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Forecasting the eddying ocean with a deep neural network

Author

Listed:
  • Yingzhe Cui

    (Ocean University of China
    Laoshan Laboratory)

  • Ruohan Wu

    (University of Science and Technology of China)

  • Xiang Zhang

    (Ocean University of China
    Laoshan Laboratory)

  • Ziqi Zhu

    (University of Science and Technology of China)

  • Bo Liu

    (University of Science and Technology of China)

  • Jun Shi

    (University of Science and Technology of China)

  • Junshi Chen

    (Laoshan Laboratory
    University of Science and Technology of China
    Joint Laboratory of Advanced Computing for Transparent Oceans between Laoshan Laboratory and University of Science and Technology of China)

  • Hailong Liu

    (Laoshan Laboratory)

  • Shenghui Zhou

    (Laoshan Laboratory)

  • Liang Su

    (Ltd)

  • Zhao Jing

    (Ocean University of China
    Laoshan Laboratory)

  • Hong An

    (Laoshan Laboratory
    University of Science and Technology of China
    Joint Laboratory of Advanced Computing for Transparent Oceans between Laoshan Laboratory and University of Science and Technology of China)

  • Lixin Wu

    (Ocean University of China
    Laoshan Laboratory)

Abstract

Mesoscale eddies with horizontal scales from tens to hundreds of kilometers are ubiquitous in the upper ocean, dominating the ocean variability from daily to weekly time scales. Their turbulent nature causes great scientific challenges and computational burdens in accurately forecasting the short-term evolution of the ocean states based on conventional physics-driven numerical models. Recently, artificial intelligence (AI)-based methods have achieved competitive forecast performance and greatly increased computational efficiency in weather forecasts, compared to numerical models. Yet, their application to ocean forecasts remains challenging due to the different dynamic characteristics of the atmosphere and the ocean. Here, we develop WenHai, a data-driven eddy-resolving global ocean forecast system (GOFS), by training a deep neural network (DNN). The bulk formulae on momentum, heat, and freshwater fluxes are incorporated into the DNN to improve the representation of air-sea interactions. Ocean dynamics is exploited in the DNN architecture design to preserve ocean mesoscale eddy variability. WenHai outperforms a state-of-the-art eddy-resolving numerical GOFS and AI-based GOFS for the temperature profile, salinity profile, sea surface temperature, sea level anomaly, and near-surface current forecasts led by 1 day to at least 10 days. Our results highlight expertise-guided deep learning as a promising pathway for enhancing the global ocean forecast capacity.

Suggested Citation

  • Yingzhe Cui & Ruohan Wu & Xiang Zhang & Ziqi Zhu & Bo Liu & Jun Shi & Junshi Chen & Hailong Liu & Shenghui Zhou & Liang Su & Zhao Jing & Hong An & Lixin Wu, 2025. "Forecasting the eddying ocean with a deep neural network," Nature Communications, Nature, vol. 16(1), pages 1-11, December.
  • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-57389-2
    DOI: 10.1038/s41467-025-57389-2
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    References listed on IDEAS

    as
    1. Kaifeng Bi & Lingxi Xie & Hengheng Zhang & Xin Chen & Xiaotao Gu & Qi Tian, 2023. "Accurate medium-range global weather forecasting with 3D neural networks," Nature, Nature, vol. 619(7970), pages 533-538, July.
    2. Shengpeng Wang & Zhao Jing & Lixin Wu & Shantong Sun & Zhaohui Chen & Xiaohui Ma & Bolan Gan, 2024. "A more quiescent deep ocean under global warming," Nature Climate Change, Nature, vol. 14(9), pages 961-967, September.
    3. Yingzhe Cui & Ruohan Wu & Xiang Zhang & Ziqi Zhu & Bo Liu & Jun Shi & Junshi Chen & Hailong Liu & Shenghui Zhou & Liang Su & Zhao Jing & Hong An & Lixin Wu, 2025. "Forecasting the eddying ocean with a deep neural network," Nature Communications, Nature, vol. 16(1), pages 1-11, December.
    4. Benjamin A. Storer & Michele Buzzicotti & Hemant Khatri & Stephen M. Griffies & Hussein Aluie, 2022. "Global energy spectrum of the general oceanic circulation," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
    5. Ce Bian & Zhao Jing & Hong Wang & Lixin Wu & Zhaohui Chen & Bolan Gan & Haiyuan Yang, 2023. "Oceanic mesoscale eddies as crucial drivers of global marine heatwaves," Nature Communications, Nature, vol. 14(1), pages 1-9, December.
    6. Kaifeng Bi & Lingxi Xie & Hengheng Zhang & Xin Chen & Xiaotao Gu & Qi Tian, 2023. "Author Correction: Accurate medium-range global weather forecasting with 3D neural networks," Nature, Nature, vol. 621(7980), pages 45-45, September.
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    1. Yingzhe Cui & Ruohan Wu & Xiang Zhang & Ziqi Zhu & Bo Liu & Jun Shi & Junshi Chen & Hailong Liu & Shenghui Zhou & Liang Su & Zhao Jing & Hong An & Lixin Wu, 2025. "Forecasting the eddying ocean with a deep neural network," Nature Communications, Nature, vol. 16(1), pages 1-11, December.

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