Science China Earth Sciences

, Volume 58, Issue 8, pp 1361–1369 | Cite as

A global empirical model for mapping zenith wet delays onto precipitable water vapor using GGOS Atmosphere data

  • YiBin Yao
  • ChaoQian Xu
  • Bao Zhang
  • Na Cao
Research Paper


The importance of water vapor in research of global climate change and weather forecast cannot be over emphasized; therefore substantial efforts have been made in exploring the optimal methods to measure water vapor. It is well-established that with a conversion factor, zenith wet delays can be mapped onto precipitable water vapor (PWV). However, the determination of the exact conversion factor depends heavily on the accurate calculation of a key variable, weighted mean temperature of the troposphere (T m). As a critical parameter in Global Positioning System (GPS) meteorology, T m has recently been modeled into a global grid known as GWMT. The GWMT model only requires the location and the day of year to calculate T m. Despite the advantages that the GWMT model offers, anomalies still exist in oceanic areas due to low sampling resolution. In this study, we refine the GWMT model by incorporating the global T m grid from Global Geodetic Observing System (GGOS) and obtain an improved model, GWMT-G. The results indicate that the GWMT-G model successfully addresses the anomaly in oceanic areas in the GWMT model and significantly improves the accuracy of T m in other regions.


GPS meteorology zenith wet delay GWMT model GWMT-G model GGOS 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Askne J, Nordius H. 1987. Estimation of tropospheric delay for mircowaves from surface weather data. Radio Sci, 22: 379–386CrossRefGoogle Scholar
  2. Bevis M, Businger S, Herring A T, et al. 1992. GPS meteorology: Remote sensing of atmospheric water vapor using the global positioning system. J Geophys Res, 97: 15787–15801CrossRefGoogle Scholar
  3. Bevis M, Businger S, Chiswell S et al. 1994. GPS meteorology: Map-ping zenith wet delays onto precipitable water. J Appl Meteorol, 33: 379–386CrossRefGoogle Scholar
  4. Boehm J, Heinkelmann R, Schuh H. 2007. Short Note: A global model of pressure and temperature for geodetic applications. J Geod, 81: 679–683CrossRefGoogle Scholar
  5. Byun S H, Bar-Sever Y E. 2009. A new type of troposphere zenith path delay product of the international GNSS service. J Geod, 83: 1–7CrossRefGoogle Scholar
  6. Chen J Y. 1998. On the error analysis for the remote sensing of atmospheric water vapor by ground based GPS. Acta Geod Cartogr Sin, 27: 113–118Google Scholar
  7. Ding J C. 2009. GPS Meteorology and Its Applications. Beijing: China Meteorological Press. 1–10Google Scholar
  8. Duan J, Bevis M, Fang P, et al. 1996. GPS meteorology: Direct estimation of the absolute value of precipitable water. J Appl Meteorol, 35: 830–838CrossRefGoogle Scholar
  9. Gu X P. 2004. Research on retrieval of GPS water vapor and method of rainfall forecast. Doctoral Dissertation. Beijing: China Agricultural University. 1–15Google Scholar
  10. Li J G, Mao J T, Li C C. 1999. The approach to remote sensing of water vapor based on GPS and linear regression T m in eastern region of China. Acta Meteorol Sin, 57: 283–292Google Scholar
  11. Rocken C, Ware R, Van Hove T, et al. 1993. Sensing atmospheric water vapor with the global positioning system. Geophys Res Lett, 20: 2631–2634CrossRefGoogle Scholar
  12. Ross R J, Rosenfeld S. 1997. Estimating mean weighted temperature of the atmosphere for Global Positioning System. J Geophys Res, 102: 21719–21730CrossRefGoogle Scholar
  13. Saastamoinen J. 1972. Atmospheric correction for the troposphere and stratosphere in radio ranging satellites. In: Soren W. Henriksen, Armando Mancini, Bernard H. Chovitz, eds. The Use of Artificial Satellites for Geodesy, Geophysics Monograph Series, Vol. 15. Washington DC: American Geophysical Union. 247–251CrossRefGoogle Scholar
  14. Xu C Q, Shi J B, Guo J M, et al. 2011. Analysis of combining ground-based GPS network and space-based COSMIC occultation observation for precipitable water vapor application within China. Geomat Inf Sci Wuhan Univ, 36: 467–470Google Scholar
  15. Yao Y, Zhu S, Yue S. 2012. A globally applicable, season-specific model for estimating the weighted mean temperature of the atmosphere. J Geod, 86: 1125–1135CrossRefGoogle Scholar
  16. Yu S J. 2011. Remote sensing of water vapor based on ground GPS observations. Doctoral Dissertation. Wuhan: Institute of Geodesy and Geophysics, Chinese Academy of Sciences. 37–50Google Scholar

Copyright information

© Science China Press and Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  1. 1.School of Geodesy and GeomaticsWuhan UniversityWuhanChina
  2. 2.Key Laboratory of Geospace Environment and Geodesy, Ministry of EducationWuhan UniversityWuhanChina

Personalised recommendations