• Corpus ID: 237635477

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

@article{McNeely2021IdentifyingDD,
  title={Identifying Distributional Differences in Convective Evolution Prior to Rapid Intensification in Tropical Cyclones},
  author={Trey McNeely and Galen B. Vincent and Rafael Izbicki and Kimberly M. Wood and Ann B. Lee},
  journal={ArXiv},
  year={2021},
  volume={abs/2109.12029}
}

• Trey McNeely, Galen B. Vincent, +2 authors Ann B. Lee
• Published 24 September 2021
• Environmental Science
• ArXiv

Tropical cyclone (TC) intensity forecasts are issued by human forecasters who evaluate spatiotemporal 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.