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.
Highlights
Atmospheric water vapor comprises only a small percentage of the atmosphere, it plays a key role in a series of weather phenomena [1,2]
The concept of water vapor tomography, which refers to the usage of global positioning system (GPS) signals as scanning rays in the tomographic grid, was first proposed by Braun et al and realized by Flores et al [15,16]
Rohm and Bosy estimated the outer part of the signal rays using the UNB3m model based on a ray-tracing method [17], Notarpietro et al used European center for medium-range weather forecasts (ECMWF) data to calculate the slant water vapor (SWV) outside the research area [19], and Chen and Liu applied the numerical weather prediction (NWP) profile data to estimate the slant wet delay (SWD) outside the modeling area [20]
Summary
Atmospheric water vapor comprises only a small percentage of the atmosphere, it plays a key role in a series of weather phenomena [1,2]. Traditional methods, which use only the first-mentioned signal rays above, reduce the utilization of GPS observation data and the accuracy of the tomographic result, especially at the bottom of the area. To solve this problem, Rohm and Bosy estimated the outer part of the signal rays using the UNB3m model based on a ray-tracing method [17], Notarpietro et al used European center for medium-range weather forecasts (ECMWF) data to calculate the SWV outside the research area [19], and Chen and Liu applied the numerical weather prediction (NWP) profile data to estimate the slant wet delay (SWD) outside the modeling area [20]. This work proposes a new method that does not feature external data and considers and models the troposphere outside the research area as unknown parameters for tomographic modeling to improve the utilization of existing GPS observations and increase the number of voxels crossed by satellite signals. Contrast methods were conducted to verify the performance of the proposed method
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