Abstract

In GNSS analysis, the tropospheric delay is parameterized by applying mapping functions (MFs), zenith delays, and tropospheric gradients. Thereby, the wet and hydrostatic MF are derived under the assumption of a spherically layered atmosphere. The coefficients of the closed-form expression are computed utilizing a climatology or numerical weather model (NWM) data. In this study, we analyze the impact of tropospheric mismodelling on estimated parameters in precise point positioning (PPP). To do so, we mimic PPP in an artificial environment, i.e., we make use of a linearized observation equation, where the observed minus modelled term equals ray-traced tropospheric delays from a high-resolution NWM. The estimated parameters (station coordinates, clocks, zenith delays, and tropospheric gradients) are then compared with the known values. The simulation study utilized a cut-off elevation angle of 3° and the standard downweighting of low elevation angle observations. The results are representative of a station located in central Europe and the warm season. In essence, when climatology is utilized in GNSS analysis, the root mean square error (RMSE) of the estimated zenith delay and station up-component equal about 2.9 mm and 5.7 mm, respectively. The error of the GNSS estimates can be reduced significantly if the correct zenith hydrostatic delay and the correct hydrostatic MF are utilized in the GNSS analysis. In this case, the RMSE of the estimated zenith delay and station up-component is reduced to about 2.0 mm and 2.9 mm, respectively. The simulation study revealed that the choice of wet MF, when calculated under the assumption of a spherically layered troposphere, does not matter too much. In essence, when the ‘correct’ wet MF is utilized in the GNSS analysis, the RMSE of the estimated zenith delay and station up-component remain at about 1.8 mm and 2.4 mm, respectively. Finally, as a by-product of the simulation study, we developed a modified wet MF, which is no longer based on the assumption of a spherically layered atmosphere. We show that with this modified wet MF in the GNSS analysis, the RMSE of the estimated zenith delay and station up-component can be reduced to about 0.5 mm and 1.0 mm, respectively. In practice, its success depends on the ability of current (future) NWM to predict the fourth coefficient of the developed closed-form expression. We provide some evidence that current NWMs are able to do so.

Highlights

  • In GNSS analysis the signal travel time delay induced by the neutral atmosphere between the satellite and the station, known as tropospheric delay, is approximated by utilizing mapping functions (MFs), zenith delays, and tropospheric gradient components [1]

  • The zenith delays can be related to the precipitable water vapor (PWV) above the considered station [3], and the gradient components can be roughly related to the horizontal PWV gradient at the respective stations [4]

  • We will only show and discuss the impact of the tropospheric mismodelling on the estimated station coordinates and tropospheric parameters, as they are the parameters of interest in precise positioning and atmospheric remote sensing

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Summary

Introduction

In GNSS analysis the signal travel time delay induced by the neutral atmosphere between the satellite and the station, known as tropospheric delay, is approximated by utilizing mapping functions (MFs), zenith delays, and tropospheric gradient components [1].The so-called hydrostatic and wet MF (the ratio of slant and zenith delays) are derived under the assumption of a spherically layered atmosphere. In one of the most demanding GNSS applications, known as precise point positioning (PPP) [2], the station coordinates are estimated with cm-level accuracy To reach this level of accuracy in the positioning domain, the inaccuracy of the underlying climatology or NWM must be taken into account. The zenith delays can be related to the precipitable water vapor (PWV) above the considered station [3], and the gradient components can be roughly related to the (first-order) horizontal PWV gradient at the respective stations [4]. These relations explain the interest of meteorology and climate research in GNSS-based atmospheric data [5]

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