Real-time GNSS tropospheric delay estimation with a novel global random walk processing noise model (GRM)

• Published in: Journal of geodesy Vol. 97; no. 12
• Main Authors: Wu, Zhilu, Lu, Cuixian, Tan, Yuxuan, Zheng, Yuxin, Liu, Yang, Liu, Yanxiong, Jin, Ke
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01-12-2023; Springer Nature B.V
• Subjects: Accuracy; Computation; Early warning systems; Earth and Environmental Science; Earth Sciences; Extreme weather; Geodetics; Geophysics/Geodesy; Modelling; Navigation; Navigation satellites; Navigation systems; Navigational satellites; Original Article; Satellites; Severe weather forecasting; Specificity; Troposphere; Warning systems; Weather forecasting; Zenith tropospheric delay; Random walk processing noise; Global navigation satellite system; Numerical weather model; Real-time
• ISSN: 0949-7714; 1432-1394
• DOI: 10.1007/s00190-023-01780-8

Accurate modeling of tropospheric delays is crucial for the global navigation satellite system (GNSS), which finds extensive applications in early warning systems of natural hazards and extreme weather forecasting. Zenith tropospheric delay (ZTD) is estimated as a random walk process with a constraint in GNSS processing. The constraint, referred to as random walk process noise (RWPN), holds significant importance in real-time ZTD estimation and exhibits geographical and temporal specificity. Presently, RWPN is treated as either a constant value or derived from a numerical weather model (NWM). To address this, our study presents a global RWPN model (GRM) by parameterizing a decade of NWM-derived RWPN data. Taking into account its spatiotemporal nature, we formulate the RWPN equation for each station by employing trigonometric, exponential, and Legendre functions. The optimum RWPN value is determined by incorporating GRM using latitude, longitude, orthometric height, and time as inputs. To validate the efficacy of GRM, we compare its performance against RWPN values derived from both JRA-55 and ERA5 datasets for the year 2020. The results indicate that the GRM-derived values exhibit enhanced accuracy in comparison with the optimal fixed RWPN values, as well as the yearly and monthly mean RWPN values. Additionally, we assess the efficacy of the GRM model in real-time ZTD estimation across 20 globally distributed GNSS stations. The results reveal an improvement exceeding 10% when compared to the results of the best fixed RWPN values. The GRM model offers an effective solution for obtaining accurate RWPN values on a global level, all while minimizing computational demands and time constraints. This notable progress significantly bolsters the precision of real-time GNSS estimates, thus facilitating their application in time-sensitive geophysical and meteorological scenarios.
Highlights
A global RWPN model is introduced for real-time GNSS tropospheric delay estimation. The model provides precise RWPN values while minimizing computation cost and time. The proposed model improves the accuracy of real-time ZTD estimation by over 10%. This model promotes GNSS in time-critical geophysical applications.