# On the Study of Influences of Different Factors on the Rapid Topospheric Tomography
## 1 Introduction  
As the most abundant greenhouse gas, atmospheric water vapor plays an important role in the climate and weather system. Due to the complex variations of water vapor in both spatial and temporal domains, measuring atmospheric water vapor contents precisely is still challenging. Compared to other water vapor measuring tools, the ground-based Global Navigation Satellite System (GNSS) has the advantages of high-accuracy, high temporal resolution, continuous and all-weather operation, which makes ground-based GNSS become a powerful technique for atmosphere sounding [1]. The relatively low temporal resolution of water vapor measurements in the operational system (e.g., radiosonde) is one of the main reasons for the poor forecasting of some extreme weather events (e.g., severe storm). Applications of ground-based GNSS water vapor measurements are therefore very promising in extreme weather forecasting [2; 3]. In addition, signal delays caused by atmospheric watervapor is also one of the main errors in geodetic data analysis such as GNSS [1,4,5], very long base (VLBI) [6,7], and satellite laser ranging (SLR) [7,8]. Information about the water vapor content and variations can therefore be used to correct GNSS and other measurements. Traditionally, in the GNSS data analysis, the Zenith Tropospheric Delay (ZTD) or the Precipitable Water Vapor (PWV) is derived, which represent the vertical integrated tropospheric delays or water vapor content over the GNSS receiver, but information about the vertical distribution of troposphere remains unknown. However, the vertical distribution of humidity fields can be valuable in some applications, such as the study of the convection system construction [9, 10, 11]. [PERSON] et al. [1] proposed the idea of using the ground-based GNSS network to reconstruct the three-dimensional (3D) distribution of water vapor in atmosphere, namely, the tropospheric tomography. The first tropospheric tomography experiment was carried out by [PERSON] et al. [12]. After that, many campaigns or experiments have been conducted over different regions to illustrate the potential of GNSS tomography to provide spatially resolved humidity fields [13, 14, 15]. However, due to the geometric distribution of the ground-based GNSS station and the geometry of the satellite constellation, some voxels in the tomographic region cannot be touched by any GNSS signal ray paths, resulting in a rank-deficient issue in the tropospheric tomography. This issue can be solved by introducing additional constraints, for example, by adding the vertical constraints [16], or taking numerical weather prediction (NWP) model as initial values [17]. Another challenging issue in the tomography is how to efficiently estimate the massive parameters, especially when the scale of the tomographic region grows or in (near) real-time applications. Most of previous troposphere tomography studies aimed at small regions, generally in the scale of several tens of kilometers with relatively dense GNSS networks [12, 18]. There have been few attentions paid to rapid tomography. However, rapid tropospheric tomography can be very useful in some applications, such as providing timely and complete pictures for some extreme weather phenomena, supporting the development of high-resolution mapping functions, and providing consistent tropospheric delay corrections for (near) real-time precise navigation or positioning users. Tomographic results can be affected by many factors. For example, [PERSON] et al. [19] compared various constraint conditions. As results show, with the water vapor changing gently in horizontal direction, the weight matrix of horizontal constraints has a large effect on the tomographic result. [PERSON] et al. [20] combined Global Positioning System (GPS) and Global Navigation Satellite System (GLONASS) precise point positioning (PPP) to obtain the three-dimensional atmospheric water vapor distribution.