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Impact Study of Assimilating Fengyun-3 GNSS-R Ocean Surface Winds in the Weather Research and Forecasting Model: Sensitivity Analysis on Observation Error Specifications.

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Title: Impact Study of Assimilating Fengyun-3 GNSS-R Ocean Surface Winds in the Weather Research and Forecasting Model: Sensitivity Analysis on Observation Error Specifications.
Authors: Wang, Guanyi1,2 (AUTHOR), Bai, Weihua1,2 (AUTHOR), Huang, Feixiong1 (AUTHOR) huangfeixiong@nssc.ac.cn, Sun, Yueqiang1,2 (AUTHOR), Xia, Junming1 (AUTHOR), Wang, Xianyi1 (AUTHOR), Meng, Xiangguang1 (AUTHOR), Hu, Peng1 (AUTHOR), Yin, Cong1 (AUTHOR), Tan, Guangyuan1 (AUTHOR), Wu, Ruhan1,2 (AUTHOR), Du, Yunlong1,2 (AUTHOR), Meng, Xiaofeng1,2 (AUTHOR)
Source: Remote Sensing. Jun2026, Vol. 18 Issue 12, p1892. 20p.
Subjects: Data assimilation, Measurement errors, Numerical weather forecasting, Sensitivity analysis, Global Positioning System, Atmospheric boundary layer, Weather forecasting, Artificial satellites
Geographic Terms: China
Abstract: Highlights: What are the main findings? Optimal assimilation of FY-3E GNSS-R winds in WRF is achieved using a static observation error of 6 m/s without data thinning. GNSS-R wind assimilation significantly improves atmospheric analyses, with impacts extending from the surface up to 700 hPa in a sensitivity experiment and higher levels in cycling OSEs. What are the implications of the main findings? The dense along-track sampling of GNSS-R observations requires careful observation error specification, which plays a critical role in data assimilation. The observation error configuration and OSEs provide a practical reference for assimilating GNSS-R winds in WRF, and can be extended to other NWP systems. The Global Navigation Satellite System Reflectometry (GNSS-R) technique provides global ocean surface wind observations unaffected by rainfall with high spatiotemporal resolution. The Fengyun-3E (FY-3E) mission, as the first operational GNSS-R satellite in China, offers low-latency data suitable for numerical weather prediction (NWP). However, the dense along-track sampling of GNSS-R winds poses challenges for observation error specification in data assimilation. In this study, FY-3E GNSS-R winds are assimilated into the Weather Research and Forecasting (WRF) model to investigate the impacts of different observation error configurations. Both static and dynamic error specifications, with and without data thinning, are evaluated through a sensitivity experiment and subsequent Observing System Experiments (OSEs). The results indicate that using a static observation error of 6 m/s without data thinning achieves the best performance. Under this configuration, GNSS-R winds influence atmospheric analyses from the surface up to approximately 700 hPa in a single assimilation case, while cycling experiments further extend the impact vertically and spatially. These findings highlight the importance of appropriate observation error specification for dense GNSS-R data and provide a practical reference for their assimilation in WRF, with potential applicability to other NWP systems. [ABSTRACT FROM AUTHOR]
Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Impact Study of Assimilating Fengyun-3 GNSS-R Ocean Surface Winds in the Weather Research and Forecasting Model: Sensitivity Analysis on Observation Error Specifications.
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  Data: <searchLink fieldCode="AR" term="%22Wang%2C+Guanyi%22">Wang, Guanyi</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Bai%2C+Weihua%22">Bai, Weihua</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Huang%2C+Feixiong%22">Huang, Feixiong</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> huangfeixiong@nssc.ac.cn</i><br /><searchLink fieldCode="AR" term="%22Sun%2C+Yueqiang%22">Sun, Yueqiang</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Xia%2C+Junming%22">Xia, Junming</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Xianyi%22">Wang, Xianyi</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Meng%2C+Xiangguang%22">Meng, Xiangguang</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Hu%2C+Peng%22">Hu, Peng</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yin%2C+Cong%22">Yin, Cong</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Tan%2C+Guangyuan%22">Tan, Guangyuan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wu%2C+Ruhan%22">Wu, Ruhan</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Du%2C+Yunlong%22">Du, Yunlong</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Meng%2C+Xiaofeng%22">Meng, Xiaofeng</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. Jun2026, Vol. 18 Issue 12, p1892. 20p.
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  Data: <searchLink fieldCode="DE" term="%22Data+assimilation%22">Data assimilation</searchLink><br /><searchLink fieldCode="DE" term="%22Measurement+errors%22">Measurement errors</searchLink><br /><searchLink fieldCode="DE" term="%22Numerical+weather+forecasting%22">Numerical weather forecasting</searchLink><br /><searchLink fieldCode="DE" term="%22Sensitivity+analysis%22">Sensitivity analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Global+Positioning+System%22">Global Positioning System</searchLink><br /><searchLink fieldCode="DE" term="%22Atmospheric+boundary+layer%22">Atmospheric boundary layer</searchLink><br /><searchLink fieldCode="DE" term="%22Weather+forecasting%22">Weather forecasting</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+satellites%22">Artificial satellites</searchLink>
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  Label: Geographic Terms
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  Data: <searchLink fieldCode="DE" term="%22China%22">China</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Highlights: What are the main findings? Optimal assimilation of FY-3E GNSS-R winds in WRF is achieved using a static observation error of 6 m/s without data thinning. GNSS-R wind assimilation significantly improves atmospheric analyses, with impacts extending from the surface up to 700 hPa in a sensitivity experiment and higher levels in cycling OSEs. What are the implications of the main findings? The dense along-track sampling of GNSS-R observations requires careful observation error specification, which plays a critical role in data assimilation. The observation error configuration and OSEs provide a practical reference for assimilating GNSS-R winds in WRF, and can be extended to other NWP systems. The Global Navigation Satellite System Reflectometry (GNSS-R) technique provides global ocean surface wind observations unaffected by rainfall with high spatiotemporal resolution. The Fengyun-3E (FY-3E) mission, as the first operational GNSS-R satellite in China, offers low-latency data suitable for numerical weather prediction (NWP). However, the dense along-track sampling of GNSS-R winds poses challenges for observation error specification in data assimilation. In this study, FY-3E GNSS-R winds are assimilated into the Weather Research and Forecasting (WRF) model to investigate the impacts of different observation error configurations. Both static and dynamic error specifications, with and without data thinning, are evaluated through a sensitivity experiment and subsequent Observing System Experiments (OSEs). The results indicate that using a static observation error of 6 m/s without data thinning achieves the best performance. Under this configuration, GNSS-R winds influence atmospheric analyses from the surface up to approximately 700 hPa in a single assimilation case, while cycling experiments further extend the impact vertically and spatially. These findings highlight the importance of appropriate observation error specification for dense GNSS-R data and provide a practical reference for their assimilation in WRF, with potential applicability to other NWP systems. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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    Identifiers:
      – Type: doi
        Value: 10.3390/rs18121892
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      – Code: eng
        Text: English
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        PageCount: 20
        StartPage: 1892
    Subjects:
      – SubjectFull: Data assimilation
        Type: general
      – SubjectFull: Measurement errors
        Type: general
      – SubjectFull: Numerical weather forecasting
        Type: general
      – SubjectFull: Sensitivity analysis
        Type: general
      – SubjectFull: Global Positioning System
        Type: general
      – SubjectFull: Atmospheric boundary layer
        Type: general
      – SubjectFull: Weather forecasting
        Type: general
      – SubjectFull: Artificial satellites
        Type: general
      – SubjectFull: China
        Type: general
    Titles:
      – TitleFull: Impact Study of Assimilating Fengyun-3 GNSS-R Ocean Surface Winds in the Weather Research and Forecasting Model: Sensitivity Analysis on Observation Error Specifications.
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              Text: Jun2026
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