SFANet：用于天气预报的空间-频率注意力网络
摘要
天气预报在 various sectors 中发挥关键作用，驱动决策和风险管理。然而，传统方法往往难以捕捉气象系统的复杂动态，尤其是在高分辨率数据存在时。本文提出 Spatial-Frequency Attention Network (SFANet)，一种新型深度学习框架，旨在解决这些挑战并提高时空天气预测的准确性。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.
引用
@article{arxiv.2405.18849,
title = {SFANet: Spatial-Frequency Attention Network for Weather Forecasting},
author = {Jiaze Wang and Hao Chen and Hongcan Xu and Jinpeng Li and Bowen Wang and Kun Shao and Furui Liu and Huaxi Chen and Guangyong Chen and Pheng-Ann Heng},
journal= {arXiv preprint arXiv:2405.18849},
year = {2024}
}