Earth, Planets and Space

, Volume 61, Issue 7, pp 845–852 | Cite as

Effect of troposphere slant delays on regional double difference GPS processing

  • Maaria Nordman
  • Reima Eresmaa
  • Johannes Boehm
  • Markku Poutanen
  • Hannu Koivula
  • Heikki Järvinen
Open Access


The demand for geodetic time series that are accurate and stable is increasing. One factor limiting their accuracy is troposphere refraction, which is hard to model and compute with sufficient resolution, both in time and space. We have studied the effect of numerical weather model (NWM)-derived troposphere slant delays and the most commonly used mapping functions, Niell and Vienna, on Global Positioning System (GPS) processing. Six months of data were calculated for a regional Finnish network, FinnRef, which consists of 13 stations, using Bernese v. 5.0 in double difference mode. The results showed that when site-specific troposphere zenith delays or gradients are not estimated, the use of NWM-based troposphere delays improved the repeatabilities of all three components of station positions (north, east and up) statistically significantly and up to 60%. The more realistic troposphere model also reduces the baseline length dependence of the solution. When site-specific troposphere delays and the horizontal gradients were estimated, there was no statistically significant improvement between the different solutions.

Key words

Troposphere slant delays geodetic time series GPS processing double difference mode 


  1. Bock, O., J. Tarniewicz, Ch. Thom, and J. Pelon, The effect of inhomogeneities in the lower atmosphere on coordinates determined from GPS measurements, Phys. Chem. Earth, 27, 323–328, 2002.CrossRefGoogle Scholar
  2. Boehm, J. and H. Schuh, Vienna mapping functions in VLBI analyses, Geophys. Res. Lett., 31, L01603, doi:10.1029/2003GL018984, 2004.CrossRefGoogle Scholar
  3. Boehm, J., B. Werl, and H. Schuh, 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, B02406, doi:10.1029/2005JB003629, 2006a.Google Scholar
  4. Boehm, J., A. Niell, P. Tregoning, and H. Schuh, Global Mapping Function (GMF): A new empirical mapping function based on numerical weather model data, Geophys. Res. Lett., 33, L07304, doi:10. 1029/2005GL025546, 2006b.CrossRefGoogle Scholar
  5. Boucher, C., Z. Altamimi, P. Sillard, and M. Feissel-Vernier, The ITRF2000 (IERS Technical Note; 31) Frankfurt am Main: Verlag des Bundesamts für Kartographie und Geodäsie, 289 pp., 2004.Google Scholar
  6. Brandt, S., Data analysis, Statistical and computational methods for scientists and engineers, Springer-Verlag, 1999.Google Scholar
  7. Dach, R., U. Hugentobler, P. Fridez, and M. Meindl (Eds.), Bernese GPS Software, Version 5.0, 612 pp., Astronomical Institute, University of Berne, 2007.Google Scholar
  8. Elgered, G., H.-P. Plag, H. van der Marel, S. Barlag, and J. Nash (Eds.), COST Action 716—Exploitation of ground-based GPS for operational numerical weather prediction and climate applications, European Union, Rep. EUR 21639, COST Office, Brussels, Belgium, 234 pp., 2005.Google Scholar
  9. Eresmaa, R. and H. Järvinen, An observation operator for ground-based GPS slant delays, Tellus, 58A, 131–140, 2006.CrossRefGoogle Scholar
  10. Eresmaa, R., H. Järvinen, M. Nordman, M. Poutanen, J. Syrjärinne, and J.-P. Luntama, Parameterization of tropospheric delay correction for mobile GNSS positioning: a case study of a cold front passage, Meteorol. Applic., 2008 (submitted).Google Scholar
  11. Hobiger, T., R. Ichikawa, T. Takasu, Y. Koyama, and T. Kondo, Ray-traced troposphere slant delays for precise point positioning, Earth Planets Space, 60, e1–e4, 2008.CrossRefGoogle Scholar
  12. Krügel, M., D. Thaller, V. Tesmer, M. Rothacher, D. Angermann, and R. Schmid, Tropospheric parameters: combination studies based on homogeneous VLBI and GPS data, J. Geod., 81, 515–527, doi:10. 1007/s00190-006-0127-8, 2007.CrossRefGoogle Scholar
  13. MacMillan, D. S. and C. Ma, Using meteorological data assimilation models in computing tropospheric delays at microwave frequencies, Phys. Chem. Earth, 23, 97–102, 1998.CrossRefGoogle Scholar
  14. Niell, A. E., Global mapping functions for the atmosphere delay at radio wavelengths, J. Geophys. Res., 101(B2), 3227–3246, 1996.CrossRefGoogle Scholar
  15. Niell, A. E., Preliminary evaluation of atmospheric mapping functions based on numerical weather models, Phys. Chem. Earth, 26, 475–480, 2001.CrossRefGoogle Scholar
  16. Niell, A. E., A. J. Coster, F. S. Solheim, V. B. Mendes, P. C. Toor, R. B. Langley, and C. A. Upham, Comparison of measurements of atmospheric wet delay by radiosonde, water vapor radiometer, GPS, and VLBI, J. Atmos. Oceanic Technol., 18, 830–850, 2001.CrossRefGoogle Scholar
  17. Nordman, M., R. Eresmaa, M. Poutanen, H. Järvinen, H. Koivula, and J.- P. Luntama, Using numerical weather prediction model derived tropospheric slant delays in GPS processing: a case study, Geophys., 43(1–2), 43–51, 2007.Google Scholar
  18. Poutanen, M., J. Jokela, M. Ollikainen, H. Koivula, M. Bilker, and H. Virtanen, Scale variation of GPS time series, in A Window on the Future of Geodesy, IAG General Assembly in Sapporo, Japan 2003, edited by F. Sansò, 15–20, IAG Symposia 128, Springer-Verlag, 2005.Google Scholar
  19. Saastamoinen, J., Contributions to the theory of atmospheric refraction, Bull. Géodésique, 107, 13–34, 1973.CrossRefGoogle Scholar
  20. Simmons, A. J. and J. K. Gibson (Eds.), The ERA-40 Project Plan, ERA- 40 Proj. Rep. Ser. 1, Eur. Cent. for Medium-Range Weather Forecasts, Reading, U.K., 2000.Google Scholar
  21. Snajdrova, K., J. Boehm, P. Willis, R. Haas, and H. Schuh, Multitechnique comparison of tropospheric zenith delays derived during the CONT02 campaign, J. Geod., 79, 613–623, doi:10.1007/s00190-005- 0010-z, 2006.CrossRefGoogle Scholar
  22. Stoyanov, B., R. Haas, and L. Gradinarsky, Calculating mapping functions from the HIRLAM numerical weather prediction model, in International VLBI Service for Geodesy and Astrometry 2004 General Meeting Proceedings, edited by N. R. Vandenberg and K. D. Baver, 471–475, NASA/CP-2004-212255, 2004.Google Scholar
  23. Tesmer, V., J. Boehm, R. Heinkelmann, and H. Schuh, Effect of different tropospheric mapping functions on the TRF, CRF and position timeseries estimated from VLBI, J. Geod., doi:10.1007/s00190-006-0126-9, 2007.Google Scholar
  24. Tregoning, P. and T. van Dam, Atmospheric pressure loading corrections applied to GPS data at the observation level, Geophys. Res. Lett., 32, L22310, doi:10.1029/2005GL024104, 2005.Google Scholar
  25. Troller, M., A. Geiger, E. Brockmann, and H.-G. Kahle, Determination of the spatial and temporal variation of tropospheric water vapour using CGPS networks, Geophys. J. Int., 167, 509–520, doi:10.1111/j.1365- 246X.2006.03101.x, 2006.CrossRefGoogle Scholar
  26. Undén, P., L. Rontu, H. Järvinen, P. Lynch, J. Calvo, G. Cats, J. Cuxart, K. Eerola, C. Fortelius, J. A. Garcia-Moya, C. Jones, G. Lenderlink, A. McDonald, R. McGrath, B. Navascués, N. Woetman Nielsen, V. Ødegaard, E. Rodriguez, M. Rummukainen, R. Rõõm, K. Sattler, B. Hansen Sass, H. Savijärvi, B. Wichers Schreur, R. Sigg, H. The, and A. Tijm, HIRLAM-5 Scientific Documentation, Available from Hirlam-5 Project, c/o Per Undén, SMHI, S-60176, Norrköping, Sweden, 144 pp., 2002.Google Scholar

Copyright information

© The Society of Geomagnetism and Earth, Planetary and Space Sciences (SGEPSS); The Seismological Society of Japan; The Volcanological Society of Japan; The Geodetic Society of Japan; The Japanese Society for Planetary Sciences; TERRAPUB. 2009

Authors and Affiliations

  • Maaria Nordman
    • 1
  • Reima Eresmaa
    • 2
  • Johannes Boehm
    • 3
  • Markku Poutanen
    • 1
  • Hannu Koivula
    • 1
  • Heikki Järvinen
    • 2
  1. 1.Finnish Geodetic InstituteMasalaFinland
  2. 2.Finnish Meteorological InstituteHelsinkiFinland
  3. 3.Institute of Geodesy and GeophysicsVienna University of TechnologyViennaAustria

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