Deep Learning Enhanced Wind Speed and Direction Forecasting for Airport Regions

Abstract This work introduces a deep learning architecture tailored for accurate wind speed and direction forecasting for airports using a grid-based input. Moving beyond the limitations of conventional forecasting methods, which struggle with rapid and localized atmospheric changes and demand substantial computational power, this study positions a machine learning approach as a superior alternative for wind nowcasting. By employing a comprehensive dataset covering 75 years, the proposed model distinguishes itself by achieving a mean absolute error of 1.26 m s −1 for wind speed and 16.18° for wind direction in the examined location, Madeira International Airport area. The model’s robustness was further validated using transfer learning across 10 global airport areas, each with diverse atmospheric conditions, resulting in consistent accuracy. Additionally, a precision forecast focused specifically on the runway was conducted, and its performance was found to be consistent with the broader spatial area forecasts. These results underscore the potential of machine learning in wind prediction for aviation, highlighting a promising pathway for future advancements in weather forecasting technology. Significance Statement This study introduces a machine learning model that demonstrates the potential for improving spatial grid and runway wind forecasts in aviation. The model achieves a mean absolute error of 1.26 m s −1 for wind speed and 16.18° for wind direction within a region measuring 10° in both latitude and longitude. It also reduces computational demands and enhances processing speed, generating 6-h ahead forecasts in just 6 s.