GPS Solutions

, 23:82 | Cite as

GNSS PPP with different troposphere models during severe weather conditions

  • Engin TunalıEmail author
  • Mustafa Tevfik Özlüdemir
Original Article


Global navigation satellite systems (GNSS)-derived zenith wet delays must be estimated precisely for monitoring weather variations and rain passages in the troposphere. We processed a set of International GNSS Service (IGS) stations within the area affected by the central European Flooding 2013 and assessed the performance of post-processed precise point positioning (PPP) during severe weather by applying different troposphere models: the Vienna Mapping Function (VMF1) together with the European Centre for Medium-Range Weather Forecasts grids, the global mapping function with the Global Pressure and Temperature 2, the Niell mapping function with the University of New Brunswick (UNB), and the VMF1 with the UNB/VMF1 from the National Centers for Environmental Prediction numerical weather model (NWM) data. Wet delay estimates from each PPP session have been verified through the IGS final troposphere products, local surface measurements and double-difference (DD) GNSS solutions performed at the same sites. All the PPP solutions agree well with the IGS. The mean residuals are all below 2.0 mm, and the PPP VMF1 performs better with RMS of 4.0 mm. The PPP solutions applying an NWM offer better agreement with the PPP solutions using real surface measurements to model the troposphere. Both PPP and DD VMF1 solutions agree with RMS of 5.4 mm, which allowed PPP to offer a strong alternative to post-processed DD solutions, even during severe weathers. With respect to the daily ITRF08 coordinate solutions, the gridded VMF1-based PPP height repeatability is better for most stations before and after the observations are corrected for atmospheric non-tidal loading effect.


GNSS PPP Zenith wet delay Zenith hydrostatic delay IGS European Flooding 2013 



This study is based on the Ph.D. dissertation of Engin Tunalı, Istanbul Technical University with the title of “Monitoring Tropospheric Water Vapor Variations with PPP during Severe Weather.” The authors would like to thank the International GNSS Service (IGS) (Dow et al. 2009) for the GNSS satellites orbits and clocks. The daily RINEX observations were downloaded from the IGS Data Center at CDDIS. We would like to thank the Astronomical Institute of the University of Bern (AIUB) for the GNSS software package Bernese v.5.2 (Dach et al. 2015) and Center for Orbit Determination in Europe (CODE) for the auxiliary files necessary for the software. The support of these institutions is appreciatively acknowledged. The reviewers are sincerely acknowledged for their valuable comments to make the article better.


  1. Askne J, Nordius H (1987) Estimation of tropospheric delay for microwaves from surface weather data. Radio Sci 22(3):379–386. CrossRefGoogle Scholar
  2. Berg H (1948) Allgemeine meteorologie. Dümmlers Verlag, BonnGoogle Scholar
  3. Bevis M, Businger S, Herring TA, Rocken C, Anthes RA, Ware RH (1992) GPS meteorology: remote sensing of atmospheric water vapor using the global positioning system. J Geophys Res 97(D14):15787–15801CrossRefGoogle Scholar
  4. Blöschl G, Nester T, Komma J, Paraijka J, Perdigao RAP (2013) The June 2013 flood in the Upper Danube Basin, and comparisons with the 2002, 1954 and 1899 floods. Hydrol Earth Syst Sci 17(12):5197–5212. CrossRefGoogle Scholar
  5. Boehm J, Niell A, Tregoning P, Schuh H (2006a) Global mapping function (GMF): a new empirical mapping function based on numerical weather model data. Geophys Res Lett 33(7):L07304. CrossRefGoogle Scholar
  6. Boehm J, Werl B, Schuh H (2006b) Troposphere mapping functions for GPS and very long baseline interferometry from European Centre for Medium-Range Weather Forecasts operational analysis data. J Geophys Res Solid Earth 111(2):B02406. CrossRefGoogle Scholar
  7. Boehm J, Heinkelmann R, Schuh H (2007) Short note: a global model of pressure and temperature for geodetic applications. J Geod 81(10):679–683. CrossRefGoogle Scholar
  8. Boehm J, Heinkelmann R, Schuh H (2009) Neutral atmosphere delays: empirical models versus discrete time series from numerical weather models. In: Drewes H (eds) Geodetic reference frames-IAG symposia, vol 134, Munich, Germany, pp 317–321. CrossRefGoogle Scholar
  9. Böhm J, Möller G, Schindelegger M, Pain G, Weber R (2015) Development of an improved empirical model for slant delays in the troposphere (GPT2w). GPS Solut 19(3):433–441. CrossRefGoogle Scholar
  10. Chen G, Herring TA (1997) Effects of atmospheric azimuthal asymmetry on the analysis of space geodetic data. J Geophys Res Solid Earth. CrossRefGoogle Scholar
  11. COESA (1966) U.S. Standard Atmosphere Supplements, 1966. U.S. Committee on Extension to the Standard Atmosphere. Sponsored by Environmental Science Services Administration, National Aeronautics and Space Administration, United States Air Force and published by the Superintendent of Documents, U.S. Government Printing Office, Washington, DCGoogle Scholar
  12. Collins JP, Langley RB (1997) A tropospheric delay model for the user of the wide area augmentation system. Final contract report for Nav Canada, Department of Geodesy and Geomatics Engineering Technical Report No. 187, University of New Brunswick, Fredericton, N.B., CanadaGoogle Scholar
  13. Dach R, Hugentobler U, Fridez P, Meindl M (2007) Bernese GPS software 5.0Google Scholar
  14. Dach R, Lutz S, Walser P, Fridez P (2015) Bernese GNSS software version 5.2. User manual. Astron Institute, Univ Bern 884(January):884.
  15. 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):1593–1607. CrossRefGoogle Scholar
  16. Dee DP et al (2011) The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Q J R Meteorol Soc 137(656):553–597. CrossRefGoogle Scholar
  17. Dousa J, Elias M (2014) An improved model for calculating tropospheric wet delay. Geophys Res Lett 41(12):4389–4397. CrossRefGoogle Scholar
  18. Dow JM, Neilan RE, Rizos C (2009) The International GNSS Service in a changing landscape of Global Navigation Satellite Systems. J Geod 83(3–4):191–198CrossRefGoogle Scholar
  19. Gao Y, Shen X (2002) A new method for carrier-phase-based precise point positioning. Navigation 49(2):109–116. CrossRefGoogle Scholar
  20. Grams CM, Binder H, Pfahl S, Piaget N, Wernli H (2014) Atmospheric processes triggering the central European floods in June 2013. Nat Hazards Earth Syst Sci 14:1691–1702. CrossRefGoogle Scholar
  21. Grubbs FE (1967) An introduction to probability theory and its applications. Technometrics. CrossRefGoogle Scholar
  22. Karabatić A, Weber R, Haiden T (2011) Near real-time estimation of tropospheric water vapour content from ground based GNSS data and its potential contribution to weather now-casting in Austria. Adv Space Res 47(10):1691–1703. CrossRefGoogle Scholar
  23. Kouba J (2009) Testing of global pressure/temperature (GPT) model and global mapping function (GMF) in GPS analyses. J Geod 83:199–208. CrossRefGoogle Scholar
  24. Kouba J, Héroux P (2001) Precise point positioning using IGS orbit and clock products. GPS Solut 5(2):12–28. CrossRefGoogle Scholar
  25. Lagler K, Schindelegger M, Böhm J, Krasna H, Nilsson T (2013) GPT2: empirical slant delay model for radio space geodetic techniques. Geophys Res Lett 40(6):1069–1073. CrossRefGoogle Scholar
  26. Landskron D, Böhm J (2018) VMF3/GPT3: refined discrete and empirical troposphere mapping functions. J Geod 92(4):349–360. CrossRefGoogle Scholar
  27. Leandro R, Santos M, Langley R (2006) UNB neutral atmosphere models: development and performance. In: Proceedings of ION NTM 2006, Institute of Navigation, Monterrey, CA, January 18–20, pp 564–573Google Scholar
  28. Malys S, Jensen PA (1990) Geodetic point positioning with GPS carrier beat phase data from the CASA UNO experiment. Geophys Res Lett 17(5):651–654CrossRefGoogle Scholar
  29. Niell AE (1996) Global mapping functions for the atmosphere delay at radio wavelengths. J Geophys Res Solid Earth 101(82):3227–3246. CrossRefGoogle Scholar
  30. Petit G, Luzum B (2010) IERS Conventions (2010). Bur Int Des Poids Mes Sevres. CrossRefGoogle Scholar
  31. Petrov L, Boy JP (2004) Study of the atmospheric pressure loading signal in VLBI observations. J Geophys Res 109(B03405):1–14. CrossRefGoogle Scholar
  32. Ray RD, Ponte RM (2003) Barometric tides from ECMWF operational analyses. Ann Geophys 21:1897–1910. CrossRefGoogle Scholar
  33. Rocken C, Van Hove T, Ware R (1997) Near real-time GPS sensing of atmospheric water vapor. Geophys Res Lett 24(3):3221–3224. CrossRefGoogle Scholar
  34. Saastamoinen J (1972) Introduction to practical computation of astronomical refraction. Bull Géodésique 106(1):383–397. CrossRefGoogle Scholar
  35. Schamm K, Ziese M, Becker A, Finger P, Meyer-Christoffer A, Schneider U, Schröder M, Stender P (2014) Global gridded precipitation over land: a description of the new GPCC First Guess Daily product. Earth Syst Sci Data 6(1):49–60. CrossRefGoogle Scholar
  36. Schröter K, Kunz M, Elmer F, Mühr B, Merz B (2015) What made the June 2013 flood in Germany an exceptional event? A hydro-meteorological evaluation. Hydrol Earth Syst Sci 19(1):309–327CrossRefGoogle Scholar
  37. Steigenberger P, Boehm J, Tesmer V (2009) Comparison of GMF/GPT with VMF1/ECMWF and implications for atmospheric loading. J Geod. CrossRefGoogle Scholar
  38. Tesmer V, Boehm J, Heinkelmann R, Schuh H (2007) Effect of different tropospheric mapping functions on the TRF, CRF and position time-series estimated from VLBI. J Geod 81(6–8):409–421. CrossRefGoogle Scholar
  39. 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. CrossRefGoogle Scholar
  40. Tregoning P, Boers R, O’Brien D, Hendy M (1998) Accuracy of absolute precipitable water vapor estimates from GPS observations. J Geophys Res Atmos 103(D22):28701–28710CrossRefGoogle Scholar
  41. Vey S, Dietrich R, Fritsche M, Rülke A, Rothacher M, Steigenberger P (2006) Influence of mapping function parameters on global GPS network analyses: comparisons between NMF and IMF. Geophys Res Lett 33(1):L01814. CrossRefGoogle Scholar
  42. Wijaya DD, Böhm J, Karbon M, Krasna H, Schuh H (2013) Atmospheric pressure loading, Chapter 4 in atmospheric effects in space geodesy. In: Böhm J, Schuh H (eds) Springer-Verlag, BerlinGoogle Scholar
  43. 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):5005–5017. CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Geomatics Engineering DepartmentHacettepe UniversityAnkaraTurkey
  2. 2.Geomatics Engineering DepartmentIstanbul Technical UniversityIstanbulTurkey

Personalised recommendations