用深度学习自动追踪台风，省时又准。
ByteStorm: a multi-step data-driven approach for Tropical Cyclones detection and tracking
- 仅用相对涡度和海平面气压数据，通过分类定位识别台风中心。
- 在多个全球台风生成区表现优异，漏检率低、误报率低。
- 适合气象研究与气候建模，提升台风追踪效率与一致性。
准确的热带气旋（TC）追踪在气象与气候科学中至关重要。传统方法依赖主观阈值，易引入区域偏差，且计算与数据成本高。本文提出高效的数据驱动框架ByteStorm，利用深度学习网络仅基于850百帕相对涡度与平均海平面气压检测台风中心（通过分类与定位），再通过BYTE算法将中心点连接成完整轨迹。ByteStorm在主要全球台风生成盆地与先进确定性追踪器对比，表现出良好的探测概率与低误报率，准确复现季节与年际变率，并重建出可靠、平滑且一致的台风路径。结果表明，结合深度学习与计算机视觉可提供鲁棒、高效且精准的台风追踪新范式。
原文摘要 · Abstract (English)
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.
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