1. Météo-France / CNRM – T. Bergot 1) Methodology 2) The assimilation procedures at local scale 3) Results for the 2002-2003 winter season Improved Site-Specific Numerical Prediction of Fog and Low Clouds COBEL-ISBA 1D model The local forecast method 1D-Var / Fog / Low Clouds Fog and low clouds Boundary layer parameters 4) Prospectives Operational forecast at Paris CDG international airport Predictability

2. 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 atmosphere ISBA COBEL

3. 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

4. Initialization / forcing (every 3h) Observations ISBA offline COBEL/ISBA Local fog forecasting formation formation visibility / vertical thickness visibility / vertical thickness clearance clearance Adjustment requirements / forecast guess Mesoscale NWP model (3D)

5. 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

6. Assimilation at local scale 1) Initialisation of dry atmosphere Methodology : variational assimilation 1D-Var Data : local observations, operational 3D NWP forecast 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 have a strong influence on the surface cooling (energy budget at the surface : spin-up problem!) Offline version of the ISBA model, driven by observed atmospheric forcings

7. 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)

8. cloud = mixed layer (moist variables) Assimilation of radiation fluxes at 2m and 45m 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 (occurrence) of low clouds! Initialisation of low clouds

9. Results for the 2002-2003 winter season : occurrence of LVP conditions / 30min LVP : visi<600m and/or ceiling<200ft Forecast time (h) Hit Ratio False Alarm Rate

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

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

12. 2002-2003 winter season : summary (dec2002-avr2003) 1) Accurate forecast requires detailled Boundary Layer obs + local assimilation scheme 2) Forecast quality Poor quality of mesoscale flow from operational NWP Measurements inside surface boundary layer (nocturnal inversion) Radiation fluxes measurement at 2 levels (divergence) Assimilation scheme at local scale 1D model can be an alternative tool for forecasting local parameters Forecast is helpful during the first 6h (poor quality of mesoscale flow from NWP!) Main problem : mesoscale clouds

13.  Ensemble forecast Study the predictability of fog and low cloud  Statistical postprocessing of local forecast One of the major pb of fog forecasting concerns the uncertainty on input parameters (extreme sensitivity in given meteorological situations) Define a common methodology of evaluation (reliability, resolution, …) : link with WG3 Uncertainties on initial data (link with WG1 + assimilation scheme at local scale) Uncertainties on mesoscale flow (clouds, mesoscale advections) Detailed local parameters (vertical inversion, etc!) Complex data (TKE, surface fluxes, etc!)

14. QUESTIONS!