Real-time retrieval of high-precision ZTD maps using GNSS observation

Abstract

Zenith Tropospheric Delay (ZTD) is an important factor that restricts the high-precision positioning of global navigation satellite system (GNSS), and it is of great significance in establishing a real-time and high-precision ZTD model. However, existing ZTD models only consider the impact of linear terms on ZTD estimation, whereas the nonlinear factors have rarely been investigated before and thus become the focus of this study. A real-time and high-precision ZTD model for large height difference area is proposed by considering the linear and nonlinear characteristics of ZTD spatiotemporal variations and is called the real-time linear and nonlinearity ZTD (RLNZ) model. This model uses the ZTD estimated from the Global Pressure and Temperature 3 (GPT3) model as the initial value. The linear impacts of periodic term and height on the estimation of ZTD difference between GNSS and GPT3 model is first considered. In addition, nonlinear factors such as geographical location and time are further used to fit the remaining nonlinear ZTD residuals using the general regression neural network method. Finally, the RLNZ-derived ZTD is obtained at an arbitrary location. The western United States, with height difference ranging from −500 to 4000 m, is selected, and the hourly ZTD of 484 GNSS stations provided by the Nevada Geodetic Laboratory (NGL) and the data of 9 radiosonde (RS) stations in the year 2021 are used. Experiment results show that a better performance of ZTD estimation can be retrieved from the proposed RLNZ model when compared with the GPT3 model. Statistical results show the averaged root mean square (RMS), Bias, and mean absolute error (MAE) of ZTD from GPT3 and RLNZ models are 33.7/0.8/25.7 mm and 22.6/0.1/17.4 mm, respectively. The average improvement rate of the RLNZ model is 33 % when compared to the GPT3 model. Finally, the application of the proposed RLNZ model in simulated real-time Precise Point Positioning (PPP) indicates that the accuracy of PPP in N, E and U components is improved by 8 %, 2 %, and 6 % when compared with that from the GPT3-based PPP. Meanwhile, the convergence time in N and U components is improved by 23 % and 7 %, respectively. Such results verify the superiority of the proposed RLNZ model in retrieving real-time ZTD maps for GNSS positioning and navigation applications.