An Interpretable Weather Forecasting Model With Separately‐Learned Dynamics and Physics Neural Networks

Abstract Machine learning (ML) offers a promising alternative for weather forecasting by reducing computational costs and modeling complex non‐linear atmospheric processes. While recent foundation models highlight this potential with advanced architectures, interpreting the “black‐box” nature of ML...

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Bibliographic Details
Main Authors: Mengxuan Chen, Jinxiao Zhang, Runmin Dong, Yidan Xu, Haoyuan Liang, Juepeng Zheng, Lanning Wang, Haohuan Fu
Format: Article
Language:English
Published: Wiley 2025-07-01
Series:Geophysical Research Letters
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Online Access:https://doi.org/10.1029/2024GL114310
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