A data-driven global ocean forecasting model with sub-daily and eddy-resolving resolution
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866914058660216832 |
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| author | Niu, Yuan Huang, Qiusheng Zhong, Xiaohui Guo, Anboyu Chen, Lei Jia, Xiaoyan Qi, Jiawei Zhang, Dianjun Li, Hao Zhang, Xuefeng |
| author_facet | Niu, Yuan Huang, Qiusheng Zhong, Xiaohui Guo, Anboyu Chen, Lei Jia, Xiaoyan Qi, Jiawei Zhang, Dianjun Li, Hao Zhang, Xuefeng |
| contents | High-fidelity ocean forecasting at high spatial and temporal resolution is essential for capturing fine-scale dynamical features, with profound implications for hazard prediction, maritime navigation, and sustainable ocean management. While conventional numerical models can generate sub-daily, eddy-resolving forecasts, they demand substantial computational resources and often struggle to maintain predictive skill at such fine scales. Data-driven models offer a promising alternative with significantly higher computational efficiency; however, most are constrained to daily outputs and show a rapid decay in accuracy when extended to sub-daily timescales. Here, we introduce TianHai, the first-of-its-kind global data-driven 6-hour forecasting model, which delivers predictions at 1/12° eddy-resolving resolution with a vertical extent down to 1,500 m. A key feature of TianHai is the integration of atmospheric forcings through FuXi-Atmosphere, a data-driven atmospheric forecasting system, which enables the explicit representation of air-sea coupling effects. Unlike conventional approaches, TianHai does not rely on numerical atmospheric models or external meteorological forecasts, making it a fully data-driven framework for coupled prediction. Benchmark experiments demonstrate that TianHai delivers state-of-the-art performance in forecasting temperature and salinity profiles, zonal and meridional currents, sea surface temperature, and sea level anomalies for lead times ranging from 1 to 10 days. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_17015 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A data-driven global ocean forecasting model with sub-daily and eddy-resolving resolution Niu, Yuan Huang, Qiusheng Zhong, Xiaohui Guo, Anboyu Chen, Lei Jia, Xiaoyan Qi, Jiawei Zhang, Dianjun Li, Hao Zhang, Xuefeng Atmospheric and Oceanic Physics High-fidelity ocean forecasting at high spatial and temporal resolution is essential for capturing fine-scale dynamical features, with profound implications for hazard prediction, maritime navigation, and sustainable ocean management. While conventional numerical models can generate sub-daily, eddy-resolving forecasts, they demand substantial computational resources and often struggle to maintain predictive skill at such fine scales. Data-driven models offer a promising alternative with significantly higher computational efficiency; however, most are constrained to daily outputs and show a rapid decay in accuracy when extended to sub-daily timescales. Here, we introduce TianHai, the first-of-its-kind global data-driven 6-hour forecasting model, which delivers predictions at 1/12° eddy-resolving resolution with a vertical extent down to 1,500 m. A key feature of TianHai is the integration of atmospheric forcings through FuXi-Atmosphere, a data-driven atmospheric forecasting system, which enables the explicit representation of air-sea coupling effects. Unlike conventional approaches, TianHai does not rely on numerical atmospheric models or external meteorological forecasts, making it a fully data-driven framework for coupled prediction. Benchmark experiments demonstrate that TianHai delivers state-of-the-art performance in forecasting temperature and salinity profiles, zonal and meridional currents, sea surface temperature, and sea level anomalies for lead times ranging from 1 to 10 days. |
| title | A data-driven global ocean forecasting model with sub-daily and eddy-resolving resolution |
| topic | Atmospheric and Oceanic Physics |
| url | https://arxiv.org/abs/2509.17015 |