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SUMMARY The basic idea of this study is the estimation of Tropospheric delay with all of possible observations and methods available in the surround area of EUREF GPS station (AUT1) of the Aristotle University of Thessaloniki. The tropospheric delay is estimated in situ from three different data sources. We estimate the tropospheric delay using 3 d...
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... Therefore, the study and evaluation of ZTD are of interest to many researchers in space geodesy and meteorology. There are many methods and tools to obtain ZTD values such as radiosonde observations (Katsougiannopoulos et al. 2006), water vapor radiometer (Teke et al. 2013), Very Long Baseline Interferometry (VLBI), (Soja et al. 2015), GNSS data processing (Niell et al. 2001;Chen et al. 2011;Pikridas et al. 2014) and global/regional ZTD models. In addition, ZTD can be obtained directly from Numerical Weather Model (NWM) (Yang et al. 2013). ...
Zenith Tropospheric delay (ZTD), as one of the error sources on the Global Navigation Satellite System (GNSS) signal, plays a significant role in determining the moisture field of the earth's atmosphere. This study investigated the statistical quality of four ZTD models (HGPT2, Hopfield, Saastamoinen, and GTrop) in Iran. To do this, ZTD estimates obtained from processing GPS observations at 27 stations were considered reference values. The average Root Mean Squares Error (RMSE) values for one-year ZTD calculated using the Hopfield, HGPT2, GTrop, and Saastamoinen models were achieved at 77 mm, 39 mm, 31.4 mm, and 26 mm, respectively. The lowest mean bias values of ZTD belong to the GTrop model, which was 5.6 mm. Moreover, taking into account the temporal changes in the behavior of the ZTD parameter, the conventional Saastamoinen model was improved. The coefficients of the modified Saastamoinen model for the Iranian region were estimated with the help of GPS_ZTD values in 14 stations. Then, in 13 other stations of the GPS network that were not involved in estimating the model parameters, the modified Saastamoinen model was evaluated. On average, the modified Saastamoinen model has reduced the quantity of bias and RMSE of the calculated tropospheric delay in all the test stations about by 80% and 27%, respectively. Also, the correlation of the ZTD values obtained from the modified Saastamoinen model with GPS_ZTD has increased by 8% compared to the corresponding values obtained from the conventional Saastamoinen model.
... The troposphere is a non-dispersive medium; therefore, the magnitude of this error component does not depend on signal frequency and cannot be determined as is the case with ionospheric delay. Tropospheric error (which is the reduction of radio navigation signal propagation speed and its deviation from the geometric path and is also commonly called tropospheric delay) can be modeled based on the fundamental meteorological parameters of the troposphere: temperature, humidity, and atmospheric pressure [6][7][8][9][10]. Various models have been developed to predict and reduce tropospheric delay with different scopes of application, including the following: the Two-Quartic Hopfield model (n/a) ((n/a)-not specified or valid for any elevation angle.) ...
... The new approach used in this study investigates the overall effect of tropospheric delay on the accuracy of the GNSS geodetic position of the selected area. Several studies [10,35] show the relationship between tropospheric delays based on radiosonde signal measurements and deviations from position accuracy where the main effect was found to be the atmospheric refraction expressed by the number of N units, which is a value that varies greatly in time and space [36][37][38]. In this way, it is possible to combine the influence of the tropospheric delays in radio signals (usually with peak values up to several tens of millimeters) with the amounts of slant tropospheric errors in the GNSS system which can reach several tens of meters. ...
... Given the current limitations of available data sources, the study was based on the well-known approach of determining the tropospheric error by determining pseudoranges [10]. The used data delimit the area from 42.60° to 46.38° North latitude and from 15.22° to 18.11° East longitude with an altitude range from 64.3 m to 457.9 m above sea level ( Figure 1). ...
Positioning error components related to tropospheric and ionospheric delays are caused by the atmosphere in positioning determined by global navigation satellite systems (GNSS). Depending on the user's requirements, the position error caused by tropospheric influences, which is commonly referred to as zenith tropospheric delay (ZTD), must be estimated during position determination or determined later by external tropospheric corrections. In this study, a new approach was adopted based on the reduction of residual tropospheric error (RTE), i.e., the unmodeled part of the tropospheric error that remains included in the total geodetic position error, along with other unmodeled systematic and random errors. The study was performed based on Global Navigation Satellite System (GLONASS) positioning solutions and accompanying meteorological parameters in a defined and harmonized temporal-spatial frame of three locations in the Republic of Croatia. A multidisciplinary approach-based analysis from a navigational science aspect was applied. The residual amount of satellite positioning signal tropospheric delay was quantitatively reduced by employing statistical analysis methods. The result of statistical regression is a model which correlates surface meteorological parameters with RTE. Considering the input data, the model has a regional character, and it is based on the Saastamoinen model of zenith tropospheric delay. The verification results show that the model reduces the RTE and thus increases the geodetic accuracy of the observed GNSS stations (with horizontal components of position accuracy of up to 3.8% and vertical components of position of up to 4.37%, respectively). To obtain these results, the Root Mean Square Error (RMSE) was used as the fundamental parameter for position accuracy evaluation. Although developed based on GLONASS data, the proposed model also shows a considerable degree of success in the verification of geodetic positions based on Global Positioning System (GPS). The purpose of the research, and one of its scientific contributions, is that the proposed method can be used to quantitatively monitor the dynamics of changes in deviations of X, Y, and Z coordinate values along coordinate axes. The results show that there is a distinct interdependence of the dynamics of Y and Z coordinate changes (with almost mirror symmetry), which has not been investigated and published so far. The resultant position of the coordinates is created by deviations of the coordinates along the Y and Z axes-in the vertical plane of space, the deviations of the coordinate X (horizontal plane) are mostly uniform and independent of deviations along the Y and Z axes. The proposed model shows the realized state of the statistical position equilibrium of the selected GNSS stations which were observed using RTE values. Although of regional character, the model is suitable for application in larger areas with similar climatological profiles and for users who do not require a maximum level of geodetic accuracy achieved by using Satellite-Based Augmentation Systems (SBAS) or other more advanced, time-consuming, and equipment-consuming positioning techniques.
... S.Katsougiannopoulos et al [9], estimated the tropospheric delay using 3 days of GPS pseudorange observations (days 157, 158, 159 in 2005), and the total zenith delay derived from the analysis centre. they compared the estimated delay from the permanent GPS data with that measured from radiosonde measurements which carried out at Thessaloniki airport, located near to GPS station . ...
When radio waves propagate through the earth's neutral atmosphere, the radio signals are affected significantly by the variability of its refractive index, which causes primarily in the delay of the arrival, usually referred to as the tropospheric delay. The Global Positioning System (GPS) is space-based radio navigation. The GPS system receiver provides exact location and time information for an unlimited number of users in all weather, day and night, anywhere in the world. The GPS used three frequencies that are (L1 = 1.57542 GHz, L2 = 1.22760 GHz and L3 = 1.38105 GHz). The work part mainly focuses on how to illustrate and model the effects of the atmospheric constituents such as, water vapor, clouds, rain and snow on the GPS frequencies.The differential phase shift (∆∅) in case of rain is caused by the oblate rain drop that is has two different axes, and because of the difference of the concentration of rain drops, also (∆∅) doesn't depend on the temperature but increase with the increase of the frequency where the values of (∆∅) are 6 deg/km for L1, 5 deg/km for L2 and 5.4 deg/km for L3. The differential attenuation (∆A) in case of rain in addition to depend on the shape of rain drop and the concentration of drops, it depends on the temperature and the frequency. The (∆∅) in case of snow caused by the irregular shape of snow particle and depends on the temperatures and the frequency. The (∆A) in case of snow has very low values in different temperatures and frequencies.
... GPS or GNSS signals have been used by meteorologists as atmospheric sensors for more than two decades (Davis et al. 1985, Bevis et al. 1992, Li et al. 2015, Mendez Astudillo et al. 2018). The GNSS signals; observed from a permanent receiver, provide the Zenith Tropospheric Delay (ZTD) or the Precipitable Water Vapour (PWV) with high accuracy and spatio-temporal resolution at a level that they are comparable to traditional weather instruments such as microwave radiometers, radiosondes, or weather satellites (Hagemann et al. 2002, Katsougiannopoulos et al. 2006, Li et al. 2015. Ground-based GNSS networks have therefore brought many advantageous towards meteorological applications including improving the Numerical Weather Prediction (NWP) (Gutman and Benjamin 2001, Gendt et al. 2004, Vedel and Huang 2004, Bennitt and Jupp 2012, validating satellites (Qin et al. 2012), and monitoring the Earth's climate (Alshawaf et al. 2017). ...
Atmospheric sensing by Global Navigation Satellite System (GNSS) signals has been exclusively studied for over two decades. This study derives the real-time Precipitable Water Vapour (PWV) by taking 1 month GNSS observation data from 11 stations. A Country-wide Pressure and Temperature (CPT) model is applied to precisely determine PWV from Zenith Tropospheric Delay (ZTD). It shows that the computed ZTD RMSE obtained from most stations is less than 15 mm, meeting a threshold of 15 mm input to its Numerical Weather Prediction (NWP) equation while the average RMSE of PWV is less than 3 mm; suitable for weather nowcasting.
... Unable to obtain the current atmosphere condition without meteorological sensors, a look-up table is used to predict the meteorological parameters. Detailed implementation steps of the UNB3M model are referred to [39], [40, p. 3], [42]. ...
Nowadays, stand-alone Global Navigation Satellite System (GNSS) positioning accuracy is not sufficient for a growing number of land users. Sub-meter or even centimeter accuracy is becoming more and more crucial in many applications. Especially for navigating rovers in the urban environment, final positioning accuracy can be worse as the dramatically lack and contaminations of GNSS measurements.To achieve a more accurate positioning, the GNSS carrier phase measurements appear mandatory. These measurements have a tracking error more precise by a factor of a hundred than the usual code pseudorange measurements. However, they are also less robust and include a so-called integer ambiguity that prevents them to be used directly for positioning.While carrier phase measurements are widely used in applications located in open environments, this thesis focuses on trying to use them in a much more challenging urban environment. To do so, Real-Time-Kinematic (RTK) methodology is used, which is taking advantage on the spatially correlated property of most code and carrier phase measurements errors. Besides, the thesis also tries to take advantage of a dual GNSS constellation, GPS and GLONASS, to strengthen the position solution and the reliable use of carrier phase measurements. Finally, to make up the disadvantages of GNSS in urban areas, a low-cost MEMS is also integrated to the final solution.Regarding the use of carrier phase measurements, a modified version of Partial Integer Ambiguity Resolution (Partial-IAR) is proposed to convert as reliably as possible carrier phase measurements into absolute pseudoranges. Moreover, carrier phase Cycle Slip (CS) being quite frequent in urban areas, thus creating discontinuities of the measured carrier phases, a new detection and repair mechanism of CSs is proposed to continuously benefit from the high precision of carrier phases.Finally, tests based on real data collected around Toulouse are used to test the performance of the whole methodology.
... In general, atmospheric bias (Ionospheric and Tropospheric) is estimated through the Kalmam Filter process along with the position, velocity and ambiguity parameters. Tropospheric bias thus can be overcome using a variety models such as UNB, Hopfield or Saastomoinen Models [8][9][10]. Dry components of the troposphere can be easily modeled, however, the wet components are relatively hard to modeled. ...
As the modernized Global Navigation Satellite System (GNSS) method, Real Time Kinematic (RTK) ensures high accuracy of position (within several centimeters). This method uses Ultra High Frequency (UHF) radio to transmit the correction data, however, due to gain and power issues, Networked Transport of RTCM via Internet Protocol (RTCM) is used to transmit the correction data for a longer baseline. This Research aims to investigate the performance of short to long-range single baseline RTK GNSS (Up to 80 KM) by applying modified LAMBDA method to resolve the ambiguity in carrier phase. The RTK solution then compared with the differential GNSS network solution. The results indicate that the differences are within RTK accuracy up to 80 km are several centimeter for horizontal solution and three times higher for vertical solution.
... The neutral atmosphere consists of a combination of several gases. The signal propagation on this layer depends on the temperature, pressure and water vapor [6]. ...
... This is particularly so during the formation and life cycle of severe mesoscale convective storm and precipitation systems. Contrary to its importance, WV remains poorly understood and inadequately measured both spatially and temporally, especially in the southern hemisphere, where meteorological data are sparse [6]. ...
... The possibility of using GNSS technology for remote sensing the atmospheric water vapor results from the development of "deterministic" least-square and Kalman filtering technique, where the effect created by zenith tropospheric delay that influence the GNSS receiver results from the recorded observation [11]. The basic idea is to calculate the tropospheric delay from GPS pseudoranges when station coordinates with high accuracy is known [6]. ...
In order to be able to be process GPS data, the GPS signal it has to pass the entire terrestrial atmosphere – both neutral atmosphere and ionosphere – which may cause an alteration of the GPS receiver to perform, resulting in large errors in the final position estimate. The dual frequency GPS receivers are affected by the influence of the atmosphere, especially by the troposphere. To estimate the delay caused by the troposphere and to obtain a high degree of accuracy, mapping function has to be used in the estimation process, which opens the door for remote sensing the atmosphere.
Because the wet component from the hydrostatic and non-hydrostatic part, is only 10% of the total neutral atmospheric part, its influence is considerate significant in the application of high-precision positioning in which GPS receivers are employed. The article presents the determination of the precipitable water vapor using relative using four permanent GPS stations. The estimation were done by using the Global Mapping Function - GMF and the apriori pressure and temperature from the GPT2 model.
... The neutral atmosphere consists of a combination of several gases. The signal propagation on this layer depends on the temperature, the pressure and the water vapor (Katsougiannopoulos et al. 2006). ...
... The possibility of using GNSS technology for remote sensing the atmospheric water vapor results from the development of "deterministic" least-square and Kalman filtering technique, where the effect created by zenith tropospheric delay that influence the GNSS receiver results from the recorded observation (Bevis et al. 1994). The basic idea is to calculate the tropospheric delay from GPS pseudoranges when station coordinates with high accuracy is known (Katsougiannopoulos et al. 2006). ...
Due to development of GPS technology and by using the combination LC of L1 and L2 frequency the first order effect of the ionosphere tends to be canceled. Thus the main source of errors in the atmosphere which causes the delay in GPS signal is the neutral part of the atmosphere, usually referred to tropospheric delay. In general, the delay is computed at the zenith direction and it is referred to zenith tropospheric delay. The zenith tropospheric delay consist of two parts: zenith hydrostatic delay and zenith wet delay. The zenith hydrostatic delay can be very well modeled which accounts for nearly 90% to 100% of the atmospheric delay. The zenith wet delay is due to the water vapor and represents the “harder” part that need to be modeled caused by “unmixed” condition of the wet atmosphere. The influence of the zenith wet delay is around 0-40 cm. The aim of the article is to present the results obtain on the network of three station which were spread around the Oradea city using different types of mapping functions. The mapping functions are: global pressure and temperature – GPT2 and Vienna mapping function – VMF1. For the vertical studies to obtain the highest accuracy, the recommended mapping function is VMF1.
... ) computed the total zenith delay as presented by (Katsougiannopoulos et al, 2006): ...
Improvements in dual frequency observations with multiple differencing techniques have led to the development of the Precise Point Positioning (PPP) observation technique. Envisaged to have reduced positioning error; the tropospheric delay and multipath error remain the most outstanding factors that mitigating its positional accuracy in PPP observations. This paper attempts to access the impact and spatio-temporal variability of tropospheric delay on PPP positioning technique in GNSS observations across Nigeria. 24 hours data from six CORS across the six geopolitical zones in the country have been processed at three months interval (January – July, 2014) using the RTKLIB software in the PPP static post processing mode. Results obtained indicate a leap frog pattern of variation in the tropospheric delay across the country with least delay observed in January (during the dry season).
... S.Katsougiannopoulos et al [9], estimated the tropospheric delay using 3 days of GPS pseudorange observations (days 157, 158, 159 in 2005), and the total zenith delay derived from the analysis centre. they compared the estimated delay from the permanent GPS data with that measured from radiosonde measurements which carried out at Thessaloniki airport, located near to GPS station . ...
