Atmospheric opacity estimation based on IWV derived from GNSS observations for VLBI applications - Publikacja - MOST Wiedzy


Atmospheric opacity estimation based on IWV derived from GNSS observations for VLBI applications


Thermal emission of atmospheric water vapor has a great influence on the calibration of radio astronomical observations at millimeter wavelengths. The phenomenon of an atmospheric water vapor emits noise signal and attenuates astronomical emission. At 22 GHz, integrated water vapor (IWV) obtained from global navigation satellite systems (GNSS) is strictly related to atmospheric opacity (τ0), which is a crucial parameter for data calibration. Therefore, providing highly precise and accurate IWV from GNSS measurements may be an alternative for microwave radiometers. Whereas it is not possible to estimate IWV directly from GNSS measurements, its value is strictly correlated with the zenith wet delay (ZWD) that is estimated together with the coordinates during the GNSS positioning. In this study, differential and Precise Point Positioning methods for ZWD estimation are tested using two different tropospheric mapping functions: Vienna mapping function (VMF) and global mapping function (GMF). After positioning, the IWV conversion is performed using meteorological parameters derived from a meteorological station located near a GNSS site. Analyses for a 3-month period from June 1 to August 30, 2016, were conducted. Based on these, we obtained a very high correlation between IWV and τ0 as measured by the Torun 32 m radio telescope, which amounts about 0.95, for both PPP and differential solutions. Thus, techniques can be successfully used to estimate IWV and calculate τ0. However, the linear regression coefficients depend on the used positioning method.


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Informacje szczegółowe

Publikacja w czasopiśmie
artykuł w czasopiśmie wyróżnionym w JCR
Opublikowano w:
GPS SOLUTIONS nr 22, strony 1 - 11,
ISSN: 1080-5370
Rok wydania:
Opis bibliograficzny:
Nykiel G., Wolak P., Figurski M.: Atmospheric opacity estimation based on IWV derived from GNSS observations for VLBI applications// GPS SOLUTIONS. -Vol. 22, nr. 9 (2018), s.1-11
Cyfrowy identyfikator dokumentu elektronicznego (otwiera się w nowej karcie) 10.1007/s10291-017-0675-9
Bibliografia: test
  1. Ahmed F, Václavovic P, Teferle FN, Douša J, Bingley R, Laurichesse D (2016) Comparative analysis of real-time precise point positioning zenith total delay estimates. GPS Solut 20(2):187-199. https://doi. org/10.1007/s10291-014-0427-z otwiera się w nowej karcie
  2. Altamimi Z, Rebischung P, Metivier L, Xavier C (2016) ITRF2014: a new release of the International Terrestrial Reference Frame modeling nonlinear station motions. J Geophys Res Solid Earth 121(8):6109-6131. otwiera się w nowej karcie
  3. Askne J, Nordius H (1987) Estimation of tropospheric delay for micro- waves from surface weather data. Radio Sci 22(3):379-386. otwiera się w nowej karcie
  4. Baldysz Z, Nykiel G, Araszkiewicz A, Figurski M, Szafranek K (2016) Comparison of GPS tropospheric delays derived from two con- secutive EPN reprocessing campaigns from the point of view of climate monitoring. Atmos Meas Tech 9(9):4861-4877. https:// otwiera się w nowej karcie
  5. Bergeot N, Chevalier JM, Bruyninx C, Pottiaux E, Aerts W, Baire Q, Legrand J, Defraigne P, Huang W (2014) Near real-time iono- spheric monitoring over Europe at the royal observatory of Bel- gium using GNSS data. J Space Weather Space Clim 4:A31. otwiera się w nowej karcie
  6. 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 97(D14):15787-15801. otwiera się w nowej karcie
  7. Boehm J, Werl B, Schuh H (2006a) Troposphere mapping functions for GPS and very long baseline interferometry from European centre for medium-range weather forecasts operational analysis data. J Geophys Res. otwiera się w nowej karcie
  8. Boehm J, Niell AE, Tregoning P, Schuh H (2006b) Global mapping function (GMF). A new empirical mapping function based on numerical weather model data. Geophys Res Lett 33:L07304-1- L07304-4. otwiera się w nowej karcie
  9. Chen G, Herring A (1997) Effects of atmospheric azimuthal asym- metry on the analysis of space geodetic data. J Geophys Res 102(B9):20489-20502. otwiera się w nowej karcie
  10. Dach R, Lutz S, Walser P, Fridez P (Eds) (2015) Bernese GNSS soft- ware version 5.2. User manual, Astronomical Institute, University of Bern, Bern Open Publishing. ISBN:978-3-906813-05-9. https:// otwiera się w nowej karcie
  11. Davis JL, Herring TA, Shapiro LI, Rogers AEE, Elgered G (1985) Geodesy by radio interferometry: effects of atmospheric modeling errors on estimates of baseline length. Radio Sci 20(6):1593-1607 otwiera się w nowej karcie
  12. Deuber B, Morland J, Martin L, Kampfer N (2005) Deriving the tropospheric integrated water vapor from tipping curve-derived opacity near 22 GHz. Radio Sci 40:RS5011. https://doi. org/10.1029/2004RS003233 otwiera się w nowej karcie
  13. Dow JM, Neilan RE, Rizos C (2009) The international GNSS service in a changing landscape of global navigation satellite systems. J Geod 83(3):191-198. otwiera się w nowej karcie
  14. Figurski M, Araszkiewicz A, Szafranek K, Nykiel G, Podkowa A (2015) CGSREFMON 2.0-coordinates stability monitoring sys- tem of the polish GNSS reference stations. In: Conference: 15th international multidisciplinary scientific GeoConference SGEM 2015, vol 2. otwiera się w nowej karcie
  15. Guerova G, Jones J, Douša J, Dick G, de Haan S, Pottiaux E, Bock O, Pacione R, Elgered G, Vedel H, Bender M (2016) Review of the state of the art and future prospects of the ground-based GNSS meteorology in Europe. Atmos Meas Tech 9:5385-5406. otwiera się w nowej karcie
  16. Hernández-Pajares M, Juan JM, Sanz J, Orus R, Garcia-Rigo A, Feltens J, Komjathy A, Schaer SC, Krankowski A (2009) The IGS VTEC maps: a reliable source of ionospheric information since 1998. J Geod 83(3):263-275. s00190-008-0266-1 otwiera się w nowej karcie
  17. Li X, Dick G, Lu C, Ge M, Nilsson T, Ning T, Wickert J, Schuh H (2015) Multi-GNSS meteorology: real-time retrieving of atmospheric water vapor from BeiDou, Galileo, GLONASS, and GPS observations. IEEE Trans Geosci Remote Sens. https:// otwiera się w nowej karcie
  18. Maddalena RJ, Johnson CH (2005) High precision calibration of data from single-dish radio telescopes. In: American astronomical society meeting 207, id.173.02; Bull Am Astron Soc, vol 37, p 1438
  19. Marvil J (2010) EVLA Memo 143: improving the frequency resolu- tion of the default atmospheric opacity model. In: National radio astronomy observatory
  20. Mendes VB (2000) Modeling the neutral-atmospheric propagation delay in radiometric space techniques. In: UNB geodesy and geomatics engineering technical report, no. 199. http://www2.
  21. Nilsson T, Elgered G (2008) Long-term trends in the atmospheric water vapor content estimated from ground-based GPS data. J Geophys Res. otwiera się w nowej karcie
  22. Nykiel G, Zanimonskiy YM, Yampolski YM, Figurski M (2017) Efficient usage of dense GNSS networks in central europe for the visualization and investigation of ionospheric TEC varia- tions. Sensors 17(10):2298. otwiera się w nowej karcie
  23. Saastamoinen J. (1972) Atmospheric correction for the troposphere and stratosphere in ranging satellites. In: The use of artificial satellites for geodesy, geophysical monography no. 15, Ameri- can Geophysical Union, pp 247-251 otwiera się w nowej karcie
  24. Schuler T (2001) On ground-based GPS tropospheric delay estima- tion. Doctor's thesis, Studiengang Geodäsie und Geoinforma- tion, Universität der Bundeswehr München, Germany, vol 73, Neubiberg
  25. Solbrig P (2000) Untersuchungen über die Nutzung numerischer Wettermodelle zur Wasserdampfbestimmung mit Hilfe des Global Positioning Systems. Diploma thesis, Institute of Geod- esy and Navigation, University FAF Munich, Germany Song DS, Grejner-Brzezinska DA (2009) Remote sensing of atmos- pheric water variation from GPS measurements during a severe weather event. Earth Planets Space 61:1117-1125. https://doi. org/10.1186/BF03352964 otwiera się w nowej karcie
  26. Steigenberger P, Boehm J, Tesmer V (2009) Comparison of GMF/ GPT with VMF1/ECMWF and implications for atmos- pheric loading. J Geod 83:943. s00190-009-0311-8 otwiera się w nowej karcie
  27. Suresh Raju C, Saha K, Thampi BV, Parameswaran K (2007) Empiri- cal model for mean temperature for Indian zone and estimation of Page 11 of 11 9 otwiera się w nowej karcie
  28. precipitable water vapor from ground based GPS measurements. Ann Geophys 25:1935-1948 otwiera się w nowej karcie
  29. Van Malderen R, Brenot H, Pottiaux E, Beirle S, Hermans C, De Mazière M, Wagner T, De Backer H, Bruyninx C (2014) A multi- site intercomparison of integrated water vapour observations for climate change analysis. Atmos Meas Tech 7:2487-2512. https:// otwiera się w nowej karcie
  30. White SM, Zauderer BA (2009) Single dish aperture efficiency meas- urements at CARMA, CARMA Memorandum Series #49, March 13
  31. Wijaya DD, Böhm J, Karbon M, Krásná H, Schuh H (2013) Atmos- pheric pressure loading. In: Böhm J, Schuh H (eds) Atmospheric effects in space geodesy. Springer, Berlin, pp 137-157. https://doi. org/10.1007/978-3-642-36932-2_4 otwiera się w nowej karcie
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