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

TL;DR

Met$^2$Net introduces a novel two-stage spatio-temporal forecasting model with separate encoders and decoders for each variable, leveraging self-attention to improve complex weather system predictions, achieving state-of-the-art results.

Contribution

The paper proposes an implicit two-stage training approach with variable-specific encoders/decoders and self-attention, addressing representation inconsistency in multivariable weather forecasting.

Findings

Reduces MSE for temperature prediction by 28.82%.

Reduces MSE for humidity prediction by 23.39%.

Achieves state-of-the-art performance on weather forecasting tasks.

Abstract

The increasing frequency of extreme weather events due to global climate change urges accurate weather prediction. Recently, great advances have been made by the \textbf{end-to-end methods}, thanks to deep learning techniques, but they face limitations of \textit{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 \textbf{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.