Search Results (168)

While space weather indices (e.g., F10.7, Dst index) are commonly employed to characterize ionospheric activity levels, the Global Mean Electron Content (GMEC) provides a more direct and comprehensive indicator of the global ionospheric state. This metric demonstrates greater potential than space weather indices for calibrating empirical ionospheric models such as IRI-2020. The COSMIC-2 constellation enables continuous, all-weather global ionospheric monitoring via radio occultation, unimpeded by land–sea distribution constraints, with over 8000 daily occultation events suitable for GMEC modeling. This study developed two lightweight GMEC models using COSMIC-2 data: (1) a POD GMEC model based on slant TEC (STEC) extracted from Level 1b podTc2 products and (2) a PROF GMEC model derived from vertical TEC (VTEC) calculated from electron density profiles (EDPs) in Level 2 ionPrf products. Both backpropagation neural network (BPNN)-based models generate hourly GMEC outputs as global spatial averages. Critically, GMEC serves as an essential intermediate step that addresses the challenges of utilizing spatially irregular occultation data by compressing COSMIC-2’s ionospheric information into an integrated metric. Building on this compressed representation, we implemented a convolutional neural network (CNN) that incorporates GMEC as an auxiliary feature to calibrate IRI-2020’s global ionospheric maps. This approach enables computationally efficient correction of systemic IRI TEC errors. Experimental results demonstrate (i) 48.5% higher accuracy in POD/PROF GMEC relative to IRI-2020 GMEC estimates, and (ii) the calibrated global IRI TEC model (designated GCIRI TEC) reduces errors by 50.15% during geomagnetically quiet periods and 28.5% during geomagnetic storms compared to the original IRI model. Full article

Phase stability at low radio frequencies is severely impacted by ionospheric propagation delays. Radio interferometers such as the giant metrewave radio telescope (GMRT) are capable of detecting changes in the ionosphere’s total electron content (TEC) over larger spatial scales and with greater sensitivity compared to conventional tools like the global navigation satellite system (GNSS). Thanks to its unique design, featuring both a dense central array and long outer arms, and its strategic location, the GMRT is particularly well-suited for studying the sensitive ionospheric region located between the northern peak of the equatorial ionization anomaly (EIA) and the magnetic equator. In this study, we observe the bright flux calibrator 3C48 for ten hours to characterize and study the low-latitude ionosphere with the upgraded GMRT (uGMRT). We outline the methods used for wideband data reduction and processing to accurately measure differential TEC ($\delta \mathrm{TEC}$) between antenna pairs, achieving a precision of< mTECU (1 mTECU = ${10}^{-3}$ TECU) for central square antennas and approximately mTECU for arm antennas. The measured $\delta \mathrm{TEC}$ values are used to estimate the TEC gradient across GMRT arm antennas. We measure the ionospheric phase structure function and find a power-law slope of $\beta =1.72\pm 0.07$, indicating deviations from pure Kolmogorov turbulence. The inferred diffractive scale, the spatial separation over which the phase variance reaches $1{\mathrm{rad}}^2$, is ∼6.66 km. The small diffractive scale implies high phase variability across the field of view and reduced temporal coherence, which poses challenges for calibration and imaging. Full article

Electrically charged particles present in this layer of the Earth’s atmosphere can alter radio waves, such as those from GPS, Galileo, or BeiDou, resulting in non-estimated errors with respect to the available navigation models for the end user. For most positioning algorithms based in sequential filters, this effect is translated into a slow convergence towards a solution around the decimeter error level. If we consider that the ionosphere’s effect varies based on the user’s location and solar activity due to the atmosphere particle composition, it becomes clear that a global accurate model, valid across wide areas accounting for different seasons and timespans, is, at the very least, quite challenging. The focus of this paper is the demonstration of a global ionosphere model designed to improve the positioning accuracy of the end user through the estimation of ionospheric corrections to the broadcasted navigation message. Mathematically, this method is based on a spherical harmonic expansion model. This approach has the advantage of reducing the dependency from a highly densified station network where the ionosphere delay must be constantly estimated in dozens of locations, in favor of a simplified model that barely needs to be adjusted with a limited set of real-time data (around 40 stations). In this case, GMV’s global station network was used, which comprises geodetic-grade receivers tracking the signal in open-sky locations around the globe. The global ionospheric model is configured to process signals from GPS and Galileo constellations. To evaluate the performances of this model on the final user position estimation, several precise point positioning (PPP) solutions were computed at different locations. The results were compared with PPP solutions calculated without ionospheric corrections at the same stations. The goal of this paper is to show the significant performance improvement observed with the implementation of the global model. Full article

This study investigates the performance of three different signal combinations (L1-L2, L1-L5, and L2-L5) for estimating ionospheric total electron content (TEC) and the rate of TEC (ROT) using Quasi-Zenith Satellite System (QZSS) observations over the Korean Peninsula. GNSS data collected from nine stations across the Korean Peninsula were analyzed for the period from Day of Year (DOY) 1 to 182 in 2024. Differential Code Bias (DCB) was estimated for QZSS satellites, showing high temporal stability with daily variations within ±0.3 ns. The TEC values derived from three different signal combinations were compared with the CODE Global Ionospheric Map (GIM). Compared to other combinations, the L1-L5 pair shows the closest agreement with the CODE GIM, yielding a mean bias of +0.25 TEC units (TECU) with a root mean square (RMS) of 3.59 TECU. In addition, the ROT analysis over the consecutive six days revealed that the L1-L5 combination consistently exhibited the lowest RMS values of about 0.027 TECU compared to other signal pairs. As a result, we suggest that the L1-L5 combination can provide better performance for QZSS-based ionospheric monitoring and TEC studies. Full article

Traditional Global Navigation Satellite Systems (GNSSs) are subject to intentional or unintentional disturbances in the northern regions of Norway, leading to loss of critical infrastructure. The VHF Data Exchange System (VDES) has been suggested as an alternative source of positioning, navigation and timing (PNT), based on statistical estimates. However, an empirical investigation into the feasibility of such a contingency-system has only recently become possible after the launch of the NorSat-TD satellite with purpose-designed VDES ranging capabilities. This paper presents an analysis of the characteristics of empirical VDE-SAT range measurements and a system-level performance analysis of a single-satellite system. In total, 1121 VDE-SAT pseudorange observations obtained from 54 satellite passes, recorded from July to October 2023, are analyzed. Residual analysis shows that these observations have a large and constant mean error of about 416 km, with a standard deviation of 335.2 m. The previously neglected atmospheric propagation effects on a VDE-SAT range measurement are shown to be significant, and the largest effect is likely to be the time-delay due to the ionosphere. The system performance analysis shows that VDE-SAT as a PNT-source has potential to be a navigation backup system, with a target metric positioning accuracy of 1000 m. This project was funded by the ESA NAVISP program. Full article

This study developed machine learning models using different algorithms, including support vector machine (SVM), random forest (RF), and backpropagation neural network (BPNN), to estimate the critical frequency of the F2 layer (foF2) and the maximum usable frequency of the F2 layer for a 3000 km circuit (MUF(3000)F2) based on the total electron content (TEC) observed by global navigation satellite system (GNSS) receivers. The ionospheric dataset used comprised TEC, foF2, and MUF(3000)F2 measurements from 11 stations in China during a solar activity period (2008–2020). The results indicate that all three machine learning models performed better than the IRI-2020 model, with varying levels of accuracy. For foF2 (MUF(3000)F2) estimation, the root mean square error (RMSE) values at Kunming and Xi’an stations were reduced by approximately 38% (26%) and 18% (11%), respectively, compared to IRI-2020. During geomagnetic disturbances, all three models were able to reproduce the variations in both foF2 and MUF(3000)F2 parameters. Nevertheless, the RF model showed significantly better performance in foF2 estimation compared to the SVM and BPNN models. Full article

In this study, we introduce a decentralized architecture aimed at enhancing the efficiency of precise point positioning real-time kinematics (PPP-RTK) in large-scale networks with a common pivot station. Initially, we partition the extensive network into multiple smaller subnetworks (SNs), each with a common pivot station. The augmentation parameters for each SN are then computed using the precise orbit corrections and ionosphere-weighted constraints. However, directly applying the estimated augmentation parameters to users across subnetworks poses challenges due to inter-subnetwork discontinuities. These discontinuities arise from variations in the network configurations and the time correlation of the Kalman filters, despite the use of the same pivot station. To address this, common augmentation parameters, such as the satellite clocks and phase biases from each SN, are integrated into a unified set of parameters and broadcast to users. The aligned common augmentation parameters are then fed back into each SN, and the Kalman filter is re-updated to mitigate the inter-subnetwork discontinuities. The proposed architecture offers a reduced computational burden compared to the centralized PPP-RTK architecture, which handles a full-scale network simultaneously. Unlike previous research on decentralized PPP-RTK, the use of a common pivot station ensures a consistent basis for the common augmentation parameters. This approach enables seamless user positioning during transitions between SNs, eliminating the need to reset the user navigation filter during handover operations and simplifying the integration process. To evaluate the effectiveness of our proposed architecture, we gather dual-frequency global positioning system (GPS) observation data from over 40 continuously observed reference stations (CORSs) in Korea. These data are then partitioned into four SNs, each sharing a common pivot station. Subsequently, we compare the static positioning error and processing time of our proposed architecture with those of the centralized architecture. Additionally, the mitigation performance of the inter-network discontinuities is shown. Full article

The relationship between ionospheric research and global navigation satellite systems (GNSS) can be analysed through two approaches. The direct approach utilises ionospheric models to mitigate its influence, while the indirect approach leverages GNSS data to model ionospheric parameters. This study presents an indirect approach in which the total electron content (TEC), a fundamental parameter for ionospheric conditions, is modelled as a harmonic function using spherical harmonic (SH) expansion. Station-specific (local) and regional ionospheric models are developed by decomposing ionospheric influence into deterministic and stochastic components. GNSS data from seven evenly distributed stations in Serbia were used to estimate TEC coefficients. Local models were provided in the ION format as SH coefficients, allowing TEC determination at any epoch, while regional models had a ${0.5}^{\circ }\times {0.5}^{\circ }$ spatial and 2 h temporal resolution. The TEC root mean square (RMS) values ranged from 0.2 to 0.5 TECU (total electron content unit), with a mean of 0.3 TECU. Validation against global ionospheric maps showed agreement within 5.0 TECU. The impact of the SH expansion degree and order on TEC values was also analysed. These results refine regional ionospheric modelling, improving GNSS positioning accuracy in Serbia and beyond. Full article

The basic concept of this study is to investigate, by correlation analysis, the relationship between geomagnetic activity and Total Electron Content (TEC) for the period from 1994 to 2023. The global TEC data used have been recalculated to a coordinate system with a modip latitude and geographical longitude. In the analysis of the parameters used, the global index of geomagnetic activity, Kp, and TEC were converted into relative values, showing the deviation from stationary (quiet) conditions. The investigation defined theoretical cross-correlation functions that allow estimating the time lag constant from the shift of the maximum cross-correlation. The seasonal dependence of the ionospheric response was investigated by splitting it into three monthly segments centered on the equinox and solstice months. The dependence of the ionospheric response on local time was studied by creating time series, including those longitudes at which, at a given moment, the local time coincides with the selected one. The results show the following peculiarities in the TEC response: the type of ionospheric response (positive or negative) in each of the latitudinal zones (auroral ovals, mid-latitude and low-latitude) depends on the season, the local time of the geomagnetic storm and the specific physical mechanism of impact. Full article

In high-orbit space missions, the significant attenuation of Global Navigation Satellite System (GNSS) signals due to long transmission distances and complex environmental interferences has led to a drastic degradation in the accuracy of traditional positioning models, which has attracted great attention in recent years. Although multi-system GNSS fusion positioning can alleviate the problem of insufficient satellite visibility, the existing methods are difficult to effectively cope with the challenges of multi-source noise coupling and inter-system error differences unique to high orbit. In this paper, we propose an adaptive GNSS positioning optimization framework for a high-orbit environment, which improves the orbiting reliability under complex signal conditions through dynamic weight allocation and a multi-system cooperative strategy. Different from the traditional weighting model, this method innovatively constructs a two-layer optimization mechanism: (1) Based on BP neural network, it evaluates the noise characteristics of pseudo-range observations in real time and realizes the adaptive suppression of receiver thermal noise, ionospheric delay, etc.; (2) it introduces Helmert variance component estimation to optimize the weighting ratio of GPS, GLONASS, BeiDou, and Galileo and reduces the impact of signal heterogeneity on the positioning solution of the multi-system. Simulation results show that the new method reduces the root-mean-square error of positioning by 32.8% compared with the traditional algorithm to 97.72 m in typical high-orbit scenarios and significantly improves the accuracy loss caused by the defective satellite geometrical configurations under multi-system synergy. Full article

Precise Point Positioning with real-time kinematic (PPP-RTK) technology, which relies on Global Navigation Satellite Systems (GNSS), encounters difficulties in achieving high-precision and rapid convergence during ionospheric active conditions such as those occurring in thunderstorms. Most existing research on PPP-RTK has primarily focused on calm ionospheric conditions, with limited analysis of its performance under ionospheric anomalies. This study analyzes 13-day data collected from 305 Australian stations, encompassing both ionospheric anomalies (from 10 to 13 May 2024) and calm periods. We evaluated the residuals of uncalibrated phase delay (UPD), the accuracy of atmospheric modeling, as well as the positioning accuracy and convergence time of PPP-RTK. The results reveal that during ionospheric anomalies, compared to calm conditions, the accuracy of wide-lane and narrow-lane UPDs decreases by 2.4% and 1.4%, respectively. Meanwhile, the accuracy of estimated ionospheric and tropospheric delays deteriorates by 167.1% and 17.3%, respectively. In terms of PPP-RTK services, for the horizontal component, the convergence times increase by 25.0%, 44.4%, and 55.6% for the GPS-only, GPS + Galileo, and GPS + Galileo + BDS solutions, respectively. For the vertical component, the increases are 56.9%, 81.6%, and 87.2%, respectively. Regarding the positioning accuracies, for the horizontal component, they decline by 5.5%, 7.4%, and 10.4% for the GPS-only, GPS + Galileo, and GPS + Galileo + BDS solutions, respectively. For the vertical component, the declines are 11.8%, 13.0%, and 18.5%, respectively. This indicates that ionospheric anomalies significantly disrupt PPP-RTK services, mainly due to the degradation of ionospheric delay estimates, which directly affects positioning results. Although the ionosphere can lead to significant degradation in positioning performance, the positioning performance can still be substantially improved with an increase in the number of satellites. This study thus offers new insights into the performance of PPP-RTK during ionospheric active conditions. Full article

The DC global electric circuit, GEC, was conceived by C.T.R. Wilson more than a century ago. Powered by thunderstorms and electrified shower clouds, an electric current I ~1 kA flows up into the ionosphere, maintaining the ionospheric potential V ~250 kV with respect to the Earth’s surface. The circuit is formed by the current I, flowing through the ionosphere all around the world, down through the atmosphere remote from the current sources (J ~2 pA/m² through a resistance R ~250 Ω), through the land and sea surface, and up to the thunderstorms as point discharge currents. This maintains a downward electric field E of magnitude ~130 V/m at the Earth’s surface away from thunderstorms and a charge Q ~−6.10⁵ C on the Earth’s surface. The theoretical modelling of ionospheric currents and the miniscule geomagnetic field perturbations (ΔB ~0.1 nT) which they cause, as derived by Denisenko and colleagues in recent years, are reviewed. The time constant of the GEC, τ = RC, where C is the capacitance of the global circuit capacitor, is estimated via three different methods to be ~7 to 12 min. The influence of stratus clouds in determining the value of τ is shown to be significant. Sudden excitations of the GEC by volcanic lightning in Iceland in 2011 and near the Tonga eruption in 2022 enable τ to be determined, from experimental observations, as ~10 min and 8 min, respectively. It has been suggested that seismic activity, or earthquake precursors, could produce large enough electric fields in the ionosphere to cause detectable effects, either by enhanced radon emission or by enhanced thermal emission from the earthquake region; a review of the quantitative estimates of these mechanisms shows that they are unlikely to produce sufficiently large effects to be detectable. Finally, some possible links between the topics discussed and human health are considered briefly. Full article

The temporal variations and spatial variations in the ionosphere during geomagnetic storms are exceptionally complex and drastic, significantly complicating ionospheric model construction. In this study, we present a multi-site, high-precision ionospheric vertical total electron content ($VTEC$) estimation method by constraining the $VTEC$ when the locations of ionospheric pierce points (IPPs), determined by multiple sites, are nearby. The root mean square error (RMSE) relative to the global ionospheric map (GIM) $VTEC$ is 3.22 TEC units (TECU), with a correlation coefficient of 0.98. This method enables the high-precision estimation of $VTEC$ at IPPs. Utilizing the Gauss–Markov Kalman filter data assimilation algorithm, we consider the relationship between various Dst indices and the ionospheric temporal scales, achieving a regional ionospheric total electron content $\left(TEC\right)$ Map during geomagnetic storms. This approach effectively monitors the impact of geomagnetic storms on the ionospheric total electron content $\left(TEC\right)$ and provides a more accurate representation of ionospheric changes during geomagnetic storms compared to the GIM $TEC$ Map and the International Reference Ionosphere (IRI)-2020 model. Full article

The Global Navigation Satellite System (GNSS) has made important progress in Earth observation and applications. With the successful design of the BeiDou Navigation Satellite System (BDS), four global navigation satellite systems are available worldwide, together with Galileo, GLONASS, and GPS. These systems have been widely employed in positioning, navigation, and timing (PNT). Furthermore, GNSS refraction, reflection, and scattering signals can remotely sense the Earth’s surface and atmosphere with powerful implications for environmental remote sensing. In this paper, the recent developments and new application progress of multi-GNSS in Earth observation are presented and reviewed, including the methods of BDS/GNSS for Earth observations, GNSS navigation and positioning performance (e.g., GNSS-PPP and GNSS-NRTK), GNSS ionospheric modelling and space weather monitoring, GNSS meteorology, and GNSS-reflectometry and its applications. For instance, the static Precise Point Positioning (PPP) precision of most MGEX stations was improved by 35.1%, 18.7%, and 8.7% in the east, north, and upward directions, respectively, with PPP ambiguity resolution (AR) based on factor graph optimization. A two-layer ionospheric model was constructed using IGS station data through three-dimensional ionospheric model constraints and TEC accuracy was increased by about 20–27% with the GIM model. Ten-minute water level change with centimeter-level accuracy was estimated with ground-based multiple GNSS-R data based on a weighted iterative least-squares method. Furthermore, a cyclone and its positions were detected by utilizing the GNSS-reflectometry from the space-borne Cyclone GNSS (CYGNSS) mission. Over the years, GNSS has become a dominant technology among Earth observation with powerful applications, not only for conventional positioning, navigation and timing techniques, but also for integrated remote sensing solutions, such as monitoring typhoons, river water level changes, geological geohazard warnings, low-altitude UAV navigation, etc., due to its high performance, low cost, all time and all weather. Full article

This study demonstrates a rich complexity of the time–frequency ionospheric signal spectrum, dependent on the measurement type and platform. Different phenomena contributing to satellite-derived and ground-derived geophysical data that only selected signal bands can be potentially sensitive to seismicity over time, and they are applicable in lithosphere–atmosphere–ionosphere coupling (LAIC) studies. In this study, satellite-derived and ground-derived ionospheric observations are filtered by a Fourier-based band-pass filter, and an experimental selection of potentially sensitive frequency bands has been carried out. This work focuses on band-pass filtered ionospheric observations and seismic activity in the region of the Aegean Sea over a two-year time period (2020–2021), with particular focus on the entire system of tectonic plate junctions, which are suspected to be a potential source of ionospheric disturbances distributed over hundreds of kilometers. The temporal evolution of seismicity power in the Aegean region is represented by the record of earthquakes characterized by M ≥ 4.5, used for the estimation of cumulative seismic energy. The ionospheric response to LAIC is explored in three data types: short inspections of in situ electron density (Ne) over a tectonic plate boundary by Swarm satellites, stationary determination of three Ne density profile parameters by the Athens Digisonde station AT138 (maximum frequency of the F2 layer: foF2; maximum frequency of the sporadic E layer: foEs; and frequency spread: ff), and stationary measure of vertical total electron content (VTEC) interpolated from a UPC-IonSAT Quarter-of-an-hour time resolution Rapid Global ionospheric map (UQRG) near Athens. The spectrograms are made with the use of short-term Fourier transform (STFT). These frequency bands in the spectrograms, which show a notable coincidence with seismicity, are filtered out and compared to cumulative seismic energy in the Aegean Sea, to the geomagnetic Dst index, to sunspot number (SN), and to the solar radio flux (F10.7). In the case of Swarm, STFT allows for precise removal of long-wavelength Ne signals related to specific latitudes. The application of STFT to time series of ionospheric parameters from the Digisonde station and GIM VTEC is crucial in the removal of seasonal signals and strong diurnal and semi-diurnal signal components. The time series formed from experimentally selected wavebands of different ionospheric observations reveal a moderate but notable correlation with the seismic activity, higher than with any solar radiation parameter in 8 out of 12 cases. The correlation coefficient must be treated relatively and with caution here, as we have not determined the shift between seismic and ionospheric events, as this process requires more data. However, it can be observed from the spectrograms that some weak signals from selected frequencies are candidates to be related to seismic processes. Full article