Incorporating Hourly Convective Cloud Data Into Tropical Cyclone Rapid Intensification Forecasting With Machine Learning
Abstract In this study, we developed a machine learning (ML) model to predict the rapid intensification (RI) of North Atlantic tropical cyclones (TCs) using 6‐hourly Statistical Hurricane Intensity Prediction Scheme (SHIPS) predictors and additional data on very deep convective clouds with an infrar...
Saved in:
| Main Authors: | Qiaoyan Wu, Tong Luo, Jiacheng Hong |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2025-03-01
|
| Series: | Journal of Geophysical Research: Machine Learning and Computation |
| Subjects: | |
| Online Access: | https://doi.org/10.1029/2025JH000595 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Diversity of Tropical Cyclones Rapid Intensification
by: Ke Peng, et al.
Published: (2024-07-01) -
North Atlantic Tropical Cyclone Intensification: Regional Drivers and Trends
by: Sharanya J. Majumdar, et al.
Published: (2023-09-01) -
Diurnal Variations in Tropical Cyclone Formation and Associated Convective Features
by: Jiacheng Hong, et al.
Published: (2024-12-01) -
Impacts of Intraseasonal Oscillations on Tropical Cyclone Rapid Intensification in the Northwestern Pacific During Winter
by: Chaodong Chen, et al.
Published: (2025-04-01) -
Influence of inner-core symmetry on tropical cyclone rapid intensification and its forecasting by a machine learning ensemble model
by: Jiali Zhang, et al.
Published: (2025-06-01)