Abstract
This paper combines ten tropospheric combined empirical models based on the atmospheric element prediction model of GPT/GPT2, the Saastamoinen and the Modified Hopfield model and the mapping function of VMF1/GMF/NMF, and combines two tropospheric combined numerical weather prediction models based on the pressure-level data of ECMWF. This paper focuses on the impact of different tropospheric models on the positioning and zenith tropospheric delay (ZTD) accuracy of multi-GNSS precise point positioning (PPP) based on International GNSS Monitoring and Assessment System (iGMAS) products. The results show that the accuracy of GPT2+Saastamoinen is 12.69% higher than UNB3M and the accuracy of Numerical Weather Model (NWM) is 63.80% higher than UNB3M based on the data of IGS ZTD. In terms of PPP positioning accuracy, the accuracy of GPT2+VMF1+Modified Hopfield is 5.30% higher than UNB3M and the accuracy of NWM (GMF) is 8.77% higher than UNB3M. This paper gives a reference for the best empirical models of GPT2+VMF1+Modified Hopfield and the best numerical weather prediction model of NWM (GMF) and provides a more accurate tropospheric model for standard point positioning (SPP), PPP, and medium and long baseline positioning.
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Acknowledgements
This work is supported by National Key R&D Program of China (2016YFB0501503-3). The authors gratefully acknowledge iGMAS for providing multi-GNSS precise orbit and clock products. Many thanks to the IGS MGEX for providing the observation data. Many thanks to the ECMWF for providing the meteorological pressure-level data.
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Jiao, G., Song, S., Su, K., Zhou, W. (2019). The Research on Optimal Tropospheric Combined Model Based on Multi-GNSS PPP. In: Sun, J., Yang, C., Yang, Y. (eds) China Satellite Navigation Conference (CSNC) 2019 Proceedings. CSNC 2019. Lecture Notes in Electrical Engineering, vol 563. Springer, Singapore. https://doi.org/10.1007/978-981-13-7759-4_11
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DOI: https://doi.org/10.1007/978-981-13-7759-4_11
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