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

Viewed

Total article views: 791 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
608 136 47 791 69 31 46
  • HTML: 608
  • PDF: 136
  • XML: 47
  • Total: 791
  • Supplement: 69
  • BibTeX: 31
  • EndNote: 46
Views and downloads (calculated since 21 Oct 2024)
Cumulative views and downloads (calculated since 21 Oct 2024)

Viewed (geographical distribution)

Total article views: 791 (including HTML, PDF, and XML) Thereof 769 with geography defined and 22 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 19 Aug 2025
Download
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.
Share