Contributions from a Local Ensemble Prediction System (LEPS) for Improving Fog and Low Cloud Forecasts at Airports

Abstract

Abstract At Paris’ international airport, named Roissy Charles de Gaulle (CdG), air traffic safety and management as well as 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. A specific event, which is called a low visibility procedure (LVP), has been defined for a visibility under 600 m and/or a ceiling under 60 m. Forecasters have to provide two LVP human predictions at 0600 and 0900 local time, providing estimates of the LVP occurrence on the airport area for the next 3 h. This estimation has a probabilistic nature since the forecasters have to classify their forecasts into the following four forecast categories: “certain,” “likely,” “unlikely,” and “excluded.” A Local Ensemble Prediction System (LEPS) has been recently designed around the Code de Brouillard á l’Echelle Locale–Interactions between Soil, Biosphere, and Atmosphere (COBEL-ISBA) numerical model and has been tested to assess the predictability of LVP events and estimate their likelihood. This work compares the operational human LVP forecasts with LEPS LVP forecasts during the winter season 2004–05. This study shows that the use of LEPS for LVP prediction can significantly improve the current design of the operational LVP forecast by providing reliable forecasts up to 12 h ahead of time. Moreover, the system can be easily run on a personal computer without high computational resources.