Assessment of GPT2 Empirical Troposphere Model and Application Analysis in Precise Point Positioning

  • Weirong ChenEmail author
  • Chengfa Gao
  • Shuguo Pan
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 304)


Precise Point Positioning (PPP) has been demonstrated to be a powerful tool in geodetic applications, such as deformation monitoring. Troposphere delay is an important error source which directly affects positioning accuracy in height direction. At the end of 2012, an improved model named Global Pressure and Temperature 2 (GPT2) was proposed. Compared with early empirical models, this new model mainly eliminates the weakness of limited spatial and temporal variability. In this study, we assess the precision of GPT2 model and apply it in PPP analysis. The analysis data of VMF1, which is produced using European Centre for Medium-Range Weather Forecasts (ECMWF), provides the nearly true value of zenith delays and mapping function for International GNSS Service (IGS) stations. Therefore a globally distributed set of 11 IGS stations is chosen to validate GPT2 model. In the case of using GPT2 as a priori model while the residual zenith delay still estimated with other unknown parameters, it would improve the zenith troposphere delay adjustments to nearly zero-mean. Therefore GPT2 is helpful to improve the efficiency in PPP data processing. However, GPT2 model is only resting upon a global 5° grid and sufficient global troposphere models are not yet available. Due to the complexity of wet zenith delay, PPP height solutions would be unsatisfactory when residual troposphere delay is not parameterized. We conclude that GPT2 is capable of predicting troposphere delay worldwide with an acceptable uncertainty. And GPT2-based PPP solution performs well only in the case of regarding residual model error as an additional unknown parameter.


Global pressure and temperature 2 Zenith troposphere delay Mapping function Precise point positioning Global navigation satellite system 


  1. 1.
    Zumberge JF, Heflin MB, Jefferson DC, Watkins MM, Webb FH (1997) Precise point positioning for the efficient and robust analysis of GPS data from large networks. J Geophys Res 102(B3):5005–5017CrossRefGoogle Scholar
  2. 2.
    Kouba J, Héroux P (2001) Precise point positioning using IGS orbit and clock products. GPS Solutions 5(2):12–28CrossRefGoogle Scholar
  3. 3.
    Hu W, Gao C (2002) GPS measurement principles and applications, 1st edn. China Communications Press, Beijing, pp 96–97Google Scholar
  4. 4.
    Leick A (1995) GPS satellite surveying, 2nd edn. Wiley, New York 220Google Scholar
  5. 5.
    Xu G (2007) GPS: theory, algorithms, and applications, 2nd edn. Springer, BerlinGoogle Scholar
  6. 6.
    McCarthy DD, Petit G (2004) IERS conventions (2003). In: International earth rotation and reference systems service (IERS), GermanyGoogle Scholar
  7. 7.
    Saastamoinen J (1972) Atmospheric correction for the troposphere and stratosphere in radio ranging satellites. Geophys Monogr Ser 15:247–251Google Scholar
  8. 8.
    Davis JL, Herring TA, Shapiro II, Rogers AEE, Elgered G (1985) Geodesy by radio interferometry: effects of atmospheric modeling errors on estimates of baseline length. Radio Sci 20(6):1593–1607CrossRefGoogle Scholar
  9. 9.
    Böhm J, Heinkelmann R, Schuh H (2007) Short note: a global model of pressure and temperature for geodetic applications. J Geodesy 81(10):679–683CrossRefzbMATHGoogle Scholar
  10. 10.
    Kouba J (2009) Testing of global pressure/temperature (GPT) model and global mapping function (GMF) in GPS analyses. J Geodesy 83(3–4):199–208CrossRefGoogle Scholar
  11. 11.
    Lagler K, Schindelegger M, Böhm J, Krásná H, Nilsson T (2013) GPT2: empirical slant delay model for radio space geodetic techniques. Geophys Res Lett 40:1069–1073CrossRefGoogle Scholar
  12. 12.
    Boehm J, Werl B, Schuh H (2006) Troposphere mapping functions for GPS and very long baseline interferometry from European centre for medium-range weather forecasts operational analysis data. J Geophys Res 111:B02406Google Scholar
  13. 13.
    Tregoning P, Herring TA (2006) Impact of a priori zenith hydrostatic delay errors on GPS estimates of station heights and zenith total delays. Geophys Res Lett 33:L23303CrossRefGoogle Scholar
  14. 14.
    Penna N, Dodson A, Chen W (2001) Assessment of EGNOS tropospheric correction model. J Navig 54(1):37–55CrossRefGoogle Scholar
  15. 15.
    Mendes VB, Prates G, Pavlis EC, Pavlis DE, Langley RB (2002) Improved mapping functions for atmospheric refraction correction in SLR. Geophys Res Lett 29(10):1414CrossRefGoogle Scholar
  16. 16.
    Niell AE (1996) Global mapping functions for the atmosphere delay at radio wavelengths. J Geophys Res 101(B1):3227–3246CrossRefGoogle Scholar
  17. 17.
    Böhm J, Niell A, Tregoning P, Schuh H (2006) Global mapping function (GMF): a new empirical mapping function based on numerical weather model data. Geophys Res Lett 33:L07304Google Scholar
  18. 18.
    Kouba J (2008) Implementation and testing of the gridded Vienna mapping function 1 (VMF1). J Geodesy 82(4–5):193–205CrossRefGoogle Scholar
  19. 19.
    Urquhart L, Nievinski FG, Santos MC (2013) Assessment of troposphere mapping functions using three-dimensional ray-tracing. GPS Solutions 1–10. doi: 10.1007/s10291-013-0334-8
  20. 20.
    Boehm J, Kouba J, Schuh H (2009) Forecast Vienna mapping functions 1 for real-time analysis of space geodetic observations. J Geodesy 83(5):397–401CrossRefGoogle Scholar
  21. 21.
    Wu JT, Wu SC, Hajj GA (1993) Effects of antenna orientation on GPS carrier phase. Man Geodetica, 18:91–98Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.School of TransportationSoutheast UniversityNanjingChina
  2. 2.School of Instrument Science and EngineeringSoutheast UniversityNanjingChina

Personalised recommendations