Numerical weather modeling-based slant tropospheric delay estimation and its enhancement by GNSS data

Abstract

Unmitigated tropospheric delay is one of the major error sources in precise point positioning (PPP). Precise Slant Tropospheric Delay (STD) estimation could help to provide cleaner observables for PPP, and improve its convergence, accuracy, and stability.STD is difficult to model accurately due to the rapid spatial and temporal variation of the water vapor in the troposphere. In the traditional approach, the STD is mapped from the zenith direction, which assumes a spherically symmetric local tropospheric profile and has limitations. In this paper, a new approach of directly estimating the STD from high resolution numerical weather modeling (NWM) products is introduced. This approach benefits from the best available meteorological information to improve real time STD estimation, with the RMS residual lower than 3.5 cm above 15° elevation, and 2 cm above 30°. Therefore, the new method can provide sufficient accuracy to improve PPP convergence time. To improve the performance of the new method in highly variable tropospheric conditions, a correction scheme is proposed which combines NWM information with multi-GNSS observations from a network of local continuously operating reference stations. It is demonstrated through a case study that this correction scheme is quite effective in reducing the STD estimation residuals and PPP convergence time.

Keywords:

• slant troposphere delay
• numerical weather modeling (NWM)
• precise point positioning (PPP)
• multi GNSS

Acknowledgments

This study is carried out as part of the project Innovative Navigation using new GNSS Signals with Hybridized Technologies (iNsight), which is funded by the UK Engineering and Physical Sciences Research Council (EPSRC). The authors acknowledge the support from the project academic partners, University College of London, Imperial College London, and University of Westminster, and also the project industry partners, Ordnance Survey, ST Microsystems, QinetiQ, EADS Astrium, Thales Research and Technology, Leica Geosystems, Civil Aviation Authority and Nottingham Scientific Limited.

The authors also acknowledge the support of the British Atmospheric Data Center (BADC) and the NERC British Isles continuous GNSS Facility(BIGF),for the NWM and GNSS archived data, respectively, and the authors of the PPP processing software suit POINT and PANDA, which have been used as the development and quality checking tools, respectively, in this paper.