Reference Station Research Articles

An ERA5 based local modelling of weighted mean temperature over hilly region in India for improved spatiotemporal analysis of extreme weather event using GNSS PWV

Precipitable water vapor (PWV) from Global Navigation Satellite System (GNSS) offers the benefits of high spatiotemporal resolution logging-consistency and all-weather operability. Estimation of water vapor weighted mean temperature (Tm) is a crucial part and a major error source in extracting GNSS PWV from atmospheric delay. Empirical modelling of Tm is a convenient approach, especially in cases where, the meteorology community suffers from inefficient and inconsistent logging of temperature and humidity profiles etc., in the existing meteorological observing systems, particularly for harsh weather conditions. In a first, a novel site specific model (Tm-SSM) is developed for the atmospheric weighted mean temperature (Tm) for 13 CORS (continuously operating reference stations) in the Survey of India (SOI) network, recently established in the hilly state of Uttarakhand (UK) using the ERA5 reanalysis data from 2016 to 2018. GNSS observations at 10 CORS are then used to observe the spatiotemporal characteristics of PWV from Tm-SSM during the heavy rainfall event in October 2021. The newly developed Tm-SSM performs better with the mean bias error (MBE) and root mean square error (RMSE) of 0.498 and 2.9 K, respectively, when compared with other globally accepted models. The Tm-SSM PWV estimates are well correlated with the precipitation data from AWS stations. The newly developed Tm-SSM is well conditioned and highly stable, with the mean stability indicator value as low as 1.043%. Further, a close relationship can be observed from the spatiotemporal characteristics of GNSS PWV during heavy rainfall event, with the in-situ precipitation measurements. It is apparent from the results that PWV steadily builds up before the arrival of rain, with sharp peaks in PWV observed just before rainfall event. It then, follows a downward slope through which it decreases to a stable value of around 30 mm when the rainfall ceases. The fluctuations in the PWV from Tm-SSM and the passage of the severe mesoscale convective system in a complex terrain such as UK were shown to have high spatial and temporal correlation, which is significant for the monitoring and forecasting such extreme weather. The findings imply that GNSS PWV represents the compendious signature of the water vapor dynamics during severe weather, demonstrating the potential to improve the early detection and sensing of severe weather.

Comparison of the Forecast Accuracy of Total Electron Content for Bidirectional and Temporal Convolutional Neural Networks in European Region

Machine learning can play a significant role in bringing new insights in GNSS remote sensing for ionosphere monitoring and modeling to service. In this paper, a set of multilayer architectures of neural networks is proposed and considered, including both neural networks based on LSTM and GRU, and temporal convolutional networks. The set of methods included 10 architectures: TCN, modified LSTM-/GRU-based deep networks, including bidirectional ones, and BiTCN. The comparison of TEC forecasting accuracy is performed between individual architectures, as well as their bidirectional modifications, by means of MAE, MAPE, and RMSE estimates. The F10.7, 10 Kp, Np, Vsw, and Dst indices are used as predictors. The results are presented for the reference station Juliusruh, three stations along the meridian 30°E (Murmansk, Moscow, and Nicosia), and three years of different levels of solar activity (2015, 2020, and 2022). The MAE and RMSE values depend on the station latitude, following the solar activity. The conventional LSTM and GRU networks with the proposed modifications and the TCN provide results at the same level of accuracy. The use of bidirectional neural networks significantly improves forecast accuracy for all the architectures and all stations. The best results are provided by the BiTCN architecture, with MAE values less than 0.3 TECU, RMSE less than 0.6 TECU, and MAPE less than 5%.

Precision comparison for Different GNSS PPP solution using online services and open-source software processing

In order to properly post-process data from the Global Navigation Satellite System, it is crucial to evaluate the dependability by means of online free processing tools because the development of GNSS offers satellite navigation consumers around the globe promising benefits. The purpose of the study is to assess the precision of several online processing tools, including GSIPOST, AUSPOS, IBGE, Magic GNSS, CSRS-PPP, and open-source software. Three stations' worth of RINEX 2 data were subjected to field observations utilising GNSS observations from IGS, with an observation time of 30 seconds. After that, both online and open-source software was used to analyze the collected data, using GPS and GPS and GLONASS. The relative differences and accuracy of the coordinates generated by each software were then assessed by comparison with those acquired from reference stations. coordinates in the ITRF14 standard for the X, Y, and Z directions. Online GNSS processing services don't require GNSS data processing skills and are easy to use, and provide more accurate results for engineering and geodetic uses. RINEX 3 data must be checked for better resolution.

A new large-area hierarchical PPP-RTK service strategy

Introducing precise atmosphere information into precise point positioning enables rapid ambiguity resolution and introduces a significant accuracy improvement. However, it can only be implemented in regions with dense networks and stable communication links. For larger areas, e.g., an intercontinental level, there is a conflict between the accuracy of corrections and the amount of atmosphere information to be disseminated. We develop a hierarchical augmentation mode to combine the advantages of the fitting model and region interpolation model to relieve the communication burden. Relying on the fitting model with fewer coefficients applied over large areas as the essential information, the unmodeled errors are calculated at each reference station, and further correction information is optional compensation depending on the magnitude of the unmodeled residuals. We perform the proposed models on 103 EUREF Permanent Network stations with 200-km station spacing and 84 stations as the external validation. The ionosphere and troposphere fitting models have an average accuracy of about 4.2 and 1.3 cm, respectively, under meteorologically calm conditions. The unmodeled error transmission determined by the magnitude of residuals can be reduced by 61% and 96% for the ionospheric and tropospheric delays, respectively, with respect to the legacy interpolation mode. Further compensation implemented, i.e., unmodeled residuals, can achieve instantaneous convergence for 83.6% of all solutions, and the overall initialization time is within 1.0 min. Thus, the proposed hierarchical positioning mode satisfies real-time positioning convergence requirements and significantly reduces massive corrections in communication.

Influence of reference stations on the stability of the geodetic control network during deformation determination in the area of Kadzielnia in Kielce

Abstract Observations of land surface deformation are one of the important tasks of surveying, especially in landslide areas. They concern the determination in time of the magnitude of the deformation, on the basis of a stable reference system based on a geodetic control points. The whole measurement process can be divided into two parts. One part concerns the observation of reference points (geodetic control points) and the other the observation of the object itself. In the first, in addition to classical methods, GNSS (Global Navigation Satellite System) techniques based on reference stations are used. In the second, common observation methods such as laser scanning or photogrammetric methods using Unmanned Aerial Vehicles (UAV) are used. These observations are carried out in a specific time period in relation to the aforementioned geodetic control points. An area such as Kadzielnia in Kielce is covered by a long-term observation programme. A key element is the survey of the constancy of the geodetic control points, which are located in the epicentre of the survey. The survey of the constancy of the control points at Kadzielnia was based on a static method using SmartNet stations. Taking into account the fact that reference stations are treated as error-free reference points and that they operate 24 hours a day, it was decided to study the variability of their position over a longer period of time, as well as to determine the influence on the geodetic control points and to observe the deformation of the object during the measurement cycles.

Prototype design of urban monitoring on several gaseous pollutants using low-cost electrochemical sensors

Monitoring atmospheric gaseous pollutants in urban areas is increasingly essential to anticipate poor urban air quality and the increasing number of acute respiratory infections caused by air pollution. Usually, the monitoring of air pollution is conducted by the government and scientists by using accurate and expensive instruments. However, this instrument’s high-cost installation and maintenance limited the number of monitoring stations that can be placed. It makes it hard to identify the pollutant’s local source, especially on an urban scale. Hence, this study designed alternative techniques to monitor several gaseous pollutants using the Low-cost Air Quality System (LAQS). LAQS has enormous potential to be a reference station’s supporting tool and increase the resolution of air pollution monitoring systems. A custom-made low-cost electrochemical sensor was used in this study to detect several gaseous pollutants, including ozone (O3), nitrogen dioxide (NO2), and carbon monoxide (CO), together with temperature and relative humidity measurements. The system was also equipped with a data logger and 4G module for data storage and transmission, respectively. In the first stage, two scenarios were employed, including its examination inside and outside the room before co-location.

METHODOLOGY TO IMPROVE THE ACCURACY OF DETERMINING THE POSITION OF UAVS EQUIPPED WITH SINGLE-FREQUENCY RECEIVERS FOR THE PURPOSES OF GATHERING DATA ON AVIATION OBSTACLES

Low-altitude photogrammetric studies are often applied in detection of aviation obstacles. The low altitude of the Unmanned Aerial Vehicle (UAV) flight guarantees high spatial resolution (X, Y) of the obtained data. At the same time, due to high temporal resolution, UAVs have become an appropriate tool for gathering data about such obstacles. In order to ensure the required accuracy of orientation of the photogrammetric block, Ground Control Points (GCPs) are measured. The recently introduced UAV positioning solutions that are based on Post-Processing Kinematic (PPK) and Real Time Kinematic (RTK) are known to effectively reduce, or, according to other sources, even completely eliminate the necessity to conduct GCP measurements. However, the RTK method involves multiple limitations that result from the need to ensure continuous communication between the reference station and the rover receiver. The main challenge lies in achieving accurate orientation of the block without the need to conduct time-consuming ground measurements that are connected to signalling and measuring the GCPs. Such solution is required if the SPP code method is applied to designation the position of the UAV. The paper presents a research experiment aimed at improving the accuracy of the determination of the coordinates of UAV for the SPP method, in real time. The algorithm of the SPP method was improved with the use of IGS products.

Monitoring mass movements using Network-RTK measurement technique and producing potential rockfall scenarios in a paleo-landslide area

Abstract Mass movements resulting from landslides cause significant losses in terms of lives and property. Periodic observations of these movements using geodetic measurement techniques help to prevent these losses. Network-RTK measurement technique produces real-time location with centimeter accuracy, based on phase observations using a network of reference stations. In this study, the paleo-landslide area in the Işıklar location of Trabzon province, Esiroğlu district, Turkey, was chosen as the application area. This study aims to measure the application area between 2019 and 2021, using the Network-RTK technique to determine the mass movements. Additionally, there is a rock block in an area with a steep slope. The possible movement of this rock block is a threat to infrastructure facilities, residential areas, agricultural areas, and life safety if the mass movement continues. Within this scope, the potential movement scenarios of the block were produced using RocPro3D software and UAV photogrammetry. Scenarios following an ongoing mass movements in the region triggering another mass movement are discussed. In the light of the results obtained, mass movements in the vertical direction of up to 28 cm were detected in the area where the rock block is located in the last 2 years. The periodic continuation of mass movements in the study area, declared a disaster-prone area, confirms the importance of the rock block in the region. In another phase of the study, possible movement scenarios of the rock block were examined using a rockfall analysis. In this context, with the help of an unmanned aerial vehicle, a digital elevation model and orthophoto map of the region where the rock block is likely to move was produced and a base map to be used in rockfall analysis was obtained. As a result of the rockfall analysis, maps showing the speed, energy, spread, possible impacts, and stopping points were produced. With the examination of these maps, it has been determined that residential areas, agricultural areas, and infrastructure facilities in the study area may be significantly damaged.

Storm‐Time Subauroral Ionospheric Plasma Density Irregularities and the Substorm Current Wedge

AbstractWe report on the development of plasma density irregularities in the subauroral ionosphere over the North American sector during the 17 March 2015 Saint Patrick's Day geomagnetic storm. Data from network of ground‐based observation instruments including the National Oceanic and Atmospheric Administration Continuously Operating Reference Station global navigation satellite system receivers and Time History of Events and Macroscale Interactions during Substorms (THEMIS) all‐sky imagers, as well as in situ measurements from the Defense Meteorological Satellite Program (DMSP) and the Van Allen Radiation Belt Storm Probes (RBSP) spacecrafts, were examined to characterize the spatial and temporal development of subauroral irregularities. The auroral electrojet (AE) index was used as primary indicator of substorm occurrences, aided by some cross‐referencing with the SYM‐H index and SYM‐H time derivative. Special attention was given to substorms that happened at the beginning of this geomagnetic storm. Analysis of Global Positioning System (GPS) rate‐of‐total electron content index (ROTI) data along an east‐west cut line near the US‐Canada border indicates that subauroral ionospheric irregularities may start to form as early as a few minutes after a substantial increase in the AE index. Contemporaneous DMSP and RBSP observations confirmed the presence of subauroral polarization streams wave structures when the aforementioned enhancement in AE index and GPS ROTI occurred. An equatorward expansion of bright auroral arc and subsequent auroral breakups were also seen in the THEMIS all‐sky imager observation data. The relatively short time scales for irregularities to develop suggest possible roles played by the penetration of magnetotail plasma flow bursts into the plasmasphere and the substorm current wedge development in their formation in the subauroral ionosphere.

Accurate time synchronization of power reference station based on BD3 system

A Beidou 3 (BD3) system-based power reference station can provide high-precision time synchronization for power distribution systems by sending synchronization data packets to devices in a multi-hop routing fashion. However, optimizing route selection to reduce both time synchronization error and delay is a challenging problem. In this paper, we establish a software-defined network-enabled power reference station time synchronization framework based on BD3. Then, we formulate the joint problem to minimize cumulative synchronization error and delay through multi-hop route selection optimization. A back propagation (BP) neural network-improved intelligent time synchronization route selection algorithm named BP-RS is proposed to learn the optimal route selection, which uses a BP neural network to dynamically adjust the exploration factor to achieve rapid convergence. Simulation results show the superior performance of BP-RS in synchronization delay, synchronization error, and adaptability with changing routing topologies.

Exploratory analysis of citizen observations of hourly precipitation over Scandinavia

Abstract. We present a comparison between Netatmo hourly precipitation amounts and observations of the same quantity from weather stations managed by national meteorological services, the latter used as reference values. The empirical distributions of the crowdsourced observations in the surroundings of reference stations are used to assess accuracy and precision of crowdsourced data. We found that reference values are typically within the distribution of the crowdsourced data. However, as the amount of precipitation increases, the spread of the crowdsourced distribution increases and the reference values are more and more frequently found towards the right tail of the distribution. These results indicate that accuracy and precision of crowdsourced data change as precipitation increases. We have studied the sensitivity of our results to the size of the neighbourhood chosen around the reference stations and we show that by aggregating the values over those neighbourhoods, crowdsourced data can be trusted in determining precipitation occurrence. We have assessed the variability of precipitation within small neighbourhoods (of radius 1, 3 and 5 km) and we provide estimates on the basis of the precipitation amounts. Our study quantifies the variability of hourly precipitation over small regions, of the size of the so-called “unresolved spatial scales” in limited area models, based on three years of data collected at several places in Scandinavia.

Assessment of GNSS zenith tropospheric delay responses to atmospheric variables derived from ERA5 data over Nigeria

Tropospheric delay is a major error caused by atmospheric refraction in Global Navigation Satellite System (GNSS) positioning. The study evaluates the potential of the European Centre for Medium-range Weather Forecast (ECMWF) Reanalysis 5 (ERA5) atmospheric variables in estimating the Zenith Tropospheric Delay (ZTD). Linear regression models (LRM) are applied to estimate ZTD with the ERA5 atmospheric variables. The ZTD are also estimated using standard ZTD models based on ERA5 and Global Pressure and Temperature 3 (GPT3) atmospheric variables. These ZTD estimates are evaluated using the data collected from the permanent GNSS continuously operating reference stations in the Nigerian region. The results reveal that the Zenith Hydrostatic Delay (ZHD) from the LRM and the Saastamoinien model using ERA5 surface pressure are of identical accuracy, having a Root Mean Square (RMS) error of 2.3 mm while the GPT3-ZHD has an RMS of 3.4 mm. For the Zenith Wet Delay (ZWD) component, the best estimates are derived using ERA5 Precipitable Water Vapour (PWV). These include the ZWD derived by the LRM having an average RMS of 20.9 mm and Bevis equation having RMS of 21.1 mm and 21.0 mm for global and local weighted mean temperatures, respectively. The evaluation of GPT3-ZWD estimates gives RMS of 45.8 mm. This study has provided a valuable insight into the application of ERA5 data for ZTD estimation. In line with the findings of the study, the ERA5 atmospheric variables are recommended for improving the accuracy in ZTD estimation, required for GNSS positioning.

Skywave Detection and Mitigation for the MF R-Mode Continuously Operating Reference Station.

There is an increasing need for an independent terrestrial navigation system, owing to the increasing reliance on global navigation satellite systems (GNSS). The medium-frequency range (MF R-Mode) system is considered a promising alternative; however, the skywave effect caused by ionospheric changes at night can degrade its positioning accuracy. To address this problem, we developed an algorithm to detect and mitigate the skywave effect on MF R-Mode signals. The proposed algorithm was tested using data collected from Continuously Operating Reference Stations (CORS) monitoring the MF R-Mode signals. The skywave detection algorithm is based on the signal-to-noise ratio (SNR) induced by the groundwave and skywave composition, whereas the skywave mitigation algorithm was derived from the I and Q components of the signals obtained through IQ modulation. The results demonstrate a significant improvement in the precision and standard deviation of the range estimation using CW1 and CW2 signals. The standard deviations decreased from 39.01 and 39.28 m to 7.94 and 9.12 m, respectively, while the precision (2-sigma) increased from 92.12 and 79.82 m to 15.62 and 17.84 m, respectively. These findings confirm that the proposed algorithms can enhance the accuracy and reliability of MF R-Mode systems.

Crustal displacement in Vietnam using CORS data during 2018 - 2021

Continuously Operation Reference Stations (CORS) networks provide surveying, mapping, and positioning services and play a crucial role in determining crustal displacement. This study processed the continuous Global Navigation Satellite System (GNSS) data collected from 55 CORS stations in the TAST’s CORS in Vietnam to determine the crustal displacement in Vietnam from 2018 to 2021. The processing was performed using online Precise Point Positioning (PPP) services and the displacement model to estimate the velocity of the position time series. The result showed that the horizontal velocity field of all CORS stations is uniform in magnitude and direction, ranging from 25.3 to 42.6 mm/year with accuracy from ±0.1 to ±1.0 mm/year in the south-east direction in the International Terrestrial Reference Frame 2014 (ITRF2014). This study confirms the stability and accuracy of GNSS data from the TAST’s CORS network for crustal displacement analysis.

Real-time high-precision landslide displacement monitoring based on a GNSS CORS network

GNSS technology is an effective all-weather, continuous and real-time method of detecting three-dimensional landslide displacement. However, current positioning methods such as the network real-time kinematic (RTK) technique based on virtual reference station (VRS) technology have some limitations, particularly a sensitivity to atmospheric modeling error. In this study, taking advantage of existing GNSS continuously operating reference stations (CORS), we propose a network RTK algorithm (virtual atmosphere constraint (VAC)) for high-precision landslide monitoring. Network modeling atmosphere delays were used as virtual observations for the terminal position solution, by which their accuracy information can be considered. Additionally, a noise reduction method that combines complementary ensemble empirical mode decomposition and median filtering was proposed to reduce GNSS signal residual errors in the original RTK coordinate time series. To validate the effectiveness of the algorithm, we processed one week of data from two monitoring receivers on the Heifangtai landslide and four surrounding CORS using the VAC and VRS methods. Results show that the original displacement time series processed with VRS was prone to jitter, whereas the VAC method exhibited better accuracy, stability, and ambiguity fixing rates. Horizontal and vertical RMS errors at a stable monitoring point were 1.7 cm and 3.9 cm for VRS-RTK and 0.9 cm and 2.3 cm for VAC-RTK; thus, VAC reduced the error by 40.1% and 20.5%, respectively. After noise reduction, the RMS values of the VAC method at the stable and the other collapsed monitoring point were both reduced to an mm-level accuracy.

Integrating low-cost sensor monitoring, satellite mapping, and geospatial artificial intelligence for intra-urban air pollution predictions

There is a growing need to apply geospatial artificial intelligence analysis to disparate environmental datasets to find solutions that benefit frontline communities. One such critically needed solution is the prediction of health-relevant ambient ground-level air pollution concentrations. However, many challenges exist surrounding the size and representativeness of limited ground reference stations for model development, reconciling multi-source data, and interpretability of deep learning models. This research addresses these challenges by leveraging a strategically deployed, extensive low-cost sensor (LCS) network that was rigorously calibrated through an optimized neural network. A set of raster predictors with varying data quality and spatial scales was retrieved and processed, including gap-filled satellite aerosol optical depth products and airborne LiDAR-derived 3D urban form. We developed a multi-scale, attention-enhanced convolutional neural network model to reconcile the LCS measurements and multi-source predictors for estimating daily PM2.5 concentration at 30-m resolution. This model employs an advanced approach by using the geostatistical kriging method to generate a baseline pollution pattern and a multi-scale residual method to identify both regional patterns and localized events for high-frequency feature retention. We further used permutation tests to quantify the feature importance, which has rarely been done in DL applications in environmental science. Finally, we demonstrated one application of the model by investigating the air pollution inequality issue across and within various urbanization levels at the block group scale. Overall, this research demonstrates the potential of geospatial AI analysis to provide actionable solutions for addressing critical environmental issues.

GNSS Signal Quality in Forest Stands for Off-Road Vehicle Navigation

One of the basic possibilities of orientation in forest stands is the use of global navigation satellite systems (GNSS). Today, these systems are used for pedestrian orientation and also for off-road vehicle navigation. This article presents the results of research aimed at measuring the quality of GNSS signal in different types of coniferous and deciduous vegetation for the purpose of optimizing the navigation of off-road vehicles. To determine the structure (density) of the forest stand, tachymetry was chosen as the reference method. The Trimble Geo 7X cm edition device with Tornado for 7X antenna devices using real time VRS (virtual reference station) method was used to measure GNSS signal quality. This article presents the results of recorded numbers of GNSS satellites (GPS, GLONASS, Galileo and BeiDou) during the driving of a terrain vehicle in two different forest locations. Significant presented results include the deviations of vehicle positions determined by GNSS from tachymetrically precisely measured and marked routes along which the vehicle was moving. The authors of the article focused on the accuracy of determining the position of the vehicle using GNNS, as the most commonly used device for off-road vehicle navigation. The measurement results confirmed the assumption that the accuracy of positioning was better in deciduous forest than in coniferous (spruce) or mixed vegetation. This research was purposefully focused on the possibilities of navigation of military vehicles, but the achieved results can also be applied to the navigation of forestry, rescue and other types of off-road vehicles.

Effects of different seasonal fitting methods on the spatial distribution characteristics of common mode errors

This study investigated the effects of various seasonal fitting techniques on the spatial distribution of the common mode errors taking the coordinate time series of the continuous GPS reference stations of the Crustal Movement Observation Network of China (CMONOC) as an example. First, the seasonal term of coordinate time series was calculated using constant amplitude harmonic fitting (CAF), continuous wavelet transform (CWT), and smoothing spline fitting (SPF). The seasonal term and linear trend were then removed to obtain the residual time series. Finally, to determine the common mode errors of residual time series, principal component analysis (PCA) was applied. The results indicate that 1) smoothing spline fitting is superior to constant amplitude harmonic fitting and continuous wavelet transform in its ability to fit short-term irregular seasonal signals. In comparison to constant amplitude harmonic fitting, N/E/U has root mean square error (RMSE) values of smoothing spline fitting that are lower by 25%, 20%, and 14.4%, respectively. Smoothing spline fitting also has a higher coefficient of determination than continuous wavelet transform and constant amplitude harmonic fitting. The coefficient of determination in the U direction is larger than that in the N and E directions. 2) Each order PC of the residual series fitted by smoothing spline fitting exhibits apparent spatial aggregation characteristics, with PC1 having a uniform spatial distribution and presenting a largely positive response. Nevertheless, the residual series obtained by constant amplitude harmonic fitting and continuous wavelet transform exhibits scattered spatial response distribution features in each order PC. Compared to N and E, U’s spatial response distribution is distinct. From north to south, the spatial response of PC1 in the U direction progressively diminishes. In addition to being much lower than that in other locations, the Sichuan–Yunnan region’s spatial response value of PC1 and PC3 also exhibits a clear negative reaction. The root mean square error value of the residual series after smoothing spline fitting is the least, and the filtering effect is the best when comparing the spatial filtering effect based on the three fitting methods. We also compared the root mean square error reduction ratio before and after spatial filtering, and the results showed that the root mean square error reduction ratio before and after the residual series obtained by smoothing spline fitting is slightly larger than that obtained by other methods.

Reference clock impact on GNSS clock outliers

Abstract With the advent of the Global Navigation Satellite System (GNSS), the need for precise and highly accurate orbit and clock products becomes crucial in processing GNSS data. Clocks in GNSS observations form the basis of positioning. Their high quality and stability enable high accuracy and the reliability of the obtained results. The clock modelling algorithms are continuously improved; thus, the accuracy of the clock products is evolving. At present, 8 Analysis Centers (ACs) contribute to the International GNSS Service final clock products. These products are based on GNSS observations on a network of reference stations, where for a given day one of the reference station clocks is the reference clock. In this paper, the authors determined the impact of the reference clock on the quality of clock product, especially outliers, for the first time. For this purpose, the multi-GNSS final clock products provided by the Center for Orbit Determination in Europe (CODE) for the period 2014–2021 (1773–2190 GPS week, 2921 days) were analysed. Analysis shows that by applying the Median Absolute Deviation (MAD) algorithm for outlier detection, the Passive Hydrogen Maser (PHM) clock installed on board the GALILEO satellites have the lowest level of noise, whereas the Block IIR GPS satellite launched in 1999 appears to have the highest levels of noise. Furthermore, the GNSS station OHIE3, when used as a reference clock, generates an increase in the level of noise, especially noticeable on the G09 and E03 satellites.

Changes in the long-term stability of GPS, GLONASS and Galileo clocks based on the IGS repro3 campaign

Abstract Time is the basis of satellite navigation systems. In precision studies, it is additionally important to ensure accuracy at the highest possible level, up to sub-millimetres. For this purpose, corrections of the clocks of satellites and GNSS reference stations are made available. This type of data is made available in real time in a navigation dispatch with an interval of 10 min–2 h depending on the GNSS system, or in a precision orbit file (interval of 15 min) or in the form of clock correction files (30 s or 300 s). This paper analyses the long-term stability of the clocks of satellites of four GNSS systems. For this purpose, IGS reprocessing data from 1994 to 2020 were used and ADEV (Allan deviation) and three related variances were adopted. The study showed the different nature of the satellite correction for each GNSS system and the increase of the stability over time.