## 2 Related Work
### Hypergraph Neural Network  
In traditional graph structures, each edge can only connect two nodes and can only represent pairwise relationships. However, in many practical scenarios, entities often have complex relationships that go beyond pairwise interactions. For this reason, hypergraphs are proposed to express high-order topological structures between multiple nodes, where a hyperedge can simultaneously associate any number of nodes [32]. Compared with ordinary graphs, [PERSON] et al. [33] pointed out that hypergraphs show stronger expressive power and modeling flexibility in depicting high-order dependencies between complex data. It is worth noting that when each hyperedge in a hypergraph only connects two nodes, it will degenerate into a standard graph structure. With the rise of deep learning, integrating graph neural networks into hypergraph structures has garnered growing interest from the research community. [PERSON] et al. [15] proposed HGNN, which introduces hyperedge convolution to capture high-order dependencies, outperforming traditional GCNs in tasks like citation classification and image recognition. [PERSON] et al. [34] developed Hyper-SAGNN, a self-attention-based model that supports variable hyperedge sizes and effectively handles both homogeneous and heterogeneous hypergraphs. [PERSON] et al. [17] introduced HyperGCN, which leverages GCNs for semi-supervised learning on hypergraphs with node attributes, improving classification performance and enabling combinatorial optimization. [PERSON] et al. [35] designed HGC-RNN by integrating hypergraph convolution with RNNs to capture structural and temporal dependencies in sensor network time series. [PERSON] et al. [16] proposed hypergraph convolution and attention operators to enhance the modeling of high-order, non-pairwise relationships, improving adaptability to real-world data structures. Building on advances in hypergraph-based learning, [PERSON] et al. [36] introduced a spatiotemporal hypergraph convolutional framework for capturing stock market trends and industry-level associations, though it treats all node relationships uniformly, potentially limiting its expressiveness. [PERSON] et al. [37] developed a dynamic spatiotemporal hypergraph neural network for subway passenger flow prediction, integrating topology and temporal travel patterns to extract high-order features. [PERSON] et al. [38] introduced a multi-information aggregation hypergraph network that models spatial correlations in meteorological data through adjacency and semantic hypergraphs, and combines aggregation and alienation convolutions with temporal modeling to achieve state-of-the-art prediction performance. Many atmospheric phenomena, such as frontal systems, monsoonal circulations, and mesoscale convective complexes, involve the simultaneous interaction of multiple spatially dispersed regions. These interactions are inherently group-based and cannot be effectively captured by pairwise relationships alone. Hypergraphs provide a natural mechanism to model such high-order dependencies by allowing a single hyperedge to connect multiple meteorologically similar stations. This facilitates joint reasoning over entire weather patterns, enabling the model to capture the coordinated evolution of regional weather systems. Compared to other higher-order structures such as simplicial complexes or global attention modules, hypergraphs offer a more interpretable and scalable framework that balances expressiveness with computational feasibility. Their flexible construction based on feature similarity makes them particularly well suited for modeling non-local, non-stationary patterns common in meteorological data. Hypergraph neural networks offer a powerful framework for modeling complex high-order relationships beyond pairwise interactions. Their ability to simultaneously capture multi-node dependencies makes them suitable for spatiotemporal data, enhancing representation learning and forecasting accuracy.