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Remote Sensing
Volume 18
Issue 12
10.3390/rs18121892
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

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

by
Guanyi Wang
1,2,†,
Weihua Bai
1,2,†,
Feixiong Huang
1,*,
Yueqiang Sun
1,
Junming Xia
1,
Xianyi Wang
1,
Xiangguang Meng
1,
Peng Hu
1,
Cong Yin
1,
Guangyuan Tan
1,
Ruhan Wu
1,2,
Yunlong Du
1,2 and
Xiaofeng Meng
1,2
1
Beijing Key Laboratory of Space Environment Exploration, National Space Science Center, Chinese Academy of Sciences (NSSC/CAS), Beijing 100190, China
2
School of Astronomy and Space Science, University of Chinese Academy of Sciences, Beijing 100049, China
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Remote Sens. 2026, 18(12), 1892; https://doi.org/10.3390/rs18121892 (registering DOI)
Submission received: 9 April 2026 / Revised: 1 June 2026 / Accepted: 5 June 2026 / Published: 8 June 2026
(This article belongs to the Special Issue Applications of Multi-Instrument Remote Sensing in Climate Change and Sustainability Monitoring)
Versions Notes

Abstract

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.
Keywords: GNSS-R; observation error; OSEs GNSS-R; observation error; OSEs

Share and Cite

MDPI and ACS Style

Wang, G.; Bai, W.; Huang, F.; Sun, Y.; Xia, J.; Wang, X.; Meng, X.; Hu, P.; Yin, C.; Tan, G.; et al. Impact Study of Assimilating Fengyun-3 GNSS-R Ocean Surface Winds in the Weather Research and Forecasting Model: Sensitivity Analysis on Observation Error Specifications. Remote Sens. 2026, 18, 1892. https://doi.org/10.3390/rs18121892

AMA Style

Wang G, Bai W, Huang F, Sun Y, Xia J, Wang X, Meng X, Hu P, Yin C, Tan G, et al. Impact Study of Assimilating Fengyun-3 GNSS-R Ocean Surface Winds in the Weather Research and Forecasting Model: Sensitivity Analysis on Observation Error Specifications. Remote Sensing. 2026; 18(12):1892. https://doi.org/10.3390/rs18121892

Chicago/Turabian Style

Wang, Guanyi, Weihua Bai, Feixiong Huang, Yueqiang Sun, Junming Xia, Xianyi Wang, Xiangguang Meng, Peng Hu, Cong Yin, Guangyuan Tan, and et al. 2026. "Impact Study of Assimilating Fengyun-3 GNSS-R Ocean Surface Winds in the Weather Research and Forecasting Model: Sensitivity Analysis on Observation Error Specifications" Remote Sensing 18, no. 12: 1892. https://doi.org/10.3390/rs18121892

APA Style

Wang, G., Bai, W., Huang, F., Sun, Y., Xia, J., Wang, X., Meng, X., Hu, P., Yin, C., Tan, G., Wu, R., Du, Y., & Meng, X. (2026). Impact Study of Assimilating Fengyun-3 GNSS-R Ocean Surface Winds in the Weather Research and Forecasting Model: Sensitivity Analysis on Observation Error Specifications. Remote Sensing, 18(12), 1892. https://doi.org/10.3390/rs18121892

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