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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/2512.07885 |
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| _version_ | 1866911545718472704 |
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| author | Donno, Davide Elia, Donatello Accarino, Gabriele De Carlo, Marco Scoccimarro, Enrico Gualdi, Silvio |
| author_facet | Donno, Davide Elia, Donatello Accarino, Gabriele De Carlo, Marco Scoccimarro, Enrico Gualdi, Silvio |
| contents | Accurate tropical cyclones (TCs) tracking represents a critical challenge in the context of weather and climate science. Traditional tracking schemes mainly rely on subjective thresholds, which may introduce biases in their skills on the geographical region of application and are often computationally and data-intensive, due to the management of a large number of variables. We present \textit{ByteStorm}, an efficient data-driven framework for reconstructing TC tracks. It leverages deep learning networks to detect TC centers (via classification and localization), using only relative vorticity (850 mb) and mean sea-level pressure. Then, detected centers are linked into TC tracks through the BYTE algorithm. \textit{ByteStorm} is benchmarked with state-of-the-art deterministic trackers on the main global TC formation basins. The proposed framework achieves good tracking skills in terms of Probability of Detection and False Alarm Rate, accurately reproduces Seasonal and Inter-Annual Variability, and reconstructs reliable, smooth and coherent TC tracks. These results highlight the potential of integrating deep learning and computer vision to provide robust, computationally efficient and skillful data-driven alternatives to TC tracking. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_07885 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ByteStorm: a multi-step data-driven approach for Tropical Cyclones detection and tracking Donno, Davide Elia, Donatello Accarino, Gabriele De Carlo, Marco Scoccimarro, Enrico Gualdi, Silvio Machine Learning Artificial Intelligence Accurate tropical cyclones (TCs) tracking represents a critical challenge in the context of weather and climate science. Traditional tracking schemes mainly rely on subjective thresholds, which may introduce biases in their skills on the geographical region of application and are often computationally and data-intensive, due to the management of a large number of variables. We present \textit{ByteStorm}, an efficient data-driven framework for reconstructing TC tracks. It leverages deep learning networks to detect TC centers (via classification and localization), using only relative vorticity (850 mb) and mean sea-level pressure. Then, detected centers are linked into TC tracks through the BYTE algorithm. \textit{ByteStorm} is benchmarked with state-of-the-art deterministic trackers on the main global TC formation basins. The proposed framework achieves good tracking skills in terms of Probability of Detection and False Alarm Rate, accurately reproduces Seasonal and Inter-Annual Variability, and reconstructs reliable, smooth and coherent TC tracks. These results highlight the potential of integrating deep learning and computer vision to provide robust, computationally efficient and skillful data-driven alternatives to TC tracking. |
| title | ByteStorm: a multi-step data-driven approach for Tropical Cyclones detection and tracking |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2512.07885 |