A New Method of GPS Water Vapor Tomography for Maximizing the Use of Signal Rays

Abstract

The spatio-temporal distribution of atmospheric water vapor information can be obtained by global positioning system (GPS) water vapor tomography. GPS signal rays pass through the tomographic area from different boundaries because the scope of the research region (latitude, longitude, and altitude) is designated in the process of tomographic modeling, the influence of the geographic distribution of receivers, and the geometric location of satellite constellations. Traditionally, only signal rays penetrating the entire tomographic area are considered in the computation of water vapor information, whereas those passing through the sides are neglected. Therefore, the accuracy of the tomographic result, especially at the bottom of the area, does not reach its full potential. To solve this problem, this paper proposes a new method that simultaneously considers the discretized tomographic voxels and the troposphere outside the research area as unknown parameters. This method can effectively improve the utilization of existing GPS observations and increase the number of voxels crossed by satellite signals, especially by increasing the proportion of voxels penetrated. A tomographic experiment is implemented using GPS data from the Hong Kong Satellite Positioning Reference Station Network. Compared to the traditional method, the proposed method increases the number of voxels crossed by signal rays and the utilization of the observed data by 15.14% and 19.68% on average, respectively. Numerical results, including comparisons of slant water vapor (SWV), precipitable water vapor (PWV), and water vapor density profile, show that the proposed method is better than traditional methods. In comparison to the water vapor density profile, the root-mean-square error (RMS), mean absolute error (MAE), standard deviation (SD), and bias of the proposed method are 1.39, 1.07, 1.30, and −0.21 gm−3, respectively. For the SWV and PWV comparison, the RMS/MAE of the proposed method are 10.46/8.17 mm and 4.00/3.39 mm, respectively.

Review on the Role of GNSS Meteorology in Monitoring Water Vapor for Atmospheric Physics

“…The technique, apart from being more efficient computationally, improved the results in some cases with respect to other methods. A different method by Yang et al [38] tried to maximize the number of rays by considering water vapor density in the upper region outside the boundaries as four (one per direction) new parameters to obtain. This addition to the traditional method allowed the use of rays that do not come from the top boundary of the study region, but from the sides.…”

“…An experiment in Hong Kong was carried out to test this method, showing better results than traditional tomography. Yang et al [38] also showed that, to improve tomography, it is more relevant to increase the number of voxels crossed by rays than than increasing the number of rays. However, the number of possible approaches is large and, therefore, Brenot et al [39] compared several variations of approaches to tomographic techniques with radiosonde and ECMWF reanalysis.…”

Development of a New Vertical Water Vapor Model for GNSS Water Vapor Tomography

“…The situations described above lead to an ill-conditioned GNSS tomographic model, i.e., the singularity condition, in which an ill-posed problem appears in the inversion of the tomographic equations, so the inverse of the design tomographic matrix cannot be directly obtained [6,7,9,29,30]. Several methods have been developed for solving the ill-posed problem, such as (1) adding inter-spatial constraints [6,8,12,13,[31][32][33][34][35]; (2) adding prior information of WV [31,[36][37][38]; (3) expanding the tomographic area to increase the number of available observations [19,37,[39][40][41][42]; (4) using multi-GNSS observations [41,[43][44][45][46][47]; (5) utilizing other observations [18,[48][49][50].…”

A new hybrid observation GNSS tomography method combining the real and virtual inverted signals

“…Haji-Aghajany et al (2020b) reduce the number of unknown parameters and empty voxels by merging voxels based on the WRF model, which improves the ill-conditioned tomographic equations. In addition, the studies in Yang et al (2019), , Zhao et al (2018), Zhao et al (2020), and Zhang et al (2020b) propose approaches to incorporate the GNSS signals passing through the side face of the tomography area. Zhao et al (2019) utilize observations of GNSS stations located outside the study area, which increases the number of the crossed voxels and the accuracy of the tomography results.…”