Predicting Short-Term Intensity Change in Tropical Cyclones Using a Convolutional Neural Network
Problem
Predicting current intensity and short-term intensity change of tropical cyclones more skillfully than existing operational methods.
Why it matters
Tropical cyclone intensity forecasts directly protect lives and property in threatened coastal regions.
What the AI did
Two CNN-based methods (D-MINT and D-PRINT) estimated current TC intensity and predicted 6-24 hour intensity change using infrared imagery, microwave imagery, and environmental scalar predictors.
Results
More skillful than three established intensity estimation methods; less skillful than NHC and consensus predictions in North Atlantic/eastern North Pacific but more skillful than other guidances for at least half of lead times.
Impact
CNN methods improved on some operational guidance but did not beat the top NHC/consensus forecasts in all basins.
Record
| Venue | Weather and Forecasting |
|---|---|
| Published | 2024 |
| Work type | article |
| Authors | Sarah M. Griffin, Anthony Wimmers, Christopher S. Velden |
| Citations | 17 (OpenAlex, at import time) |
| References | 49 |
| Model or method | D-MINT and D-PRINT (convolutional neural networks) |
| Organization | not stated in source |
| Primary source | https://doi.org/10.1175/waf-d-23-0085.1 |
| Open access | closed |
| Keywords | tropical cyclones, intensity prediction, convolutional neural network, satellite imagery, forecasting |
Importance rating
significant — thousands to millions affected
Tropical cyclone intensity forecasting protects lives and property in coastal regions worldwide; methods beat several operational guidances but not NHC/consensus in all basins, so partial improvement.
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