Assessment of tomographic window and sampling rate effects on GNSS water vapor tomography

Abstract

AbstractThe ground-based Global Navigation Satellite System (GNSS) water vapor tomography is increasingly important in GNSS meteorology. As the multi-GNSS and more ground-based GNSS sites can be incorporated into the regional water vapor tomographic model, determining the tomographic window and sampling rate is crucial for the modelling of the water vapor tomography. These two factors affect not only the number of available signal rays from the satellites, but also the number of tomographic voxels crossed by the signal rays. This study uses Hong Kong as the research area to explore the impact of 12 schemes with different tomographic window and sampling rate on the three water vapor tomography methods, including Least squares, Kalman filtering, and Multiplicative Algebraic Reconstruction Technique (MART). Numerical results show that the tomographic results with the three methods get better as the width of the tomographic window decreases and the sampling rate increases in these 12 schemes, and it is found that the Least squares method is most affected by the two factors, followed by Kalman filtering and MART methods. It is recommended to set a tomographic window width of 10 min and a sampling rate of 300 s in a GNSS water vapor tomographic experiment with dense GNSS site like Hong Kong.