Advertisement

GPS Solutions

, 23:64 | Cite as

A new troposphere tomography algorithm with a truncation factor model (TFM) for GNSS networks

  • Qingzhi ZhaoEmail author
  • Kefei Zhang
  • Yibin Yao
  • Xin Li
Original Article
  • 212 Downloads

Abstract

For previous studies of global navigation satellite system (GNSS) troposphere tomography, only the GNSS observations derived from ground-based stations located inside the tomographic area were considered; however, stations distributed outside the area of interest in a dense regional network were neglected. This wastes valuable GNSS data and decreases the number of voxels traveled by satellite rays. This becomes the focus of this work, which tries to use GNSS receivers located outside the tomographic region to participate in the establishment of a tomographic observation equation. A new troposphere tomography algorithm is proposed with a truncation factor model (TFM), while the ability of the TFM to calculate the sectional slant water vapor inside the tomographic area, derived from the receivers outside this area, has been verified. The proposed algorithm is validated using the observed data collected over 31 days from the continuously operating reference system network of Zhejiang Province, China. At elevation angle masks of 10°, the number of satellite rays used has increased by 21.27% while the number of voxels transited by satellite rays has increased by 13.97% from 65.44 to 79.23% when adopting the TFM. The compared result of integrated water vapor with those from radiosonde data reveals that the RMS error and bias of the proposed algorithm are 4.1 mm and 0.06 mm, respectively, while those of the conventional method are 4.8 mm and − 0.34 mm, respectively. Water vapor profile comparison also shows that the RMS error and bias of the proposed algorithm are superior with average values of 1.17 g m−3 and 0.02 g m−3 to that of the conventional algorithm with values of 1.44 g m−3 and 0.03 g m−3, respectively. The PWV differences between tomography and GAMIT further indicate a good performance of the proposed algorithm with the values of RMS error and bias of 8.7 mm and 0.5 mm, respectively, while those of the traditional method are 12.6 mm and 0.9 mm, respectively.

Keywords

GNSS Troposphere tomography Truncation factor model (TFM) Radiosonde 

Notes

Acknowledgements

The authors would like to thank IGAR for providing access to the web-based IGAR data. The Zhejiang administration of surveying mapping and geoinformation is also acknowledged for providing the experimental data. This research was supported by the State Key Program of National Natural Science Foundation of China (41730109), Scientific Research Program of Shaanxi Provincial Education Department (18JK0508), the Excellent Youth Science and Technology Fund Project of Xi’an University of Science and Technology (2018YQ3-12) and the Startup Foundation for Doctor of Xi’an University of Science and Technology (2017QDJ041).

References

  1. Adeyemi B, Joerg S (2012) Analysis of water vapor over Nigeria using radiosonde and satellite data. J Appl Meteorol Climatol 51(51):1855–1866CrossRefGoogle Scholar
  2. Alshawaf F (2013) Constructing water vapor maps by fusing InSAR, GNSS and WRF data. Doctoral dissertation, Karlsruhe. Karlsruher Institut für Technologie (KIT)Google Scholar
  3. Askne J, Nordius H (1987) Estimation of tropospheric delay for microwaves from surface weather data. Radio Sci 22(3):379–386CrossRefGoogle Scholar
  4. Bender M, Raabe A (2007) A preconditions to ground based GPS water vapour tomography. Annales Geophysicae. European Geosciences Union 25(8):1727–1734Google Scholar
  5. Benevides P, Catalão J, Miranda PM (2014) Experimental GNSS tomography study in Lisbon (Portugal). Física de la Tierra 26:65–79CrossRefGoogle Scholar
  6. Benevides P, Nico G, Catalao J, Miranda P (2015a) Merging SAR interferometry and GPS tomography for high-resolution mapping of 3D tropospheric water vapour. In: 2015 IEEE international geoscience and remote sensing symposium (IGARSS), pp 3607–3610Google Scholar
  7. Benevides P, Catalao J, Nico G, Miranda PM (2015b) Inclusion of high resolution MODIS maps on a 3D tropospheric water vapor GPS tomography model. In: Proceedings of the SPIE 9640, remote sensing of clouds and the atmosphere XX, 96400R.  https://doi.org/10.1117/12.2194857
  8. 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 Atmos 97(D14):15787–15801CrossRefGoogle Scholar
  9. Bi Y, Mao J, Li C (2006) Preliminary results of 4-D water vapor tomography in the troposphere using GPS. Adv Atmos Sci 23(4):551–560CrossRefGoogle Scholar
  10. 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(7):L07304Google Scholar
  11. Braun J, Rocken C, Meertens C, Ware R (1999) Development of a water vapor tomography system using low cost L1 GPS receivers. In: 9th ARM science team meeting proceedings, San Antonio, TX, vol, 2226, pp 22–26Google Scholar
  12. Brenot H, Walpersdorf A, Reverdy M, Van Baelen J, Ducrocq V, Champollion C, Giroux P (2014) A GPS network for tropospheric tomography in the framework of the Mediterranean hydrometeorological observatory Cévennes-Vivarais (southeastern France). Atmos Meas Tech 7(2):553–578CrossRefGoogle Scholar
  13. Champollion C, Masson F, Bouin MN, Walpersdorf A, Doerflinger E, Bock O, Van Baelen J (2005) GPS water vapour tomography: preliminary results from the ESCOMPTE field experiment. Atmos Res 74(1):253–274CrossRefGoogle Scholar
  14. Chen B, Liu Z (2014) Voxel-optimized regional water vapor tomography and comparison with radiosonde and numerical weather model. J Geodesy 88(7):691–703CrossRefGoogle Scholar
  15. De Lathauwer L, De Moor B, Vandewalle J (2000) A multilinear singular value decomposition. SIAM J Matrix Anal Appl 21(4):1253–1278CrossRefGoogle Scholar
  16. Elósegui P, Rius A, Davis JL, Ruffini G, Keihm S, Bürki B, Kruse LP (1998) An experiment for estimation of the spatial and temporal variations of water vapor using GPS data. Phys Chem Earth Parts A/B/C 23(1):125–130CrossRefGoogle Scholar
  17. Flores A, Ruffini G, Rius A (2000) 4D tropospheric tomography using GPS slant wet delays. Ann Geophys 18(2):223–234CrossRefGoogle Scholar
  18. Herring TA, King RW, McClusky SC (2010) Documentation of the GAMIT GPS analysis software release 10.4. Department of Earth and Planetary Sciences, Massachusetts Institute of Technology, Cambridge, MAGoogle Scholar
  19. Heublein M, Zhu XX, Alshawaf F, Mayer M, Bamler R, Hinz S (2015) Compressive sensing for neutrospheric water vapor tomography using GNSS and InSAR observations. In: 2015 IEEE international geoscience and remote sensing symposium (IGARSS), pp 5268–5271Google Scholar
  20. Kacmarík M, Douša J, Dick G, Zus F, Brenot H, Möller G, Pottiaux E, Kapłon J, Hordyniec P, Václavovic P, Morel L (2017) Inter-technique validation of tropospheric slant total delays. Atmos Meas Tech 10(6):2183–2208CrossRefGoogle Scholar
  21. Liu Z, Man SW, Nichol J, Chan PW (2013) A multi-sensor study of water vapour from radiosonde, MODIS and AERONET: a case study of Hong Kong. Int J Climatol 33(33):109–120CrossRefGoogle Scholar
  22. Mendes VB (1999) Modeling the neutral-atmosphere propagation delay in radiometric space technique, Ph.D. dissertation, University of New Brunswick, Fredericton, New Brunswick, CanadaGoogle Scholar
  23. Niell AE (2001) Comparison of measurements of atmospheric wet delay by radiosonde, water vapor radiometer, GPS, and VLBI. J Atmos Ocean Technol 18(6):830–850CrossRefGoogle Scholar
  24. Nilsson T, Gradinarsky L (2006) Water vapor tomography using GPS phase observations: simulation results. IEEE Trans Geosci Remote Sens 44(10):2927–2941CrossRefGoogle Scholar
  25. Notarpietro R, Cucca M, Gabella M, Venuti G, Perona G (2011) Tomographic reconstruction of wet and total refractivity fields from GNSS receiver networks. Adv Space Res 47(5):898–912CrossRefGoogle Scholar
  26. Perler D, Geiger A, Hurter F (2011) 4D GPS water vapor tomography: new parameterized approaches. J Geodesy 85(8):539–550CrossRefGoogle Scholar
  27. Rius A, Ruffini G, Cucurull L (1997) Improving the vertical resolution of ionospheric tomography with GPS occultations. Geophys Res Lett 24(18):2291–2294CrossRefGoogle Scholar
  28. Rohm W (2013) The ground GNSS tomography—unconstrained approach. Adv Space Res 51(3):501–513CrossRefGoogle Scholar
  29. Rohm W, Bosy J (2009) Local tomography troposphere model over mountains area. Atmos Res 93(4):777–783CrossRefGoogle Scholar
  30. Rohm W, Bosy J (2011) The verification of GNSS tropospheric tomography model in a mountainous area. Adv Space Res 47(10):1721–1730CrossRefGoogle Scholar
  31. Saastamoinen J (1972) Atmospheric correction for the troposphere and stratosphere in radio ranging of satellites. In: The use of artificial satellites for geodesy, American Geophysical Union (AGU), vol 15, pp 247–251CrossRefGoogle Scholar
  32. Seko H, Shimada S, Nakamura H, Kato T (2000) 3-d distribution of water vapor estimated from tropospheric delay of GPS data in a mesoscale precipitation system of the Baiu front. Earth Planets Space 52(11):927–933CrossRefGoogle Scholar
  33. Skone S, Hoyle V (2005) Troposphere modeling in a regional GPS network. J Glob Position Syst 4:230–239CrossRefGoogle Scholar
  34. Troller MR (2004) GPS based determination of the integrated and spatially distributed water vapor in the troposphere. Doctoral dissertation, ETH, ZurichGoogle Scholar
  35. Van Baelen J, Reverdy M, Tridon F, Labbouz L, Dick G, Bender M, Hagen M (2011) On the relationship between water vapour field evolution and the life cycle of precipitation systems. Q J R Meteorol Soc 137(S1):204–223CrossRefGoogle Scholar
  36. Xia P, Cai C, Liu Z (2013) GNSS troposphere tomography based on two-step reconstructions using GPS observations and COSMIC profiles. Ann Geophys 31(10):1805–1815CrossRefGoogle Scholar
  37. Yao Y, Zhao Q (2016a) Maximally using GPS observation for water vapour tomography. IEEE Trans Geosci Remote Sens 54(12):7185–7196CrossRefGoogle Scholar
  38. Yao Y, Zhao Q (2016b) A novel, optimized approach of voxel division for water vapor tomography. Meteorol Atmos Phys 129(1):57–70CrossRefGoogle Scholar
  39. Yao Y, Zhao Q, Zhang B (2016) A method to improve the utilisation of GNSS observation for water vapour tomography. Ann Geophys 34(1):143–152CrossRefGoogle Scholar
  40. Ye S, Xia P, Cai C (2016) Optimization of GPS water vapor tomography technique with radiosonde and COSMIC historical data. Ann Geophys 34(9):789–799CrossRefGoogle Scholar
  41. Zhao Q, Yao Y (2017) An improved troposphere tomographic approach considering the signals coming from the side face of the tomographic area. Ann Geophys 35(1):87–95CrossRefGoogle Scholar
  42. Zhao Q, Yao Y, Yao W, Xia P (2018) An optimal tropospheric tomography approach with the support of an auxiliary area. Ann Geophys 36(4):1037–1046CrossRefGoogle Scholar

Copyright information

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

Authors and Affiliations

  1. 1.College of GeomaticsXi’an University of Science and TechnologyXi’anChina
  2. 2.Satellite Positioning for Atmosphere, Climate and Environment (SPACE) Research CentreRMIT UniversityMelbourneAustralia
  3. 3.School of Environment Science and Spatial InformaticsChina University of Mining and TechnologyXuzhouChina
  4. 4.School of Geodesy and GeomaticsWuhan UniversityWuhanChina
  5. 5.College of Geology Engineering and GeomaticsChang’an UniversityXi’anChina

Personalised recommendations