SHAtropE—A Regional Gridded ZTD Model for China and the Surrounding Areas

Abstract

A regional zenith tropospheric delay (ZTD) empirical model, referred to as SHAtropE (SHanghai Astronomical observatory tropospheric delay model—Extended), is developed and provides tropospheric propagation delay corrections for users in China and the surrounding areas with improved accuracy. The SHAtropE model was developed based on the ZTD time series of the continuous GNSS sites from the Crustal Movement Observation Network of China (CMONOC) and GNSS sites of surrounding areas. It combines the exponential and periodical functions and is provided as regional grids with a resolution of 2.5° × 2.0° in longitude and latitude. At each grid point, the exponential function converts the ZTD from the site height to the ellipsoid, and the periodical terms, including both annual and semi-annual periods, describe ZTD’s temporal variation. Moreover, SHAtropE also provides the predicted ZTD uncertainty, which is valuable in Precise Point Positioning (PPP) with ZTD being constrained for faster convergence. The data of 310 GNSS sites over 7 years were used to validate the new model. Results show that the SHAtropE ZTD has an accuracy of 3.5 cm in root mean square (RMS) quantity, which has a mean improvement of 35.2% and 5.4% over the UNB3m (5.4 cm) and GPT3 (3.7 cm) models, respectively. The predicted uncertainty of SHAtropE ZTD shows seasonal variations, where the values are larger in summer than in winter. By applying the SHAtropE model in the static PPP, the convergence time of GPS-only and BDS-only solutions are reduced by 8.1% and 14.5% respectively compared to the UNB3m model, and the reductions are 6.9% and 11.2% respectively for the GPT3 model. As no meteorological data are required for the implementation of the model, the SHAtropE could thus be a refined tropospheric model for GNSS users in mainland China and the surrounding areas. The method of modeling the ZTD uncertainty can also be used in further global tropospheric delay modeling.

Modeling wide-area tropospheric delay corrections for fast PPP ambiguity resolution

“…To decorrelate the ZTD and station coordinate parameters during the GNSS processing, precise external tropospheric delay products are used as a priori constraint, e.g., the tropospheric delay from numerical weather model (NWM) (Hobiger et al 2008;Andrei and Chen 2009;Lu et al 2017;Wilgan et al 2017), empirical tropospheric delay models such as global pressure and temperature 2 (GPT2w) and GZTD2 (Böhm et al 2015;Yao et al 2016;Chen et al 2020), in situ instrument measurements such as water vapor radiometer (Ware et al 1993;Alber et al 1997), and Raman lidar (Bock et al 2001;Bosser et al 2009). On the other hand, using the tropospheric delay estimates of a regional GNSS reference network, a correction model can be generated and broadcasted to the users within the region, that is, the tropospheric delay regional augmentation (Takeichi et al 2009;Fund et al 2011;Kalinnikov et al 2012).…”

Development and Assessment of Improved Global Pressure and Temperature Series Models

“…With the widespread use of GPT series models, many relevant studies have emerged to improve or validate the performance of these models [11][12][13][14][15][16][17][18][19][20][21][22]. However, most validations are conducted at the surface rather than at higher altitudes.…”

BDS Satellite-Based Augmentation Service Correction Parameters and Performance Assessment

“…The initial satellite orbits and clocks were calculated from the broadcast ephemeris, and they were corrected using the received BDS SBAS corrections by Equations (2) and (4) at each epoch. The tropospheric delay was corrected using the SHAtropE model [27] and GMF mapping function [28], which were identical to the processing model of BDS GCS. It should be noted that the rest of the wet tropospheric delay was not necessary to be estimated as a random walk parameter since the PCC contains tropospheric model error and this part of the error would be modelled very well in areas near the partition center [20].…”