Met^2Net：面向复杂氣象系統的解耦兩階段時空預測模型

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

全球氣候變化導致極端天氣事件頻發，這對精確的天氣預報提出了迫切需求。近年來，得益於深度學習技術，端到端方法取得了顯著進展，但這些方法在多變量整合中面臨表示不一致性的限制，且難以有效捕捉複雜天氣系統中所需的變量間依賴關係。將不同變量視為獨立模態並採用多模態模型的兩階段訓練方法，雖能部分緩解此問題，但由於兩階段訓練任務的不一致性，結果往往未達最佳。為應對這些挑戰，我們提出了一種隱式的兩階段訓練方法，為每個變量配置獨立的編碼器與解碼器。具體而言，在第一階段，翻譯器被凍結，而編碼器與解碼器學習共享的潛在空間；在第二階段，編碼器與解碼器被凍結，翻譯器則捕捉變量間的交互作用以進行預測。此外，通過在潛在空間中引入自注意力機制進行多變量融合，性能得到了進一步提升。實驗結果表明，我們的方法達到了最先進的性能水平，具體而言，在近地表氣溫和相對濕度預測上，均方誤差分別降低了28.82%和23.39%。源代碼已公開於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.