Journal of Geodesy

, Volume 88, Issue 3, pp 273–282 | Cite as

Improved one/multi-parameter models that consider seasonal and geographic variations for estimating weighted mean temperature in ground-based GPS meteorology

  • Yibin YaoEmail author
  • Bao Zhang
  • Chaoqian Xu
  • Feng Yan
Original Article


In ground-based GPS meteorology, weighted mean temperature is the key parameter to calculate the conversion factor which will be used to map zenith wet delay to precipitable water vapor. In practical applications, we can hardly obtain the vertical profiles of meteorological parameters over the site, thus cannot use the integration method to calculate weighted mean temperature. In order to exactly calculate weighted mean temperature from a few meteorological parameters, this paper studied the relation between weighted mean temperature and surface temperature, surface water vapor pressure and surface pressure, and determined the relationship between, on the one hand, the weighted mean temperature, and, on the other hand, the surface temperature and surface water vapor pressure. Considering the seasonal and geographic variations in the relationship, we employed the trigonometry functions with an annual cycle and a semi-annual cycle to fit the residuals (seasonal and geographic variations are reflected in the residuals). Through the above work, we finally established the GTm-I model and the PTm-I model with a \(2^{\circ }\times 2.5^{\circ }(\mathrm{lat}\times \mathrm{lon})\) resolution. Test results show that the two models both show a consistent high accuracy around the globe, which is about 1.0 K superior to the widely used Bevis weighted mean temperature–surface temperature relationship in terms of root mean square error.


GPS meteorology Weighted mean temperature Zenith wet delay Precipitable water vapor 



Constellation observing system of meteorology, ionosphere, and climate


European Centre for Medium-Range Weather Forecasts


Global Positioning System


Integrated global radiosonde archive


International GNSS service


Numerical weather prediction


Precipitable water vapor


Root mean square error


Zenith hydrostatic delay


Zenith total delay


Zenith wet delay



The authors would like to thank IGRA for providing access to the web-based IGRA data and “GGOS Atmosphere” for providing grids of \(T_\mathrm{m}\) and COSMIC for the occultation data and ECMWF for temperature and dew point temperature data. We will also thank Prof. Johannes Böhm for his kind help. This research was supported by the National Natural Science Foundation of China (41174012; 41274022) and The National High Technology Research and Development Program of China (2013AA122502).

Supplementary material

190_2013_684_MOESM1_ESM.docx (37 kb)
Supplementary material 1 (docx 37 KB)


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.School of Geodesy and GeomaticsWuhan UniversityWuhanChina

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