Development and evaluation of the refined zenith tropospheric delay (ZTD) models

Fei Yang,Jiming Guo,Debao Yuan,Ming Chen,Xiaolin Meng

doi:10.1186/s43020-021-00052-0

Abstract

The tropospheric delay is a significant error source in Global Navigation Satellite System (GNSS) positioning and navigation. It is usually projected into zenith direction by using a mapping function. It is particularly important to establish a model that can provide stable and accurate Zenith Tropospheric Delay (ZTD). Because of the regional accuracy difference and poor stability of the traditional ZTD models, this paper proposed two methods to refine the Hopfield and Saastamoinen ZTD models. One is by adding annual and semi-annual periodic terms and the other is based on Back-Propagation Artificial Neutral Network (BP-ANN). Using 5-year data from 2011 to 2015 collected at 67 GNSS reference stations in China and its surrounding regions, the four refined models were constructed. The tropospheric products at these GNSS stations were derived from the site-wise Vienna Mapping Function 1 (VMP1). The spatial analysis, temporal analysis, and residual distribution analysis for all the six models were conducted using the data from 2016 to 2017. The results show that the refined models can effectively improve the accuracy compared with the traditional models. For the Hopfield model, the improvement for the Root Mean Square Error (RMSE) and bias reached 24.5/49.7 and 34.0/52.8 mm, respectively. These values became 8.8/26.7 and 14.7/28.8 mm when the Saastamoinen model was refined using the two methods. This exploration is conducive to GNSS navigation and positioning and GNSS meteorology by providing more accurate tropospheric prior information.

Highlights

During propagating through the neutral atmosphere, Global Navigation Satellite System (GNSS) signals from a satellite to a receiver will be delayed and bent due to their interaction with dry gases and water particles, which is called tropospheric delay (Bevis et al, 1992; Yao et al, 2018)
To refine the Saastamoinen model and the Hopfield model, two methods were introduced, namely the method by adding annual and semi-annual periodic terms and the method based on the Back-Propagation Artificial Neutral Network (BP-Artificial Neural Network (ANN))
Four refined Zenith Tropospheric Delay (ZTD) models are established using the ZTD products provided by the site-wise Vienna Mapping Function 1 (VMF1)

Summary

Introduction

During propagating through the neutral atmosphere, Global Navigation Satellite System (GNSS) signals from a satellite to a receiver will be delayed and bent due to their interaction with dry gases and water particles, which is called tropospheric delay (Bevis et al, 1992; Yao et al, 2018). The ZTDs estimated by the above two types of empirical models have generally poorer results than those with Saastamoinen and Hopfield model based on the measured meteorological data.

Results

Conclusion