Machine Learning-Based Fog Nowcasting for Aviation with the Aid of Camera Observations

Abstract

In aviation, fog is a severe phenomenon, causing difficulties in airport traffic management; thus, accurate fog forecasting is always appreciated. The current paper presents a fog forecast at the Poprad-Tatry Airport, Slovakia, where various methods of machine learning algorithms (support vector machine, decision trees, k-nearest neighbors) are adopted to predict fog with visibility below 300 m for a lead time of 30 min. The novelty of the study is represented by the fact that beyond the standard meteorological variables as predictors, the forecast models also make use of information on visibility obtained through remote camera observations. Cameras observe visibility using tens of landmarks in various distances and directions from the airport. The best performing model reached a score level of 0.89 (0.23) for the probability of detection (false alarm ratio). One of the most important findings of the study is that the predictor, defined as the minimum camera visibilities from eight cardinal directions, helps improve the performance of the constructed machine learning models in terms of an enhanced ability to forecast the initiation and dissipation of fog, i.e., the moments when a no-fog event turns into fog and vice versa. Camera-based observations help to overcome the drawbacks of the automated sensors (predominantly point character of measurements) and the human observers (complex, but lower frequency observations), and offer a viable solution for certain situations, such as the recent periods of the COVID-19 pandemic.