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<p>Efficient and accurate prediction of ocean surface latent heat fluxes is essential for understanding and modeling climate dynamics. Conventional estimation methods have low resolution and lack accuracy. The transformer model, with its self-attention mechanism, effectively captures long-range dependencies, leading to a degradation of accuracy over time. Due to the non-linearity and uncertainty of physical processes, the transformer model encounters the problem of error accumulation, leading to a degradation of accuracy over time. To solve this problem, we combine the Data Assimilation (DA) technique with the transformer model and continuously modify the model state to make it closer to the actual observations. In this paper, we propose a deep learning model called TransNetDA, which integrates transformer, convolutional neural network and DA methods. By combining data-driven and DA methods for spatiotemporal prediction, TransNetDA effectively extracts multi-scale spatial features and significantly improves prediction accuracy. The experimental results indicate that the TransNetDA method surpasses traditional techniques in terms of root mean square error and <i>R</i><sup>2</sup> metrics, showcasing its superior performance in predicting latent heat fluxes at the ocean surface.</p>

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A deep learning model for ocean surface latent heat flux based on transformer and data assimilation

  • 期刊论文

  • 发表日期：

    2025年5月1日

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  • Yahui Liu,
  • Hengxiao Li,
  • Jichao Wang

摘要

Efficient and accurate prediction of ocean surface latent heat fluxes is essential for understanding and modeling climate dynamics. Conventional estimation methods have low resolution and lack accuracy. The transformer model, with its self-attention mechanism, effectively captures long-range dependencies, leading to a degradation of accuracy over time. Due to the non-linearity and uncertainty of physical processes, the transformer model encounters the problem of error accumulation, leading to a degradation of accuracy over time. To solve this problem, we combine the Data Assimilation (DA) technique with the transformer model and continuously modify the model state to make it closer to the actual observations. In this paper, we propose a deep learning model called TransNetDA, which integrates transformer, convolutional neural network and DA methods. By combining data-driven and DA methods for spatiotemporal prediction, TransNetDA effectively extracts multi-scale spatial features and significantly improves prediction accuracy. The experimental results indicate that the TransNetDA method surpasses traditional techniques in terms of root mean square error and R2 metrics, showcasing its superior performance in predicting latent heat fluxes at the ocean surface.