Met^2Net：面向复杂气象系统的解耦式双阶段时空预测模型

Met^2Net: A Decoupled Two-Stage Spatio-Temporal Forecasting Model for Complex Meteorological Systems

July 23, 2025

作者: Shaohan Li, Hao Yang, Min Chen, Xiaolin Qin

cs.AI

摘要

全球气候变化导致极端天气事件频发，亟需提高天气预报的准确性。近年来，得益于深度学习技术，端到端方法取得了显著进展，但在多变量整合中存在表征不一致的局限，难以有效捕捉复杂天气系统中变量间的依赖关系。将不同变量视为独立模态并采用多模态模型的两阶段训练方法虽能部分缓解此问题，但由于两阶段训练任务的不一致性，结果往往不尽如人意。为应对这些挑战，我们提出了一种隐式两阶段训练方法，为每个变量配置独立的编码器和解码器。具体而言，第一阶段冻结翻译器，让编码器和解码器学习共享的潜在空间；第二阶段则冻结编码器和解码器，由翻译器捕捉变量间的交互以进行预测。此外，通过在潜在空间中引入自注意力机制进行多变量融合，性能得到进一步提升。大量实验表明，我们的方法达到了业界领先水平，特别是在近地表气温和相对湿度预测上，均方误差分别降低了28.82%和23.39%。源代码已发布于https://github.com/ShremG/Met2Net。

English

The increasing frequency of extreme weather events due to global climate change urges accurate weather prediction. Recently, great advances have been made by the end-to-end methods, thanks to deep learning techniques, but they face limitations of representation inconsistency in multivariable integration and struggle to effectively capture the dependency between variables, which is required in complex weather systems. Treating different variables as distinct modalities and applying a two-stage training approach from multimodal models can partially alleviate this issue, but due to the inconformity in training tasks between the two stages, the results are often suboptimal. To address these challenges, we propose an implicit two-stage training method, configuring separate encoders and decoders for each variable. In detailed, in the first stage, the Translator is frozen while the Encoders and Decoders learn a shared latent space, in the second stage, the Encoders and Decoders are frozen, and the Translator captures inter-variable interactions for prediction. Besides, by introducing a self-attention mechanism for multivariable fusion in the latent space, the performance achieves further improvements. Empirically, extensive experiments show the state-of-the-art performance of our method. Specifically, it reduces the MSE for near-surface air temperature and relative humidity predictions by 28.82\% and 23.39\%, respectively. The source code is available at https://github.com/ShremG/Met2Net.