2003-2004 and 2004-2005 winter seasons -applications / limits 3) Conclusions / prospectives

  2. 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

  3. Mesoscale terms : ALADIN • Advections • Geostrophic wind • clouds COBEL Radiative processes (IR+vis) Turbulent processes (stable cases) Microphysical processes (condensation-evaporation, sedimentation) Exchanges between soil, vegetation and atmosphere ISBA

  4. International Paris CdG airport Ground measurements : T / W inside the soil (between ground and –50cm) short- and long-wave radiations Meteorological tower of 30m : T / Hu% Since december 2002 Airport terminal 1: T / H% Radiation fluxes

  5. Improved site-specific numerical prediction Mesoscale NWP model (3D) Observations ISBA offline guess COBEL/ISBA Local assimilation scheme • Local forecasting : • Fog onset • visibility / vertical thickness • clearance forecaster

  6. Results for 3 winter seasons at Paris CdG LVP Visi<600m or Ceil<200ft False Alarm Rate Hit Ratio Fog Visi<600m Hit Ratio False Alarm Rate

  7. Sensitivity to local assimilation LVP : visi<600m and/or ceiling<200ft Hit Ratio Forecast time (h) False Alarm Rate Forecast time (h)

  8. Limits 1) Forecast quality • 1D model can be an alternative tool to forecast local parameters • Forecast is helpful during the first 6h 2) Accurate forecast requires : integrated approach • Accurate high resolution model • Dedicated measurements inside surface boundary layer (nocturnal inversion) • Adaptive assimilation scheme at local scale

  9. Conclusions / perspectives 1) Operational forecast : Paris CdG • 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) 2) Other sites in France : Paris-Orly, Lyon-St Exupery

  10. Conclusions / Prospective Collaboration : US C&V (http://www.ll.mit.edu/AviationWeather/cvp.html) • 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) 2) Collaboration : Morocco – Casablanca airport • dedicated observations = sounding + SYNOP/METAR • Optimization of local assimilation scheme • Test of Cobel-Isba assimilation / forecast system

  11. QUESTIONS!

  12. The Cobel 1D model (Bergot 1993 ; Bergot and Guedalia 1994 ;Guedalia and Bergot, 1994) http://www.rap.ucar.edu/staff/tardif/COBEL Physical parameterizations • High resolution radiation scheme (232 spectral intervals) • Turbulence scheme : turbulent kinetic energy (TKE) Fine mesh vertical grid • First level : 0.5m • 20 levels below 200m

  13. Assimilation at local scale 1) Local 1D-Var • Adaptive variational assimilation scheme • dedicated observations 2) Initialisation of fog / low clouds • 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) 3) Initialisation of soil parameters • Soil temperature andmoisture : linear interpolation of measurements

  14. 1D-Var : T / q surface boundary layer • Guess = previous COBEL-ISBA forecast (3h) • Altitude « observations » = 3D NWP Aladin forecast • Surface observations = local data (30m tower, 2m obs.) Temperature at 1m (initial conditions) 2002-2003 Winter Bias = 0.0°C Std. Dev. = 0.3°C Temperature at 1m (CI Cobel-Isba) Temperature at 1m (observation)

  15. Results for the 2002-2003 winter season: 2m temperature

  16. Results for the 2002-2003 winter season : IR radiative fluxes

  17. Sensitivity to local initialisation : low clouds IR fluxes when low clouds are detected low clouds from local assimilation bias=-1.0W/m2 Low clouds from Aladin bias=-41.9W/m2 3D operational NWP models are not able to give realistic forecasts of low clouds!

  18. Results for 3 winter seasons at Paris CdG 00UTC 03UTC LVP N~=50 N~=50 06UTC 09UTC N~=30 N~=15

  19. Results for 3 winter seasons at Paris CdG 12UTC 15UTC LVP N~=15 N~=10 18UTC 21UTC N~=20 N~=20

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