Global Navigation Satellite System Stations Research Articles

Global, spatially explicit modelling of zenith wet delay with XGBoost

Radio signals transmitted by Global Navigation Satellite System (GNSS) satellites experience tropospheric delays. While the hydrostatic part, referred to as zenith hydrostatic delay (ZHD) when mapped to the zenith direction, can be analytically modelled with sufficient accuracy, the wet part, referred to as zenith wet delay (ZWD), is much more difficult to determine and needs to be estimated. Thus, there exist several ZWD models which are used for various applications such as positioning and climate research. In this study, we present a data-driven, global model of the spatial ZWD field, based on the Extreme Gradient Boosting (XGBoost). The model takes the geographical location, the time, and a number of meteorological variables (in particular, specific humidity at several pressure levels) as input, and can predict ZWD anywhere on Earth as long as the input features are available. It was trained on ZWDs at 10718 GNSS stations and tested on ZWDs at 2684 GNSS stations for the year 2019. Across all test stations and all observations, the trained model achieved a mean absolute error of 6.1 mm, respectively, a root mean squared error of 8.1 mm. Comparisons of the XGBoost-based ZWD predictions with independently computed ZWDs and baseline models underline the good performance of the proposed model. Moreover, we analysed regional and monthly models, as well as the seasonal behaviour of the ZWD predictions in different climate zones, and found that the global model exhibits a high predictive skill in all regions and across all months of the year.

Assessment of ERA5 derived zenith tropospheric delay data over East African region

The Global Navigation Satellite Systems (GNSS) provide valuable data for computing accurate and reliable Zenith Tropospheric Delay (ZTD) products used in GNSS precise positioning, climate, and meteorological studies. However, inconsistent GNSS data streaming in the East African region, caused by poor internet connectivity, equipment failure, and electrical power outages, results in data gaps that pose an impact on the applications of the ZTD products. To ensure continuous ZTD data availability, alternative approaches are necessary to fill the gaps. This paper evaluates the accuracy and feasibility of computing ZTD over East Africa using the European Centre for Medium-Range Weather Forecasts (ECMWF) 5th Re-Analysis (ERA5) model. The ERA5 ZTD is compared with ZTD data from 13 GNSS stations for the years 2013 to 2020 from the Nevada Geodetic Laboratory (NGL) products. Seven statistical evaluation metrics, including mean bias (MnB), root mean squared error (RMSE), Pearson correlation coefficient (R), coefficient of determination (R2), refined index of agreement (IA), Nash–Sutcliffe coefficient of efficiency (NSE), and Taylor skill score (TSS) were used to determine the agreement between ERA5 and GNSS tropospheric products. The results indicate that ERA5ZTD products match well with GNSS ZTD products, with an average MnB of −0.80 mm, RMSE of 8.94 mm, R of 0.976, R2 of 0.952, IA of 0.986, TSS of 0.975, and NSE of 0.945. The high accuracy and stability of ERA5 data in computing ZTD make it an excellent alternative source of ZTD data to augment GNSS ZTD products for positioning and meteorological applications in the East African region.

Assessing seismic displacements and early warning using kinematic PPP with MADOCA real-time products: A case study of 2023 Kahramanmaraş (Türkiye) earthquakes

Early warning and emergency hazard response studies in large earthquakes require rapid and reliable determination of earthquake magnitude. The magnitude of an earthquake can be estimated based on peak ground displacement (PGD) values captured from high-rate GNSS records. In this study, two major earthquakes that hit Kahramanmaraş Province on February 6, 2023, were investigated by using the real-time Global Navigation Satellite System (GNSS) Precise Point Positioning (PPP) technique to confirm the usability of GNSS in early warning. Data from high-rate (1-Hz) GNSS stations were evaluated with the real-time kinematic PPP approach utilizing the multi-GNSS advanced demonstration tool for orbit and clock analysis (MADOCA) products and JPL's GipsyX software. In addition, static PPP was also used to identify the permanent displacements caused by earthquakes. Static PPP results demonstrated that significant displacements were detected at stations close to the epicenters, particularly the 446.86 cm horizontal displacement in the EKZ1 station. Considering the kinematic PPP results revealed that when PGD values calculated from seismic waves, functional model, and updated coefficients were implemented, the moment magnitude of the Pazarcık earthquake was estimated as Mw 7.78, which was reported as Mw 7.8 by USGS and Mw 7.7 by AFAD, whilst the magnitude of the Elbistan earthquake was computed as Mw 7.56, which was reported as Mw 7.5 by USGS and 7.6 by AFAD. Overall, this study validates the effectiveness of the real-time GNSS PPP as an attractive alternative for analyzing coseismic events and supporting early warning.

GNSS Ground deformation observation network optimization assisted using prior InSAR-derived ground surface deformation and multiscale iteration estimation

ABSTRACT Ground surface deformation must be monitored to understand and prevent geological hazards. Global navigation satellite system (GNSS) technology has been widely used in deformation monitoring; however, current GNSS station layout strategies are often subjective and lack theoretical guidance. Therefore, in this study, a GNSS deformation observation network optimization method was developed based on interferometric synthetic aperture radar (InSAR) prior to deformation assistance. This method considers the deformation distribution, cost, accuracy, topography, and actual monitoring requirements and uses kriging interpolation and multiscale iterative optimization to obtain the reference number, spatial distribution, and deformation capture accuracy of GNSS stations in areas experiencing deformation. Simulation experiments showed that the proposed method strengthened the spatial deformation monitoring ability of GNSS by 46%–68% compared with the traditional station layout methods, which ignore the distribution of the deformation. Then, based on the small baseline subset (SBAS) InSAR technology and applying the new method, we evaluated the number and location of the most suitable GNSS stations for a mining area and found that 89.6% of the error was less than 1.5 cm, which further illustrates the practicability and reliability of the proposed method. This method provides an effective supplement to station layouts for monitoring GNSS deformation.

Stability Analysis of GNSS Stations Affected by Samos Earthquake

An earthquake cycle can cause meters of displacement on the surface and at Global Navigation Satellite System (GNSS) stations. This study focuses on the identification of GNSS stations that have significant displacement because of a Mw 7.0 earthquake near Samos Island on 30 October 2020. The S-transformation method is used to examine 3D, 2D and 1D coordinate systems along with threshold and statistical test approaches. The highest coseismic offset among the 21 GNSS stations is displayed by SAMO, and CESM, MNTS, IZMI and IKAR also experience significant displacement. Significantly displaced stations are successfully identified in both 3D and 2D analyses. In the up component, SAMO is the only unstable station. The coordinate S-transformation method can be used in detecting unstable points in a GNSS network and provide valuable information about the effects of an earthquake on GNSS stations.

THE EFFECT OF SEASONAL VARIATION ON GNSS ZENITH TROPOSPHERIC DELAY

Abstract. The Global Navigation Satellite System (GNSS) signal experiences delays caused by the atmosphere, leading to the lengthening of the geometric path of the ray, commonly referred to as tropospheric delay. This delay is a significant source of error in GNSS positioning, contributing to a bias in the height component of several centimeters, even when meteorological data are simultaneously recorded and used in tropospheric models. In this study, considering seasonal variations, we investigated the impact of tropospheric delay on the GNSS height component. GNSS stations, part of the Turkish RTK CORS Network known as TUSAGA-Active (Turkish National Permanent GNSS Network Active), covered different heights over the 2014–2019 period. Daily coordinates of GNSS stations and tropospheric zenith delay were obtained through the GAMIT/GLOBK software solution.In the study, temperature, pressure, and relative humidity data of meteorological stations at different heights were converted to mean sea level. By using these values, interpolation estimates were made for the continuous GNSS stations in the same region with the IDW method. The most significant delay in GNSS signals occurs in July and August. This effect, which causes periodic changes in the zenith delay, varies inversely with the station's height. With the increase in the amount of water vapor in the atmosphere in parallel with the rise in the temperature in the summer months, it is seen that the stations at a low height are more exposed to the tropospheric effect than the stations at higher heights. In addition, GNSS stations' reduced meteorological values (temperature, pressure and relative humidity) show that the zenith delay values changed directly proportional to the temperature and inversely proportional to the pressure and relative humidity.

Drought‐Induced Vertical Displacements and Water Loss in the Po River Basin (Northern Italy) From GNSS Measurements

AbstractWe study vertical ground displacement time series from Global Navigation Satellite System (GNSS) stations to measure deformation associated with hydrological drought in the Po river basin. Focusing on interannual trend changes, rather than seasonal (annual) components, we found a clear spatially correlated deformation signal that is temporally (anti)correlated with changes in the Po river level and the SPEI‐12 drought index, with stations moving upward during periods of river/index level decrease and vice versa. In the 2021–2022 time span, which culminated in the most severe drought of the last two centuries, we estimate the amount and spatial distribution of water loss in the basin and its surroundings. Excluding the seasonal signals, between January 2021 and August 2022, the GNSS stations underwent uplift, up to 7 mm, which corresponds to ∼70 Gtons of water loss. Compared to Global Land Data Assimilation System and Gravity Recovery and Climate Experiment estimates, GNSS results show a similar temporal evolution of water content but a more heterogeneous distribution of values. We show that continuous GNSS networks provide an effective way to monitor multiannual trend changes in water storage even in small water basins and serve as a reliable indicator of drought severity.

Modeling trends and periodic components in geodetic time series: a unified approach

Geodetic time series are usually modeled with a deterministic approach that includes trend, annual, and semiannual periodic components having constant amplitude and phase-lag. Although simple, this approach neglects the time-variability or stochasticity of trend and seasonal components, and can potentially lead to inadequate interpretations, such as an overestimation of global navigation satellite system (GNSS) station velocity uncertainties, up to masking important geophysical phenomena. In this contribution, we generalize previous methods for determining trends and seasonal components and address the challenge of their time-variability by proposing a novel linear additive model, according to which (i) the trend is allowed to evolve over time, (ii) the seasonality is represented by a fractional sinusoidal waveform process (fSWp), accounting for possible non-stationary cyclical long-memory, and (iii) an additional serially correlated noise captures the short term variability. The model has a state space representation, opening the way for the evaluation of the likelihood and signal extraction with the support of the Kalman filter (KF) and the associated smoothing algorithm. Suitable enhancements of the basic methodology enable handling data gaps, outliers, and offsets. We demonstrate the advantage of our method with respect to the benchmark deterministic approach using both observed and simulated time series and provide a fair comparison with the Hector software. To that end, various geodetic time series are considered which illustrate the ability to capture the time-varying stochastic seasonal signals with the fSWp.

Improving GNSS PPP Performance in the South China Under Different Weather Conditions by Using the Weather Research and Forecasting (WRF) Model‐Derived Wet Delay Corrections

AbstractAtmospheric wet delay caused by Precipitable Water Vapor (PWV) significantly impacts the performance of many geodetic surveying systems such as Global Navigation Satellite System (GNSS). In this study, we use wet delay corrections forecast by the Weather Research and Forecasting (WRF) model to enhance GNSS Precise Point Positioning (PPP) during two observation periods with two different weather conditions, that is, period 1: March 01 to 14, 2020 (average PWV: 23.5 kg/m2) and period 2: June 02 to 15, 2020 (flooding weather with average PWV: 55.6 kg/m2), over the South China. PWV data from 277 to 263 GNSS stations are assimilated into WRF model to enhance the WRF water vapor forecasting capability for period 1 and period 2, respectively. Wet delay corrections from two different WRF configurations, that is, WRF no data assimilation and WRF with assimilation of GNSS PWV, are used to augment the PPP. Totally, eight WRF‐enhanced PPP schemes are tested. The results show that WRF‐enhanced PPP schemes generally have a better positioning performance in the up component than traditional PPP. After using WRF wet delay corrections, for static mode, the vertical positioning accuracy is improved by 14.6% and 33.7% for period 1 and period 2, respectively. The corresponding convergence time are reduced by 41.8% and 25.0% for period 1 and period 2, respectively. For kinematic mode, the positioning accuracy improvements in the up component reach 13.8% and 19.0% for period 1 and period 2, respectively. The kinematic PPP convergence time is reduced by up to 8.2% for period 1.

Studying the Repeatability of Measurements Obtained via Network Real-Time Kinematic Positioning at Different Times of the Day

The network real-time kinematic (NRTK) positioning technique is currently used in numerous applications. The aim of this study was to better understand the process of obtaining accurate positions by statistically evaluating the significance of differences between repeated measurements for a single point at different times of the day (morning, noon, and evening) using the Virtual Reference Station (VRS), Flächen Korrektur Parameter (FKP), and Master Auxiliary Concept (MAC) correction methods. An analysis of variance (ANOVA) was used to this effect. Further analysis was carried out to determine the accuracy and precision of the coordinate differences obtained via a static GNSS (global navigation satellite system) and by averaging the repeated measurements. It was determined that the accuracy and precision of the vertical component of the coordinates were lower than that of the horizontal component. The FKP correction method yielded the best results. It was observed that the accuracy and precision of the measurements taken at noon were the lowest. The ANOVA showed that the differences between repeated measurements were statistically significant and that there were outlier measurements. The results of this study are important for NRTK users to be able to statistically evaluate different measurement configurations and obtain positions with the desired accuracy and precision.

Slip rate and interseismic coupling along the Moxi-Shimian segment of the Xianshuihe fault, Eastern Qinghai-Xizang Plateau

On September 5, 2022, the Luding M6.8 earthquake occurred in the Moxi-Shimian segment of the Xianshuihe fault, coinciding with the historical ruptured zone of the 1786 Moxi earthquake. Its seismogenic environment provides a foundation for comprehending the mechanism of the earthquake and its future hazard. In the Moxi-Shimian segment, we establish a series of near-field Global Navigation Satellite Systems (GNSS) stations to enhance the spatial resolution of observational data for the inversion of the interseismic kinematic parameters. In this study, with an elastic screw dislocation model constrained by GNSS observations, the slip rate of the Moxi-Shimian segment is estimated to be 10.9 ± 1.0 mm/yr, while the locking depth is 15.7 ± 6.2 km. Additionally, we utilize a block-dislocation model to invert the interseismic fault coupling along the Kangding-Moxi-Shimian segment. The result indicates a gradual deepening of the locking depth along the section from Kangding to Shimian. The coseismic rupture of the 2022 event occurred within the high coupling regions in the Kangding-Moxi-Shimian segment, which indicates that the rupture kinematics in this event might be controlled by the interseismic deformation. The seismic moment accumulated within the ruptured zone of the Luding earthquake since 1786 ranges in [1.42–3.40] × 1019 N·m, which is significantly greater than the seismic moment released during the 2022 event. As a result, we infer that the Luding earthquake released only a portion of the accumulated energy within the original rupture zone since 1786, indicating that the 2022 event has not caused a complete rupture in the Moxi-Shimian segment. Consequently, there remains a substantial seismic hazard in this area.

Investigation of 2020–2022 extreme floods and droughts in Sichuan Province of China based on joint inversion of GNSS and GRACE/GFO data

The Global Navigation Satellite System (GNSS) surface deformation measurement and Gravity Recovery and Climate Experiment (GRACE)/GRACE Follow-on (GFO) satellite gravimetry provide complementary strengths in estimating terrestrial water storage (TWS) changes, which helps quantify the evolutions of extreme floods and droughts. We implemented a joint inversion framework that only incorporates the contributions of GNSS and GRACE/GFO to recover the TWS changes for monitoring extreme floods and droughts in the Sichuan Province of China (SCP). The performance of TWS changes from the joint inversion is evaluated through closed-loop simulation and independent hydrometeorological data (i.e., precipitation (P), evapotranspiration (ET), and runoff (R)) based on the water balance equation (named P-ET-R) in the spatio-temporal domains. The simulation analysis results show that joint inversion presents better performance in recovering TWS changes than GNSS-only and GRACE/GFO-only solutions in the spatio-temporal scales. The measured joint inversion results reveal more spatial details of TWS changes than GNSS-only and GRACE/GFO-only solutions and present better correlations with GLDAS- and ERA5-derived P-ET-R (0.77 vs. 0.79) than GNSS-only (0.49 vs. 0.54) and GRACE/GFO-only (0.61 vs. 0.68) in the spatial domain. Meanwhile, the joint inversion results effectively characterize the spatio-temporal dynamics of extreme floods and droughts in SCP during 2020–2022, the average TWS surpluses are 33.61 and 32.86 mm in 2020 and 2022 flood events and the average TWS deficits are −36.78 and −63.87 mm in 2021 and 2022 drought events. In addition, the drought propagation time generally presents an increasing trend from southwestern to northeastern SCP. Furthermore, the average and minimum recovery time of drought and flood in the Sichuan basin is longer than those in the western mountains during 2020–2022. The proposed approach provides an effective estimation strategy for recovering reliable TWS changes for the investigation of extreme floods and droughts in regions where GNSS stations are distributed.

Relationship between GIX, SIDX, and ROTI ionospheric indices and GNSS precise positioning results under geomagnetic storms

Ionospheric indices give information about ionospheric perturbations, which may cause absorption, diffraction, refraction, and scattering of radio signals, including those from global navigation satellite systems (GNSS). Therefore, there may be a relationship between index values and GNSS positioning results. A thorough understanding of ionospheric indices and their relationship to positioning results can help monitor and forecast the reliability and accuracy of GNSS positioning and support the precision and safety of life applications. In this study, we present the relationship between three indices: Gradient Ionosphere indeX (GIX), Sudden Ionospheric Disturbance indeX (SIDX), and Rate of Total electron content Index (ROTI) in relation to precise positioning results. We used two approaches: precise point positioning (PPP) and relative positioning for long baselines. We focus on GNSS stations located in Europe for two selected geomagnetic storms: March 17, 2015, and May 22, 2015. Our results show that in the case of PPP, positioning degradation occurred mainly at high latitudes and was mostly caused by rapid small-scale changes in ionospheric electron content represented by SIDX and ROTI. We also showed a significant correlation between cycle slips of GNSS signals and ROTI (0.88). The most significant degradations for relative positioning for low and medium latitudes were associated with large spatial gradients reflected by the GIX.

Improving GNSS PPP-RTK through global forecast system zenith wet delay augmentation

The precise point positioning real-time kinematic (PPP-RTK) is a high-precision global navigation satellite system (GNSS) positioning technique that combines the advantages of wide-area coverage in precise point positioning (PPP) and of rapid convergence in real-time kinematic (RTK). However, the PPP-RTK convergence is still limited by the precision of slant ionospheric delays and tropospheric zenith wet delay (ZWD), which affects the PPP-RTK network parameters estimation and user positioning performance. The present study aims to construct a PPP-RTK model augmented with a priori ZWD values derived from the global forecast system (GFS) product (a global numerical weather prediction (NWP) model) to improve the PPP-RTK performance. This study gives a priori ZWD values and conversion based on the GFS products, and the full-rank GFS-augmented undifferenced and uncombined (UDUC) PPP-RTK network model is derived. To verify the performance of GFS-augmented UDUC PPP-RTK, a comprehensive evaluation using 10-day GNSS observation data from three different GNSS station networks in the United States (US), Australia, and Europe is conducted. The results show that with the GFS ZWD a priori information, PPP-RTK performance significantly improves at the initial filtering stage, but this advantage gradually decays over time. Based on 10-day positioning results for all user stations, the GFS ZWD-augmented PPP-RTK approach reduces the average convergence time by 46% from 10.0 to 5.4 min, the three-dimensional root-mean-square (3D-RMS) error by 5.7% from 3.5 to 3.3 cm, and the time to first fix (TTFF) value by 35.8% from 6.7 to 4.3 min, all when compared to the traditional PPP-RTK without GFS ZWD constraints.

New Baseline Correction Method for Near-Fault Ground-Motion Records Based on Continuous Wavelet Transform

Abstract On 6 February 2023, a magnitude 7.8 earthquake struck south-central Türkiye. It was followed by many aftershocks, the largest of which was a magnitude 7.5 aftershock. This earthquake caused considerable loss of life and property. Numerous near-fault ground-motion records were collected during the 2023 Türkiye earthquake. Most existing baseline correction methods for near-fault ground motions disregard the commonly used filtering techniques due to their potential elimination of actual permanent displacement. To address this, a new automatic baseline correction method is proposed based on the continuous wavelet transform, which incorporates filtering while preserving permanent displacement. This method is compatible with existing filtering techniques and demonstrates good applicability. In this approach, the raw record is decomposed into a pulse signal containing permanent displacement and a nonpulse signal. The nonpulse signal is high-pass filtered to obtain the corrected nonpulse signal, and then the true ground motion is obtained by combining the pulse signal with the corrected nonpulse signal. The effectiveness of the proposed method is evaluated across five aspects using the 1999 Chi-Chi earthquake records: time histories, baseline offsets, response spectra, peak ground velocities, and peak ground displacements. Furthermore, the method is applied to 36 station records (108 components) of the 2023 Türkiye earthquake. The analysis results indicate that the corrected displacement time histories exhibit sustained flatness in their tails and demonstrate a closer agreement with the Global Navigation Satellite System (GNSS) measurements than the previous methods. This method can also incorporate static GNSS offsets to accurately determine true ground motions with precise permanent displacements. The corrected results can be utilized for nonlinear seismic calculations and structural damage analysis of fault-crossing structures such as bridges and tunnels.

Present-day kinematics of the northwest Moroccan Atlantic Margin from GNSS data: west southwest extrusion at the western end of the High Atlas

The Global Navigation Satellite System (GNSS) has emerged as a practical and effective technique for studying slow and steady geodynamic movements, enabling continuous monitoring and precise quantification of deformation over different timescales. In Morocco, a network of GNSS stations has been established, offering valuable insights into tectonic processes. This paper focuses on investigating the geodynamic motion of the northwest Moroccan Atlantic Margin. By utilizing GNSS data, subsidence rates and horizontal velocity fields were determined for the first time, providing valuable information for oil and gas exploration activities. The study reveals an active uplift rate of 1 mm/year and a westward horizontal motion of 2.04 mm/year in the Essaouira segment. The paper presents a case study of the Essaouira–Agadir basin (EAB) onshore segment and investigates the anomalous displacement observed in this region compared to other coastal GNSS stations. Possible explanations for the observed movements include local processes such as salt tectonics and regional northwest–southeast compression related to Africa–Eurasia convergence. We suggest that the anomalous movement detected in this work is due to the regional northwest–southeast compression related to Africa–Eurasia convergence imparting an extrusion of the EAB to the west. This research contributes to a better understanding of the geodynamics in the northwest Moroccan Atlantic margin, thereby providing valuable insights for ongoing efforts in oil and gas exploration. Furthermore, it indicates the continued activity of the Agadir fault, which would exhibit a sinistral wrench movement, thus posing a threat to the city of Agadir and its inhabitants.

Development of a Dynamic Prediction Model for Underground Coal-Mining-Induced Ground Subsidence Based on the Hook Function

Underground coal-mining-induced ground subsidence deformation is a common geological disaster impacting buildings, transportation and water supplies. Models predicting ground subsidence dynamically with high precision are important for the prevention of damage derived from ground subsidence. In this paper, the Hook function is utilized to develop a model describing the velocity of ground subsidence due to underground coal mining. Based on the subsidence velocity model, a dynamic subsidence model is established by taking an integral of the velocity model. Coefficients of the model, which depend on maximum subsidence, maximum subsidence velocity and the time corresponding to the maximum subsidence velocity, are related to the geological and mining conditions of the coal seam being investigated. A Levenberg–Marquardt-algorithm-based method is also proposed to calculate the optimal model coefficients based on subsidence velocity observations. Four continuously operating Global Navigation Satellite System (GNSS) stations were constructed above a typical longwall coal mining working face in the Jining mining area, China. These GNSS stations collected subsidence observations over two years, which were used to validate the developed prediction model. The results show that the root-mean-square (RMS) of the model-predicted ground subsidence error is 56.1 mm, and the maximum relative error is 2.5% for all four GNSS stations, when the ground subsidence is less than 6000 mm.

The Alaska Makushin Volcano 2016–2018 inflation and its potential relation to the 2020 earthquake swarm, from GNSS observations

Makushin volcano on Unalaska Island along the Aleutian Chain in Alaska experienced a period of unrest from 2016 to 2020, including 2016–2018 surface inflation observed by 5 continuous Global Navigation Satellite System stations, and an earthquake swarm in the second half of 2020 consisting of hundreds of earthquakes, including two greater than M4. A Bayesian inversion of the geodetic data applied to a range of analytical source models suggests that the observed deformation pattern can be explained by an inflating pressure point source located 2 km east of the summit at a depth of approximately 5 km below sea level, with a chamber volume increase of approximately 0.004 km3. A stress change analysis suggests that the 2016–2018 magma intrusion may have played a role in advancing the 2020 seismicity anomaly by modulating the regional stress field. Our study highlights the importance of stress transfer between magmatic and tectonic processes and the potential for using this information to better understand and mitigate the risk of volcanic eruptions.

A compliance assessment of GNSS station networks in Serbia

Since the early 21st century, Global Positioning System (GPS) technology has dominated geodetic reference networks. Almost all countries established a permanent Global Navigation Satellite System (GNSS) station network to augment all available GNSS systems. By the end of 2005, Serbia completed the Active Geodetic Reference Network as a particular project of Serbia's Republic Geodetic Authority (RGA). Besides RGA, two private companies, Vekom and Geotaur, have established permanent station networks. This paper assesses the compliance of all the three networks, and network results are evaluated against the spatial distance determined by classical geodetic methods. When all available GNSS constellations are utilized, NAVSTAR, GLONASS, BEIDOU, and GALILEO, in the processing procedure, the established networks in Serbia align within a margin of about 3 mm across all coordinate axes. The results obtained within the research indicate that by using GNSS networks, it is possible to provide the coordinates of the points for the establishment of the national spatial reference system of Serbia, the reference system in almost all engineering fields, reference systems for the maintenance works of the real estate cadastre, and it is also possible to provide coordinates of points that can be used to define local, national, and world reference heights surfaces.

Storm‐Time Characteristics of Ionospheric Model (MSAP) Based on Multi‐Algorithm Fusion

AbstractGeomagnetic storms induce ionospheric disturbances, affecting short‐wave radio communication systems. Accurate ionospheric total electron content (TEC) prediction is vital for accurately describing the short‐wave radio environment of the ionosphere. We use the Multi‐Step Auxiliary Prediction (MSAP) model, a deep learning algorithm, to forecast TEC during geomagnetic storms. The MSAP model integrates Bi‐LSTM networks, an auxiliary model, and convolutional processes for spatiotemporal modeling. Our validation shows the MSAP model outperforms the IRI‐2016 model in predicting global TEC for the next 6 days in the test set. We assess its performance during 116 geomagnetic storm events, considering storm intensity, solar activity, month, and Universal Time (UT). The MSAP model exhibits a weak correlation with storm intensity and a strong correlation with solar activity. Monthly variation displays similar strong correlations in root mean square error (RMSE) and R2 for both models. For UT variation, the other metrics exhibit a weak correlation with the number of Global Navigation Satellite System stations, except for the RMSE of the MSAP and IRI‐2016 models.