Optimum stochastic modeling for GNSS tropospheric delay estimation in real-time

Abstract

In GNSS data processing, the station height, receiver clock and tropospheric delay (ZTD) are highly correlated to each other. Although the zenith hydrostatic delay of the troposphere can be provided with sufficient accuracy, zenith wet delay (ZWD) has to be estimated, which is usually done in a random walk process. Since ZWD temporal variation depends on the water vapor content in the atmosphere, it seems to be reasonable that ZWD constraints in GNSS processing should be geographically and/or time dependent. We propose to take benefit from numerical weather prediction models to define optimum random walk process noise. In the first approach, we used archived VMF1-G data to calculate a grid of yearly and monthly means of the difference of ZWD between two consecutive epochs divided by the root square of the time lapsed, which can be considered as a random walk process noise. Alternatively, we used the Global Forecast System model from National Centres for Environmental Prediction to calculate random walk process noise dynamically in real-time. We performed two representative experimental campaigns with 20 globally distributed International GNSS Service (IGS) stations and compared real-time ZTD estimates with the official ZTD product from the IGS. With both our approaches, we obtained an improvement of up to 10% in accuracy of the ZTD estimates compared to any uniformly fixed random walk process noise applied for all stations.

Spatiotemporal variability of water vapor over Turkey from GNSS observations during 2009–2017 and predictability of ERA-Interim and ARMA model

“…Hence, following ionospheric layer, the tropospheric layer has been playing a challenging role in the Communication, Navigation and Surveillance (CNS) applications by offering neutral atmospheric delays in the traversing signals (Ansari et al 2018). In particular, the modern navigation and positioning operations through the Global Navigation Satellite System (GNSS) signals suffer a considerable range delay error due the water vapor, temperature and pressure content in the troposphere which in turn degrades the positioning precision (Hadas et al 2016). Unlike ionospheric (dispersive) delays, the tropospheric (neutral) delay in dual frequency GNSS receivers cannot be mitigated through linear combination of frequencies method (Panda and Gedam 2016).…”

“…There are hardly any accurate models for the wet component. Hence, the ZWD is usually estimated as unidentified parameters (Hadas et al 2016). Incidentally, the slant wet delay in the GNSS signals can be mapped into zenith direction by using suitable mapping functions like Niell mapping function (Niell 1996) and VMF1 (Boehm et al 2009) etc.…”

Analysis of Factors Affecting Asynchronous RTK Positioning with GNSS Signals

“…The atmosphere error in the ARTK model mainly includes ionosphere and troposphere errors. The change rate of zenith troposphere error is usually under 0.01 mm/s [25,26] and the variation of slant troposphere error will be less than 1 mm during a few seconds of latency for a satellite with 10 • elevation angle. As a result, the time variation of troposphere error in ARTK processing can be neglected for short latency time.…”

GPS signal derived precipitable water vapor and its applications in rainfall prediction

“…In literature, there are no accurate models for determining the wet delays. Therefore, wet delays are estimated as unknowns along with other parameters like station coordinates and receiver clock-offsets during the GNSS processing [6,56,79]. In literature, two different modes are commonly used for processing these observations: 1) double-difference (DD) solution mode and 2) Precise Point Positioning (PPP) mode [6,48,80].…”

“…As we know that the ZW D values are the difference between the two bigger delays ZT D and ZHD, the errors in the ZW D values are mostly from the errors in the estimated ZT D and ZHD values. The reported quality of ZTD estimates from near real-time processing is 3-10 mm [79]. There is a linear dependency between the daily mean of the total electron content (TEC) unit and the estimated vertical position [108].…”