# On the Study of Influences of Different Factors on the Rapid Topospheric Tomography
## 5 Tomography Experiments and Results
### Influences of Ground Meteorological Observation Data  
In order to study the influences of adding ground meteorological data, three cases were carried out, namely, (1) only using GFS data as background field without input observation data (CTRL); (2) using GFS data as background field with GPS SWD as inputs (CS01); (3) using GFS data as background field with GPS observation data and ground meteorological observation data as inputs (CS02). All the tomography input data (SWD and meteorological observations) are simulated following procedures described in Section 3.1. As mentioned previously, the ERA5 product, which is used as the basis for SWD and meteorological observation simulation, is again taken as the reference for tomographic result evaluation. The wet refractivity error profiles derived from CTRL, CS01 and CS02 are presented in Figure 6 where the horizontal axis denotes the vertical layer and the vertical axis is the standard deviation of wet refractive differences for all voxels with GNSS ray passed between tomographic outputs and ERA5-derived _Nw_. It can be seen from Figure 6 that _Nw_ error at the lowest layer (0 km) in CTRL and CS01 are almost the same (about 6.3 mm/km), but _Nw_ error in CS02 at bottom layer decreases to 4.5 mm/km, with improvements of about 28% after adding ground meteorological data. At middle troposphere, for example, the 8 th layer (2 km height), _Nw_ error is about 3.6 mm/km in CTRL and about 3.1 mm/km in both CS01 and CS02. At upper layers, for example, the 21 th layer (10 km), there are no significant differences among three cases due to small amount of water vapor, with _Nw_ errors of only about 0.25 mm/km. This can be explained with the help of Figure 7 where the horizontal cross-section of \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Case** & **Short** & **MET** & **GNSS** & **Horizontal** & **Station Distribution** & **Number** & **Number** & **Computational** \\
**Name** & **Name** & **Obs** & **Systems** & **Resolution** & **of Station** & **of Voxel** & **Time** \\ \hline Control & CTRL & No & No & \(0.5^{\circ}\times 0.5^{\circ}\) & N/A & N/A & 5760 & 1.5 s \\ Case 01 & CS01 & No & GPS & \(0.5^{\circ}\times 0.5^{\circ}\) & Real GNSS stations & 93 & 5760 & 7.5 s \\ Case 02 & CS02 & Yes & GPS & \(0.5^{\circ}\times 0.5^{\circ}\) & Real GNSS stations & 93 & 5760 & 8 s \\  & & GPS + & & GPS + & & & & \\ Case 03 & CS03 & Yes & GLONASS + & \(0.5^{\circ}\times 0.5^{\circ}\) & Real GNSS stations & 93 & 5760 & 21 s \\  & & Galileo + BDS & & & & & & \\ Case 04 & CS04 & Yes & GPS & \(0.5^{\circ}\times 0.5^{\circ}\) & Simulated \(1.0^{\circ}\times 1.0^{\circ}\) & 45 & 5760 & 5 s \\ Case 05 & CS05 & Yes & GPS & \(0.5^{\circ}\times 0.5^{\circ}\) & Simulated \(0.5^{\circ}\times 0.5^{\circ}\) & 153 & 5760 & 14 s \\ Case 06 & CS06 & Yes & GPS & \(0.5^{\circ}\times 0.5^{\circ}\) & Simulated \(0.2^{\circ}\times 0.2^{\circ}\) & 861 & 5760 & 47 s \\ Case 07 & CS07 & Yes & GPS & \(0.25^{\circ}\times 0.25^{\circ}\) & Simulated \(0.2^{\circ}\times 0.2^{\circ}\) & 861 & 21528 & 255 s \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Setup of tomographic experiments. Figure 6: Comparisons of wet refractivity error profiles for CTRL, CS01, CS02, and CS03 for a study of influences of ground observations and ground multi-GNSS observations on tomography. four grids at different layers are taken as examples. Compared to the middle layer (8 th layer with height of 2 km) (Figure 7b), the puncture points of GNSS signals at the bottom layer for each station are more concentrated, indicating the measurements used for estimating values at the grid nodes have stronger correlations, which will result in less contributions to the estimates compared to the middle layer. Therefore, adding the GNSS observations has more significant improvements for the middle layer than the for the bottom layer. For the upper layer, due to the small amount of water vapor, the improvements are not significant although the puncture points are more discretely distributed.