Work

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

Year: 2021

Type: preprint

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.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2109.12029

Authors: McNeely, Trey, Vincent, Galen, Izbicki, Rafael, Wood, Kimberly M., Lee, Ann B.

Institutions: Carnegie Mellon University, Universidade Federal de São Carlos, Mississippi State University

Language: English

Topic: Tropical and Extratropical Cyclones Research

Subfield: Atmospheric Science

Field: Earth and Planetary Sciences

Domain: Physical Sciences

Open Access status: green

Funders: National Science Foundation, Mississippi State University, Fundação de Amparo à Pesquisa do Estado de São Paulo, Conselho Nacional de Desenvolvimento Científico e Tecnológico

Awards: G1317009434, Neural networks in statistical inference problems, G295657101, G298771608, Statistical Procedures and Performance Measures for Simulator-Based Frequentist Inference, AI Institute: Planning: Physics of the Future

OpenAlex ID: W3202025347

Cites: 18

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Related to: 20