In this study, the correlation between Tm, a key variable in GNSS water vapor inversion, and surface temperature (Ts) was calculated on a global scale based on the global geodetic observing system (GGOS) atmosphere Tm 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 Tm results by linear regression model. Based on these facts, “GGOS atmosphere” Tm data and ECMWF Ts 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 Tm–Ts relationship.
Correlation Linear regression Zenith wet delay Water vapor GPS
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Thank the ECMWF for providing surface temperature data and “GGOS atmosphere” for providing global Tm data.
Rocken C, Van Hove T, Ware RH (1997) Near real-time GPS sensing of atmospheric water vapor. Geophys Res Lett 24:3221–3224CrossRefGoogle Scholar
Rocken C, Anthes R, Exner M (1997) Analysis and validation of GPS/MET data in the neutral atmosphere. J Geophys Res 102:29849–29866CrossRefGoogle Scholar
Li CG, Mao JT, Li JG et al (1999) Remote sensing precipitable water with GPS. Chin Sci Bull 44:1041–1044CrossRefGoogle Scholar
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 20:1593–1607CrossRefGoogle Scholar
Bevis M, Businger S, Chiswell S et al (1994) GPS meteorology: mapping zenith wet delays onto precipitable water. J Appl Meteorol 33:379–386CrossRefGoogle Scholar
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 97:15787–15801CrossRefGoogle Scholar
Ross RJ, Rosenfeld S (1997) Estimating mean weighted temperature of the atmosphere for global positioning system applications. J Geophys Res 102:21719–21730CrossRefGoogle Scholar
Ross RJ, Rosenfeld S (1999) Correction to “estimating mean weighted temperature of the atmosphere for global positioning system application”. J Geophys Res 104:27625CrossRefGoogle Scholar
Chen JY (1998) On the error analysis for the remote sensing of atmospheric water vapor by ground based GPS. Acta Geod Cartrogr Sin 27:114–118Google Scholar
Li JG, Mao JT, Li CC (1999) The approach to remote sensing of water vapor based on GPS and linear regression Tm in eastern region of China. Acta Meteorol Sin 57:284–291Google Scholar
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 40:5–15CrossRefGoogle Scholar
Baltink HK, van der Marel H, van der Hoeven AGA (2002) Integrated atmospheric water vapor estimates from a regional GPS network. J Geophys Res 107:4025CrossRefGoogle Scholar
Bokoye AI, Royer A, O’Neill NT et al (2003) Multisensor analysis of integrated atmospheric water vapor over Canada and Alaska. J Geophys Res 108:4480CrossRefGoogle Scholar
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 32:152–154Google Scholar
Jade S, Vijayan MSM (2008) GPS-based atmospheric precipitable water vapor estimation using meteorological parameters interpolated from NCEP global reanalysis data. J Geophys Res 113:1–12Google Scholar
Yao YB, Zhu S, Yue SQ (2012) A globally applicable, season-specific model for estimating the weighted mean temperature of the atmosphere. J Geod 86:1125–1135CrossRefGoogle Scholar