Met^2Net: A Decoupled Two-Stage Spatio-Temporal Forecasting Model for
  Complex Meteorological Systems
Authors: name: Shaohan Li, name: Hao Yang, name: Min Chen, name: Xiaolin Qin

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.