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Contribution of permanent stations GPS data to estimate the water vapor content over Algeria

  • Hassen AbdellaouiEmail author
  • Naima Zaourar
  • Salem Kahlouche
Original Paper
  • 28 Downloads

Abstract

In the last decade, important studies have demonstrated that GPS can be also used as an efficient tool for measuring the integrated water vapor (IWV) in the atmosphere which is a useful quantity for climatological and weather forecasting applications. This study presents the first results obtained by using the time series GPS stations of six local stations belonging to the continuously operating Algerian network, and 13 stations of the IGS (International GNSS Service) for the estimation of the value of the IWV locally. In this paper, tropospheric parameters are obtained from double difference processing of GPS observations, collected from 2008 to 2015, using the Bernese 5.2 software. For the validation of GPS IWV values, three approaches are used. In the first, the GPS IWV are compared with the corresponding ERA-Interim values derived from interpolations in time and space. The results show a good agreement with correlation coefficients exceeding 85% and an RMS (root mean square) between 2.22 and 5.53 kg m−2. In the second approach, we compare GPS IWV and radiosondes over two stations, where the results showed an acceptable concordance and equivalent to those of the first approach. In the third approach, the GPS ZWD (zenith wet delay), roughly IWV, values are compared with the daily rainfall data provided by the Algerian Meteorological Office. The results show that the temporal variation of ZWD and the high rainfall collected by rain gauges (not far from those of GPS) present a perfect coincidence over the surrounding observed peaks. Finally, the analysis of the annual time cycle of ZWD and precipitation carried out on the data of geographically and climatically different GPS stations shows that these two parameters depend on the latitude of the site. The first experimental results of this study further strengthen the strong potential of GPS in meteorological applications.

Keywords

GNSS/GPS Integrated water vapor ERA-Interim Precipitations 

Notes

Acknowledgments

We are particularly grateful to our colleagues from geophysical and geodesic laboratories of Luxembourg University especially Addisu H. and Kibrom E. A. We thank the National Meteorological Office for allowing us to use their data. Thanks to the team of the National Institute of Cartography and Remote Sensing, which participated in the installation of the stations and observations campaigns.

References

  1. Abraha KE, Lewi E, Masson F, Boy JP, Doubre C (2017) Spatial–temporal variations of water vapor content over Ethiopia: a study using GPS observations and he ECMWF model. GPS Solutions 21(1):89–99.  https://doi.org/10.1007/s10291-015-0508-7 CrossRefGoogle Scholar
  2. Baltink HK, Van Der Marel H, Van der Hoeven AG (2002) Integrated atmospheric water vapor estimates from a regional GPS network. J Geophys Res: Atmospheres 107(D3).  https://doi.org/10.1029/2000JD000094
  3. Berrisford P, Dee DPKF, Fielding K, Fuentes M, Kallberg P, Kobayashi S, Uppala S (2009) The ERA-interim archive. ERA report series 1:1–16Google Scholar
  4. Bevis M, Businger S, Herring TA, Rocken C, Anthes RA, Ware H (1992) GPS meteorology: remote sensing of atmospheric water vapor using the global positioning system. J Geophys Res: Atmospheres 97(D14):15787–15801.  https://doi.org/10.1029/92JD01517 CrossRefGoogle Scholar
  5. Bevis M, Businger S, Chiswell S, Herring TA, Anthes RA, Rocken C, Ware RH (1994) GPS meteorology: mapping zenith wet delays onto precipitable water. J Appl Meteorol 33(3):379–386.  https://doi.org/10.1175/15200450(1994)033<0379:GMMZWD>2.0.CO;2 CrossRefGoogle Scholar
  6. Bock O, Doerflinger E (2001) Atmospheric modeling in GPS data analysis for high accuracy positioning. Physics and Chemistry of the Earth, Part A: Solid Earth and Geodesy 26(6 8):373–383.  https://doi.org/10.1016/S1464-1895(01)00069-2 CrossRefGoogle Scholar
  7. Bock O, Guichard F, Janicot S, Lafore JP, Bouin MN, Sultan B (2007a) Multiscale analysis of precipitable water vapor over Africa from GPS data and ECMWF analyses. Geophys Res Lett 34:L09705.  https://doi.org/10.1029/2006GL028039 CrossRefGoogle Scholar
  8. Bock O, Bouin M, Walpersdorf A, Lafore JP, Janicot S, Guichard F, Agusti Panareda A (2007b) Comparison of ground based GPS precipitable water vapour to independent observations and NWP model reanalyzes over Africa. Q.J.R. Meteorol Soc 133:2011–2027.  https://doi.org/10.1002/qj.185 CrossRefGoogle Scholar
  9. Boehm J, Werl B, Schuh H (2006) 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(B2).  https://doi.org/10.1029/2005JB003629
  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)  https://doi.org/10.1029/2005GL025546
  11. Boutiouta S, Lahcene A (2013) Preliminary study of GNSS meteorology techniques in Algeria. Int J Remote Sens 34(14):5105–5118.  https://doi.org/10.1080/01431161.2013.786850 CrossRefGoogle Scholar
  12. Chinowsky P, Schweikert A, Strzepek N, Manahan K, Strzepek K, Schlosser CA (2013) Climate change adaptation advantage for African road infrastructure. Clim Chang 117(1-2):345–361.  https://doi.org/10.1007/s10584-012-0536-z CrossRefGoogle Scholar
  13. Dach R, Hugentobler U, Fridez P, Meindl M (2007) Bernese GPS software version 5.0. Astronomical institute, University of Bern, 640, 114Google Scholar
  14. Dee DP, Uppala SM, Simmons AJ, Berrisford P, Poli P, Kobayashi S, Bechtold P (2011) The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Q J R Meteorol Soc 137(656):553–597.  https://doi.org/10.1002/qj.828 CrossRefGoogle Scholar
  15. Ge M, Gendt G, Rothacher MA, Shi C, Liu J (2008) Resolution of GPS carrier-phase ambiguities in precise point positioning (PPP) with daily observations. J Geodesy 82(7):389–399.  https://doi.org/10.1007/s00190-007-0187-4 CrossRefGoogle Scholar
  16. Guerova G (2003) Application of GPS derived water vapor for numerical weather prediction in Switzerland. PhD thesis, Institute of Applied Physics, University of Bern, BerneGoogle Scholar
  17. Guerova G, Jones J, Dousa J, Dick G, de Haan S, Pottiaux E, Bender M (2016) Review of the state of the art and future prospects of the ground-based GNSS meteorology in Europe. Atmos Meas Tech 9(11):5385–5406.  https://doi.org/10.5194/amt-9-5385-2016 CrossRefGoogle Scholar
  18. Hagemann S, Bengtsson L, Gendt G (2003) On the determination of atmosphericwater vapor from GPS measurements. J Geophys Res: Atmospheres 108(D21).  https://doi.org/10.1029/2002JD003235
  19. Haase J, Ge M, Vedel H, Calais E (2003) Accuracy and variability of GPS tropospheric delay measurements of water vapor in the western Mediterranean. J Appl Meteorol 42(11):1547–1568CrossRefGoogle Scholar
  20. Heise S, Dick G, Gendt G, Schmidt T, Wickert J (2009) Integrated water vapor from IGS ground-based GPS observations: initial results from a global 5-min dataset. Ann Geophys 27(7):2851–2859.  https://doi.org/10.5194/angeo-27-2851-2009 CrossRefGoogle Scholar
  21. Jones J (2010) An assessment of the quality of GPS water vapor estimates and their usein operational meteorology and climate monitoring. PhD thesis, University of Nottingham. http://eprints.nottingham.ac.uk/11287/1/JJ_Thesis_Final.pdf
  22. Li Z, Muller JP, Cross P (2003) Comparison of precipitable water vapor derived from radiosonde, GPS, and moderate-resolution imaging spectroradiometer measurements. J Geophys Res 108(D20):4651.  https://doi.org/10.1029/2003JD003372 CrossRefGoogle Scholar
  23. Namaoui H, Kahlouche S, Belbachir AH, Van Malderen R, Brenot H, Pottiaux E (2017) GPS water vapor and its comparison with radiosonde and ERA-Interim data in Algeria. Adv Atmos Sci 34(5):623–634.  https://doi.org/10.1007/s00376-016-6111-1 CrossRefGoogle Scholar
  24. Rocken C, Ware RH, Hove TV, Solheim F, Alber C, Johnson J, Bevis M, Businger S (1993) Sensing atmospheric water vapor with the global positioning system. Geophys Res Lett 20:2631–2634CrossRefGoogle Scholar
  25. Roeckner E, Bäuml G, Bonaventura L, Brokopf R, Esch M, Giorgetta M, Rhodn A (2003) The atmospheric general circulation model ECHAM 5. PART I: model description. Report/MPI für Meteorologie, 349. http://pubman.mpdl.mpg.de/pubman/item/escidoc:995269/component/escidoc:995268/max_scir p_349.pdf
  26. Vey S, Dietrich R., Fritsche M, Rulke A, Steigenberger P, Rothacher M (2009) On the homogeneity and interpretation of precipitale water time series derived from global GPS observations. J Geophys Res (D: Atmos), 114,  https://doi.org/10.1029/2008JD010415.
  27. Wang J, Zhang L (2009) Climate applications of a global, 2-hourly atmospheric precipitable water dataset derived from IGS tropospheric products. J Geodesy 83(3):209–217.  https://doi.org/10.1007/s00190-008-0238-5 CrossRefGoogle Scholar
  28. Wolfe DE, Gutman SI (2000) Developing an operational, surface-based, GPS, water vapor observing system for NOAA: network design and results. J Atmos Ocean Technol 17:426–440CrossRefGoogle Scholar

Copyright information

© Saudi Society for Geosciences 2019

Authors and Affiliations

  • Hassen Abdellaoui
    • 1
    Email author
  • Naima Zaourar
    • 1
  • Salem Kahlouche
    • 2
  1. 1.Geophysics Department-FSTGATUniversity of Sciences and Technology Houari Boumediene (USTHB)AlgiersAlgeria
  2. 2.Department of Space GeodesyCenter for Space TechniquesArzewAlgeria

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