# On the Study of Influences of Different Factors on the Rapid Topospheric Tomography
## 6 Conclusions  
Water vapor is a precursor of cloud, rain, snow, sleet, hail, and other precipitations, all of which constitute the basic phenomenon of weather as people experience every day. The water vapor distribution plays a key role in the evolution of atmospheric storm systems and vertical stability of the atmosphere. It is therefore important to get the information of atmospheric water vapor content and distribution. In recent years, GNSS including the US GPS, Russian GLONASS, EU Galileo, and Chinese Beidou has been widely employed as an operational tool for retrieving atmospheric water vapor. Compared to other water vapor measurement tools, the ground-based GNSS has the advantages of high accuracy, high temporal resolution and operations in all weather conditions. The ground-based GNSS can only provide the vertical integral of water vapor or refractivity directly. However, for some applications, the three dimensional of humidity fields are of great importance, and the tropospheric tomography technique can be used for this purpose. Near real-time tropospheric tomography can  
Figure 9: Horizontal cross-section at (**a**) 250 m layer in CS04; (**b**) 2000 m layer in CS04; (**c**) 250 m layer in CS05; (**d**) 2000 m layer in CS05; (**e**) 250 m layer in CS06; (**f**) 2000 m layer in CS06. Triangles denote the to-be-estimated grid nodes. Black dots represent the puncture points of GNSS signals. provide useful information to weather phenomena study and navigation and positioning, but it faces challenges in rank-deficit issue and massive parameter rapid estimation which were solved by using NWP forecasting products and ART technique in this study. The tropospheric tomographic results may be affected by many factors, such as the inclusion of ground meteorological measurements, the multi-GNSS observations, the ground-based station distribution, the tomographic resolution and so on. In order to exclude the discrepancy between the input observations (GNSS-derived slant delays) and the references (i.e., radiosondes or reanalysis products), the input observations for the tomographic system were simulated on the basis of the ERA5 reanalysis products and the reanalysis were then taken as references for the investigation of influences of different factor on tomography. It is found that when adding GPS observation data, compared with only using GFS forecast product as background field, the tomographic results were improved by about 7%, and can be further improved by about 6% at the bottom layers when using the ground meteorological measurements. The inclusion of multi-GNSS observations does not considerably improve the tomographic results compared with GPS-only observations. This is mainly due to the geometry of the tomographic voxel, namely, much wider in horizontal than vertical, resulting in almost unchanged ray touched voxel percentage when adding multi-GNSS observations, and the dispersion of the puncture points of the GNSS signals at different layers does not change significantly. Finally, the effects of station density and grid partition on the results of tomography are investigated. The experiments were carried out with different stations distribution (horizontal separation of 1\({}^{\circ}\), 0.5\({}^{\circ}\) and 0.2\({}^{\circ}\)) and grid horizontal resolution (0.5\({}^{\circ}\) and 0.25\({}^{\circ}\)). Results show that the _Nw_ accuracy can be further improved when the station and the grid partition become denser. However, too dense station distribution and grid partition will significantly increase the burden of tomographic solution. Therefore, in real situations, a compromise needs to be made between the accuracy and computational efficiency, especially for near real-time applications. By excluding the discrepancy between the input observations and references, which are existed in most current studies, the investigation of influences of different factors on the tomographic results in this study can provide some indications and references for tomographic research and applications in the future.