Surface pressure is a necessary meteorological variable for the accurate determination of integrated water vapor (IWV) using Global Navigation Satellite System (GNSS). The lack of pressure observations is a big issue for the conversion of historical GNSS observations, which is a relatively new area of GNSS applications in climatology. Hence the use of the surface pressure derived from either a blind model (e.g., Global Pressure and Temperature 2 wet, GPT2w) or a global atmospheric reanalysis (e.g., ERA-Interim) becomes an important alternative solution. In this study, pressure derived from these two methods is compared against the pressure observed at 108 global GNSS stations at four epochs (00:00, 06:00, 12:00 and 18:00 UTC) each day for the period 2000–2013. Results show that a good accuracy is achieved from the GPT2w-derived pressure in the latitude band between −30 and 30° and the average value of 6 h root-mean-square errors (RMSEs) across all the stations in this region is 2.5 hPa. Correspondingly, an error of 5.8 mm and 0.9 kg m⁻² in its resultant zenith hydrostatic delay (ZHD) and IWV is expected. However, for the stations located in the mid-latitude bands between −30 and −60° and between 30 and 60°, the mean value of the RMSEs is 7.3 hPa, and for the stations located in the high-latitude bands from −60 to −90° and from 60 to 90°, the mean value of the RMSEs is 9.9 hPa. The mean of the RMSEs of the ERA-Interim-derived pressure across at the selected 100 stations is 0.9 hPa, which will lead to an equivalent error of 2.1 mm and 0.3 kg m⁻² in the ZHD and IWV, respectively, determined from this ERA-Interim-derived pressure. Results also show that the monthly IWV determined using pressure from ERA-Interim has a good accuracy − with a relative error of better than 3 % on a global scale; thus, the monthly IWV resulting from ERA-Interim-derived pressure has the potential to be used for climate studies, whilst the monthly IWV resulting from GPT2w-derived pressure has a relative error of 6.7 % in the mid-latitude regions and even reaches 20.8 % in the high-latitude regions. The comparison between GPT2w and seasonal models of pressure–ZHD derived from ERA-Interim and pressure observations indicates that GPT2w captures the seasonal variations in pressure–ZHD very well.

The Global Positioning System (GPS) is a powerful atmospheric observing system for determining precipitable water vapour (PWV). In the detection of PWV using GPS, the atmospheric weighted mean temperature (T_m) is a crucial parameter for the conversion of zenith tropospheric delay (ZTD) to PWV since the quality of PWV is affected by the accuracy of T_m. In this study, an improved voxel-based T_m model, named GWMT-D, was developed using global reanalysis data over a 4-year period from 2010 to 2013 provided by the United States National Centers for Environmental Prediction (NCEP). The performance of GWMT-D was assessed against three existing empirical T_m models – GTm-III, GWMT-IV, and GTm_N – using different data sources in 2014 – the NCEP reanalysis data, surface T_m data provided by Global Geodetic Observing System and radiosonde measurements. The results show that the new GWMT-D model outperforms all the other three models with a root-mean-square error of less than 5.0 K at different altitudes over the globe. The new GWMT-D model can provide a practical alternative T_m determination method in real-time GPS-PWV remote sensing systems.