Articles | Volume 18, issue 2
https://doi.org/10.5194/os-18-419-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/os-18-419-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
28 Mar 2022
Research article |
| 28 Mar 2022
| 28 Mar 2022
Forecasting hurricane-forced significant wave heights using a long short-term memory network in the Caribbean Sea
Brandon J. Bethel
School of Marine Sciences, Nanjing University of Information
Science and Technology, Nanjing 210044, China
Wenjin Sun
School of Marine Sciences, Nanjing University of Information
Science and Technology, Nanjing 210044, China
Southern Ocean Science and Engineering Guangdong Laboratory
(Zhuhai), Zhuhai 519000, China
Changming Dong
CORRESPONDING AUTHOR
School of Marine Sciences, Nanjing University of Information
Science and Technology, Nanjing 210044, China
Southern Ocean Science and Engineering Guangdong Laboratory
(Zhuhai), Zhuhai 519000, China
Department of Atmospheric and Oceanic Sciences, University of
California, Los Angeles, CA 90095, USA
Dongxia Wang
China State Shipbuilding Corporation (Chongqing) Haizhuang
Windpower Equipment Co., Ltd., Chongqing 400021, China
Related authors
No articles found.
Xinxin Wang, Jiuke Wang, Wenfang Lu, Changming Dong, Hao Qin, and Haoyu Jiang
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-181, https://doi.org/10.5194/gmd-2024-181, 2024
Preprint under review for GMD
Short summary
Short summary
Large-scale wave modeling is essential for science and society, typically relying on resource-intensive numerical methods to simulate wave dynamics. In this study, we introduce a rolling AI-based method for modeling global significant wave height. Our model achieves accuracy comparable to traditional numerical methods while significantly improving speed, making it operable on standard laptops. This work demonstrates AI's potential to enhance the accuracy and efficiency of global wave modeling.
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Short summary
Ocean surface waves forced by tropical cyclones can cause tremendous damage to offshore structures and coastal communities. As such, forecasting their evolution is of utmost importance. This study investigates the usage of a long short-term memory neural network to forecast hurricane-forced waves in the Caribbean Sea. Results strongly suggest that forecasts can be performed to a high degree of accuracy up to 12 h at minimal computational expense and even outperform a wave model.
Ocean surface waves forced by tropical cyclones can cause tremendous damage to offshore...
