Weather forecasting plays a critical role in various sectors, driving
decision-making and risk management. However, traditional methods often
struggle to capture the complex dynamics of meteorological systems,
particularly in the presence of high-resolution data. In this paper, we propose
the Spatial-Frequency Attention Network (SFANet), a novel deep learning
framework designed to address these challenges and enhance the accuracy of
spatiotemporal weather prediction. Drawing inspiration from the limitations of
existing methodologies, we present an innovative approach that seamlessly
integrates advanced token mixing and attention mechanisms. By leveraging both
pooling and spatial mixing strategies, SFANet optimizes the processing of
high-dimensional spatiotemporal sequences, preserving inter-component
relational information and modeling extensive long-range relationships. To
further enhance feature integration, we introduce a novel spatial-frequency
attention module, enabling the model to capture intricate cross-modal
correlations. Our extensive experimental evaluation on two distinct datasets,
the Storm EVent ImageRy (SEVIR) and the Institute for Climate and Application
Research (ICAR) - El Ni\~{n}o Southern Oscillation (ENSO) dataset, demonstrates
the remarkable performance of SFANet. Notably, SFANet achieves substantial
advancements over state-of-the-art methods, showcasing its proficiency in
forecasting precipitation patterns and predicting El Ni\~{n}o events.

提出了 Spatial-Frequency Attention Network（SFANet），这是一个新颖的深度学习框架，通过解决传统方法难以捕捉气象系统复杂动力学的问题，在高分辨率数据存在的情况下提高时空天气预测的准确性。通过整合先进的令牌混合和注意机制，SFANet 优化处理高维时空序列的过程，以捕捉组件间的关系信息和建模广泛的长距离关系。具有空间频率注意模块的 SFANet 能够捕捉复杂的跨模态相关性，并在两个不同数据集上展示了惊人的性能，证明其在预测降水模式和预测 El Niño 事件方面具有卓越的能力。