【摘 要】
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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
【机 构】
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Shanghai Astronomical Observatory, Chinese Academy of Sciences, Shanghai 200030, China,University of
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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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