Identifying Distributional Differences in Convective Evolution Prior to Rapid Intensification in Tropical Cyclones
This work provides forecasters and scientists with key insights into tropical cyclone behavior, though it appears incremental as it combines existing AI and statistical methods for a specific domain problem.
The study tackled the challenge of extracting interpretable patterns from complex tropical cyclone data to improve intensity forecasts, using AI prediction algorithms and statistical inference to identify distributional differences in convective evolution prior to rapid intensification.
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.