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With the gradual improvement of Galileo and the opening of BDS-3 services, water vapor tomography based on multi-GNSS can be effectively carried out to reconstruct three-dimensional water vapor distribution. In this paper, experiments in Hong Kong were conducted to analyze and assess the performances of GPS, BDS, GLONASS, and Galileo and their combinations in water vapor tomography. Numerical results show that the number of available signal rays varies widely in the four satellite systems, and the value can be increased by the combination of satellite systems; the combinations also increase the number of voxels crossed by signal rays, but this value is not directly related to the number of available signal rays; the number and distribution of the voxels with sufficient signal rays, which most closely related to the structure of the tomographic model, show no obvious differences in the four satellite systems and their combinations. Comparative results of slant water vapor (SWV) estimated by GNSS data and water vapor density derived from radiosonde data reveal that the differences in the water vapor tomography of the four satellite systems are small, and their combinations have limited improvement in the tomographic results. [ABSTRACT FROM AUTHOR]

Water vapour is an important substituent of the atmosphere but its spatial and temporal distribution is difficult to detect. Global Positioning System (GPS) water vapour tomography, which can sense three-dimensional water vapour distribution, has been developed as a research area in the field of GPS meteorology. In this paper, a new water vapour tomography method based on a genetic algorithm (GA) is proposed to overcome the ill-conditioned problem. The proposed approach does not need to perform matrix inversion, and it does not rely on excessive constraints, a priori information or external data. Experiments in Hong Kong under rainy and rainless conditions using this approach show that there is a serious ill-conditioned problem in the tomographic matrix by grayscale and condition numbers. Numerical results show that the average root mean square error (RMSE) and mean absolute error (MAE) for internal and external accuracy are 1.52/0.94 and 10.07/8.44 mm, respectively, with the GAMIT-estimated slant water vapour (SWV) as a reference. Comparative results of water vapour density (WVD) derived from radiosonde data reveal that the tomographic results based on GA with a total RMSE / MAE of 1.43/1.19 mm are in good agreement with that of radiosonde measurements. In comparison to the traditional least squares method, the GA can achieve a reliable tomographic result with high accuracy without the restrictions mentioned above. Furthermore, the tomographic results in a rainless scenario are better than those of a rainy scenario, and the reasons are discussed in detail in this paper. [ABSTRACT FROM AUTHOR]

The delays of radio signals transmitted by global navigation satellite system (GNSS) satellites and induced by neutral atmosphere, which are usually represented by zenith tropospheric delay (ZTD), are required as critical information both for GNSS positioning and navigation and GNSS meteorology. Establishing a stable and reliable ZTD model is one of the interests in GNSS research. In this study, we proposed a regional ZTD model that makes full use of the ZTD calculated from regional GNSS data and the corresponding ZTD estimated by global pressure and temperature 3 (GPT3) model, adopting the artificial neutral network (ANN) to construct the correlation between ZTD derived from GPT3 and GNSS observations. The experiments in Hong Kong using Satellite Positioning Reference Station Network (SatRet) were conducted and three statistical values, i.e., bias, root mean square error (RMSE), and compound relative error (CRE) were adopted for our comparisons. Numerical results showed that the proposed model outperformed the parameter ZTD model (Saastamoinen model) and the empirical ZTD model (GPT3 model), with an approximately 56%/52% and 52%/37% RMSE improvement in the internal and external accuracy verification, respectively. Moreover, the proposed method effectively improved the systematic deviation of GPT3 model and achieved better ZTD estimation in both rainy and rainless conditions. [ABSTRACT FROM AUTHOR]