Wind speed retrieval using GNSS-R technique with geographic partitioning
Abstract In this paper, the effect of geographical location on Cyclone Global Navigation Satellite System (CYGNSS) observables is demonstrated for the first time. It is found that the observables corresponding to the same wind speed vary with geographic location regularly. Although latitude and long...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2023-02-01
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| Series: | Satellite Navigation |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s43020-022-00093-z |
| _version_ | 1811165613770407936 |
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| author | Zheng Li Fei Guo Fade Chen Zhiyu Zhang Xiaohong Zhang |
| author_facet | Zheng Li Fei Guo Fade Chen Zhiyu Zhang Xiaohong Zhang |
| author_sort | Zheng Li |
| collection | DOAJ |
| description | Abstract In this paper, the effect of geographical location on Cyclone Global Navigation Satellite System (CYGNSS) observables is demonstrated for the first time. It is found that the observables corresponding to the same wind speed vary with geographic location regularly. Although latitude and longitude information is included in the conventional method, it cannot effectively reduce the errors caused by geographic differences due to the non-monotonic changes of observables with respect to latitude and longitude. Thus, an improved method for Global Navigation Satellite System Reflectometry (GNSS-R) wind speed retrieval that takes geographical differences into account is proposed. The sea surface is divided into different areas for independent wind speed retrieval, and the training set is resampled by considering high wind speed. To balance between the retrieval accuracies of high and low wind speeds, the results with the random training samples and the resampling samples are fused. Compared with the conventional method, in the range of 0–20 m/s, the improved method reduces the Root Mean Square Error (RMSE) of retrieved wind speeds from 1.52 to 1.34 m/s, and enhances the correlation coefficient from 0.86 to 0.90; while in the range of 20–30 m/s, the RMSE decreases from 8.07 to 4.06 m/s, and the correlation coefficient increases from 0.04 to 0.45. Interestingly, the SNR observations are moderately correlated with marine gravities, showing correlation coefficients of 0.5–0.6, which may provide a useful reference for marine gravity retrieval using GNSS-R in the future. |
| first_indexed | 2024-04-10T15:40:33Z |
| format | Article |
| id | doaj.art-0a104f6ef9bf40d4a470bc456d71634a |
| institution | Directory Open Access Journal |
| issn | 2662-9291 2662-1363 |
| language | English |
| last_indexed | 2024-04-10T15:40:33Z |
| publishDate | 2023-02-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Satellite Navigation |
| spelling | doaj.art-0a104f6ef9bf40d4a470bc456d71634a2023-02-12T12:25:25ZengSpringerOpenSatellite Navigation2662-92912662-13632023-02-014111510.1186/s43020-022-00093-zWind speed retrieval using GNSS-R technique with geographic partitioningZheng Li0Fei Guo1Fade Chen2Zhiyu Zhang3Xiaohong Zhang4School of Geodesy and Geomatics, Wuhan UniversitySchool of Geodesy and Geomatics, Wuhan UniversitySchool of Geodesy and Geomatics, Wuhan UniversitySchool of Geodesy and Geomatics, Wuhan UniversitySchool of Geodesy and Geomatics, Wuhan UniversityAbstract In this paper, the effect of geographical location on Cyclone Global Navigation Satellite System (CYGNSS) observables is demonstrated for the first time. It is found that the observables corresponding to the same wind speed vary with geographic location regularly. Although latitude and longitude information is included in the conventional method, it cannot effectively reduce the errors caused by geographic differences due to the non-monotonic changes of observables with respect to latitude and longitude. Thus, an improved method for Global Navigation Satellite System Reflectometry (GNSS-R) wind speed retrieval that takes geographical differences into account is proposed. The sea surface is divided into different areas for independent wind speed retrieval, and the training set is resampled by considering high wind speed. To balance between the retrieval accuracies of high and low wind speeds, the results with the random training samples and the resampling samples are fused. Compared with the conventional method, in the range of 0–20 m/s, the improved method reduces the Root Mean Square Error (RMSE) of retrieved wind speeds from 1.52 to 1.34 m/s, and enhances the correlation coefficient from 0.86 to 0.90; while in the range of 20–30 m/s, the RMSE decreases from 8.07 to 4.06 m/s, and the correlation coefficient increases from 0.04 to 0.45. Interestingly, the SNR observations are moderately correlated with marine gravities, showing correlation coefficients of 0.5–0.6, which may provide a useful reference for marine gravity retrieval using GNSS-R in the future.https://doi.org/10.1186/s43020-022-00093-zCYGNSSGeographical differencesOcean wind speedGNSS reflectometryMarine gravity |
| spellingShingle | Zheng Li Fei Guo Fade Chen Zhiyu Zhang Xiaohong Zhang Wind speed retrieval using GNSS-R technique with geographic partitioning Satellite Navigation CYGNSS Geographical differences Ocean wind speed GNSS reflectometry Marine gravity |
| title | Wind speed retrieval using GNSS-R technique with geographic partitioning |
| title_full | Wind speed retrieval using GNSS-R technique with geographic partitioning |
| title_fullStr | Wind speed retrieval using GNSS-R technique with geographic partitioning |
| title_full_unstemmed | Wind speed retrieval using GNSS-R technique with geographic partitioning |
| title_short | Wind speed retrieval using GNSS-R technique with geographic partitioning |
| title_sort | wind speed retrieval using gnss r technique with geographic partitioning |
| topic | CYGNSS Geographical differences Ocean wind speed GNSS reflectometry Marine gravity |
| url | https://doi.org/10.1186/s43020-022-00093-z |
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