Accurate prediction of sea surface temperature (SST) is crucial in the field of oceanography, as it has a significant impact on various physical, chemical, and biological processes in the marine environment. In this study, we propose a physics-guided attention-based neural network (PANN) to address the spatiotemporal SST prediction problem. The PANN model incorporates data-driven spatiotemporal convolution operations and the underlying physical dynamics of SSTs using a cross-attention mechanism. First, we construct a spatiotemporal convolution module (SCM) using convolutional long short-term memory (ConvLSTM) to capture the spatial and temporal correlations present in the time series of the SST data. We then introduce a physical constraint module (PCM) to mimic the transport dynamics in fluids based on data assimilation techniques used to solve partial differential equations (PDEs). Consequently, we employ an attention fusion module (AFM) to effectively combine the data-driven and PDE-constrained predictions obtained from the SCM and PCM, aiming at enhancing the accuracy of the predictions. To evaluate the performance of the proposed model, we conduct short-term SST forecasts in the East China Sea (ECS) with forecast lead times ranging from one to ten days, by comparing it with several state-of-the-art models, including ConvLSTM, PredRNN, temporal convolutional transformer network (TCTN), convolutional gated recurrent unit (ConvGRU), and SwinLSTM. The experimental results demonstrate that our proposed model outperforms these models in terms of multiple evaluation metrics for short-term predictions.

中文翻译：

---

用于海面温度预测的物理引导的基于注意力的神经网络


海面温度（SST）的准确预测在海洋学领域至关重要，因为它对海洋环境中的各种物理、化学和生物过程具有重大影响。在这项研究中，我们提出了一种物理引导的基于注意力的神经网络（PANN）来解决时空海表温度预测问题。 PANN 模型结合了数据驱动的时空卷积运算和使用交叉注意力机制的 SST 的底层物理动力学。首先，我们使用卷积长短期记忆（ConvLSTM）构建时空卷积模块（SCM）来捕获 SST 数据时间序列中存在的空间和时间相关性。然后，我们引入物理约束模块 (PCM)，基于用于求解偏微分方程 (PDE) 的数据同化技术来模拟流体中的输运动力学。因此，我们采用注意力融合模块（AFM）来有效地结合从 SCM 和 PCM 获得的数据驱动和 PDE 约束的预测，旨在提高预测的准确性。为了评估所提出模型的性能，我们通过与几个最先进的模型进行比较，对东海（ECS）进行了短期海表温度预报，预报周期为一到十天，包括ConvLSTM、PredRNN、时间卷积变换网络 (TCTN)、卷积门控循环单元 (ConvGRU) 和 SwinLSTM。实验结果表明，我们提出的模型在短期预测的多个评估指标方面优于这些模型。