Diagnosis and Assessment of Pre-Fog in the Mainland Portuguese International Airports: Statistical and Neural Network Models Comparison

Abstract

The prediction of fog is a challenging task in operational weather forecast. Due to its dependency on small-scale processes, numerical weather models struggle to deal with under scale features, resulting in uncertainties in the fog forecast. Unawareness of the onset time and the duration of fog leads to disproportionate impact on open-air activities, especially in aviation. Nevertheless, in a small sized country such as Portugal mainland, the fog varies greatly. The traffic of the two busiest Portuguese international airports of Porto and Lisbon is affected by the occurrence of fog at different times of the year. The fog occurrence at Porto is a predominant winter phenomenon and a summer one at Lisbon. Observational variables and their trend are local indicators of favouring conditions to the fog’s onset, such as cooling, water vapour saturation and turbulent mixing. A dataset corresponding to 17 years of half-hourly METAR from the airports of Porto and Lisbon is used to diagnose the pre-fog conditioning. Two diagnostic models are proposed to assess pre-fog conditions. The first model is adapted from the statistical method proposed by Menut et al. (2014), which performs a diagnosis from key variables trend, such as temperature, wind speed and relative humidity. Thresholds are defined from the METAR samples in the 6 h period prior to the formation of fog. Due to the local character of fog, the presented thresholds are the most appropriate ones for each airport. The predictability of fog is then assessed using observations. The second approach consists of neural networks such as a fully connected (FC) network and a recurrent neural network (RNN), which are especially well suited for time series. By experimenting with different types of neural networks (NN), we will try to capture the connection between the temporal evolution of measured variables in the dataset and the fog onset. These experiments will include different time windows to measure its influence on prediction performance.