Spherical multigrid neural operator for improving autoregressive global weather forecasting

Spherical multigrid neural operator for improving autoregressive global weather forecasting

Abstract Data-driven approaches for global weather forecasting have shown great potential. However, conventional architectures of these models struggle with spherical distortions, leading to unstable autoregressive forecasts. Although methods such as spherical Fourier neural operator (SFNO) based on...

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Bibliographic Details
Main Authors: Yifan Hu, Fukang Yin, Weimin Zhang, Kaijun Ren, Junqiang Song, Kefeng Deng, Di Zhang
Format: Article
Language:English
Published: Nature Portfolio 2025-04-01
Series:Scientific Reports
Subjects:
Spherical convolution
Autoregressive forecasts
Multigrid neural operator
Spherical shallow water equations
Global weather forecasting
Online Access:https://doi.org/10.1038/s41598-025-96208-y
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https://doi.org/10.1038/s41598-025-96208-y

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