Multi-Resolution Analysis of the Convective Structure of Tropical Cyclones for Short-Term Intensity Guidance
This work addresses the challenge of interpreting complex satellite data for disaster mitigation in the Atlantic TC basin, offering an incremental improvement in forecasting methods.
The paper tackles the problem of short-term tropical cyclone intensity forecasting by proposing a multi-resolution analysis approach using discrete wavelet transform to quantify fine structural features from satellite imagery, which strongly correlate with rapid intensity changes and can be used for deep-learning-based guidance.
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.