A Local Ensemble Prediction System for Fog and Low Clouds: Construction, Bayesian Model Averaging Calibration, and Validation

Abstract

At main international airports, air traffic safety and economic issues related to poor visibility conditions are crucial. Meteorologists face the challenge of supplying airport authorities with accurate forecasts of fog and cloud ceiling. These events are difficult to forecast because conditions evolve on short space and time scales during their life cycle. To obtain accurate forecasts of fog and low clouds, the Code de Brouillard à l?Echelle Locale (the local scale fog code)?Interactions between Soil, Biosphere, and Atmosphere (COBEL?ISBA) local numerical forecast system was implemented at Charles de Gaulle International Airport in Paris. However, even with dedicated observations and initialization, uncertainties remain in both initial conditions and mesoscale forcings. A local ensemble prediction system (LEPS) has been designed around the COBEL?ISBA numerical model and tested to assess the predictability of low visibility procedures events, defined as a visibility less than 600 m and/or a ceiling below 60 m. This work describes and evaluates a local ensemble strategy for the prediction of low visibility procedures. A Bayesian model averaging method has been applied to calibrate the ensemble. The study shows that the use of LEPS for specific local event prediction is well adapted and useful for low visibility prediction in the aeronautic context. Moreover, a wide range of users, especially those with low cost?loss ratios, can expect economic savings with the use of this probabilistic system.