Global Navigation Satellite System Reference Stations Research Articles

High energy events as a combined effect of human impact and geoenvironmental factors - the case study based on GNSS data

In the Upper Silesian Coal Basin (USCB) mining region in Poland, coal production has been gradually reduced for many years. Nevertheless, the number of high-energy tremors remains at a similar level or decreases much slower than production. We analyze this problem in the aspect of geological setting and on the base of geodetic data. We explain the paradox by specific interaction between man-induced and natural tectonic stress as seasonal hydrological effects. Anomalous energies released during large seismic events exceeded the predetermined threshold, typical for mining tremors in the area. Further, the authors point out the seasonal occurrence of these events. Temporal variations of distances between continuously operating GNSS (Global Navigation Satellite Systems) reference stations were analyzed, and linear strain, inferred from these geodetic observations, corresponded to high-energy tremors in the area. Consequently, the seismic events usually occurred when the analyzed baseline performance demonstrated significant seasonal increases or decreases of evaluated temporal distribution of strain. The aim of the analysis was to evaluate the relationships between the characteristics of the time series of deformations and the occurrence of seismic tremors of energy E ≥ 3 x 107J occur. Seasonal occurrences of high-energy seismic events as the energy they released suggest the influence of environmental factors.

Interpolation of GNSS Position Time Series Using GBDT, XGBoost, and RF Machine Learning Algorithms and Models Error Analysis

The global navigation satellite system (GNSS) position time series provides essential data for geodynamic and geophysical studies. Interpolation of the GNSS position time series is necessary because missing data will produce inaccurate conclusions made from the studies. The spatio-temporal correlations between GNSS reference stations cannot be considered when using traditional interpolation methods. This paper examines the use of machine learning models to reflect the spatio-temporal correlation among GNSS reference stations. To form the machine learning problem, the time series to be interpolated are treated as output values, and the time series from the remaining GNSS reference stations are used as input data. Specifically, three machine learning algorithms (i.e., the gradient boosting decision tree (GBDT), eXtreme gradient boosting (XGBoost), and random forest (RF)) are utilized to perform interpolation with the time series data from five GNSS reference stations in North China. The results of the interpolation of discrete points indicate that the three machine learning models achieve similar interpolation precision in the Up component, which is 45% better than the traditional cubic spline interpolation precision. The results of the interpolation of continuous missing data indicate that seasonal oscillations caused by thermal expansion effects in summer significantly affect the interpolation precision. Meanwhile, we improved the interpolation precision of the three models by adding data from five stations which have high correlation with the initial five GNSS reference stations. The interpolated time series for the North, East, and Up (NEU) are examined by principal component analysis (PCA), and the results show that the GBDT and RF models perform interpolation better than the XGBoost model.

Multi-GNSS positioning for landslide monitoring: a case study at the Recica landslide

Global Navigation Satellite System (GNSS) positioning has characteristics of simple operation, high efficiency and high precision technique for landslide surface monitoring. In recent years, finalization of modern GNSS systems Galileo and BeiDou has brought a possibility of multi-GNSS positioning. The paper focuses on evaluation of possible benefits of multi-GNSS constellations in landslide monitoring. While simulating observational conditions of selected Recica landslide in the Czech Republic, one-month data from well-established permanent GNSS reference stations were processed. Besides various constellation combinations, differential and Precise Point Positioning techniques, observation data lengths and observation sampling intervals were evaluated. Based on the results, using a combination of GPS and GLONASS, or GPS, GLONASS and Galileo systems can be recommended, together with a static differential technique and observation periods for data collection exceeding eight hours. In the last step, data from GNSS repetitive campaigns realized at the Recica landslide during two years were processed with optimal setup and obtained displacement results were compared to standard geotechnical measurements.

IMPACT OF GNSS SIGNAL OUTAGE ON EOPS USING FORWARD KALMAN FILTER AND SMOOTHING ALGORITHM

Abstract. The global navigation satellite system (GNSS) has been playing the principal role in positioning applications in past decades. Position robustness degrades with a standalone receiver due to GNSS signal outage in mobile mapping systems. The GNSS and inertial measurement unit (IMU) integration is used to solve positioning degradation. This article studies the GNSS/IMU integration processing (i.e., forward Kalman filter (KF) and smoothing algorithm) using a single or a network of nearby GNSS reference stations. In addition, we analyze the impact of simulated GNSS signal outage on exterior orientation parameters (EOPs). The outcomes confirm that the smoothing algorithm works better than the forward KF and improves the accuracy for position and orientation in the case when there is no GNSS signal outage. Also, it improves the position and orientation accuracy by about 95% and 60% when there is a 180 second GNSS signal outage, respectively.

Assessing the Impact of Positioning Errors in Car-Borne Repeat-Pass SAR Interferometry With a Controlled Rail-Based Experiment

Agile synthetic aperture radar (SAR) platforms such as car-borne and UAV-borne SAR systems require combined inertial navigation systems (INS) and global navigation satellite systems (GNSS) to measure the radar sensor trajectories used for focusing and interferometric processing. Measurement inaccuracies from INS/GNSS systems lead to residual phase errors in the SAR products whose minimisation is crucial to derive accurate topographic and deformation information. In this work, we analyse the impact of residual positioning errors on car-borne repeat-pass SAR interferometry at L-band for different INS/GNSS measurement configurations and for the typical car-borne acquisition geometry. The positioning errors are evaluated both during single SAR acquisitions with long integration times and between different acquisitions as a function of the distance of the radar platform from the GNSS reference stations. We show the reduction of interferometric phase errors achievable by additionally using a GNSS receiver mounted in the vicinity of the SAR platform as compared to remote reference stations of the national network of permanent GNSS receivers. Test results obtained in a controlled setup with a rail-based SAR system equipped with a navigation-grade INS/GNSS system show maximum repeat-pass trajectory errors on the order of 1–2 cm using a local GNSS reference station and up to 10–15 cm using the remote reference stations, leading to azimuth and range phase trends in the interferometric products.

A Method for dSTEC Interpolation: Ionosphere Kernel Estimation Algorithm

Ionospheric structure is important for estimating ionospheric delay for user stations in Global Navigation Satellite System (GNSS). However, most existing parameter estimation methods suffer from challenges due to data inaccuracy and unavailability of limited and sparse scattered data at ground reference stations. The high variability of active low latitude or disturbed ionosphere leads to GNSS signal scintillation. It is critical to capture ionospheric random structure and estimate ionospheric parameter using data of disperse receivers to improve accuracy. This paper proposes a unifying method named Ionosphere Kernel Estimation Algorithm (IKEA) to retrieve the information of ionospheric spatial structure. The proposed model utilities the semi-parametric representation theorem to incorporate prior information and constraints. The multiple kernel technique is adopted firstly to include physical correlations. Additionally, a learning approach is deployed to determine model parameters. The IKEA model has been verified based on simulated and experimental data at active low latitudes from a network of ground GNSS reference stations from all visible GPS and GALILEO satellites. The IKEA model reduces approximately 19.5% and 24.2% of differential Slant Total Electron Content (dSTEC) in the root mean square error with respect to Inverse Distance Weighting (IDW) and the Kriging model during high ionospheric activities. The IKEA architecture has been demonstrated effective to make robust ionospheric estimation, which may be further extended for various GNSS applications and beyond.

Antenna Array-Aided Real-Time Kinematic Using Moving Base Stations

A global navigation satellite systems (GNSS) real-time kinematic (RTK) using multiple base stations is referred as an antenna array-aided RTK. This positioning method more reliably resolves carrier phase integer ambiguities by processing additional measurements provided from multiple GNSS reference stations whose antenna locations are accurately surveyed. In this study, a conventional antenna array-aided RTK is extended to improve RTK performance with multiple moving base stations. Because accurately surveyed antenna coordinates are not available, we propose an antenna array-aided Kalman filter approach to first check the sanity of the fixed integer ambiguities and baselines estimated by moving reference stations and use them as a-priori information in RTK measurement formulations. Our flight test results with three unmanned aerial vehicles confirmed that the proposed Kalman filter formulation outperformed the conventional RTK and a priori antenna array-aided RTK approaches in the aspects of positioning accuracy, time to first fix, and success probability of integer ambiguity fixes.

Algorithm Research Using GNSS-TEC Data to Calibrate TEC Calculated by the IRI-2016 Model over China

The International Reference Ionosphere (IRI) is an empirical model widely used to describe ionospheric characteristics. In the previous research, high-precision total ionospheric electron content (TEC) data derived from global navigation satellite system (GNSS) data were used to adjust the ionospheric global index IG12 used as a driving parameter in the standard IRI model; thus, the errors between IRI-TEC and GNSS-TEC were minimized, and IRI-TEC was calibrated by modifying IRI with the updated IG12 index (IG-up). This paper investigates various interpolation strategies for IG-up values calculated from GNSS reference stations and the calibrated TEC accuracy achieved using the modified IRI-2016 model with the interpolated IG-up values as driving parameters. Experimental results from 2015 and 2019 show that interpolating IG-up with a 2.5° × 5° spatial grid and a 1-h time resolution drives IRI-2016 to generate ionospheric TEC values consistent with GNSS-TEC. For 2015 and 2019, the mean absolute error (MAE) of the modified IRI-TEC is improved by 78.57% and 77.42%, respectively, and the root mean square error (RMSE) is improved by 78.79% and 77.14%, respectively. The corresponding correlations of the linear regression between GNSS-TEC and the modified IRI-TEC are 0.986 and 0.966, more than 0.2 higher than with the standard IRI-TEC.

Development and evaluation of the refined zenith tropospheric delay (ZTD) models

The tropospheric delay is a significant error source in Global Navigation Satellite System (GNSS) positioning and navigation. It is usually projected into zenith direction by using a mapping function. It is particularly important to establish a model that can provide stable and accurate Zenith Tropospheric Delay (ZTD). Because of the regional accuracy difference and poor stability of the traditional ZTD models, this paper proposed two methods to refine the Hopfield and Saastamoinen ZTD models. One is by adding annual and semi-annual periodic terms and the other is based on Back-Propagation Artificial Neutral Network (BP-ANN). Using 5-year data from 2011 to 2015 collected at 67 GNSS reference stations in China and its surrounding regions, the four refined models were constructed. The tropospheric products at these GNSS stations were derived from the site-wise Vienna Mapping Function 1 (VMP1). The spatial analysis, temporal analysis, and residual distribution analysis for all the six models were conducted using the data from 2016 to 2017. The results show that the refined models can effectively improve the accuracy compared with the traditional models. For the Hopfield model, the improvement for the Root Mean Square Error (RMSE) and bias reached 24.5/49.7 and 34.0/52.8 mm, respectively. These values became 8.8/26.7 and 14.7/28.8 mm when the Saastamoinen model was refined using the two methods. This exploration is conducive to GNSS navigation and positioning and GNSS meteorology by providing more accurate tropospheric prior information.

Study of the Spatiotemporal Characteristics of the Equatorial Ionization Anomaly Using Shipborne Multi-GNSS Data: A Case Analysis (120–150°E, Western Pacific Ocean, 2014–2015)

Ground-based GNSS (Global Navigation Satellite System) reference stations lack the capacity to provide data for ocean regions with sufficient spatial-temporal resolution, limiting the detailed study of the equatorial ionization anomaly (EIA). Thus, this study collected kinematic multi-GNSS data on the ionospheric Total Electron Content (TEC) during two research cruises across the equator in the Western Pacific Ocean in 2014 (31 October–8 November) and 2015 (29 March–6 April). The purpose of the study was to use sufficient spatial–temporal resolution data to conduct a detailed analysis of the diurnal variation of the equatorial ionization anomaly in different seasons. The two-year data collected were used to draw the following conclusions. During the test in 2014, the EIA phenomenon in the Northern and Southern Hemispheres was relatively obvious. The maximum values occurred at local time (LT) 15:00 (~136TECu) and LT22:00 (~107TECu) in the Northern Hemisphere and at LT14:00 (100TECu) and LT22:00 (80TECu) in the Southern Hemisphere. During the test in 2015, the EIA in the Southern Hemisphere reached its maximum level at LT14:00 (~115TECu) and LT20:00 (~85TECu). However, the EIA phenomenon in the Northern Hemisphere was weakened, and a maximum value occurred only at LT 15:00 (~85TECu). The intensity contrast was reversed. The EIA phenomenon manifests a strong hemisphere asymmetry in this region.

Antennas as Precise Sensors for GNSS Reference Stations and High-Performance PNT Applications on Earth and in Space.

Satellite navigation is more and more important in a plethora of very different application fields, ranging from bank transactions to shipping, from autonomous driving to aerial applications, such as commercial avionics as well as unmanned aerial vehicles (UAVs). In very precise positioning, navigation, and timing (PNT) applications, such as in reference stations and precise timing stations, it is important to characterize all errors present in the system in order to account possibly for them or calibrate them out. Antennas play an important role in this respect: they are indeed the “sensor” that capture the signal in space from global navigation satellite systems (GNSS) and thereby strongly contribute to the overall achievable performance. This paper reviews the currently available antenna technologies, targeting specifically reference stations as well as precise GNSS antennas for space applications, and, after introducing performance indicators, summarizes the currently achievable performance. Finally, open research issues are identified, and possible approaches to solve them are discussed.

Assessment of the Steering Precision of a Hydrographic USV along Sounding Profiles Using a High-Precision GNSS RTK Receiver Supported Autopilot

Unmanned Surface Vehicles (USV) are increasingly used to perform numerous tasks connected with measurements in inland waters and seas. One of such target applications is hydrography, where traditional (manned) bathymetric measurements are increasingly often realized by unmanned surface vehicles. This pertains especially to restricted or hardly navigable waters, in which execution of hydrographic surveys with the use of USVs requires precise maneuvering. Bathymetric measurements should be realized in a way that makes it possible to determine the waterbody’s depth as precisely as possible, and this requires high-precision in navigating along planned sounding profiles. This paper presents research that aimed to determine the accuracy of unmanned surface vehicle steering in autonomous mode (with a Proportional-Integral-Derivative (PID) controller) along planned hydrographic profiles. During the measurements, a high-precision Global Navigation Satellite System (GNSS) Real Time Kinematic (RTK) positioning system based on a GNSS reference station network (positioning accuracy: 1–2 cm, p = 0.95) and a magnetic compass with the stability of course maintenance of 1°–3° Root Mean Square (RMS) were used. For the purpose of evaluating the accuracy of the vessel’s path following along sounding profiles, the cross track error (XTE) measure, i.e., the distance between an USV’s position and the hydrographic profile, calculated transversely to the course, was proposed. The tests were compared with earlier measurements taken by other unmanned surface vehicles, which followed the exact same profiles with the use of much simpler and low-cost multi-GNSS receiver (positioning accuracy: 2–2.5 m or better, p = 0.50), supported with a Fluxgate magnetic compass with a high course measurement accuracy of 0.3° (p = 0.50 at 30 m/s). The research has shown that despite the considerable difference in the positioning accuracy of both devices and incomparably different costs of both solutions, the authors proved that the use of the GNSS RTK positioning system, as opposed to a multi-GNSS system supported with a Fluxgate magnetic compass, influences the precision of USV following sounding profiles to an insignificant extent.

GNSS-TS-NRS: An Open-Source MATLAB-Based GNSS Time Series Noise Reduction Software

The global navigation satellite system (GNSS) has seen tremendous advances in measurement precision and accuracy, and it allows researchers to perform geodynamics and geophysics studies through the analysis of GNSS time series. Moreover, GNSS time series not only contain geophysical signals, but also unmodeled errors and other nuisance parameters, which affect the performance in the estimation of site coordinates and related parameters. As the number of globally distributed GNSS reference stations increases, GNSS time series analysis software should be developed with more flexible format support, better human–machine interaction, and with powerful noise reduction analysis. To meet this requirement, a new software named GNSS time series noise reduction software (GNSS-TS-NRS) was written in MATLAB and was developed. GNSS-TS-NRS allows users to perform noise reduction analysis and spatial filtering on common mode errors and to visualize GNSS position time series. The functions’ related theoretical background of GNSS-TS-NRS were introduced. Firstly, we showed the theoretical background algorithms of the noise reduction analysis (empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD)). We also developed three improved algorithms based on EMD for noise reduction, and the results of the test example showed our proposed methods with better effect. Secondly, the spatial filtering model supported five algorithms on a separate common model error: The stacking filter method, weighted stacking filter method, correlation weighted superposition filtering method, distance weighted filtering method, and principal component analysis, as well as with batch processing. Finally, the developed software also enabled other functions, including outlier detection, correlation coefficient calculation, spectrum analysis, and distribution estimation. The main goal of the manuscript is to share the software with the scientific community to introduce new users to the GNSS time series noise reduction and application.

Optimizing Global Navigation Satellite Systems network real-time kinematic infrastructure for homogeneous positioning performance from the perspective of tropospheric effects.

Real-time centimetre-level precise positioning from Global Navigation Satellite Systems (GNSS) is critical for activities including landslide, glacier and coastal erosion monitoring, flood modelling, precision agriculture, intelligent transport systems, autonomous vehicles and the Internet of Things. This may be achieved via the real-time kinematic (RTK) GNSS approach, which uses a single receiver and a network of continuously operating GNSS reference stations (CORS). However, existing CORS networks have often been established simply by attempting regular spacing or in clusters around cities, with little consideration of weather, climate and topography effects, which influence the GNSS tropospheric delay, a substantial GNSS positional error and which prevents homogeneous RTK accuracy attainment. Here, we develop a framework towards optimizing the design of CORS ground infrastructure, such that tropospheric delay errors reduce to 1.5 mm worth of precipitable water vapour (PWV) globally. We obtain average optimal station spacings of 52 km in local summer and 70 km in local winter, inversely related to the atmospheric PWV variation, with denser networks typically required in the tropics and in mountainous areas. We also consider local CORS network infrastructure case studies, showing how after network modification interpolated PWV errors can be reduced from around 2.7 to 1.4 mm.

Realization of an Optimal Dynamic Geodetic Reference Frame in China: Methodology and Applications

China Geodetic Coordinate System 2000 (CGCS2000) has been used for several years as a formal published reference frame. The coordinates of all global navigation satellite system (GNSS) stations in China need to be corrected to align with the CGCS2000 frame. Different strategies can be adopted for the realization of an optimal reference frame. However, different strategies lead to different results, with differences as great as several decimeters when GNSS station coordinates are transformed into CGCS2000-defined coordinates. The two common methods for the coordinate correction of a GNSS station are quasi-stable adjustment under CGCS2000 and plate movement correction, and the differences between their results can be greater than 10 cm. In this study, a statistic method called “supervised clustering” is applied to the selection of GNSS reference stations; a new scheme named “partition spacing” for the grouping of all processed GNSS stations is proposed; and the plate movement correction method is used to correct the coordinates of all GNSS stations from the GNSS epoch to the CGCS2000 epoch. The results from the new partitioning method were found to be significantly better than those from the conventional station-blocking approach. When coordinates from the stations without grouping were used as the standard, the accuracy of all the three-dimensional coordinate components from the new partitioning method was better than 2 mm. The root mean squares (RMSs) of the velocities in the x, y, and z directions resulting from the supervised clustering method were 0.19, 0.45, and 0.32 mm∙a−1, respectively, which were much smaller than the values of 0.92, 0.72, and 0.97 mm∙a−1 that resulted from the conventional approach. In addition, singular spectrum analysis (SSA) was used to model and predict the position nonlinear movements. The modeling accuracies of SSA were better than 3, 2, and 5 mm in the east (E), north (N), and up (U) directions, respectively; and its prediction accuracies were better than 5 mm and 1 cm for the horizontal and vertical domains, respectively.

Accuracy Assessment of Satellite-Based Correction Service and Virtual GNSS Reference Station for Hydrographic Surveying

The aim of this paper is to assess the performance of satellite-based correction service, Trimble PP-RTX, and Virtual Reference Stations (VRS) for bathymetry determination, and check how far these techniques meet the minimum standards of the International Hydrography Organization (IHO) for hydrographic surveys. To this end, a three-hour duration session was conducted at Sharm Obhur using KAU-Hydrography 1 vessel. This session includes Global Navigation Satellite System (GNSS) data at the base station using Trimble SPS855 GNSS receiver, multibeam records using Kongsberg EM 712 multibeam echo sounder, sound velocity profile using Valeport’s sound velocity profiler, Applanix POS MV measurements, and real-time PP-RTX corrections. Moreover, the VRS GNSS data was generated using Kingdom of Saudi Arabia Continuous Operation Reference Station network (KSA-CORS). It is shown that the Total Horizontal Uncertainty (THU) and Total Vertical Uncertainty (TVU) of the PP-RTX technique are 5.50 cm and 5.90 cm, respectively, which meets the IHO minimum standards for all survey orders at 95% confidence level. The THU and TVU of the VRS technique, on the other hand, are 5.75 cm and 7.05 cm at 95% confidence level, respectively. These values meet the IHO standards for all survey orders as well. Statistical analysis of the seabed surface differences showed a −0.07 cm average difference between the PP-RTX seabed surface and the reference seabed surface with a standard deviation of 3.60 cm. However, the average difference between the VRS-based seabed surface and the reference seabed surface is −0.03 cm and a standard deviation of 3.61 cm.

Effects and Correction of Atmospheric Pressure Loading Deformation on GNSS Reference Stations in Mainland China

Atmospheric pressure loading (APL) deformation is one component of nontectonic deformation for Global Navigation Satellite System (GNSS) time series and is a kind of deformation response caused by a redistribution of atmospheric pressure. In this paper, we present an atmospheric data processing strategy to compute the APL based on a spherical harmonic expansion of the global atmosphere pressure changes. We also provide a sample model to describe the relativity between the global atmosphere pressure changes and APL vertical deformation. The results show that the variation of air mass has a major impact on the north-eastern area of East China, the eastern area of North China, and Northeast China, and the vertical crustal displacement caused by the atmosphere changes in these regions can reach about 20 mm. The correction of APL for vertical time series of GNSS reference stations in different regions indicates that the arid area of the Northwest China, Northeast China, Central China, and North China are greatly affected by APL. While for the station located in Sichuan-Yunnan region, the amplitude and period change are small after correction of APL for vertical time series of GNSS reference stations, which reveals that the area is seriously affected by tectonic movement and water migration loading. The correlation between atmospheric pressure changes and crustal deformation is analyzed, which shows that APL has a serious impact on the north-eastern area of North China, the Northeast China, and the eastern area of Central China when the variations in atmospheric pressure in mainland China are the same. The research results of this paper will provide some reference value for the study of crustal structural deformation and the establishment of geodetic datum.

Global Ionospheric Model Accuracy Analysis Using Shipborne Kinematic GPS Data in the Arctic Circle

The global ionospheric model built by the International Global Navigation Satellite System (GNSS) Service (IGS) using GNSS reference stations all over the world is currently the most widely used ionospheric product on a global scale. Therefore, analysis and evaluation of this ionospheric product’s accuracy and reliability are essential for the practical use of the product. In contrast to the traditional way of assessing global ionospheric models with ground-based static measurements, our study used shipborne kinematic global positioning system (GPS) measurements collected over 18 days to perform a preliminary analysis and evaluation of the accuracy of the global ionospheric models; our study took place in the Arctic Circle. The data from the International GNSS Service stations near the Arctic Circle were used to verify the ionospheric total electron contents derived from the kinematic data. The results suggested that the global ionospheric model had an approximate regional accuracy of 12 total electron content units (TECu) within the Arctic Circle and deviated from the actual ionospheric total electron content value by about 4 TECu.

Analysis of GNSS sensed precipitable water vapour and tropospheric gradients during the derecho event in Poland of 11th August 2017

Nowadays, one of the techniques that meets the increasing requirements of meteorologist in terms of monitoring of severe weather events is global navigation satellite systems (GNSS), which can provide information about the tropospheric state independently of the weather conditions and even in real time. In this paper we present the usage of GNSS sensed tropospheric data to monitor sudden and intense weather events. Our analyses were performed on the example of a derecho event in Poland of 11th August 2017. We used GPS/GLONASS observations from 278 GNSS reference stations located in Poland and, using Bernese GNSS Software ver. 5.2, we estimated the zenith tropospheric delays (ZTD) and tropospheric gradients with 5 and 15-min intervals respectively. Next, using meteorological data from the synoptic stations, we estimated the precipitable water vapour (PWV) which gives information about the amount of water vapour in the atmosphere. By applying a dense network of GNSS receivers, we were able to create the PWV, rate of the PWV (ROP), and the tropospheric gradients maps, which in turn allowed us to monitor the derecho event. We also conducted case studies for the selected GNSS stations where a comparison between PWV, reflectivity, and microwave radiometer were performed. During the main phase of the event we obtained the maximum value of PWV equal to 52.1 mm at 20:30 UTC. We also acquired high agreement between the PWV/ROP maps and reflectivity derived from meteorological radars. This applies to both the direction and speed of the phenomena as well as the time and place of the occurrence of the main phase of the derecho. This was possible only owing to the use of data derived from the dense network of the GNSS receivers.

REAL-TIME TROPOSPHERIC PRODUCT ESTABLISHMENT AND ACCURACY ASSESSMENT IN CHINA

Abstract. Tropospheric delay has always been an important issue in Global Navigation Satellite System (GNSS) processing. Empirical tropospheric delay models are difficult to simulate complex and volatile atmospheric environments, resulting in poor accuracy of the empirical model and difficulty in meeting precise positioning demand. In recent years, some scholars proposed to establish real-time tropospheric product by using real-time or near-real-time GNSS observations in a small region, and achieved some good results. This paper uses real-time observing data of 210 Chinese national GNSS reference stations to estimate the tropospheric delay, and establishes ZWD grid model in the country wide. In order to analyze the influence of tropospheric grid product on wide-area real-time PPP, this paper compares the method of taking ZWD grid product as a constraint with the model correction method. The results show that the ZWD grid product estimated based on the national reference stations can improve PPP accuracy and convergence speed. The accuracy in the north (N), east (E) and up (U) direction increase by 31.8 %,15.6 % and 38.3 %, respectively. As with the convergence speed, the accuracy of U direction experiences the most improvement.