Determination of zenith hydrostatic delay and its impact on GNSS-derived integrated water vapor

Abstract

Abstract. 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., ERAInterim) 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 ERAInterim-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 ERAInterim 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 highlatitude 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.

Adaptive Node Parameterization for Dynamic Determination of Boundaries and Nodes of GNSS Tomographic Models

“…For example, GNSS tomography utilizes GNSS-derived slant wet delays (SWDs) as the observations to construct tomographic models. The models have the potential to be used to detect and monitor the evolution of heavy rain events (Chen et al, 2017;Liu et al, 2013;Wang et al, 2017;Zhang et al, 2015). Flores et al (2000) built the first GNSS tomographic model using 4 × 4 × 40 voxels and developed software called Local Tropospheric Tomography Software (LOTTOS) for simulation and processing of GNSS data.…”

Real-Time Tropospheric Delay Retrieval from Multi-GNSS PPP Ambiguity Resolution: Validation with Final Troposphere Products and a Numerical Weather Model

“…Significant development has been achieved in the past few years [2][3][4][5][6][7][8]. In comparison with established atmospheric sounding techniques, GPS has extensive application prospects for sounding water vapor since it has the unique advantages of all-weather availability, high accuracy, long-term stability, and high time resolution [9,10].…”

A new approach for GNSS tomography from a few GNSS stations

“…Based on GNSS measurements collected from a regional or global GNSS reference network, a regional or a global tomographic model, which is three-dimensional (3-D), can be constructed. The tomographic model reflects the spatial variation of water vapor in the time period investigated, thus it has the potential to be used to investigate the evolution of heavy rain events for severe weather forecast (Wang et al, 2017;Chen et al, 2017;Zhang et al, 2015).…”