用小波分析量化台风结构，辅助预测强度突变
Multi-Resolution Analysis of the Convective Structure of Tropical Cyclones for Short-Term Intensity Guidance
- 通过离散小波变换实现多分辨率结构分析
- 识别出与快速增强强相关的物理结构特征
- 为深度学习提供可解释的输入，适合气象预报研究者
在大西洋台风区，24小时时效的台风短时强度预报对防灾减灾至关重要。由于多数台风远离陆基观测网，卫星图像成为监测关键，但其复杂的高分辨率空间结构难以被预报员实时定性解读。本文提出一种简洁、可解释且描述性强的方法，利用离散小波变换实现多分辨率分析（MRA），量化台风精细结构，帮助数据分析师识别与快速强度变化强相关的物理结构特征。此外，该MRA可作为深度学习模型的输入，用于短期强度预测。
原文摘要 · Abstract (English)
Accurate tropical cyclone (TC) short-term intensity forecasting with a 24-hour lead time is essential for disaster mitigation in the Atlantic TC basin. Since most TCs evolve far from land-based observing networks, satellite imagery is critical to monitoring these storms; however, these complex and high-resolution spatial structures can be challenging to qualitatively interpret in real time by forecasters. Here we propose a concise, interpretable, and descriptive approach to quantify fine TC structures with a multi-resolution analysis (MRA) by the discrete wavelet transform, enabling data analysts to identify physically meaningful structural features that strongly correlate with rapid intensity change. Furthermore, deep-learning techniques can build on this MRA for short-term intensity guidance.
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