In recent years, there has been an increased interest in understanding and predicting the weather using weather station data with Spatial-Temporal Graph Neural Networks (STGNN). However, it has large prediction errors as a result of the inherent non-linearities and the influence of dynamic spatio-temporal auto-correlation. Using a continuously-varying graph topology chronologically, while embedding domain knowledge to enforce validity, can effectively resolve the issue, but the implementation of such concept constitutes an interdisciplinary challenge for researchers. A Dynamic Graph Former (DGFormer) model is proposed to address this challenge. It combines a topology learner through a deep generative layer with domain knowledge enhancement inserted into the STGNN structure, where the derived physics-guided method allows for an efficient integration with the earth system. For capture of the optimal topology, we merge a node-embedding-based similarity metric learning and the superposition principle as physical assistants into the dynamic graph module. We evaluate our model with a real-world weather dataset on short-term (12 hours) and medium-range (360 hours) prediction tasks. DGFormer achieves outstanding performance with obvious improvements by up to 34.84% at short-term prediction and by up to 23.25% at medium-range prediction compared with the state-of-the-art methods. We also conducted detailed analyses for cities in three regions and visualized the dynamic graph, revealing the characteristics, advantages, and graph visualization of our model.

近年来，人们对使用气象站数据和时空图神经网络（STGNN）来理解和预测天气越来越感兴趣。但由于其固有的非线性和动态时空自相关的影响，其预测误差较大。按时间顺序使用连续变化的图拓扑，同时嵌入领域知识来增强有效性，可以有效解决该问题，但这种概念的实施对研究人员构成了跨学科的挑战。提出了动态图形成器（DGFormer）模型来应对这一挑战。它将拓扑学习器通过深度生成层与插入到 STGNN 结构中的领域知识增强相结合，其中派生的物理引导方法允许与地球系统的有效集成。为了捕获最佳拓扑，我们将基于节点嵌入的相似性度量学习和叠加原理作为物理助手合并到动态图模块中。我们使用真实世界天气数据集在短期（12 小时）和中期（360 小时）预测任务上评估我们的模型。 DGFormer 取得了出色的性能，与最先进的方法相比，短期预测提高了 34.84%，中期预测提高了 23.25%。我们还对三个地区的城市进行了详细的分析，并可视化了动态图，揭示了我们的模型的特点、优势和图形可视化。