Acta Geophysica

, Volume 63, Issue 4, pp 1103–1125 | Cite as

Investigation of the 16-year and 18-year ZTD Time Series Derived from GPS Data Processing

  • Zofia BałdyszEmail author
  • Grzegorz Nykiel
  • Mariusz Figurski
  • Karolina Szafranek
  • Krzysztof KroszczyńSki
Open Access


The GPS system can play an important role in activities related to the monitoring of climate. Long time series, coherent strategy, and very high quality of tropospheric parameter Zenith Tropospheric Delay (ZTD) estimated on the basis of GPS data analysis allows to investigate its usefulness for climate research as a direct GPS product. This paper presents results of analysis of 16-year time series derived from EUREF Permanent Network (EPN) reprocessing performed by the Military University of Technology. For 58 stations Lomb-Scargle periodograms were performed in order to obtain information about the oscillations in ZTD time series. Seasonal components and linear trend were estimated using Least Square Estimation (LSE) and Mann—Kendall trend test was used to confirm the presence of a linear trend designated by LSE method. In order to verify the impact of the length of time series on trend value, comparison between 16 and 18 years were performed.

Key words

GPS ZTD time series troposphere 


  1. Bengtsson, L., S. Hagemann, and K.I. Hodges (2004), Can climate trends be calculated from reanalysis data? J. Geophys. Res. 109, D11, D1111, DOI: 10.1029/2004JD004536.Google Scholar
  2. Bevis, M., S. Businger, T.A. Herring, C. Rocken, R.A. Anthes, and R.H. Ware (1992), GPS meteorology: Remote sensing of atmospheric water vapor using the global positioning system, J. Geophys. Res. 97, D14, 15787–15801, DOI: 10.1029/92JD01517.CrossRefGoogle Scholar
  3. Bock, O., M.-N. Bouin, A. Walpersdorf, J.-P. Lafore, S. Janicot, F. Guichard, and A. Agusti-Panareda (2007), Comparison of ground-based GPS precipitable water vapour to independent observations and numerical weather prediction model reanalyses over Africa, Q. J. Roy. Meteor. Soc. 133, 629, 2011–2027, DOI: 10.1002/qj.185.CrossRefGoogle Scholar
  4. Bock, O., P. Willis, J. Wang, and C. Mears (2014), A high-quality, homogenized, global, long-term (1993-2008) DORIS precipitable water data set for climate monitoring and model verification, J. Geophys. Res.–Atmos. 119, 12, 7209–7230, DOI: 10.1002/2013JD021124.CrossRefGoogle Scholar
  5. Bruyninx, C. (2004), The EUREF Permanent Network: a multi-disciplinary network serving surveyors as well as scientists, GeoInformatics 7, 5, 32–35.Google Scholar
  6. Byun, S.H., and Y.E. Bar-Server (2009), A new type of troposphere zenith path delay product of the international GNSS service, J. Geodesy 83, 3–4, 367–373, DOI: 10.1007/s00190-008-0288-8.Google Scholar
  7. COST (2012), Memorandum of understanding for the implementation of a European Concerted Research Action, COST Action ES1206, Advanced global Navigation Satellite Systems tropospheric products for monitoring severe weather events and climate (GNSS4SWEC), European Cooperation in Science and Technology.Google Scholar
  8. Dach, R., U. Hugentobler, P. Fridez, and M. Meindl (eds.) (2007), Bernese GPS software version 5.0, User manual, Astronomical Institute, University of Bern, Bern, Switzerland.Google Scholar
  9. Figurski, M., P. Kamiński, and A. Kenyeres (2009), Preliminary results of the complete EPN reprocessing computed by the MUT EPN Local Analysis Centre, Bull. Geod. Geomatics 1, 163–174.Google Scholar
  10. Goosens, C., and A. Berger (1986), Annual and seasonal climatic variations over the northern hemisphere and Europe during the last century, Ann. Geophys. 4, 4, 385–400.Google Scholar
  11. Guerova, G. (2013), Ground-based GNSS meteorology, Gfg2 Summer School, 2 July 2013, Potsdam, Germany.Google Scholar
  12. Hagemann, S., L. Bengtsson, and G. Gendt (2003), On the determination of atmospheric water vapor from GPS measurements, J. Geophys. Res. 108, D21, 4678, DOI: 10.1029/2002JD003235.CrossRefGoogle Scholar
  13. Held, I.M., and B.J. Soden (2006), Robust responses of the hydrological cycle to global warming, J. Climate 19, 21, 5686–5699, DOI: 10.1175/JCLI3990.1.CrossRefGoogle Scholar
  14. Herring, T.A. (1992), Modeling atmospheric delays in the analysis of space geodetic data. In: J.C. de Munck, and T.A.T. Spoelstra (eds.), Proc. Symp. Refraction of Transatmospheric Signals in Geodesy, 19–22 May 1992, Hague, The Netherlands, 157–164.Google Scholar
  15. Hocke, K. (1998), Phase estimation with Lomb-Scargle periodogram method, Ann. Geophys. 16, 3, 356–358.Google Scholar
  16. Jin, S., J.-U. Park, J.-H. Cho, and P.-H. Park (2007), Seasonal variability of GPS-derived zenith tropospheric delay (1994–2006) and climate implications, J. Geophys. Res. 112, D9, D09110, DOI: 10.1029/2006JD007772.Google Scholar
  17. Karmeshu, N. (2012), Trend detection in annual temperature and precipitation using Mann Kendall test–A case study to assess climate change on select states in the Northeastern United States, M.Sc. Thesis, University of Pennsylvania, Philadelphia, USA.Google Scholar
  18. Kendall, M.G., and A. Stuart (1970), The Advanced Theory of Statistics. Vol. 2: Interference and Relationship, 3rd ed., Hafner, New York.Google Scholar
  19. Lomb, N.R. (1976), Least-squares frequency analysis of unequally spaced data, Astrophys. Space Sci. 39, 2, 447–462, DOI: 10.1007/BF006483.CrossRefGoogle Scholar
  20. Mann, H.B. (1945), Nonparametric tests against trend, Econometrica 13, 3, 245–259, DOI: 10.2307/1907187.CrossRefGoogle Scholar
  21. Marini, J.W. (1972), Correction of satellite tracking data for an arbitrary tropospheric profile, Radio Sci. 7, 2, 223–231, DOI: 10.1029/RS007i002p00223.CrossRefGoogle Scholar
  22. Mavromatis, T., and D. Stathis (2011), Response of the water balance in Greece to temperature and precipitation trends, Theor. Appl. Climatol. 104, 1–2, 13–24, DOI: 10.1007/s00704-010-0320-9.CrossRefGoogle Scholar
  23. Niell, A.E. (1996), Global mapping functions for the atmospheric delay at radio wavelengths, J. Geophys. Res. 101, B2, 3227–3246, DOI: 10.1029/95JB03048.CrossRefGoogle Scholar
  24. Nilsson, T., and G. Elgered (2008), Long-term trends in the atmospheric water vapor content estimated from ground-based GPS data, J. Geophys. Res. 113, D19, D19101, DOI: 10.1029/2008JD010110.Google Scholar
  25. Ning, T. (2012), GPS meteorology: with focus on climate application, Ph.D. Thesis, Department of Earth and Space Sciences, Chalmers University of Technology, Göteborg, Sweden.Google Scholar
  26. Pacione, R., B. Pace, and G. Bianco (2014), An homogeneously reprocessed Zenith Total Delay long-term time series over Europe. In: EGU General Assembly, 27 April–2 May 2014, Vienna, Austria, id. 2945.Google Scholar
  27. Press, W.H., S.A. Teukolsky, W.T. Vetterling, and B.P. Flannery (1992), Numerical recipes in Fortran, 2nd ed., Cambridge University Press, Cambridge.Google Scholar
  28. Ross, R.J., and W.P. Elliott (2001), Radiosonde-based northern hemisphere tropospheric water vapor trends, J. Climate 14, 7, 1602–1612, DOI: 10.1175/1520-0442(2001)014<1602:RBNHTW>2.0.CO;2.CrossRefGoogle Scholar
  29. Schüler, T. (2001), On ground-based GPS tropospheric delay estimation, Ph.D. Thesis, Universität der Bundeswehr, München, Germany, 364 pp.Google Scholar
  30. Söhne, W., M. Figurski, and K. Szafranek (2010), Homogeneous Zenith Total Delay parameter estimation from European permanent GNSS sites, Bull. Geod. Geomatics 69, 1, 11–22.Google Scholar
  31. Steigenberger, P., M. Rothacher, R. Dietrich, M. Fritsche, A. Rülke, and S. Vey (2006), Reprocessing of a global GPS network, J. Geophys. Res. 111, B5, B05402, DOI: 10.1029/2005JB003747.Google Scholar
  32. van Malderen, R., H. Brenot, E. Pottiaux, S. Beirle, C. Hermans, M. de Mazière, T. Wagner, H. de Backer, and C. Bruyninx (2014), A multi-site intercomparison of integrated water vapour observations for climate change analysis, Atmos. Meas. Tech. 7, 8, 2487–2512, DOI: 10.5194/amt-7-2487-2014.CrossRefGoogle Scholar
  33. Wang, J., and L. Zhang (2009), Climate applications of a global, 2-hourly atmospheric precipitable water dataset derived from IGS tropospheric products, J. Geodesy 83, 3–4, 209–217, DOI: 10.1007/s00190-008-0238-5.CrossRefGoogle Scholar
  34. Yong, W., Y. Binyun, W. Debao, and L. Yanping (2008), Zenith Tropospheric Delay from GPS monitoring climate change of Chinese Mainland. In: Int. Workshop on Education Technology and Training and on Geoscience and Remote Sensing, 21–22 December 2008, Shanghai, China, Vol. 1, 365–368, DOI: 10.1109/ETTandGRS.2008.43.Google Scholar

Copyright information

© Bałdysz et al. 2015

Authors and Affiliations

  • Zofia Bałdysz
    • 1
    Email author
  • Grzegorz Nykiel
    • 1
  • Mariusz Figurski
    • 1
  • Karolina Szafranek
    • 1
  • Krzysztof KroszczyńSki
    • 1
  1. 1.Faculty of Civil Engineering and GeodesyMilitary University of TechnologyWarszawaPoland

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