Establishment of a Site-Specific Tropospheric Model Based on Ground Meteorological Parameters over the China Region

Abstract

China is a country of vast territory with complicated geographical environment and climate conditions. With the rapid progress of the Chinese BeiDou satellite navigation system (BDS); more accurate tropospheric models must be applied to improve the accuracy of navigation and positioning. Based on the formula of the Saastamoinen and Callahan models; this study develops two single-site tropospheric models (named SAAS_S and CH_S models) for the Chinese region using radiosonde data from 2005 to 2012. We assess the two single-site tropospheric models with radiosonde data for 2013 and zenith tropospheric delay (ZTD) data from four International GNSS Service (IGS) stations and compare them to the results of the Saastamoinen and Callahan models. The experimental results show that: the mean accuracy of the SAAS_S model (bias: 0.19 cm; RMS: 3.19 cm) at all radiosonde stations is superior to those of the Saastamoinen (bias: 0.62 cm; RMS: 3.62 cm) and CH_S (bias: −0.05 cm; RMS: 3.38 cm) models. In most Chinese regions; the RMS values of the SAAS_S and CH_S models are about 0.51~2.12 cm smaller than those of their corresponding source models. The SAAS_S model exhibits a clear improvement in the accuracy over the Saastamoinen model in low latitude regions. When the SAAS_S model is replaced by the SAAS model in the positioning of GNSS; the mean accuracy of vertical direction in the China region can be improved by 1.12~1.55 cm and the accuracy of vertical direction in low latitude areas can be improved by 1.33~7.63 cm. The residuals of the SAAS_S model are closer to a normal distribution compared to those of the Saastamoinen model. Single-site tropospheric models based on the short period of the most recent data (for example 2 years) can also achieve a satisfactory accuracy. The average performance of the SAAS_S model (bias: 0.83 cm; RMS: 3.24 cm) at four IGS stations is superior to that of the Saastamoinen (bias: −0.86 cm; RMS: 3.59 cm) and CH_S (bias: 0.45 cm; RMS: 3.38 cm) models.

Establishment and Evaluation of a New Meteorological Observation-Based Grid Model for Estimating Zenith Wet Delay in Ground-Based Global Navigation Satellite System (GNSS)

“…The RMSs are larger in the low-latitude regions and smaller in the high-latitude regions, whereas the RRMSs are larger in the high-latitude regions and smaller in the low-latitude regions. The difference between the geographic distribution of RMSs and RRMSs is linked to to the increased humidity and the less synoptic variability in the low-latitude regions [3,4,8]. The GridZWD model shows smaller uncertainty than the other three models in all of the latitude bands, indicating the performance of the new built model for estimating ZWD with higher accuracy and robustness.…”

“…The coefficients of four grid points closest to the GNSS receiver are used to calculate the coefficients of this location employing bilinear interpolation methodology. Then, the ZWD at the height of the water vapor pressure can be derived using Equations (7) and (8) with the obtained water vapor pressure. If the height of the water vapor pressure and the height of the GNSS receiver are different, a vertical correction is necessary to reduce the uncertainty of the derived ZWD value.…”

“…The Saastamoinen [6] and the Hopfield models [7] are widely used in-situ meteorological observation-based models. Note that the Saastamoinen-ZHD model can rather accurately estimate ZHD with measured pressure, whereas the Saastamoinen-ZWD model shows lower accuracy for the sophisticated variation of ZWD [8]. The empirical model does not depend on meteorological data and only the location (latitude, longitude and altitude) of a GNSS receiver and the specific time are required as inputs.…”

“…The coefficients of the existing in-situ meteorological observation-based models are usually globally uniform [8], rendering them insufficient to express geographic difference and reflect regional characteristics. ZWD series derived from empirical models are always "too smooth", thus having low capability to capture the sudden changes or spike-shaped peaks emerging during the real meteorological process.…”

A Global Model for Estimating Tropospheric Delay and Weighted Mean Temperature Developed with Atmospheric Reanalysis Data from 1979 to 2017

“…The existing empirical models are roughly divided into two types. The first type models meteorological parameters and uses traditional tropospheric delay models, e.g., the Saastamoinen model or similar models, to calculate tropospheric delay [8][9][10][11][12][13][14][15]. The second type models tropospheric delay directly, which provides empirical tropospheric delay free of meteorological parameters [16][17][18].…”