Acta Geodaetica et Geophysica

, Volume 53, Issue 2, pp 259–274 | Cite as

Developing a global model for the conversion of zenith wet tropospheric delays to integrated water vapour

Original Study
  • 21 Downloads

Abstract

The tropospheric wet delay is a significant systematic error of GNSS positioning, nevertheless it carries important information to meteorologists. It is closely related to the integrated water vapour that is the upper limit of precipitable water. The zenith wet delay can be converted to the integrated water vapour using a simple conversion factor. This conversion factor can be determined with the empirical formulae derived from radiosonde observations. In the past decades, numerous models were derived for this purpose, but all of these models rely on radiosonde observations stemming from a limited area of the globe. Although these models are valid for the area, where the underlying radiosonde observations were measured, there are several examples that these empirical formulae are used to validate GNSS based integrated water vapour estimations all over the globe. Our aim is to create a global model for the conversion of the zenith tropospheric delay to the integrated water vapour for realtime and nearrealtime applications using globally available Numerical Weather Models (NWM). Thus our model takes into consideration the fact that the model parameters strongly depend on the geographical location. 10 years of monthly mean ECMWF (European Center for Medium-Range Weather Forecast) dataset were used for the derivation of the model parameters in a grid with the resolution of 1° × 1°. The empirical coefficients of the developed models depend on two input parameters, namely the geographical location and the surface temperature measured at the station. Thus, the new models can be used for both realtime and near-realtime GNSS meteorological applications. The developed models were validated using 6 years of independent global ECMWF monthly mean analysis datasets (2011–2016). The results showed, that the application of the original models outside the area of the underlying radiosonde data sets can result in a relative systematic error of 7–8% in the estimation of the conversion factor as well as the estimated IWV values.

Keywords

Wet tropospheric delay Integrated water vapour Conversion factor GNSS meteorology 

References

  1. Askne J, Nordius H (1987) Estimation of tropospheric delay for microwaves from surface weather data. Radio Sci 22(3):379–386CrossRefGoogle Scholar
  2. Bevis M, Businger S, Herring TA, Rocken C, Anthes A, Ware R (1992) GPS meteorology: remote sensing of atmospheric water vapour using the global positioning system. J Geophys Res 97:15787–15801.  https://doi.org/10.1029/92JD01517 CrossRefGoogle Scholar
  3. Boehm J, Schuh H (2003) Vienna mapping functions. 16. Working meeting on European VLBI for geodesy and astrometry, pp 131–143Google Scholar
  4. Deniz I, Mekik C, Gurbuz G (2016) Spherical harmonics functions modelling of meteorological parameters in PWV estimation. In: Conference paper ‘living planet symposium 2016’, Prague, Czech Republic, 9–13 May 2016Google Scholar
  5. Emardson TR, Derks HJP (2000) On the relation between the wet delay and the integrated precipitable water vapour in the European atmosphere. Meteorol Appl 7:61–68.  https://doi.org/10.1017/S1350482700001377 CrossRefGoogle Scholar
  6. 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):1547–1568.  https://doi.org/10.1175/1520-0450(2003)042%3C1547:AAVOGT%3E2.0.CO;2 CrossRefGoogle Scholar
  7. Igondová M, Cibulka D (2010) Precipitable water vapour and zenith total delay time series and models over Slovakia and vicinity. Contrib Geophys Geodesy 40(4):299–312.  https://doi.org/10.2478/v10126-010-0012-6 Google Scholar
  8. ISO 2533: 1975 standard, Standard Atmosphere (1975)Google Scholar
  9. Mekik C, Deniz I (2016) Modelling and validation of the weighted mean temperature for Turkey. Meteorol Appl 24(1):92–100.  https://doi.org/10.1002/met.1608 CrossRefGoogle Scholar
  10. Nafisi V, Madzak M, Böhm J, Ardalan A, Schuh H (2012) Ray-traced tropospheric delays in VLBI analysis. Radio Sci 47:2.  https://doi.org/10.1029/2011RS004918 CrossRefGoogle Scholar
  11. Rocken C, Sokolovskiy S, Johnson JM, Hunt D (2001) Improved mapping of tropospheric delays. J Atmos Ocean Technol 18:1205–1213.  https://doi.org/10.1175/1520-0426(2001)018%3C1205:IMOTD%3E2.0.CO;2 CrossRefGoogle Scholar
  12. Rózsa SZ, Dombai F, Németh P, Ablonczy D (2009) Integrált vízgőztartalom becslése GPS adatok alapján Geomatikai Közlemények XII. pp 187–196Google Scholar
  13. Rózsa SZ, Weidinger T, Gyöngyösi AZ, Kenyeres A (2012) The role of GNSS infrastructure in the monitoring of atmospheric water vapor. Q J Hung Meteorol Serv 116:1–20Google Scholar
  14. Thayer GD (1974) An improved equation for the radio refractive index of air. Radio Sci 9:803–807CrossRefGoogle Scholar
  15. World Meteorological Organization (WMO) (2008) Guide to meteorological instruments and methods of observation. WMO-No. 8Google Scholar

Copyright information

© Akadémiai Kiadó 2018

Authors and Affiliations

  1. 1.Department of Geodesy and SurveyingBudapest University of Technology and EconomicsBudapestHungary

Personalised recommendations