Editorial Type:
Article
Article Type:
Research Article

Predicting Rapid Intensification in North Atlantic and Eastern North Pacific Tropical Cyclones Using a Convolutional Neural Network

Sarah M. Griffin aCooperative Institute for Meteorological Satellite Studies, University of Wisconsin–Madison, Madison, Wisconsin

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Anthony Wimmers aCooperative Institute for Meteorological Satellite Studies, University of Wisconsin–Madison, Madison, Wisconsin

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Christopher S. Velden aCooperative Institute for Meteorological Satellite Studies, University of Wisconsin–Madison, Madison, Wisconsin

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Online Publication:
19 Jul 2022
Print Publication:
01 Aug 2022
DOI:
https://doi.org/10.1175/WAF-D-21-0194.1
Page(s):
1333–1355
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Abstract

This study develops a probabilistic model based on a convolutional neural network to predict rapid intensification (RI) in both North Atlantic and eastern North Pacific tropical cyclones (TCs). Coined “I-RI,” an advantage of using a convolutional neural network to predict RI is that it is designed to learn from spatial fields, like two-dimensional satellite imagery, as well as scalar features. The resulting model RI probability output is validated against two operational RI guidances—an empirical and a deterministic method—to assess skill at predicting RI over 12-, 24-, 36-, 48-, and 72-h lead times. Results indicate that in North Atlantic TCs, AI-RI is more skillful at predicting RI over 12- and 24-h lead times compared to both operational RI guidances. In eastern North Pacific TCs, AI-RI is more skillful than the empirical operational RI guidance at most RI thresholds, but less skillful than the deterministic RI guidance at all thresholds. For TCs north of 15°N, where the deterministic skill was lower, AI-RI was more skillful than the deterministic operational guidance for over half of the RI thresholds. It is also found that AI-RI struggles to reach the higher RI probabilities produced by both of the operational RI guidances in both basins. This work demonstrates that the two-dimensional structures within the satellite imagery of TCs and the evolution of these structures identified using the difference in satellite images, captured by a convolutional neural network, yield better 12–24-h indicators of RI than existing scalar assessments of satellite brightness temperature.

Significance Statement

The purpose of this study is to develop a method to predict tropical cyclone rapid intensification using artificial intelligence. The developed model uses a convolutional neural network, which can identify features in satellite imagery that are indicative of rapid intensification. The results suggest that, compared with current operational rapid intensification models, a convolutional neural network approach is generally more skillful at predicting rapid intensification.

© 2022 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Sarah M. Griffin, sarah.griffin@ssec.wisc.edu

Abstract

This study develops a probabilistic model based on a convolutional neural network to predict rapid intensification (RI) in both North Atlantic and eastern North Pacific tropical cyclones (TCs). Coined “I-RI,” an advantage of using a convolutional neural network to predict RI is that it is designed to learn from spatial fields, like two-dimensional satellite imagery, as well as scalar features. The resulting model RI probability output is validated against two operational RI guidances—an empirical and a deterministic method—to assess skill at predicting RI over 12-, 24-, 36-, 48-, and 72-h lead times. Results indicate that in North Atlantic TCs, AI-RI is more skillful at predicting RI over 12- and 24-h lead times compared to both operational RI guidances. In eastern North Pacific TCs, AI-RI is more skillful than the empirical operational RI guidance at most RI thresholds, but less skillful than the deterministic RI guidance at all thresholds. For TCs north of 15°N, where the deterministic skill was lower, AI-RI was more skillful than the deterministic operational guidance for over half of the RI thresholds. It is also found that AI-RI struggles to reach the higher RI probabilities produced by both of the operational RI guidances in both basins. This work demonstrates that the two-dimensional structures within the satellite imagery of TCs and the evolution of these structures identified using the difference in satellite images, captured by a convolutional neural network, yield better 12–24-h indicators of RI than existing scalar assessments of satellite brightness temperature.

Significance Statement

The purpose of this study is to develop a method to predict tropical cyclone rapid intensification using artificial intelligence. The developed model uses a convolutional neural network, which can identify features in satellite imagery that are indicative of rapid intensification. The results suggest that, compared with current operational rapid intensification models, a convolutional neural network approach is generally more skillful at predicting rapid intensification.

© 2022 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Sarah M. Griffin, sarah.griffin@ssec.wisc.edu
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