Articles | Volume 18, issue 16
https://doi.org/10.5194/gmd-18-5101-2025
https://doi.org/10.5194/gmd-18-5101-2025
Development and technical paper
 | 
19 Aug 2025
Development and technical paper |  | 19 Aug 2025

Data-driven rolling model for global wave height

Xinxin Wang, Jiuke Wang, Wenfang Lu, Changming Dong, Hao Qin, and Haoyu Jiang

Model code and software

Data-driven rolling model for global wave height Xinxin Wang https://doi.org/10.5281/zenodo.14244061

Video supplement

Supplementary Movies S1 & S2 Xinxin Wang https://doi.org/10.5281/zenodo.15612386

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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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