A Statistical Learning Approach to Mediterranean Cyclones

TL;DR

This paper presents a Bayesian statistical method using Latent Dirichlet Allocation to classify and track Mediterranean cyclones based on wind velocity data, aiding in understanding these less-studied but impactful weather events.

Contribution

It introduces a novel application of Bayesian algorithms for classifying Mediterranean cyclones, enabling effective dimensionality reduction and improved detection and tracking.

Findings

Effective classification of cyclones achieved

Dimensionality reduction facilitates better tracking

Potential for improved climate impact assessments

Abstract

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.