[2512.07885] ByteStorm: a multi-step data-driven approach for Tropical Cyclones detection and tracking

Computer Science > Machine Learning arXiv:2512.07885 (cs) [Submitted on 28 Nov 2025 (v1), last revised 26 Mar 2026 (this version, v2)] Title:ByteStorm: a multi-step data-driven approach for Tropical Cyclones detection and tracking Authors:Davide Donno, Donatello Elia, Gabriele Accarino, Marco De Carlo, Enrico Scoccimarro, Silvio Gualdi View a PDF of the paper titled ByteStorm: a multi-step data-driven approach for Tropical Cyclones detection and tracking, by Davide Donno and 5 other authors View PDF Abstract: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 Int...