• Journal of Navigation and Port Research
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• v.36 no.10
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• pp.825-832
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• 2012

GPS signal delay that caused by dry gases and water vapor in troposphere is a main error source of GPS point positioning and it must be eliminated for precise point positioning. In this paper, we implemented to generate tropospheric delay grid map over the Korean Peninsula based on post-processing method by using the GPS permanent station network in order to determine the availability of tropospheric delay generation algorithm. GIPSY 5.0 was used for GPS data process and nationwide AWS observation network was used to calculate the amount of dry delay and wet delay separately. As the result of grid map's accuracy analysis, the RMSE between grid map data and GPS site data was 0.7mm in ZHD, 7.6mm in ZWD and 8.5mm in ZTD. After grid map accuracy analysis, we applied the calculated tropospheric delay grid map to single frequency relative positioning algorithm and analyzed the positioning accuracy enhancement. As the result, positioning accuracy was improved up to 36% in case of relative positioning of Suwon(SUWN) and Mokpo(MKPO), that the baseline distance is about 297km.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.1
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• pp.1-7
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• 2019

In this study, we estimated the tropospheric delay of GNSS (Global Navigation Satellite System) signals during passing through the atmosphere in relation to weather and seasonal factors. For this purpose, we chose four CORS (Continuously Operating Reference Station) stations from inland (CCHJ and PYCH) and on the coast (GEOM and CHJU). A total of 48 days for each station (one set of data for each week) were downloaded from the NGII (National Geographic Information Institute) and processed it using the scientific GNSS software. The average tropospheric delays in winter are less than 2,400 mm, which is about 200 mm less than those in summer. The estimated tropospheric delay shows a similar pattern from all stations except the absolute bias in magnitude, while a large delay was observed for the station located on the coast. In addition, the delay during the day was relatively stable in winter, and the average tropospheric delay was strongly related to the orthometric height. The inland stations have tropospheric delays by the precipitation rather than humidity due to dry weather and difference in temperature. On the contrary, it was primarily caused by the humidity on the sea. The correlation between temperature and water vapor pressure is 0.9 or larger for all stations, and the tropospheric delay showed a high linear relationship with temperature. It is necessary to analyze the GNSS data with higher temporal resolution (e.g. all RINEX data of the year) to improve the stability and reliability of the correlation results.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.31 no.5
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• pp.421-427
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• 2013

In general, it can be assumed that the tropospheric effect are removed through double-differencing technique in short-baseline GPS data processing. This means that the high-accuracy positioning can be obtained because various error sources can be eliminated and the number of unknown can be decreased in the adjustment computation procedure. As a consequence, short-baseline data processing is widely used in the fields such as deformation monitoring which require precise positioning. However, short-baseline data processing is limited to achieve high positioning accuracy when the height difference between the reference and the rover station is significant. In this study, the effects of tropospheric delays on the determination of short-baseline is analyzed, which depends on the orientation of baseline. The GPS measurements which include tropospheric effect and measurement noises are generated by simulation, and then rover coordinates are computed by short-baseline data processing technique. The residuals of rover coordinates are analyzed to interpret the tropospheric effect on the positioning. The results show that the magnitudes of the biases in the coordinate residuals increase as the baseline length gets longer. The increasing rate is computed as 0.07cm per meter in baseline length. Therefore, the tropospheric effects should be carefully considered in short-baseline data processing when the significant height difference between the reference and rover is observed.

• Journal of Astronomy and Space Sciences
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• v.16 no.1
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• pp.61-68
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• 1999

The radio waves transmitted from GPS satellites is delayed by the troposphere as they propagate to Earth-based GPS receivers. The troposphere delay is usually divided into two parts, the dry delay due to the atmospheric gases and the wet delay due to the water vapor. In this study for the month of May in 1998 the GPS data from two stations(Taejon, Suwon) were used to estimate the total troposphere delay in the zenith direction by the least square method. The dry delay in the zenith direction can be evaluated by using surface pressure values at the station, then the zenith wet delay is obtained by removing the zenith dry delay from the total delay. The zenith wet delay is strongly correlated with the total precipitable water. The quality of the estimate has been assessed by comparison with radiosonde data at Osan. We found the food agreement in precipitable water of the GPS estimates and the radiosonde data. The standard deviation of the difference of the difference between the GPS and radiosonde observations was 3.68mm at Suwon.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.11
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• pp.2569-2575
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• 2015

Network RTK generally uses a linear interpolation method by using the corrections from reference stations. This minimizes the spatial decorrelation error caused by the increase of distance between the reference station's baseline and user's baseline. However, tropospheric delay, a function of the meteorological data can cause a spatial decorrelation characteristic among reference stations within a network by local meteorological difference. A non-linear characteristic of tropospheric delay can deteriorate Network RTK performance. In this paper, the modeling of tropospheric delay irregularity is made from the data when the typhoon is occurred. By using this modeling, analyzing the effect of meteorological difference between reference stations on correction is performed. Finally, we analyze an effect of non-linear characteristics of tropospheric delay among reference stations to Network RTK user.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.27 no.5
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• pp.565-574
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• 2009

The water vapor weighted vertically mean temperature(Tm) models, which were developed by the consideration of seasonal characteristics over the Korea, was used in the retrieval of precipitable water vapor (PWV) from GPS data which were observed at four GPS permanent stations. Since the weighted mean temperature relates to the water vapor pressure and temperature profile at a site, the accuracy of water vapor information which were estimated from GPS tropospheric wet delay is proportional to the accuracy of the weighted mean temperature. The adaption of Korean seasonal weighted mean temperature model, as an alternative to other formulae which are suggested from other nation, provides an improvement in the accuracy of the GPS PWV estimation. Therefore, it can be concluded that the seasonally appropriate weighted mean temperature model, which is used to convert actual zenith wet delay (ZWD) to the PWV, can be more reduced the relative biases of PWV estimated from GPS signal delays in the troposphere than other annual model, so that it would be useful for GPS PWV estimation with high accuracy.

• 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 the Korea Institute of Military Science and Technology
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• v.19 no.2
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• pp.163-170
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• 2016

Tropospheric delay is one of the largest error source in pseudolite navigation system. Because a pseudolite is installed on the ground and transmits its signal to a user in the air or on the ground, the conventional tropospheric delay model developed for a satellite navigation doesn't work properly. In this paper, performance analysis of several pseudolite tropospheric delay models has been done using meteorological data. Based on the result, a new compensation method for Hopfield model has been proposed.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2013.10a
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• pp.362-365
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• 2013

최근 기존 관측장비들에 비해 저렴한 GPS를 이용한 기상 모니터링이 많이 활용되고 있다. 이용자에게 보다 정밀한 위치정보의 제공을 연구하기 위하여 위성항법중앙사무소 기반시스템을 응용하여, 대류권 습윤정보를 추출하여 기상과 위치오차와의 상관성을 분석하고자 한다.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.6D
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• pp.1033-1041
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• 2006

In this study, we calculated a space-time variation of GPS precipitable water vapor using GPS meteorology technique during a progress of the typhoon EWINIAR had made an effect on Korean peninsular at 10 July, 2006. We estimated tropospheric dry delay and wet delay for one hourly using 22 GPS permanent stations and precipitable water vapor was conversed by using surface meteorological data. The Korean weighted mean temperature and air-pressure of versa-reduction to the mean sea level have been used for an accuracy improvement of GPS precipitable water vapor estimation. Finally, we compared MTSAT water vapor image, radar image and precipitable water vapor map during a passage of the typhoon EWINIAR.