Skip to main content
SpringerLink
Log in

Analysis of the global T m–T s correlation and establishment of the latitude-related linear model

  • Article
  • Geophysics
  • Published:
Chinese Science Bulletin

Abstract

In this study, the correlation between T m, a key variable in GNSS water vapor inversion, and surface temperature (T s) was calculated on a global scale based on the global geodetic observing system (GGOS) atmosphere T m data and European centre for medium-range weather forecasts (ECMWF) surface temperature data. The results show that their correlation is mainly affected by latitudes, and the correlation is stronger at high latitudes and weaker at low latitudes. Although the correlation is relatively weak in the tropic areas, the temperature changes so little in a year in these areas that we can still achieve good T m results by linear regression model. Based on these facts, “GGOS atmosphere” T m data and ECMWF T s data from 2005 to 2011 were used to establish the global latitude-related linear regression model. The new model has root mean square error (RMSE) of 3.2, 3.3, and 4.4 K, respectively, compared with respect to the “GGOS atmosphere” data, COSMIC data, and radiosonde data and is more accurate than the Bevis T m–T s relationship.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. Rocken C, Van Hove T, Ware RH (1997) Near real-time GPS sensing of atmospheric water vapor. Geophys Res Lett 24:3221–3224

    Article  Google Scholar 

  2. Rocken C, Anthes R, Exner M (1997) Analysis and validation of GPS/MET data in the neutral atmosphere. J Geophys Res 102:29849–29866

    Article  Google Scholar 

  3. Li CG, Mao JT, Li JG et al (1999) Remote sensing precipitable water with GPS. Chin Sci Bull 44:1041–1044

    Article  Google Scholar 

  4. Davis JL, Herring TA, Shapiro II et al (1985) Geodesy by radio interferometry: effects of atmospheric modeling errors on estimates of baseline length. Radio Sci 20:1593–1607

    Article  Google Scholar 

  5. Bevis M, Businger S, Chiswell S et al (1994) GPS meteorology: mapping zenith wet delays onto precipitable water. J Appl Meteorol 33:379–386

    Article  Google Scholar 

  6. Bevis M, Businger S, Herring TA et al (1992) GPS meteorology: remote sensing of atmospheric water vapor using the global positioning system. J Geophys Res 97:15787–15801

    Article  Google Scholar 

  7. Ross RJ, Rosenfeld S (1997) Estimating mean weighted temperature of the atmosphere for global positioning system applications. J Geophys Res 102:21719–21730

    Article  Google Scholar 

  8. Ross RJ, Rosenfeld S (1999) Correction to “estimating mean weighted temperature of the atmosphere for global positioning system application”. J Geophys Res 104:27625

    Article  Google Scholar 

  9. Chen JY (1998) On the error analysis for the remote sensing of atmospheric water vapor by ground based GPS. Acta Geod Cartrogr Sin 27:114–118

    Google Scholar 

  10. Li JG, Mao JT, Li CC (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 57:284–291

    Google Scholar 

  11. Liou YA, Teng YT, Van Hove T et al (2001) Comparison of precipitable water observations in the near tropics by GPS, microwave radiometer, and radiosondes. J Appl Meteorol 40:5–15

    Article  Google Scholar 

  12. Baltink HK, van der Marel H, van der Hoeven AGA (2002) Integrated atmospheric water vapor estimates from a regional GPS network. J Geophys Res 107:4025

    Article  Google Scholar 

  13. Bokoye AI, Royer A, O’Neill NT et al (2003) Multisensor analysis of integrated atmospheric water vapor over Canada and Alaska. J Geophys Res 108:4480

    Article  Google Scholar 

  14. Wang Y, Liu LT, Xu HZ et al (2007) Retrieving change of precipitable water vapor in Chinese Mainland by GPS technique. Geom Inf Sci Wuhan Univ 32:152–154

    Google Scholar 

  15. Jade S, Vijayan MSM (2008) GPS-based atmospheric precipitable water vapor estimation using meteorological parameters interpolated from NCEP global reanalysis data. J Geophys Res 113:1–12

    Google Scholar 

  16. Yao YB, Zhu S, Yue SQ (2012) A globally applicable, season-specific model for estimating the weighted mean temperature of the atmosphere. J Geod 86:1125–1135

    Article  Google Scholar 

Download references

Acknowledgements

Thank the ECMWF for providing surface temperature data and “GGOS atmosphere” for providing global T m data.

Author information

Authors and Affiliations

  1. School of Geodesy and Geomatics, Wuhan University, Wuhan, 430079, China

    Yibin Yao, Bao Zhang, Chaoqian Xu & Jiajun Chen

Authors
  1. Yibin Yao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bao Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Chaoqian Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jiajun Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yibin Yao.

About this article

Cite this article

Yao, Y., Zhang, B., Xu, C. et al. Analysis of the global T m–T s correlation and establishment of the latitude-related linear model. Chin. Sci. Bull. 59, 2340–2347 (2014). https://doi.org/10.1007/s11434-014-0275-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11434-014-0275-9

Keywords

Search

Navigation