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Open AccessArticle

A Method to Improve the Distribution of Observations in GNSS Water Vapor Tomography

by Fei Yang 1,2,3, Jiming Guo 1,2,3,*, Junbo Shi 1,2, Lv Zhou 1,4, Yi Xu 1 and Ming Chen 1,5
School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China
Key Laboratory of Precise Engineering and Industry Surveying of National Administration of Surveying, Mapping and Geoinformation, Wuhan University, Wuhan 430079, China
Research Center for High Accuracy Location Awareness, Wuhan University, Wuhan 430079, China
College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541004, China
National Geomatics Center of China, Beijing 100830, China
Author to whom correspondence should be addressed.
Sensors 2018, 18(8), 2526;
Received: 18 June 2018 / Revised: 28 July 2018 / Accepted: 30 July 2018 / Published: 2 August 2018
(This article belongs to the Special Issue High-Precision GNSS in Remote Sensing Applications)
Water vapor is an important driving factor in the related weather processes in the troposphere, and its temporal-spatial distribution and change are crucial to the formation of cloud and rainfall. Global Navigation Satellite System (GNSS) water vapor tomography, which can reconstruct the water vapor distribution using GNSS observation data, plays an increasingly important role in GNSS meteorology. In this paper, a method to improve the distribution of observations in GNSS water vapor tomography is proposed to overcome the problem of the relatively concentrated distribution of observations, enable satellite signal rays to penetrate more tomographic voxels, and improve the issue of overabundance of zero elements in a tomographic matrix. Numerical results indicate that the accuracy of the water vapor tomography is improved by the proposed method when the slant water vapor calculated by GAMIT is used as a reference. Comparative results of precipitable water vapor (PWV) and water vapor density (WVD) profiles from radiosonde station data indicate that the proposed method is superior to the conventional method in terms of the mean absolute error (MAE), standard deviations (STD), and root-mean-square error (RMS). Further discussion shows that the ill-condition of tomographic equation and the richness of data in the tomographic model need to be discussed separately. View Full-Text
Keywords: GNSS remote sensing; atmospheric sounding; water vapor tomography; meteorology GNSS remote sensing; atmospheric sounding; water vapor tomography; meteorology
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MDPI and ACS Style

Yang, F.; Guo, J.; Shi, J.; Zhou, L.; Xu, Y.; Chen, M. A Method to Improve the Distribution of Observations in GNSS Water Vapor Tomography. Sensors 2018, 18, 2526.

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