1. 1 Operational low visibility statistical prediction Frédéric Atger (Météo-France)

2. 2 Methodology Linear discriminant analysis Probabilistic forecast of minimum visibility observed between H-1h et H+1h Predictors :  Arpège forecasts  Observations (Visi, T, Td, etc)  Hybrid predictors Ascending progressive selection of predictors

3. 3 Hybrid predictors Mixing ratio inversion  Humidity is computed from the forecast temperature under the assumption that the last observed mixing ratio is conserved Vertical gradient of temperature and pseudo- adiabatic temperature Vertical gradient of relative humidity Wind divergence, humidity advection, gradient of humidity advection  lower impact

4. 4 Evaluation context  4 « winter » seasons (October to March)  3 seasons for learning, 1 season for testing  46 stations with regular, frequent observations  200m et 600m thresholds not frequent enough to be predictable  800m, 1000m, 1500m, 3000m and 5000m thresholds forecast for 16 stations

5. 5 Selected predictors Temperature gradient  often in 1st position Mixing ratio inversion, humidity and humidity gradient, wind speed and direction  often in 2nd position Solar radiation at the surface  often in 3rd or 4th position

6. 6 Evaluation  Contingence tables for 100 probability thresholds  false alarm rate (FAR) and detection rate (DR)  « Pseudo-ROC » curve DR=f(FAR)  Target : top left quadrant (DR>0.5, FAR<0.5)  Target hardly reached in the best case (example: Nancy for 2 different periods)

7. 7 Results  False alarm rate for a detection rate above 0.5 (2 periods of 2 seasons)  Green : FAR  50% (target is reached)  Blue : FAR = 55-60% (almost acceptable)  Red : FAR  65% (not acceptable)  16 towns/airports selected for operational production

8. 8 Operational production Daily production from the 12 UTC Arpège run and 15 UTC observations  forecast available at 16:30 UTC Probabilistic forecast for tomorrow 06 UTC Deterministic forecast obtained by comparing probabilities to the probability thresholds allowing to reach a 50% detection rate on the evaluation sample Experimental product available on the intranet

9. 9 Perspectives (1) Non linear methods  Non linear regression and neural networks coupled to a flexible discriminant analysis  No improvement  More informative predictors are needed  From 1D modelling  3D model « Liquid water content » predictor (CEPMMT, Arpège later)  Other hybrid predictors

10. 10 Perspectives (2) For operational purposes  Several lead times  Several updates in one day  Forecasting fog occurrence for a period of several hours, e.g. 02 UTC to 10 UTC  Extrapolation of forecast probabilities for thresholds below 800 m