Identifying Distributional Differences in Convective Evolution Prior to Rapid Intensification in Tropical Cyclones

TL;DR

This paper uses advanced AI and statistical methods to identify key patterns in tropical cyclone convective evolution that precede rapid intensification, aiding forecasters' understanding.

Contribution

It introduces a novel approach combining AI prediction algorithms with statistical inference to interpret convective evolution in tropical cyclones.

Findings

Identified specific convective patterns associated with rapid intensification.

Demonstrated the effectiveness of AI methods in extracting scientific insights.

Provided a framework for real-time analysis of cyclone evolution.

Abstract

Tropical cyclone (TC) intensity forecasts are issued by human forecasters who evaluate spatio-temporal observations (e.g., satellite imagery) and model output (e.g., numerical weather prediction, statistical models) to produce forecasts every 6 hours. Within these time constraints, it can be challenging to draw insight from such data. While high-capacity machine learning methods are well suited for prediction problems with complex sequence data, extracting interpretable scientific information with such methods is difficult. Here we leverage powerful AI prediction algorithms and classical statistical inference to identify patterns in the evolution of TC convective structure leading up to the rapid intensification of a storm, hence providing forecasters and scientists with key insight into TC behavior.