Visibility and Ceiling Nowcasting Using Artificial Intelligence Techniques for Aviation Applications

Abstract

This work presents a novel approach for simulating visibility (Vis) and ceiling base height (Hc) in up to 1 h using several machine learning (ML) algorithms. Ten years of meteorological data at 15 min intervals for Santos Dumont airport (SDA), Rio de Janeiro, Brazil were used in the ML method training and testing process. In the investigation, several categorical and regressive algorithms were trained and tested, and the results were verified with observations. The forecast results reveal that the categorical methods produced satisfactory results only up to 15 min for visibility prediction with the probability of detection greater than 85%. On the other hand, the regressive methods were found to be more capable of generating an accurate prediction of Vis and Hc compared to categorical method up to 60 min. The forecast evaluation metrics for Vis and Hc had correlation coefficients of 0.99 ± 0.00 and 0.96 ± 0.00, with mean absolute errors of 324 ± 77 m, and 167 ± 21 m, respectively. Results suggested that ML methods can improve the prediction of Vis and Hc up to 1 h when accurate observations are used for the analysis.