• Journal of the Korean earth science society
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• v.21 no.4
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• pp.380-388
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• 2000

The estimation of tropospheric zenith delay over the Seoul-Jecheon area using GPS is presented. Over the past ten years, the world-wide industrial nations have been intensively concerned over increasing GPS surveyings in the various fields of earth science. To preserve precise positioning under various weather conditions, relationships between tropospheric zenith delay and GPS accuracy are analyzed. GPS accuracies are compared with tropospheric zenith delay produced by Bernese 4.0 software. Errors of tropospheric delay are 20cm in mean and reduced up to 5cm when tropospheric correction models are used. Correlation between error of GPS and tropospheric zenith delay plays a positive role to monitor the migration of weather front in the established Korean GPS network.

• Journal of Positioning, Navigation, and Timing
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• v.10 no.4
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• pp.371-377
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• 2021

This research proposes the best combination of tropospheric delay models for Korean Positioning System (KPS). The overall results are based on real observation data of Japanese Quasi-Zenith satellite system (QZSS), whose constellation is similar to the proposed constellation of KPS. The tropospheric delay models are constructed as the combinations of three types of zenith path delay (ZPD) models and four types of mapping functions (MFs). Two sets of International GNSS Service (IGS) stations with the same receiver are considered. Comparison of observation residuals reveals that the ZPD models are more influential to the measurement model rather than MFs, and that the best tropospheric delay model is the combination of GPT3 with 5 degrees grid and Vienna Mapping Function 1 (VMF1). While the bias of observation residual depends on the receivers, it still remains to be further analyzed.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.1
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• pp.23-28
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• 2021

Data analysis research using deep learning has recently been studied in various field. In this paper, we conduct a GNSS (Global Navigation Satellite System)-based meteorological study applying deep learning by estimating the ZWD (Zenith tropospheric Wet Delay) through MLP (Multi-Layer Perceptron) and LSTM (Long Short-Term Memory) models. Deep learning models were trained with meteorological data and ZWD which is estimated using zenith tropospheric total delay and dry delay. We apply meteorological data not used for learning to the learned model to estimate ZWD with centimeter-level RMSE (Root Mean Square Error) in both models. It is necessary to analyze the GNSS data from coastal areas together and increase time resolution in order to estimate ZWD in various situations.

• Journal of Navigation and Port Research
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• v.40 no.5
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• pp.265-270
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• 2016

Extreme tropospheric anomalies such as typhoons or regional torrential rain can degrade positioning accuracy of the GPS signal. It becomes one of the main error terms affecting high-precision positioning solutions in network RTK. This paper proposed a detection algorithm to be used during atmospheric anomalies in order to detect the tropospheric irregularities that can degrade the quality of correction data due to network errors caused by inhomogeneous atmospheric conditions between multi-reference stations. It uses an atmospheric grid that consists of four meteorological stations and estimates the troposphere zenith total delay difference at a low performance point in an atmospheric grid. AWS (automatic weather station) meteorological data can be applied to the proposed tropospheric anomaly detection algorithm when there are different atmospheric conditions between the stations. The concept of probability density distribution of the delta troposphere slant delay was proposed for the threshold determination.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.20 no.3
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• pp.283-291
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• 2002

Since GPS's SA cancellation, the interest is converged in correction of errors such as atmospheric delay and multipath that weight had been small relatively, which can improve the accuracy of positioning through modelling research. The aim of this study have an extensive comparison of the various tropospheric delay models (Goad&Goodman, A&K, Hopfield and Sasstamoinen) and mapping functions(Niell, Chao, and Marini). Expecially, the tropospheric delay amounts by change of the GPS satellite elevations, and the delay by various combination between zenith delay models and mapping functions, compared and examined. For this, programmed the total delay models and the combined models which can be described as a product of the delay at the zenith and a mapping function. The result of study, especially, as the minimum elevation of included data is reduced under $10^{\circ}$, it was considered to be reasonable that the prediction of tropospheric delay considering combination and mapping character of functions about the transition of the zenith delay to a delay with arbitrary zenith angle.

• Journal of Advanced Navigation Technology
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• v.20 no.1
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• pp.23-28
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• 2016

In this paper, we verified accuracy of the satellite based augmentation system (SBAS) tropospheric delay correction model for the Korean region. We employed the precise data of the tropospheric zenith path delay (ZPD) which is provided by the international GNSS service (IGS). In addition, we compared the verification results with that of the Saastamoinen model and the Hopfield model. Consequently, the bias residual error of the SBAS tropospheric delay correction model is about 50 mm, whereas the Saastamoinen model and the Hopfield model are more accurate. This residual error by the tropospheric delay model can affect the SBAS user position accuracy, but there is no problem in SBAS accuracy requirement. If we modified the meteorological parameters for SBAS tropospheric model to appropriate in Korean weather environment, we can provide better SBAS service to the Korean user.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.6
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• pp.1317-1322
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• 2014

This paper shows effects of tropospheric delay models and mapping functions among delay features occurred when GPS code signal is transferred for GPS Time Transfer. GPS time transfer uses CGGTTS as the international standard format. For geodetic GPS receiver, ROB has provided r2cggtts software which generates CGGTTS data from RINEX data and all laboratories participated in TAI link use this software and send the CGGTTS results periodically. Though Saastamoinen zenith path model and Niell mapping function are commonly used in space geodesy, r2cggtts software applied NATO zenith path model and CHAO mapping function to the tropospheric delay model. Hence, this paper shows effects of two tropospheric delay models by implementing Saastamoinen model and Niell mapping function for the time offset.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.703-706
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• 2004

Tropospheric parameters, in the form of Total Zenith Delay (TZD) corrections, were estimated with the current GPS network of Korea. We estimated the TZD using the Korea Astronomy Observatory GPS Network of nine permanent stations. About four years of data were processed to get the continuous time series of the TZD. The longest time series is obtained from the site DAEJ, which has been in operation for about 10 years. We analyzed the seasonal and annual signals in the TZD estimates at DAEJ and spatial correlations among eight sites.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.20 no.2
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• pp.215-222
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• 2002

As the GPS signals propagate from the GPS satellites to the receivers on the ground, they are delayed by the atmosphere. The tropospheric delay consists of two components. The hydrostatic (or "dry") component that is dependent on the dry air gasses in the atmosphere and accounts for approximately 90% of the delay. And the "wet" component that depends on the moisture content of the atmosphere and accounts for the remaining effect of the delay. The Zenith Hydrostatic Delay (ZHD) can be calculated from the local surface pressure. The Total Zenith Delay (TZD) will be estimated and the wet component extracted later. Integrated water Vapor (IWV) gives the total amount of water vapor that a signal from the zenith direction would encounter. Precipitable Water Vapor (PWV) is the IWV scaled by the density of water. The quality of this PWV has been verified by comparison with radiosonde data(at Osan). We processed data for JULY 2 and JULY 14, 1999 from four stations(Cheju, Kwangju, Suwon, Daegu). We found the coincidence between PWV of the estimations using GPS and PWV of pressing the radiosonde data. The average of the difference between PWV using GPS and PWV using radiosonde was 3.77 mm(Std. ＝ $\pm$0.013 mm) and 2.70 mm(Std. = $\pm$0.0011 mm) at Suwon & Kwangju.

• Journal of Navigation and Port Research
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• v.40 no.5
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• pp.271-278
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• 2016

Localized atmospheric conditions between multi-reference stations can bring the tropospheric delay irregularity that becomes an error terms affecting positioning accuracy in network RTK environment. Imbalanced network error can affect the network solutions and it can corrupt the entire network solution and degrade the correction accuracy. If an anomaly could be detected before the correction message was generated, it is possible to eliminate the anomalous satellite that can cause degradation of the network solution during the tropospheric delay anomaly. An atmospheric grid that consists of four meteorological stations was used to detect an inhomogeneous weather conditions and tropospheric anomaly applied AWSs (automatic weather stations) meteorological data. The threshold of anomaly detection algorithm was determined based on the statistical weather data of AWSs for 5 years in an atmospheric grid. From the analytic results of anomaly detection algorithm it showed that the proposed algorithm can detect an anomalous satellite with an anomaly flag generation caused tropospheric delay anomaly during localized atmospheric conditions between stations. It was shown that the different precipitation condition between stations is the main factor affecting tropospheric anomalies.