Sea surface temperature (SST) is crucial for studying global oceans and evaluating ecosystems. Accurately predicting short and mid-term daily SST has been a significant challenge in oceanography. Traditional deep learning methods can handle temporal data and spatial features but often struggle with long-range spatiotemporal dependencies. To address this, we propose a coordination attention residual U-Net(CResU-Net) model designed to better capture the dynamic spatiotemporal correlations of high-resolution SST. The model integrates coordinate attention mechanisms, multiple residual modules, and depthwise separable convolutions to enhance prediction capabilities. The spatiotemporal variations of SST across different areas of the South China Sea are complex, making accurate predictions challenging. Experiments across various regions of the South China Sea show the model’s effectiveness and robust generalization in predicting high-resolution daily SST. For a 10-day forecast period, the model achieves approximately 0.3 °C in RMSE, outperforming several advanced models.

中文翻译：

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用于增强中短期海面温度预测的协调注意残差 U-Net 模型


海面温度 （SST） 对于研究全球海洋和评估生态系统至关重要。准确预测短期和中期每日 SST 一直是海洋学领域的一项重大挑战。传统的深度学习方法可以处理时态数据和空间特征，但通常会难以处理长距离时空依赖性。为了解决这个问题，我们提出了一种协调注意力残差 U-Net （CResU-Net） 模型，旨在更好地捕捉高分辨率 SST 的动态时空相关性。该模型集成了坐标注意力机制、多个残差模块和深度可分离卷积，以增强预测能力。南海不同区域的海温时空变化很复杂，因此难以准确预测。在南海不同地区的实验表明，该模型在预测高分辨率每日 SST 方面的有效性和稳健的泛化性。在 10 天的预测期内，该模型在 RMSE 中达到大约 0.3 °C，优于几个高级模型。