Advertisement

Meteorology and Atmospheric Physics

, Volume 126, Issue 3–4, pp 193–205 | Cite as

4D tomographic reconstruction of the tropospheric wet refractivity using the concept of virtual reference station, case study: northwest of Iran

  • Zohre AdaviEmail author
  • Masoud Mashhadi-Hossainali
Original Paper
First Online:

Abstract

Iran enjoys a variety of climatological conditions. Moreover, numerical weather prediction (NWP) models are not assimilated with the meteorological data in Iran, the country suffering from poor spatial and temporal resolution of radiosonde measurements. These facts make modeling of troposphere impossible using the measurements and NWP. On the other hand, the global positioning system (GPS) has been emerged as a valuable tool for modeling and remote sensing of Earth’s atmosphere. This research is the first attempt to address the tropospheric wet refractivity modeling by GPS measurements in Iran. Changes of topography in the study area are taken into account. As a leading work, virtual reference stations (VRS) are used to fix the rank deficiency of the problem. The model space resolution matrix is used to achieve the optimum spatial resolution of the tomographic model and the optimum number of VRS stations. The accuracy of the developed model (KNTU1) is investigated by deploying radiosonde measurements.

Keywords

Global Position System Tomographic Reconstruction Virtual Station Global Position System Signal Global Position System Station 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This is a preview of subscription content, log in to check access.

Notes

Acknowledgments

During this research, Mr. Akbar who is a member of the meteorological organization of Iran, kindly provided us valuable remarks. His cooperation is appreciated here. The meteorological organization of Iran provided the radiosonde profiles with dense pressure levels of the Tabriz station and the observation files of Synoptic stations in the northwestern part of the country for this research. This collaboration is also appreciated here. We are grateful to the National Cartographic Center (NCC) of Iran for providing the observation files of the Azerbaijan sub-network of the Iranian Permanent GPS Network too.

References

  1. Al-Shaery A, Lim S, Rizos C (2011) Investigation of different interpolation models used in network-RTK for the virtual reference station technique. J Glob Position Syst 10:136–148. doi: 10.5081/jgps.10.2.136 CrossRefGoogle Scholar
  2. Aster R, Borchers B, Thurber C (2005) Parameter estimation and inverse problems, vol 90. Elsevier Academic Press, USACrossRefGoogle Scholar
  3. Becker M, Bruyninx C, Fernandez R (2002) Processing and Submission Guidelines for GPS Solutions to be Integrated to a WEGENER Data Base,Proceedings of WEGENER 2002. Athens, Greece, University of Athens, Jun 12-14Google Scholar
  4. Bender M, Raabe A (2007) Preconditions to ground based GPS water vapour tomography. Ann Geophys 25(8):1727–1734CrossRefGoogle Scholar
  5. Bender M et al (2009) Estimates of the information provided by GPS slant data observed in Germany regarding tomographic applications. J Geophys Res. doi: 10.1029/2008JD011008 Google Scholar
  6. Bender M et al (2011) Development of a GNSS water vapour tomography system using algebraic reconstruction techniques. Adv Space Res 47:1704–1720. doi: 10.1016/j.asr.2010.05.034 CrossRefGoogle Scholar
  7. Bertero M, Boccacci P (1998) Introduction to inverse problem in imaging. Institute of Physics, LondonCrossRefGoogle Scholar
  8. Bevis M, Businger S, Herring T, Rocken C, RA A, Ware RH (1992) GPS meteorology: remote sensing of atmospheric water vapor using the global positioning system. J Geophys Res 97(D14):15787–15801CrossRefGoogle Scholar
  9. 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. doi: 10.1029/2005GL02554 Google Scholar
  10. Bosy J, Rohm W, Sierny J (2010) The concept of the near real time atmosphere model based on the GNSS and the meteorological data from the ASG-EUPOS reference stations. Acta Geodyn Geomater 7:253–261Google Scholar
  11. Brenot H et al (2014) A GPS network for tropospheric tomography in the framework of the Mediterranean hydrometeorological observatory Cévennes-Vivarais (southeastern France). Atmos Meas Tech 7:553–578. doi: 10.5194/amt-7-553-2014 CrossRefGoogle Scholar
  12. Cai G, Chen BM, Lee TH (2011) Unmanned rotorcraft systems. Springer, New YorkCrossRefGoogle Scholar
  13. Dach R, Hugentobler U, Fridez P, Meindl M (2007) Bernese GPS Software Version 5.0. Astronomical Institute, University of Bern, BernGoogle Scholar
  14. Dai L, Han S, Wang J, Rizos C (2001) A study of GPS/GLONASS multiple reference station techniques for precise real-time carrier phase–based positioning. In: 14th Internationl Technical Meeting of the Satellite Division of the US Institute of Navigation, Salt Lake City, Utah, 11–14 September, pp 392–403Google Scholar
  15. Davis JL, Herring TA, Shapiro II, Rogers EE, Elgered G (1985) Geodesy by radio interferometry: effects of atmospheric modeling errors onestimates of baseline length. Radio Sci 20(6):1593–1607CrossRefGoogle Scholar
  16. De Brito Mendes V (1999) Modeling the neutral-atmosphere propagation delayin radiometric space techniques. University of New Brunswick, FrederictonGoogle Scholar
  17. Elfving T, Nikazad T, Hansen PC (2010) Semi-convergence and relaxation parameters for a class of sirt algorithms. Electron Trans Numer Anal 37:321–336Google Scholar
  18. Emardson TR, Elgered G, Johansson JM (1998) Three months of continuous monitoring of atmospheric water vapor with a network of Global Positioning System receivers. J Geophys Res 103:1807–1820CrossRefGoogle Scholar
  19. Erhu Wei E, Chai H, An Z (2006) VRS virtual observations generation algorithm. J Global Position Syst 1–2:76–81Google Scholar
  20. Euler HJ, Keenan CR, Zebhauser BE, Wübbena G (2001) Study of a simplified approach in utilizing information from permanent reference station arrays. Paper presented at the Proceedings 14th International Technical Meeting of the Satellite Division of the Institute of Navigation, Salt Lake City, USA, ION GPS-2001, September 11–14Google Scholar
  21. Flores A, Ruffini G, Rius A (2000) 4D tropospheric tomography using gps slant wet delays. Ann Geophys 18(2):223–234. doi: 10.1007/s00585-000-0223-7 CrossRefGoogle Scholar
  22. Foelsche U, Kirchengast G (2001) Tropospheric water vapor imaging by combination of ground-based and space born GNSS sounding data. J Geophys Res 106(D21):27221–27231CrossRefGoogle Scholar
  23. Fotopoulos G, Cannon ME (2001) An overview of multi-reference station methods for Cm-level positioning. GPS solutions 4:1–10Google Scholar
  24. Golub GH, Matt U (1997) Generalized cross-validation for large scale problems. J Comput Graph Stat 6(1):1–34. doi: 10.1080/10618600.1997.10474725
  25. Grewal MS, Weill RL, Andrews AP (2007) Global positioning systems, inertial navigation, and integration. Wiley, New YorkCrossRefGoogle Scholar
  26. Guerova G (2003) Application of GPS derived water vapour for numerical weather prediction in Switzerland. University of Bern, BernGoogle Scholar
  27. Gurtner W (1994) RINEX: The reciever-independent exchange format GPS World, Las Cruces: 48–52Google Scholar
  28. Hansen PC (1998) Rank-deficient and discrete ILL-posed problems: numerical aspect of linear inversion, PhiladelphiaGoogle Scholar
  29. Hirahara K (2000) Local GPS tropospheric tomography. Earth planet space 52(11):935–939CrossRefGoogle Scholar
  30. Hoyle VA (2005) Data assimilation for 4-D wet refractivity modelling in a regional GPS network. Calgary, AlbertaGoogle Scholar
  31. Hu GR, Khoo VHS, Goh PC, Law CL (2002) Internet-based GPS VRS RTK positioning with a multiple reference station network. J Global Position Syst 1:113–120CrossRefGoogle Scholar
  32. Jarlemark POJ, Johansson JM, Emardson TR (1998) Wet delay variability calculated from radiometric measurements and its role in space geodetic parameter estimation. Radio Sci 33:719–730CrossRefGoogle Scholar
  33. Kleijier F (2004) Troposphere modeling and filtering for precise GPS leveling. Delft, The NetherlandsGoogle Scholar
  34. Landweber L (1951) An iteration formula for Fredholm integral equations of the first kind. Am J Math 73:615–624CrossRefGoogle Scholar
  35. Langley R (1998b) Propagation of the GPS Signals, Chap 3, In: Teunissen, Kleusberg, pp 111–149Google Scholar
  36. Lutz SM (2008) High-resolution GPS tomography in view of hydrological hazard assessment. ETH Zurich, SwitzerlandGoogle Scholar
  37. Manning T, Zhang K, Rohm W, Choy S, Hurter F (2012) Detecting severe weather using GPS tomography: an australian case study. J Global Position Syst 11:58–70. doi: 10.5081/jgps.11.1.58 Google Scholar
  38. Marel H-v-d (1998) Virtual GPS reference stations in the Netherlands. In: Paper presented at the Proc 11th International Technical Meeting of the Satellite Division of the US Institute of Navigation, ION GPS-98, Nashville, TN, September 15–18Google Scholar
  39. Menke W (2012) Geophysical data analysis: discrete inverse theory MATLAB Edition. doi: 10.1016/B978-0-12-397160-9.00001-1
  40. Mervart L (1995) Ambiguity resolution techniques in geodetic and geodynamic applications of global positioning system. University of Bern, BernGoogle Scholar
  41. Nikazad T (2007) The use of land weber algorithm in image reconstruction. Linköpings University, LinköpingsGoogle Scholar
  42. Nilsson T, Gradinarsky L (2006) Water vapor tomography using GPS phase observations: simulation results. IEEE Trans Geosci Remote Sens 44:2927–2941CrossRefGoogle Scholar
  43. Odijk D (2002) Fast precise GPS positioning in the presence of ionospheric delays. University of Delft, The NetherlandsGoogle Scholar
  44. Raquet J, Lachapelle G (2001) Efficient precision positioning: RTK positioning with multiple reference stations. GPS World 12(48):53Google Scholar
  45. Rasmussen JM (2001) Compact linear operators and Krylov subspace methods. Technical University Denmark, DenmarkGoogle Scholar
  46. Rizos C, Han S, Chen HY (2000a) Regional-Scale multiple reference stations for carrier phase-based GPS positioning a correction generation algorithm Earth. Planet Space 52(10):795–800CrossRefGoogle Scholar
  47. Rizos C, Han S, Chen HY (2000b) Regional-scale multiple reference stations for carrier phase-based GPS positioning a correction generation algorithm Earth. Planet Space 52:795–800CrossRefGoogle Scholar
  48. Rohm W, Bosy J (2009) Local tomography troposphere model over mountains area. Atmos Res 93(4):777–783. doi: 10.1016/j.atmosres.2009.03.013 CrossRefGoogle Scholar
  49. Rohm W, Bosy J (2011) The verification of GNSS tropospheric tomography model in a mountainous area. Adv Space Res 47:1721–1730. doi: 10.1016/j.asr.2010.04.017 CrossRefGoogle Scholar
  50. Rothacher M, Schaer S, Beutler G, Schlüter W, Hase HO (1996) Phase center variations of GPS antennas derived from GPS observations of specially designed calibration campaigns supplement to EOS, American Geophysical Union, 1996 Springer Meeting, May 20–24 77:G11A-16Google Scholar
  51. Rothacher M, Springer TA, Schaer S, Beutler G (1998) Processing strategies for regional GPS networks. In: Brunner Fk (ed) International Association of Geodesy Symposia, vol 118. Advances in Positioning and Reference Frames, Springer, Berlin Heidelberg, pp 93–100. doi: 10.1007/978-3-662-03714-0_14
  52. Saastamoinen J (1973) Contributions to the theory of atmospheric refraction. Part II: refraction corrections in satellite geodesy. Bull Geod 107:13–34CrossRefGoogle Scholar
  53. Schaer S (1999) Mapping and predicting the earth’s Ionosphere using the global positioning system. University of Bern, BernGoogle Scholar
  54. Schüler T (2001) On ground-based GPS tropospheric delay estimationGoogle Scholar
  55. Shangguan M, Bender M, Wickert J, Raabe A (2011) Validation of GNSS water vapour tomography with radiosonde data. In: Geodätische Woche NürnbergGoogle Scholar
  56. Shangguan M, Bender M, Ramatschi M, Dick G, Wickert J, Raabe A, Gales R (2013) GPS tomography: validation of reconstructed 3-D humidity fiDiescludsssions with radiosonde profiles. Ann Geophys 31:1491–1505CrossRefGoogle Scholar
  57. Spilker J (1996d) Tropospheric Effects on GPS. In: Parkinson Spilker (eds) 1,Chap.13:517-546Google Scholar
  58. Vedel H, Huang XY (2004) Impact of ground based GPS data on numerical weather prediction. J Meteorol Soc Jpn 82(1B):459–472CrossRefGoogle Scholar
  59. Vollath U, Buecherl A, Landau H, Pagels C, Wager B (2000) Multi-base RTK positioning using virtual reference stations.In: Paper presented at the Proceedings 13th International Technical Meeting of the Satellite Division of the US Institute of Navigation, ION GPS-2000, Salt Lake City, September, 19–22Google Scholar
  60. Wanninger L (1997) Real-time differential GPS-error modeling in regional reference station networks. Paper presented at the Proceedings of the IAG scientific assembly, Rio de Janeiro, SepGoogle Scholar
  61. Watkins DS (2002) Fundamentals of matrix computations. Wiley, USACrossRefGoogle Scholar
  62. Wu L (2003) A parameter choice method for Tikhonov regularization ENTA Kent State University 6:22Google Scholar
  63. Wu S (2009) Performance of regional atmospheric error models for NRTK in GPSnet and the implementation of a NRTK system. RMIT University, AustraliaGoogle Scholar
  64. Xia P, Cai C, Liu Z (2013) GNSS troposphere tomography based on two-step reconstructions using GPS observations and COSMIC profiles. Annales Geophysicae 31:1805–1815. doi: 10.5194/angeo-31-1805-2013 CrossRefGoogle Scholar
  65. de Haan S, Barlag S, Klein Baltink H, Debie F, van der Marel H (2004) Synergetic use of GPS water vapor and Meteosat images for synoptic weather forecasting. J Appl Meteorol 43:514–518CrossRefGoogle Scholar
  66. Zhang K, Roberts C Network-based real-time kinematic positioning system: current development in Australia. In: Geoinformatics and Surveying Conference, Malaysia, 7 Apr 2003Google Scholar
  67. Zhang S, Lim S, Rizos C, Guo J Atmospheric decomposition for VRS based network-RTK system. In: 22nd International Technical Meeting of the Satellite Division of the US, Institution of Navigation, Savannah, Georgia, 22–25 September 2009, pp 2707–2716Google Scholar

Copyright information

© Springer-Verlag Wien 2014

Authors and Affiliations

  1. 1.Department of Geodesy and Geomatics EngineeringK. N.Toosi University of TechnologyTehranIran

Personalised recommendations

Cite article

Buy options