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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| Format: | Article |
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Nature Portfolio
2025-04-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-96208-y |
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| author | Yifan Hu Fukang Yin Weimin Zhang Kaijun Ren Junqiang Song Kefeng Deng Di Zhang |
| author_facet | Yifan Hu Fukang Yin Weimin Zhang Kaijun Ren Junqiang Song Kefeng Deng Di Zhang |
| author_sort | Yifan Hu |
| collection | DOAJ |
| description | 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 spherical harmonic convolution can alleviate these problems, they face the challenge of high computational cost. Here, we introduce a spherical multigrid neural operator (SMgNO) that integrates spherical harmonic convolution and low resolution SFNO in the multigrid framework, effectively alleviating data distortions while requiring few computational resources. Experiments for spherical shallow water equations and medium-range global weather forecasting demonstrate the effectiveness and robustness of SMgNO. For 500 hPa geopotential height with a 7 days lead time, SMgNO achieves a 9.31% and 6.83% improvement in anomaly correlation coefficient over IFS T42 and SFNO, respectively. Furthermore, SMgNO requires only 10% floating-point operations of SFNO for forward propagation and 30.90% less GPU memory than SFNO during training. |
| format | Article |
| id | doaj-art-1089decfe28a437cb8083cc862a1b51d |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-1089decfe28a437cb8083cc862a1b51d2025-08-20T03:07:40ZengNature PortfolioScientific Reports2045-23222025-04-0115111210.1038/s41598-025-96208-ySpherical multigrid neural operator for improving autoregressive global weather forecastingYifan Hu0Fukang Yin1Weimin Zhang2Kaijun Ren3Junqiang Song4Kefeng Deng5Di Zhang6College of Computer Science and Technology, National University of Defense TechnologyCollege of Meteorology and Oceanography, National University of Defense TechnologyCollege of Meteorology and Oceanography, National University of Defense TechnologyCollege of Meteorology and Oceanography, National University of Defense TechnologyCollege of Meteorology and Oceanography, National University of Defense TechnologyCollege of Meteorology and Oceanography, National University of Defense TechnologyCollege of Meteorology and Oceanography, National University of Defense TechnologyAbstract 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 spherical harmonic convolution can alleviate these problems, they face the challenge of high computational cost. Here, we introduce a spherical multigrid neural operator (SMgNO) that integrates spherical harmonic convolution and low resolution SFNO in the multigrid framework, effectively alleviating data distortions while requiring few computational resources. Experiments for spherical shallow water equations and medium-range global weather forecasting demonstrate the effectiveness and robustness of SMgNO. For 500 hPa geopotential height with a 7 days lead time, SMgNO achieves a 9.31% and 6.83% improvement in anomaly correlation coefficient over IFS T42 and SFNO, respectively. Furthermore, SMgNO requires only 10% floating-point operations of SFNO for forward propagation and 30.90% less GPU memory than SFNO during training.https://doi.org/10.1038/s41598-025-96208-ySpherical convolutionAutoregressive forecastsMultigrid neural operatorSpherical shallow water equationsGlobal weather forecasting |
| spellingShingle | Yifan Hu Fukang Yin Weimin Zhang Kaijun Ren Junqiang Song Kefeng Deng Di Zhang Spherical multigrid neural operator for improving autoregressive global weather forecasting Scientific Reports Spherical convolution Autoregressive forecasts Multigrid neural operator Spherical shallow water equations Global weather forecasting |
| title | Spherical multigrid neural operator for improving autoregressive global weather forecasting |
| title_full | Spherical multigrid neural operator for improving autoregressive global weather forecasting |
| title_fullStr | Spherical multigrid neural operator for improving autoregressive global weather forecasting |
| title_full_unstemmed | Spherical multigrid neural operator for improving autoregressive global weather forecasting |
| title_short | Spherical multigrid neural operator for improving autoregressive global weather forecasting |
| title_sort | spherical multigrid neural operator for improving autoregressive global weather forecasting |
| topic | Spherical convolution Autoregressive forecasts Multigrid neural operator Spherical shallow water equations Global weather forecasting |
| url | https://doi.org/10.1038/s41598-025-96208-y |
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