Development of a regional tropospheric delay model for GPS-based navigation with emphasis to the Indian Region

Radio Science ◽  
2008 ◽  
Vol 43 (4) ◽  
pp. n/a-n/a ◽  
Author(s):  
K. Parameswaran ◽  
Korak Saha ◽  
C. Suresh Raju
Keyword(s):  
Indian Region ◽  
Tropospheric Delay ◽  
Delay Model

scholarly journals An improved global zenith tropospheric delay model GZTD2 considering diurnal variations

2016 ◽  
Author(s):  
YiBin Yao ◽  
YuFeng Hu ◽  
Chen Yu ◽  
Bao Zhang ◽  
JianJian Guo
Keyword(s):  
Diurnal Variations ◽  
Atmospheric Parameter ◽  
Tropospheric Delay ◽  
Delay Model ◽  
Grid Data ◽  
International Gnss Service ◽  
Data Set ◽  
Average Bias ◽  
Global Average ◽  
Zenith Tropospheric Delay

Abstract. The zenith tropospheric delay (ZTD) is an important atmospheric parameter in the wide application of GNSS technology in geoscience. Given that the temporal resolution of the current Global Zenith Tropospheric Delay model (GZTD) is only 24 h, an improved model GZTD2 has been developed by taking the diurnal variations into consideration and modifying the model expansion function. The data set used to establish this model is the global ZTD grid data provided by Global Geodetic Observing System (GGOS) Atmosphere spanning from 2002 to 2009. We validated the proposed model with respect to ZTD grid data from GGOS Atmosphere, which was not involved in modeling, as well as International GNSS Service (IGS) tropospheric product. The obtained results of ZTD grid data show that the global average Bias and RMS for GZTD2 model are 0.2 cm and 3.8 cm respectively. The global average Bias is comparable to that of GZTD model, but the global average RMS is improved by 3 mm. The Bias and RMS are far better than EGNOS model and the UNB series models. The testing results from global IGS tropospheric product show the Bias and RMS (−0.3 cm and 3.9 cm) of GZTD2 model are superior to that of GZTD (−0.3 cm and 4.2 cm), suggesting higher accuracy and reliability compared to the EGNOS model, as well as the UNB series models.

scholarly journals Empirical Orthogonal Function Analysis and Modeling of Global Tropospheric Delay Spherical Harmonic Coefficients

2021 ◽  
Vol 13 (21) ◽  
pp. 4385
Author(s):  
Yongchao Ma ◽  
Hang Liu ◽  
Guochang Xu ◽  
Zhiping Lu
Keyword(s):  
Empirical Orthogonal Function ◽  
Spherical Harmonic ◽  
Function Analysis ◽  
Meteorological Data ◽  
Regional Scale ◽  
Global Scale ◽  
Tropospheric Delay ◽  
Delay Model ◽  
Product Model ◽  
Orthogonal Function

Based on the ERA-5 meteorological data from 2015 to 2019, we establish the global tropospheric delay spherical harmonic (SH) coefficients set called the SH_set and develop the global tropospheric delay SH coefficients empirical model called EGtrop using the empirical orthogonal function (EOF) method and periodic functions. We apply tropospheric delay derived from IGS stations not involved in modeling as reference data for validating the dataset, and statistical results indicate that the global mean Bias of the SH_set is 0.08 cm, while the average global root mean square error (RMSE) is 2.61 cm, which meets the requirements of the tropospheric delay model applied in the wide-area augmentation system (WAAS), indicating the feasibility of the product strategy. The tropospheric delay calculated with global sounding station and tropospheric delay products of IGS stations in 2020 are employed to validate the new product model. It is verified that the EGtrop model has high accuracy with Bias and RMSE of −0.25 cm and 3.79 cm, respectively, with respect to the sounding station, and with Bias and RMSE of 0.42 cm and 3.65 cm, respectively, with respect to IGS products. The EGtrop model is applicable not only at the global scale but also at the regional scale and exhibits the advantage of local enhancement.

scholarly journals Establishment of a Real-Time Local Tropospheric Fusion Model

2019 ◽  
Vol 11 (11) ◽  
pp. 1321 ◽  
Author(s):  
Yibin Yao ◽  
Xingyu Xu ◽  
Chaoqian Xu ◽  
Wenjie Peng ◽  
Yangyang Wan
Keyword(s):  
Real Time ◽  
Meteorological Data ◽  
Rapid Development ◽  
Water Vapor Pressure ◽  
Auxiliary Information ◽  
Satellite System ◽  
Tropospheric Delay ◽  
Navigation Systems ◽  
Delay Model ◽  
Fusion Model

The tropospheric delay is one major error source affecting the precise positioning provided by the global navigation satellite system (GNSS). This error occurs because the GNSS signals are refracted while travelling through the troposphere layer. Nowadays, various types of model can produce the tropospheric delay. Among them, the globally distributed GNSS permanent stations can resolve the tropospheric delay with the highest accuracy and the best continuity. Meteorological models, such as the Saastamoinen model, provide formulae to calculate temperature, pressure, water vapor pressure and subsequently the tropospheric delay. Some grid-based empirical tropospheric delay models directly provide tropospheric parameters at a global scale and in real time without any auxiliary information. However, the spatial resolution of the GNSS tropospheric delay is not sufficient, and the accuracy of the meteorological and empirical models is relatively poor. With the rapid development of satellite navigation systems around the globe, the demand for real-time high-precision GNSS positioning services has been growing dramatically, requiring real-time and high-accuracy troposphere models as a critical prerequisite. Therefore, this paper proposes a multi-source real-time local tropospheric delay model that uses polynomial fitting of ground-based GNSS observations, meteorological data, and empirical GPT2w models. The results show that the accuracy in the zenith tropospheric delay (ZTD) of the proposed tropospheric delay model has been verified with a RMS (root mean square) of 1.48 cm in active troposphere conditions, and 1.45 cm in stable troposphere conditions, which is significantly better than the conventional tropospheric GPT2w and Saastamoinen models.

A simplified GNSS tropospheric delay model based on the nonlinear hypothesis

GPS Solutions ◽  
2017 ◽  
Vol 21 (4) ◽  
pp. 1735-1745 ◽  
Author(s):  
Jinlong Sun ◽  
Zhilu Wu ◽  
Zhendong Yin ◽  
Bo Ma
Keyword(s):  
Tropospheric Delay ◽  
Delay Model ◽  
Model Based

New versions of the BDS/GNSS zenith tropospheric delay model IGGtrop

2014 ◽  
Vol 89 (1) ◽  
pp. 73-80 ◽  
Author(s):  
Wei Li ◽  
Yunbin Yuan ◽  
Jikun Ou ◽  
Yanju Chai ◽  
Zishen Li ◽  
...  
Keyword(s):  
Tropospheric Delay ◽  
Delay Model ◽  
Zenith Tropospheric Delay

Evaluation of EGNOS Tropospheric Delay Model in South-Eastern Europe

2009 ◽  
Vol 62 (2) ◽  
pp. 341-349 ◽  
Author(s):  
Tomislav Kos ◽  
Maja Botincan ◽  
Ivan Markezic
Keyword(s):  
Real Time ◽  
Meteorological Parameters ◽  
Prediction Models ◽  
Signal Propagation ◽  
Electromagnetic Signal ◽  
Tropospheric Delay ◽  
Delay Model ◽  
Signal Delay ◽  
One Year ◽  
Delay Prediction

The troposphere affects electromagnetic signal propagation causing signal path bending and the alteration of the electromagnetic wave velocity. Tropospheric delay can introduce a considerable error in satellite positioning if it is not properly estimated. The GPS signal delay can vary from 2 to 20 m depending on the elevation angles between the receiver and the satellite. Two basic types of delay prediction models exist. The first use surface meteorological parameters to estimate the value of the tropospheric delay, and the other models that do not require real-time meteorological input use average and seasonal variation data related to the receiver's latitude and day-of-year. This paper compares the performance of both types of model over a period of one year, comprising all seasons, to verify their accuracy over a longer period. The Saastamoinen model, known as one of the best performing prediction models, was taken as a reference and the global EGNOS model was used to check how the global estimates of the yearly averages of the meteorological parameters and their related seasonal variations comply with the real-time surface parameters.

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

2020 ◽  
Vol 12 (1) ◽  
pp. 165 ◽  
Author(s):  
Junping Chen ◽  
Jungang Wang ◽  
Ahao Wang ◽  
Junsheng Ding ◽  
Yize Zhang
Keyword(s):  
Meteorological Data ◽  
Grid Point ◽  
Mainland China ◽  
Propagation Delay ◽  
Convergence Time ◽  
Tropospheric Delay ◽  
Delay Model ◽  
Observation Network ◽  
Surrounding Areas ◽  
Improved Accuracy

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.

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