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Operational low visibility statistical prediction

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

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Operational low visibility statistical prediction

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

  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. 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. 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. 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. 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. 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. 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. 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. 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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