Search Results (150)

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

Predicting global ionospheric total electron content (TEC) is critical for high-precision GNSS applications, but some existing models fail to jointly capture spatial heterogeneity and multiscale temporal trends. To address the problem, this work proposes a spatio-temporal graph neural network (STGNN) that addresses these limitations through (1) a trainable positional attention mechanism to dynamically infer node dependencies without fixed geographical constraints and (2) a GRU–Transformer sequential module to hierarchically model local and global temporal patterns. The proposed network is validated by different solar and geomagnetic activities. With a training dataset with a time span between 2008 and 2018, the proposed model is tested in a high solar phase for the year 2015 and a low solar phase for the year 2018. For 2015, the experimental results show a 21.9% RMSE reduction at low latitudes compared to the results of the iTransformer model. For the geomagnetic storm event, the proposed STGNN achieves 16.0% higher stability. For the one-week (84 step) prediction test, the STGNN shows a 27.0% lower error compared to the MLPMultivariate model. The model’s self-adaptive spatial learning and multiscale temporal modeling uniquely enable TEC forecasting under diverse geophysical conditions. Full article

This study presents a comprehensive analysis of the impact of Storm Laura, which was observed over Europe and the Baltic Sea on 12 March 2020, on the thermosphere–ionosphere system. The investigation of ionospheric disturbances caused by the meteorological storm was carried out using a combined modeling approach, incorporating the regional AtmoSym and the global GSM TIP models. This allowed for the consideration of acoustic and internal gravity waves (AWs and IGWs) generated by tropospheric convective sources and the investigation of wave-induced effects in both the neutral atmosphere and ionosphere. The simulation results show that, three hours after the activation of the additional heat source, an area of increased temperature exceeding 100 K above the background level formed over the meteorological storm region. This temperature change had a significant impact on the meridional component of the thermospheric wind and total electron content (TEC) variations. For example, meridional wind changes reached 80 m/s compared a the meteorologically quiet day, while TEC variations reached 1 TECu. Good agreement was obtained with experimental TEC maps from CODE (Center for Orbit Determination in Europe), MOSGIM (Moscow Global Ionospheric Map), and WD IZMIRAN (West Department of Institute of Terrestrial Magnetism, Ionosphere and Radio Wave Propagation Russian Academy of Sciences), which revealed a negative TEC value effect over the meteorological storm region. 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

In this paper, we study the geomagnetic storm that occurred on 23–24 April 2023. We present variations in the values of interplanetary magnetic field (IMF-Bz), solar wind parameters (Vsw, Nsw, Tsw, and Psw), geomagnetic index (SYM-H), and vertical total electron content (VTEC) obtained from 18 GPS-TEC stations situated in equatorial, mid-latitude, and high-latitude regions. We analyze the variations in total electron content (TEC) before, during, and after the storm using VTEC plots, dTEC% plots, and global ionospheric maps for each GNSS receiver station, all referenced to universal time (UT). Our results indicate that GNSS receiver stations located at high latitudes detected an increase in ionospheric density during the main phase and a decrease during the recovery phase. In contrast, stations in equatorial and mid-latitude regions detected a decrease in ionospheric density during the main phase and an increase during the recovery phase. Large dTEC% values ranging from −80 to 190 TECU were observed a few hours before and during the storm period (23–24 April 2023); these can be compared to values ranging from −10 to 20 TECU on the day before (22 April 2023) and the day after (25 April 2023). Notably, higher dTEC% values were observed at stations in high and middle latitudes compared to those in the equatorial region. As the storm progressed, the TEC intensification observed on global ionospheric maps appeared to shift from east to west. A detailed analysis of these maps showed that equatorial and low-latitude regions experienced larger spatial and temporal TEC variations during the storm period compared to higher-latitude regions. Full article

This study proposes a deep learning-based vertical decomposition model for ionospheric Total Electron Content (TEC), which establishes a nonlinear mapping from macroscale TEC data to vertically layered electron density (Ne) spanning 60–800 km by integrating geomagnetic indices (AE, SYM-H) and solar activity parameters (F10.7). Utilizing global TEC grid data (spatiotemporal resolution: 1 h/5.625° × 2.8125°) provided by the International GNSS Service (IGS), a Multilayer Perceptron (MLP) model was developed, taking spatiotemporal coordinates, altitude, and space environment parameters as inputs to predict logarithmic electron density ln(Ne). Experimental validation against COSMIC-2 radio occultation observations in 2019 demonstrates the model’s capability to capture ionospheric vertical structures, with a prediction performance significantly outperforming the International Reference Ionosphere model IRI-2020: root mean square error (RMSE) decreased by 34.16%, and the coefficient of determination (R²) increased by 28.45%. This method overcomes the reliance of traditional electron density inversion on costly radar or satellite observations, enabling high-spatiotemporal-resolution global ionospheric profile reconstruction using widely available GNSS-TEC data. It provides a novel tool for space weather warning and shortwave communication optimization. Current limitations include insufficient physical interpretability and prediction uncertainty in GNSS-sparse regions, which could be mitigated in future work through the integration of physical constraints and multi-source data assimilation. Full article

The G5 geomagnetic storm of May 2024 provided a significant opportunity to investigate global ionospheric disturbances using vertical total electron content (VTEC) data derived from 422 GNSS-IGS stations and GIM. This study presents a comprehensive spatio-temporal analysis of VTEC modulation before, during, and after the storm, focusing on hemispheric asymmetries and longitudinal variations. The primary objective of this study is to analyze the spatial and temporal modulation of VTEC under extreme geomagnetic conditions, assess the hemispheric asymmetry and longitudinal disruptions, and evaluate the influence of geomagnetic indices on storm-time ionospheric variability. The indices examined reveal intense geomagnetic activity, with the dst index plunging to −412 nT, the Kp index reaching 9, and significant fluctuations in the auroral electrojet indices (AE, AL, AU), all indicative of severe space weather conditions. The results highlight storm-induced hemispheric asymmetries, with positive storm effects (VTEC enhancement) in the Northern Hemisphere and negative storm effects (VTEC depletion) in the Southern Hemisphere. These anomalies are primarily attributed to penetration electric fields, neutral wind effects, and composition changes in the ionosphere. The storm’s peak impact on DoY 132 exhibited maximum disturbances at ±90° and ±180° longitudes, emphasizing the role of geomagnetic forces in plasma redistribution. Longitudinal gradients were strongly amplified, disrupting the usual equatorial ionization anomaly structure. Post-storm recovery on DoY 136 demonstrated a gradual return to equilibrium, although lingering effects persisted at mid- and high latitudes. These findings are crucial for understanding space weather-induced ionospheric perturbations, directly impacting GNSS-based navigation, communication systems, and space weather forecasting. Full article

Real-time ionospheric products can accelerate the convergence of real-time precise point positioning (PPP) to improve the real-time positioning services of global navigation satellite systems (GNSSs), as well as to achieve continuous monitoring of the ionosphere. This study applied an extended Kalman filter (EKF) to total electron content (TEC) modeling, proposing a regional real-time EKF-based ionospheric model (REIM) with a spatial resolution of 1° × 1° and a temporal resolution of 1 h. We examined the performance of REIM through a 7-day period during geomagnetic storms. The post-processing model from the China Earthquake Administration (IOSR), CODG, IGSG, and the BDS geostationary orbit satellite (GEO) observations were utilized as reference. The consistency analysis showed that the mean deviation between REIM and IOSR was 0.97 TECU, with correlation coefficients of 0.936 and 0.938 relative to IOSR and IGSG, respectively. The VTEC mean deviation between REIM and BDS GEO observations was 4.15 TECU, which is lower than those of CODG (4.68 TECU), IGSG (5.67 TECU), and IOSR (6.27 TECU). In the real-time single-frequency PPP (RT-SF-PPP) experiments, REIM-augmented positioning converges within approximately 80 epochs, and IGSG requires 140 epochs. The REIM-augmented east-direction positioning error was 0.086 m, smaller than that of IGSG (0.095 m) and the Klobuchar model (0.098 m). REIM demonstrated high consistencies with post-processing models and showed a higher accuracy at IPPs of BDS GEO satellites. Moreover, the correction results of the REIM model are comparable to post-processing models in RT-SF-PPP while achieving faster convergence. 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

Global navigation satellite systems provide important data sets that can be used to study the influence of various space weather factors. We analyzed the effects of the main phase of the May 2024 extreme geomagnetic storm on the ionosphere and GPS kinematic precise point positioning (PPP). ROTI and global ionospheric maps showed the ionospheric dynamics. The auroral oval expanded up to low latitudes: up to 30°N in the American sector and up to 45°N in the European–Asian sector during the main phase of the geomagnetic storm. The ROTI peaked at 2 TECU/min, which is four times as much against the background. The equatorial anomaly crest intensified considerably (up to 200 TECU) and shifted poleward in the American sector. The counter-propagation finally caused the equatorial anomaly to cross the auroral oval boundary. The ROTI correlated with errors in the kinematic PPP. Positioning errors increased 1.5–5 times at the boundary of the auroral oval. Increased positioning errors propagated according to the shift of the auroral oval boundary. The geomagnetic storm significantly affected the positioning and the ionosphere, threatening various applications based on navigation and communication. Full article

High-precision total electron content (TEC) prediction can improve the accuracy of the Global Navigation Satellite System (GNSS)-based applications. The existing deep learning models for TEC prediction mainly include long short-term memory (LSTM), convolutional long short-term memory (ConvLSTM), and their variants, which contain only one temporal memory. These models may result in fuzzy prediction results due to neglecting spatial memory, as spatial memory is crucial for capturing the correlations of TEC within the TEC neighborhood. In this paper, we draw inspiration from the predictive recurrent neural network (PredRNN), which has dual memory states to construct a TEC prediction model named Multichannel ED-PredRNN. The highlights of our work include the following: (1) for the first time, a dual memory mechanism was utilized in TEC prediction, which can more fully capture the temporal and spatial features; (2) we modified the n vs. n structure of original PredRNN to an encoder–decoder structure, so as to handle the problem of unequal input and output lengths in TEC prediction; and (3) we expanded the feature channels by extending the Kp, Dst, and F10.7 to the same spatiotemporal resolution as global TEC maps, overlaying them together to form multichannel features, so as to fully utilize the influence of solar and geomagnetic activities on TEC. The proposed Multichannel ED-PredRNN was compared with COPG, ConvLSTM, and convolutional gated recurrent unit (ConvGRU) from multiple perspectives on a data set of 6 years, including comparisons at different solar activities, time periods, latitude regions, single stations, and geomagnetic storm periods. The results show that in almost all cases, the proposed Multichannel ED-PredRNN outperforms the three comparative models, indicating that it can more fully utilize temporal and spatial features to improve the accuracy of TEC prediction. Full article

Although the traditional Carrier-to-Code Leveling (CCL) method can provide ideal slant total electron content (STEC) observables for establishing ionospheric models, it must rely on dual-frequency (DF) receivers, which results in high hardware costs. In this study, an ionosphere-weight (IW) single-frequency (SF) precise point positioning (PPP) method for extracting STEC observables is proposed, and multi-global navigation satellite system (GNSS)-integrated processing is adopted to improve the spatial resolution of the ionospheric model. To investigate the advantages of this novel method, 41 European stations are used to establish the regional ionospheric model, and both low- and high-solar-activity conditions are considered. The results show that the IW SFPPP-derived regional ionospheric model has a significantly better quality of vertical total electron content (VTEC) than the CCL method when using the final global ionospheric map (GIM) as a reference, especially in areas with sparse monitoring stations. Compared with the CCL method, the RMS VTEC accuracy of the IW SFPPP method can be improved by 17.4% and 12.7% to 1.09 and 2.83 total electron content unit (TECU) in low- and high-solar-activity periods, respectively. Regarding GNSS carrier-phase-derived STEC variation (dSTEC) as the reference, the dSTEC accuracy of the IW SFPPP method is comparable to that of the CCL method, and its RMS values are about 1.5 and 2.8 TECU in low- and high-solar-activity conditions, respectively. This indicates that the proposed method using SF-only observations can achieve the same external accord accuracy as the CCL method in regional ionospheric modeling. 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

Atmospheric responses to earthquakes or volcanic eruptions have become an interesting topic and can potentially contribute to future forecasting of these events. Extensive anomalies of the total electron content (TEC) are most often linked with geomagnetic storms or Earth-dependent phenomena, like earthquakes, volcanic eruptions, or nuclear explosions. This study extends rarely discussed, but very frequent, interactions between tectonic plate boundaries and the ionosphere. Our investigations focus on the very frequent occurrence of TEC enhancements not exclusively linked with individual seismic phenomena but located over tectonic plate boundaries. The objective of this study is to provide a review of the global spatiotemporal distribution of TEC anomalies, facilitating the discussion of their potential relations with tectonic activity. We apply a Kriging-based UPC-IonSAT quarter-of-an-hour time resolution rapid global ionospheric map (UQRG) from the Polytechnic University of Catalonia (UPC) IonSAT group for the detection of relative vertical TEC (VTEC) changes. Our study describes global relative and normalized VTEC variations, which have spatial and temporal behaviours strongly indicating their relationship both with geomagnetic changes and the tectonic plate system. The variations in geomagnetic fields, including the storms, disturb the ionosphere and amplify TEC variations persisting for several hours over tectonic plate boundaries, mostly over the diverging ones. The seismic origin of the selected parts of these TEC enhancements and depletions and their link with tectonic plate edges are suspected from their duration, shape, and location. The changes in TEC originating from both sources can be observed separately or together, and therefore, there is an open question about the directions of the energy transfers. However, the importance of geomagnetic field lines seems to be probable, due to the frequent common occurrence of both types of TEC anomalies. This research also proves that permanent observation of global lithosphere–atmosphere–ionosphere coupling (LAIC) is also important in time periods without strong earthquake or volcanic events. The occurrence of TEC variations over diverging tectonic plate boundaries, sometimes combined with travelling anomalies of geomagnetic origin, can add to the studies on earthquake precursors and forecasting. Full article

Solar eclipses present a valuable opportunity for controlled in situ ionosphere studies. This work explores the response of the upper atmosphere’s F-layer during the total eclipse of 8 April 2024, which was primarily visible across North and South America. Employing a multi-instrument approach, we analyze the impact on the ionosphere’s Total Electron Content (TEC) and Very Low Frequency (VLF) signals over a three-day period encompassing the eclipse (7–9 April 2024). Ground-based observations leverage data from ten International GNSS Service (IGS)/Global Positioning System (GPS) stations and four VLF stations situated along the eclipse path. We compute vertical TEC (VTEC) alongside temporal variations in the VLF signal amplitude and phase to elucidate the ionosphere’s response. Notably, the IGS station data reveal a decrease in VTEC during the partial and total solar eclipse phases, signifying a reduction in ionization. While VLF data also exhibit a general decrease, they display more prominent fluctuations. Space-based observations incorporate data from Swarm and COSMIC-2 satellites as they traverse the eclipse path. Additionally, a spatiotemporal analysis utilizes data from the Global Ionospheric Map (GIM) database and the DLR’s (The German Aerospace Center’s) database. All space-based observations consistently demonstrate a significant depletion in VTEC during the eclipse. We further investigate the correlation between the percentage change in VTEC and the degree of solar obscuration, revealing a positive relationship. The consistent findings obtained from this comprehensive observational campaign bolster our understanding of the physical mechanisms governing ionospheric variability during solar eclipses. The observed depletion in VTEC aligns with the established principle that reduced solar radiation leads to decreased ionization within the ionosphere. Finally, geomagnetic data analysis confirms that external disturbances do not significantly influence our observations. Full article