Met^2Net: A Decoupled Two-Stage Spatio-Temporal Forecasting Model for
  Complex Meteorological Systems
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.
68887f9daf872d625c10c6f0
Shaohan Li
claimed_verified
2025-07-29T12:50:52.185Z
65a54d243a09e652a32245bf
https://cdn-avatars.huggingface.co/v1/production/uploads/65a54d243a09e652a32245bf/R6I2KaIxuxqu0ts3biw0D.jpeg
Shaohan Li
user
guaishou1
68887f9daf872d625c10c6f1
Hao Yang
68887f9daf872d625c10c6f2
Min Chen
68887f9daf872d625c10c6f3
Xiaolin Qin
Met^2Net: A Decoupled Two-Stage Spatio-Temporal Forecasting Model for
  Complex Meteorological Systems
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.
An implicit two-stage training method with separate encoders, decoders, and a translator, enhanced by self-attention, improves weather prediction accuracy.
end-to-end methods deep learning representation inconsistency two-stage training approach multimodal models implicit two-stage training encoders decoders Translator self-attention mechanism multivariable fusion latent space MSE