FuXi-Ocean: A Global Ocean Forecasting System with Sub-Daily Resolution
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arXiv
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| Format: | Preprint |
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2025
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| author | Huang, Qiusheng Niu, Yuan Zhong, Xiaohui Guo, Anboyu Chen, Lei Zhang, Dianjun Zhang, Xuefeng Li, Hao |
| author_facet | Huang, Qiusheng Niu, Yuan Zhong, Xiaohui Guo, Anboyu Chen, Lei Zhang, Dianjun Zhang, Xuefeng Li, Hao |
| contents | Accurate, high-resolution ocean forecasting is crucial for maritime operations and environmental monitoring. While traditional numerical models are capable of producing sub-daily, eddy-resolving forecasts, they are computationally intensive and face challenges in maintaining accuracy at fine spatial and temporal scales. In contrast, recent data-driven approaches offer improved computational efficiency and emerging potential, yet typically operate at daily resolution and struggle with sub-daily predictions due to error accumulation over time. We introduce FuXi-Ocean, the first data-driven global ocean forecasting model achieving six-hourly predictions at eddy-resolving 1/12° spatial resolution, reaching depths of up to 1500 meters. The model architecture integrates a context-aware feature extraction module with a predictive network employing stacked attention blocks. The core innovation is the Mixture-of-Time (MoT) module, which adaptively integrates predictions from multiple temporal contexts by learning variable-specific reliability , mitigating cumulative errors in sequential forecasting. Through comprehensive experimental evaluation, FuXi-Ocean demonstrates superior skill in predicting key variables, including temperature, salinity, and currents, across multiple depths. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_03210 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | FuXi-Ocean: A Global Ocean Forecasting System with Sub-Daily Resolution Huang, Qiusheng Niu, Yuan Zhong, Xiaohui Guo, Anboyu Chen, Lei Zhang, Dianjun Zhang, Xuefeng Li, Hao Machine Learning Artificial Intelligence Atmospheric and Oceanic Physics Accurate, high-resolution ocean forecasting is crucial for maritime operations and environmental monitoring. While traditional numerical models are capable of producing sub-daily, eddy-resolving forecasts, they are computationally intensive and face challenges in maintaining accuracy at fine spatial and temporal scales. In contrast, recent data-driven approaches offer improved computational efficiency and emerging potential, yet typically operate at daily resolution and struggle with sub-daily predictions due to error accumulation over time. We introduce FuXi-Ocean, the first data-driven global ocean forecasting model achieving six-hourly predictions at eddy-resolving 1/12° spatial resolution, reaching depths of up to 1500 meters. The model architecture integrates a context-aware feature extraction module with a predictive network employing stacked attention blocks. The core innovation is the Mixture-of-Time (MoT) module, which adaptively integrates predictions from multiple temporal contexts by learning variable-specific reliability , mitigating cumulative errors in sequential forecasting. Through comprehensive experimental evaluation, FuXi-Ocean demonstrates superior skill in predicting key variables, including temperature, salinity, and currents, across multiple depths. |
| title | FuXi-Ocean: A Global Ocean Forecasting System with Sub-Daily Resolution |
| topic | Machine Learning Artificial Intelligence Atmospheric and Oceanic Physics |
| url | https://arxiv.org/abs/2506.03210 |