Assessment of the Accuracy of the Saastamoinen Model and VMF1/VMF3 Mapping Functions with Respect to Ray-Tracing from Radiosonde Data in the Framework of GNSS Meteorology

Abstract

In this paper, we assess, in the framework of Global Navigation Satellite System (GNSS) meteorology, the accuracy of GNSS propagation delays corresponding to the Saastamoinen zenith hydrostatic delay (ZHD) model and the Vienna Mapping function VMF1/VMF3 (hydrostatic and wet), with reference to radiosonde ray-tracing delays over a three-year period on 28 globally distributed sites. The results show that the Saastamoinen ZHD estimates have a mean root mean square (RMS) error of 1.7 mm with respect to the radiosonde. We also detected some seasonal signatures in these Saastamoinen ZHD estimates. This indicates that the Saastamoinen model, based on the hydrostatic assumption and the ground pressure, is insufficient to capture the full variability of the ZHD estimates over time with the accuracy needed for GNSS meteorology. Furthermore, we found that VMF3 slant hydrostatic delay (SHD) estimates outperform the corresponding VMF1 SHD estimates (equivalent SHD RMS error of 4.8 mm for VMF3 versus 7.1 mm for VMF1 at 5° elevation angle), with respect to the radiosonde SHD estimates. Unexpectedly, the situation is opposite for the VMF3 slant wet delay (SWD) estimates compared to VMF1 SWD estimates (equivalent SWD RMS error of 11.4 mm for VMF3 versus 7.0 mm for VMF1 at 5° elevation angle). Our general conclusion is that the joint approach using ZHD models and mapping functions must be revisited, at least in the framework of GNSS meteorology.

Temporal Analysis of GNSS-Based Precipitable Water Vapor during Rainy Days over the Philippines from 2015 to 2017

“…Essentially, the Saastamonein model is used for high-precision geodetic application on a global scale because it performs the best for all elevation angles [34]. This model is commonly used to compute for zenith hydrostatic delay (ZHD):…”

Investigation of Antarctic Precipitable Water Vapor Variability and Trend from 18 Year (2001 to 2018) Data of Four Reanalyses Based on Radiosonde and GNSS Observations

“…It should be noted that negative GNSS-derived PWV results were removed for quality assurance. Negative GNSS-derived PWV values are due to a poor performance of the underlying ZHD models (Saastamoinen model) over Antarctica [46]. In Antarctica, the PWV contents are far smaller than in other parts of the world and the GNSS-derived PWV must be considered with great care even if they are positive.…”

Analysis of Systematic Biases in Tropospheric Hydrostatic Delay Models and Construction of Correction Model

“…Moreover, with the help of succeeding researchers, uncertainties of model ZHDs have been limited in sub-millimetre level, especially using closed formulae induced from precise integral method based on hydrostatic equilibrium condition (Davis et al, 1985;Zhang et al, 2016). Thus, in fields including GNSS and VLBI, calculated figures from the ZHD models are widely used even as true values (Wang et al, 2005;Tuka and El-Mowafy, 2013;Liu et al, 2017;Feng et al, 2020), which facilitate quite a few advanced models, such as GPT series, VMF series (Boehm et al, 2006;Boehm et al, 2007;Böhm et al, 2015;Landskron and Böhm, 2018) and extrapolation models (Li et al, 2018;Hu and Yao, 2019;Li et al, 2020;Wang et al, 2022).…”

“…However, hydrostatic equilibrium will be broken if vertical wind acceleration occurs, which shall probably be influential to the accuracy of traditional ZHD models depicted by closed formulae. In fact, it has been noticed that these models show some certain systematic biases when compared with the precise integral method, which vary with locations or time yet are easily neglected (Liu et al, 2000;Chen et al, 2009;Yan et al, 2011;Dai and Zhao, 2013;Zhang et al, 2016;Feng et al, 2020). In the direction of zenith or high-altitude angle, the estimates of technologies like GNSS can hardly be affected by this kind of bias.…”