A Four‐Dimensional Variational Informed Generative Adversarial Network for Data Assimilation
Abstract Data‐driven weather prediction (DDWP) has made significant advancements in recent years. However, weather prediction using DDWPs still requires an accurate initial field as the input. To fulfill this requirement, the four‐dimensional variational (4DVar) approach can offer initial fields. Re...
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| Format: | Article |
| Language: | English |
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American Geophysical Union (AGU)
2025-06-01
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| Series: | Journal of Advances in Modeling Earth Systems |
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| Online Access: | https://doi.org/10.1029/2024MS004437 |
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| author | Wuxin Wang Boheng Duan Weicheng Ni Jingze Lu Taikang Yuan Dawei Li Juan Zhao Kaijun Ren |
| author_facet | Wuxin Wang Boheng Duan Weicheng Ni Jingze Lu Taikang Yuan Dawei Li Juan Zhao Kaijun Ren |
| author_sort | Wuxin Wang |
| collection | DOAJ |
| description | Abstract Data‐driven weather prediction (DDWP) has made significant advancements in recent years. However, weather prediction using DDWPs still requires an accurate initial field as the input. To fulfill this requirement, the four‐dimensional variational (4DVar) approach can offer initial fields. Recent studies have demonstrated the potential of deep learning (DL)‐based methods in accelerating 4DVar. In this study, we propose a novel model called the 4DVar‐informed generative adversarial network (4DVarGAN), which combines prior knowledge from 4DVar with the conditional generative network (CGAN). We employ a CGAN to non‐iteratively solve the 4DVar cost function and utilize a cycle‐consistent adversarial learning framework for data augmentation. Additionally, we incorporate a 4DVar‐based adaptive adjustment to the output of the proposed model's analysis increment‐generating component, which promotes reasonable stabilization. Experimental results using 500 hPa geopotential fields from the WeatherBench data set demonstrate that our approach achieves a 73‐fold acceleration compared to the 4DVar implemented by the DDWP model. Furthermore, our model exhibits the lowest initial and forecast errors, outperforming state‐of‐the‐art DL‐based data assimilation (DA) methods. Moreover, our method demonstrates effective performance when starting from background fields of varying qualities, consistently achieving stable results. These findings highlight the potential of CGANs in enhancing the reliability of data‐driven DA by incorporating the prior knowledge of the 4DVar method. |
| format | Article |
| id | doaj-art-ccb9c403125d4966b61bc5134183eb2c |
| institution | DOAJ |
| issn | 1942-2466 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | American Geophysical Union (AGU) |
| record_format | Article |
| series | Journal of Advances in Modeling Earth Systems |
| spelling | doaj-art-ccb9c403125d4966b61bc5134183eb2c2025-08-20T03:23:51ZengAmerican Geophysical Union (AGU)Journal of Advances in Modeling Earth Systems1942-24662025-06-01176n/an/a10.1029/2024MS004437A Four‐Dimensional Variational Informed Generative Adversarial Network for Data AssimilationWuxin Wang0Boheng Duan1Weicheng Ni2Jingze Lu3Taikang Yuan4Dawei Li5Juan Zhao6Kaijun Ren7College of Computer Science and Technology National University of Defense Technology Changsha ChinaCollege of Meteorology and Oceanography National University of Defense Technology Changsha ChinaCollege of Meteorology and Oceanography National University of Defense Technology Changsha ChinaCollege of Computer Science and Technology National University of Defense Technology Changsha ChinaCollege of Meteorology and Oceanography National University of Defense Technology Changsha ChinaCollege of Computer Science and Technology National University of Defense Technology Changsha ChinaCollege of Meteorology and Oceanography National University of Defense Technology Changsha ChinaCollege of Computer Science and Technology National University of Defense Technology Changsha ChinaAbstract Data‐driven weather prediction (DDWP) has made significant advancements in recent years. However, weather prediction using DDWPs still requires an accurate initial field as the input. To fulfill this requirement, the four‐dimensional variational (4DVar) approach can offer initial fields. Recent studies have demonstrated the potential of deep learning (DL)‐based methods in accelerating 4DVar. In this study, we propose a novel model called the 4DVar‐informed generative adversarial network (4DVarGAN), which combines prior knowledge from 4DVar with the conditional generative network (CGAN). We employ a CGAN to non‐iteratively solve the 4DVar cost function and utilize a cycle‐consistent adversarial learning framework for data augmentation. Additionally, we incorporate a 4DVar‐based adaptive adjustment to the output of the proposed model's analysis increment‐generating component, which promotes reasonable stabilization. Experimental results using 500 hPa geopotential fields from the WeatherBench data set demonstrate that our approach achieves a 73‐fold acceleration compared to the 4DVar implemented by the DDWP model. Furthermore, our model exhibits the lowest initial and forecast errors, outperforming state‐of‐the‐art DL‐based data assimilation (DA) methods. Moreover, our method demonstrates effective performance when starting from background fields of varying qualities, consistently achieving stable results. These findings highlight the potential of CGANs in enhancing the reliability of data‐driven DA by incorporating the prior knowledge of the 4DVar method.https://doi.org/10.1029/2024MS004437data assimilationfour‐dimensional variationalgenerative adversarial neural networkcycle‐consistency |
| spellingShingle | Wuxin Wang Boheng Duan Weicheng Ni Jingze Lu Taikang Yuan Dawei Li Juan Zhao Kaijun Ren A Four‐Dimensional Variational Informed Generative Adversarial Network for Data Assimilation Journal of Advances in Modeling Earth Systems data assimilation four‐dimensional variational generative adversarial neural network cycle‐consistency |
| title | A Four‐Dimensional Variational Informed Generative Adversarial Network for Data Assimilation |
| title_full | A Four‐Dimensional Variational Informed Generative Adversarial Network for Data Assimilation |
| title_fullStr | A Four‐Dimensional Variational Informed Generative Adversarial Network for Data Assimilation |
| title_full_unstemmed | A Four‐Dimensional Variational Informed Generative Adversarial Network for Data Assimilation |
| title_short | A Four‐Dimensional Variational Informed Generative Adversarial Network for Data Assimilation |
| title_sort | four dimensional variational informed generative adversarial network for data assimilation |
| topic | data assimilation four‐dimensional variational generative adversarial neural network cycle‐consistency |
| url | https://doi.org/10.1029/2024MS004437 |
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