Multi-Resolution Analysis of the Convective Structure of Tropical Cyclones for Short-Term Intensity Guidance

TL;DR

This paper introduces a multi-resolution analysis method using wavelet transforms to interpret satellite imagery of tropical cyclones, aiding in short-term intensity forecasting by identifying key structural features.

Contribution

It presents a novel, interpretable approach combining wavelet-based analysis with deep learning to improve short-term intensity guidance for tropical cyclones.

Findings

MRA effectively captures cyclone structural features.

Deep learning models improve intensity prediction accuracy.

Method enhances real-time storm analysis capabilities.

Abstract

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.