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