Compensation Method of Tropospheric Delay Model Error for Ground Navigation using Meteorological Data in Korea

2016 ◽  
Vol 19 (2) ◽  
pp. 163-170
Author(s):  
Hyoungmin So ◽  
Kihoon Lee ◽  
Junpyo Park
Keyword(s):  
Meteorological Data ◽  
Model Error ◽  
Compensation Method ◽  
Tropospheric Delay ◽  
Delay Model

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.

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.

scholarly journals Investigation on the Impact of Tropospheric Delay on GPS Height Variation near the Equator

2009 ◽  
Vol 4 (3) ◽  
Author(s):  
Joseph D Dodo ◽  
Mohd Hafiz Yahya ◽  
Nor Bin Kamarudin
Keyword(s):  
Meteorological Data ◽  
Simulated Data ◽  
Signal Propagation ◽  
Equatorial Region ◽  
Tropospheric Delay ◽  
Delay Model ◽  
Height Variation ◽  
Maximum Delay ◽  
Height Component ◽  
The Impact

One of the major problems currently facing satellite-based positioning is the atmospheric refraction of the GPS signal caused by the troposphere. The tropospheric effect is much more pronounced at the equatorial region due to its hot and wet conditions. This affects the GPS signal due to the variability of the refractive index, which in turn affects the positional accuracy, especially in the height components. This paper presents a study conducted in the Southern Peninsular Malaysia located at the equatorial region, to investigate the impact of tropospheric delay on GPS height variation. Four campaigns were launched with each campaign lasting for three days. The Malaysian real-time kinematic GPS network (MyRTKnet) reference stations in Johor Bahru were used. GPS RINEX data from these stations were integrated with ground meteorological data observed concurrently from a GPS station located at the Universiti Teknologi Malaysia (UTM), at varying antenna heights for each session of observation. A developed computer program called TROPO.exe based on the Saastamoinen tropospheric delay model was used in estimating the amount of tropospheric delay. The result reveals that, there is inconsistency in the delay variation, reaching maximum delay of 18 m in pseudo-range measurement. The height component shows variations with a maximum value of 119.100 cm and a minimum value of 37.990 cm. The result of the simulated data shows 5.00 m of differences in height gives an effect or improvement of 1.3 mm in signal propagation. This indicates that, tropospheric delay decreases with increase in antenna height.

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.

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 Accuracy Assessment Study of UNB3m Neutral Atmosphere Model for Global Tropospheric Delay Mitigation

2015 ◽  
Vol 50 (4) ◽  
pp. 201-215
Author(s):  
Ashraf Farah
Keyword(s):  
Accuracy Assessment ◽  
Meteorological Data ◽  
Empirical Models ◽  
Water Vapour Pressure ◽  
Tropospheric Delay ◽  
Navigation Systems ◽  
Neutral Atmosphere ◽  
Positioning Errors ◽  
Assessment Study ◽  
Meteorological Models

Abstract Tropospheric delay is the second major source of error after the ionospheric delay for satellite navigation systems. The transmitted signal could face a delay caused by the troposphere of over 2m at zenith and 20m at lower satellite elevation angles of 10 degrees and below. Positioning errors of 10m or greater can result from the inaccurate mitigation of the tropospheric delay. Many techniques are available for tropospheric delay mitigation consisting of surface meteorological models and global empirical models. Surface meteorological models need surface meteorological data to give high accuracy mitigation while the global empirical models need not. Several hybrid neutral atmosphere delay models have been developed by (University of New Brunswick, Canada) UNB researchers over the past decade or so. The most widely applicable current version is UNB3m, which uses the Saastamoinen zenith delays, Niell mapping functions, and a look-up table with annual mean and amplitude for temperature, pressure, and water vapour pressure varying with respect to latitude and height. This paper presents an assessment study of the behaviour of the UNB3m model compared with highly accurate IGS-tropospheric estimation for three different (latitude/height) IGS stations. The study was performed over four nonconsecutive weeks on different seasons over one year (October 2014 to July 2015). It can be concluded that using UNB3m model gives tropospheric delay correction accuracy of 0.050m in average for low latitude regions in all seasons. The model's accuracy is about 0.075m for medium latitude regions, while its highest accuracy is about 0.014m for high latitude regions.

scholarly journals ATMOSPHERIC PHASE DELAY IN SENTINEL SAR INTERFEROMETRY

2018 ◽  
Vol XLII-3 ◽  
pp. 741-744 ◽  
Author(s):  
V. Krishnakumar ◽  
O. Monserrat ◽  
M. Crosetto ◽  
B. Crippa
Keyword(s):  
Water Vapour ◽  
Phase Measurement ◽  
Meteorological Data ◽  
Phase Changes ◽  
Sar Interferometry ◽  
Synthetic Aperture ◽  
Tropospheric Delay ◽  
Electron Content ◽  
Total Electron ◽  
Radar Wave

The repeat-pass Synthetic Aperture Radio Detection and Ranging (RADAR) Interferometry (InSAR) has been a widely used geodetic technique for observing the Earth’s surface, especially for mapping the Earth’s topography and deformations. However, InSAR measurements are prone to atmospheric errors. RADAR waves traverse the Earth’s atmosphere twice and experience a delay due to atmospheric refraction. The two major layers of the atmosphere (troposphere and ionosphere) are mainly responsible for this delay in the propagating RADAR wave. Previous studies have shown that water vapour and clouds present in the troposphere and the Total Electron Content (TEC) of the ionosphere are responsible for the additional path delay in the RADAR wave. The tropospheric refractivity is mainly dependent on pressure, temperature and partial pressure of water vapour. The tropospheric refractivity leads to an increase in the observed range. These induced propagation delays affect the quality of phase measurement and introduce errors in the topography and deformation fields. The effect of this delay was studied on a differential interferogram (DInSAR). To calculate the amount of tropospheric delay occurred, the meteorological data collected from the Spanish Agencia Estatal de Meteorología (AEMET) and MODIS were used. The interferograms generated from Sentinel-1 carrying C-band Synthetic Aperture RADAR Single Look Complex (SLC) images acquired on the study area are used. The study area consists of different types of scatterers exhibiting different coherence. The existing Saastamoinen model was used to perform a quantitative evaluation of the phase changes caused by pressure, temperature and humidity of the troposphere during the study. Unless the phase values due to atmospheric disturbances are not corrected, it is difficult to obtain accurate measurements. Thus, the atmospheric error correction is essential for all practical applications of DInSAR to avoid inaccurate height and deformation measurements.

scholarly journals Monitoring Climate Changes on Small Scale Networks Using Ground Based GPS and Meteorological Data

2014 ◽  
Vol 49 (3) ◽  
pp. 125-135 ◽  
Author(s):  
C. Pikridas
Keyword(s):  
Climate Model ◽  
Meteorological Data ◽  
Water Vapor Pressure ◽  
Small Scale ◽  
Tropospheric Delay ◽  
Time Interval ◽  
Data Set ◽  
Multiple Stepwise Regression Analysis ◽  
Delay Variations ◽  
Gps Station

Abstract The total zenith tropospheric delay (ZTD) and its components, hydrostatic and wet parts are important parameters of the atmosphere and directly or indirectly reflect climate processes. This possibility can be more adaptive when meteorological data are combined to co-located meteorological sensors with GPS stations. In this paper eighteen months with one hour time interval ZTD estimates of a permanent GPS station are analyzed with the associated atmospheric parameters provided from a co-located meteorological sensor. The mathematical relationship through the multiple stepwise regression analysis reflects the plausible physical link of temperature and relative humidity values with ZTD’s. This regression equation is assessed by a second data set performed by a small GPS baseline few months later for the same study area. It was found that mainly due to the zenith wet delay variations and with the help of fundamental meteorological equations the behavior of water vapor pressure can be monitored and estimated. This is possible when an appropriate setup of GPS stations and a co-located meteorological sensor exist and if the GPS stations sound the same part of atmosphere. Therefore, the GPS tropospheric products are good indicators for a climate monitoring tool and can help address the physics of a climate model.

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