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

• URL: http://arxiv.org/abs/2109.12029v1
• Date: Fri, 24 Sep 2021 15:33:29 GMT
• Title: Identifying Distributional Differences in Convective Evolution Prior to Rapid Intensification in Tropical Cyclones
• Authors: Trey McNeely, Galen Vincent, Rafael Izbicki, Kimberly M. Wood, and Ann B. Lee
• Abstract summary: Tropical cyclone (TC) intensity forecasts are issued by human forecasters every 6 hours. Within these time constraints, it can be challenging to draw insight from such data. Here we leverage powerful AI prediction algorithms and classical statistical inference to identify patterns in the evolution of TC structure leading up to the rapid intensification of a storm.
• Score: 4.925967492198013
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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