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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2501.15694 |
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| _version_ | 1866915123268943872 |
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| author | Roveri, L. Fery, L. Cavicchia, L. Grotto, F. |
| author_facet | Roveri, L. Fery, L. Cavicchia, L. Grotto, F. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_15694 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Statistical Learning Approach to Mediterranean Cyclones Roveri, L. Fery, L. Cavicchia, L. Grotto, F. Atmospheric and Oceanic Physics Machine Learning 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. |
| title | A Statistical Learning Approach to Mediterranean Cyclones |
| topic | Atmospheric and Oceanic Physics Machine Learning |
| url | https://arxiv.org/abs/2501.15694 |