A Refined Tomographic Window for GNSS-Derived Water Vapor Tomography
Global navigation satellite system (GNSS) tomography can effectively sense the three-dimensional structure of tropospheric water vapor (WV) using the GNSS observations. Numerous studies have utilized a tomographic window to include more epochs of observations, which significantly increases the numbe...
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MDPI AG
2020-09-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/12/18/2999 |
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| author | Yibin Yao Chen Liu Chaoqian Xu Yu Tan Mingshan Fang |
| author_facet | Yibin Yao Chen Liu Chaoqian Xu Yu Tan Mingshan Fang |
| author_sort | Yibin Yao |
| collection | DOAJ |
| description | Global navigation satellite system (GNSS) tomography can effectively sense the three-dimensional structure of tropospheric water vapor (WV) using the GNSS observations. Numerous studies have utilized a tomographic window to include more epochs of observations, which significantly increases the number of valid signals. However, considering the tomography grid limits, a massive number of valid signals inevitably exhibits linear dependence. This dependence makes it impossible to improve the rank score of the tomography coefficient matrix by blindly introducing a large number of valid rays. Furthermore, excessive valid signals may lead to a high condition number in the coefficient matrix (ill-condition problem), which causes unstable results using the GNSS-WV tomography. Considering these problems, we proposed an improved tomographic approach, which applies a refined tomographic window. It differs from the general tomographic window in that the window is refined to traverse the valid signals available 15 min before and after the target epoch while retaining only the linearly independent parts (characteristic signal). Compared to the conventional method, the proposed method can filter the characteristic signal, which increases the rank score of the coefficient matrix and improves the stability of the tomography model. In this paper, we used GNSS observations from the Hong Kong Satellite Positioning Reference Station Network (SatRef) to validate the performance of the proposed method over the day-of-year (DOY) periods of 130–132, 2019 and 146–148, 2019. The numerical results showed that, by using a refined tomographic window, the proposed method obtained superior WV products in comparison with that of the conventional method. |
| first_indexed | 2024-03-10T16:19:31Z |
| format | Article |
| id | doaj.art-5b942a9d386e42c1bb59805efb848ace |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T16:19:31Z |
| publishDate | 2020-09-01 |
| publisher | MDPI AG |
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| series | Remote Sensing |
| spelling | doaj.art-5b942a9d386e42c1bb59805efb848ace2023-11-20T13:47:16ZengMDPI AGRemote Sensing2072-42922020-09-011218299910.3390/rs12182999A Refined Tomographic Window for GNSS-Derived Water Vapor TomographyYibin Yao0Chen Liu1Chaoqian Xu2Yu Tan3Mingshan Fang4School of Geodesy and Geomatics, Wuhan University, 129 Luoyu Road, Wuhan 430079, ChinaSchool of Geodesy and Geomatics, Wuhan University, 129 Luoyu Road, Wuhan 430079, ChinaSchool of Geodesy and Geomatics, Wuhan University, 129 Luoyu Road, Wuhan 430079, ChinaCollege of Civil Engineering and Architecture, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, ChinaZhejiang Zhoushan Northward Great Routeway Company Limited, Anbo Road, Ningbo 315040, ChinaGlobal navigation satellite system (GNSS) tomography can effectively sense the three-dimensional structure of tropospheric water vapor (WV) using the GNSS observations. Numerous studies have utilized a tomographic window to include more epochs of observations, which significantly increases the number of valid signals. However, considering the tomography grid limits, a massive number of valid signals inevitably exhibits linear dependence. This dependence makes it impossible to improve the rank score of the tomography coefficient matrix by blindly introducing a large number of valid rays. Furthermore, excessive valid signals may lead to a high condition number in the coefficient matrix (ill-condition problem), which causes unstable results using the GNSS-WV tomography. Considering these problems, we proposed an improved tomographic approach, which applies a refined tomographic window. It differs from the general tomographic window in that the window is refined to traverse the valid signals available 15 min before and after the target epoch while retaining only the linearly independent parts (characteristic signal). Compared to the conventional method, the proposed method can filter the characteristic signal, which increases the rank score of the coefficient matrix and improves the stability of the tomography model. In this paper, we used GNSS observations from the Hong Kong Satellite Positioning Reference Station Network (SatRef) to validate the performance of the proposed method over the day-of-year (DOY) periods of 130–132, 2019 and 146–148, 2019. The numerical results showed that, by using a refined tomographic window, the proposed method obtained superior WV products in comparison with that of the conventional method.https://www.mdpi.com/2072-4292/12/18/2999GNSS tomographywater vaportomographic windowcharacteristic signalradiosonde |
| spellingShingle | Yibin Yao Chen Liu Chaoqian Xu Yu Tan Mingshan Fang A Refined Tomographic Window for GNSS-Derived Water Vapor Tomography Remote Sensing GNSS tomography water vapor tomographic window characteristic signal radiosonde |
| title | A Refined Tomographic Window for GNSS-Derived Water Vapor Tomography |
| title_full | A Refined Tomographic Window for GNSS-Derived Water Vapor Tomography |
| title_fullStr | A Refined Tomographic Window for GNSS-Derived Water Vapor Tomography |
| title_full_unstemmed | A Refined Tomographic Window for GNSS-Derived Water Vapor Tomography |
| title_short | A Refined Tomographic Window for GNSS-Derived Water Vapor Tomography |
| title_sort | refined tomographic window for gnss derived water vapor tomography |
| topic | GNSS tomography water vapor tomographic window characteristic signal radiosonde |
| url | https://www.mdpi.com/2072-4292/12/18/2999 |
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