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

Fog is one of the severe meteorological phenomena, causing difficulties in transportation activities by air, road, or sea. Improving fog prediction methods is of great importance to society as a whole. Our study presents a fog forecast at the Poprad-Tatry Airport, Slovakia, where we used various machine learning algorithms (support vector machine, decision trees, k-nearest neighbors) to predict fog with visibility below 300 m for a lead time of 30 minutes. This research was carried out in the framework of the SESAR research project PJ.04-29.2. 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. The cameras help observe visibility using tens of landmarks at various distances and directions from the airport. The best-performing model achieved 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 overcome the drawbacks of automated sensors (which predominantly measure points) and human observers (which have lower frequency observations but are more complex), and offer a viable solution for certain situations, such as the recent periods of the COVID-19 pandemic.