Articles | Volume 18, issue 11
https://doi.org/10.5194/essd-18-8149-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-18-8149-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Daily global sea surface ageostrophic current dataset from 1993–2023 via physics-informed deep learning
Guangxi Cui
State Key Laboratory of Internet of Things for Smart City, Department of Ocean Science and Technology, University of Macau, Macau, China
Center for Ocean Research in Hong Kong and Macau (CORE), Hong Kong, China
Ka-Veng Yuen
State Key Laboratory of Internet of Things for Smart City, Department of Civil and Environmental Engineering, University of Macau, Macau, China
Ying Chen
State Key Laboratory of Internet of Things for Smart City, Department of Ocean Science and Technology, University of Macau, Macau, China
Center for Ocean Research in Hong Kong and Macau (CORE), Hong Kong, China
Zhiqiang Liu
Department of Ocean Science and Engineering and Center for Complex Flows and Soft Matter Research, Southern University of Science and Technology, Shenzhen, China
Dingqi Yang
State Key Laboratory of Internet of Things for Smart City, Department of Computer and Information Science, University of Macau, Macau, China
Guangliang Liu
State Key Laboratory of Physical Oceanography, Institute of Oceanographic Instrumentation, Shandong Academy of Sciences, Qingdao, China
Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Shandong Computer Science Center, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China
State Key Laboratory of Internet of Things for Smart City, Department of Ocean Science and Technology, University of Macau, Macau, China
Center for Ocean Research in Hong Kong and Macau (CORE), Hong Kong, China
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Short summary
Most long-term satellite-based global current datasets capture only large-scale flows driven by pressure differences, missing important nonlinear motions. We used satellite data and a physics-informed deep learning method to create a new global dataset, including these previously missing components. Our currents are more accurate than traditional products, and we show that motions driven by nonlinear effects strongly influence the transport of heat, carbon, and nutrients in ocean.
Most long-term satellite-based global current datasets capture only large-scale flows driven by...
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