Machine Learning Forecasting of Extreme Winds: A Study on the Mediterranean West Coast in the Valencian Community

The occurrence of extreme wind events poses a significant threat to human populations, putting lives at risk and causing substantial damage to vital infrastructures. Coastal regions, in particular, face heightened vulnerability due to the unpredictable nature of the land-sea interface, presenting a formidable   challenge for accurate modeling. Thus, there is an urgent need for robust and efficient predictive techniques to anticipate and manage the impact of these severe wind phenomena. In response to this imperative, our study explores the application of an innovative machine learning forecasting methodology tailored for extreme winds, specifically focusing on the Mediterranean west coast in the Valencian community. Our approach involves analysis of historical meteorological station data from the Spanish meteorological agency. This data, combined with an extensive set of reanalysis data spanning from 1961 to 2019, is utilized for the identification and classification of extreme wind events. Employing a train-test procedure, we implemented a Random Forest binary classification model, enabling successful forecasting of extreme wind episodes up to 48h in advance. Notably, the precision of our model exhibited a remarkable range between 73% and 92%, varying with the lead-time across the considered regions. This methodology not only enhances forecasting capabilities but also provides insights into the intricate dependencies of meteorological variables, thereby advancing our understanding of complex atmospheric processes. This pioneering study, driven by artificial intelligence, contributes to the ongoing exploration of the complex dynamics of winds in coastal regions. The insights gained from our research extend beyond the Mediterranean west coast and have the potential for broader applicability  in other coastal areas. The results underscore the pivotal role of adaptive strategies in mitigating the impact of extreme weather events, emphasizing the importance of proactive measures in the face of escalating climate-related challenges.