A Statistical Learning Approach to Mediterranean Cyclones
This work addresses the challenge of understanding extreme meteorological events in the Mediterranean region, which is increasingly impacted by climate change, but it appears incremental as it adapts existing methods to a new domain.
The authors tackled the problem of characterizing Mediterranean cyclones by applying a Bayesian algorithm (Latent Dirichlet Allocation) to wind velocity data, achieving a drastic dimensional reduction that enabled the use of supervised statistical learning techniques for detection and tracking.
Mediterranean cyclones are extreme meteorological events of which much less is known compared to their tropical, oceanic counterparts. The raising interest in such phenomena is due to their impact on a region increasingly more affected by climate change, but a precise characterization remains a non trivial task. In this work we showcase how a Bayesian algorithm (Latent Dirichlet Allocation) can classify Mediterranean cyclones relying on wind velocity data, leading to a drastic dimensional reduction that allows the use of supervised statistical learning techniques for detecting and tracking new cyclones.