The prediction of ocean temperature using sea surface data is crucial for studying ocean-related events and climate change. However, current temperature predictions mainly focus on surface data and rarely consider the temporal relationship of ocean temperature. In this study, we propose a novel deep-learning model to predict ocean temperature for the next two months, which fully considers both temporal and spatial features. The input consists of satellite remote sensing data from the past month, including weekly sea surface temperature, salinity, height, and velocity. The model consists of four modules: convolutional, attention, residual, and transposed convolutional. We compare the model estimation with reanalysis data and conduct temporal, spatial, and vertical distribution analyses. The results demonstrate that the model can accurately predict ocean temperature at different lead time, depths, and locations. Finally, we compare the predicted temperature with actual sea observations to ensure the model's good performance in practical applications. This study shows the tremendous potential of the proposed model in predicting 4-D ocean temperature, providing powerful data support for ocean-related events and climate change research.

中文翻译：

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基于新颖的深度学习模型，使用卫星数据预测时空 4 维海洋温度

利用海面数据预测海洋温度对于研究海洋相关事件和气候变化至关重要。然而，目前的温度预测主要集中在表面数据，很少考虑海洋温度的时间关系。在这项研究中，我们提出了一种新颖的深度学习模型来预测未来两个月的海洋温度，该模型充分考虑了时间和空间特征。输入由过去一个月的卫星遥感数据组成，包括每周的海面温度、盐度、高度和速度。该模型由四个模块组成：卷积、注意力、残差和转置卷积。我们将模型估计与再分析数据进行比较，并进行时间、空间和垂直分布分析。结果表明，该模型可以准确预测不同提前时间、深度和位置的海洋温度。最后，我们将预测温度与实际海洋观测结果进行比较，以确保模型在实际应用中具有良好的性能。这项研究展示了所提出的模型在预测4维海洋温度方面的巨大潜力，为海洋相关事件和气候变化研究提供了强有力的数据支持。