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.

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grid random walk process noise troposphere centres for environmental prediction yearly international gnss service igs stations archived vmf1g data temporal variation monthly means of the difference of zwd ztd product gnss processing zenith hydrostatic delay numerical weather prediction models water vapor receiver clock time lapsed global forecast system model

.Optimum stochastic modeling for GNSS tropospheric delay estimation in real-time. (),1–13.

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

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