Ionospheric Electron Content Research Articles

Intensity of 27-day variations in solar emission and ionospheric electron content

The rotation of the Sun modulates solar emission with 27-day variations. Large-scale structures in the solar atmosphere, like sunspots and plages, rotate with the Sun and emit at extreme ultraviolet (EUV) and microwaves that produce the varying part of the emission. As EUV emission is the main driver of ionization in the Earth ionosphere, its variations are reflected in the electron content. In this paper, we analyze wide range of data from radio emission in 245–17000 MHz range and the entire EUV spectrum to total and global electron contents (TEC and GEC, respectively) in the ionosphere to find out where the 27-day variations are most prominent. We show that the relative contribution of 27-day variations to radio emission is maximal at 1000 MHz and decreases at 245 and 17000 MHz. The variations are pronounced throughout the EUV spectrum, their intensity vary at different spectral lines and, on average, is higher than in radio emission. The distribution of the variations in global ionospheric maps (GIM) shows an intensification of 27-day variations in TEC in the North America region, which can be related to the winter anomaly as it is also more significant in this region. Temporal variations of the 27-day component of emission are similar in shape for radio and EUV emission, as well as for GEC, and have a high correlation, up to 0.8. Cycle-averaged temporal variations imply that the 27-day component weakens significantly during solar minimum but can intensify during the remaining phases of the solar cycle. We also find that the flux at 1000 MHz, which originate from free-free emission in contrast to F10.7, can be used as a proxy index of solar activity because it has the highest intensity of 27-day variations and has been observed since 1956.

Optimizing Deep Learning Models with Improved BWO for TEC Prediction.

The prediction of total ionospheric electron content (TEC) is of great significance for space weather monitoring and wireless communication. Recently, deep learning models have become increasingly popular in TEC prediction. However, these deep learning models usually contain a large number of hyperparameters. Finding the optimal hyperparameters (also known as hyperparameter optimization) is currently a great challenge, directly affecting the predictive performance of the deep learning models. The Beluga Whale Optimization (BWO) algorithm is a swarm intelligence optimization algorithm that can be used to optimize hyperparameters of deep learning models. However, it is easy to fall into local minima. This paper analyzed the drawbacks of BWO and proposed an improved BWO algorithm, named FAMBWO (Firefly Assisted Multi-strategy Beluga Whale Optimization). Our proposed FAMBWO was compared with 11 state-of-the-art swarm intelligence optimization algorithms on 30 benchmark functions, and the results showed that our improved algorithm had faster convergence speed and better solutions on almost all benchmark functions. Then we proposed an automated machine learning framework FAMBWO-MA-BiLSTM for TEC prediction, where MA-BiLSTM is for TEC prediction and FAMBWO for hyperparameters optimization. We compared it with grid search, random search, Bayesian optimization algorithm and beluga whale optimization algorithm. Results showed that the MA-BiLSTM model optimized by FAMBWO is significantly better than the MA-BiLSTM model optimized by grid search, random search, Bayesian optimization algorithm, and BWO.

On the development of HARMONY Machine learning (ML) IONO demonstrator

HARMONY (macHine leARning to MOdel gNss sYstems) demonstrator is intended to overcome the limitations of the simulations based on a GNSS macro-model through the implementation of a ML model that models a GNSS system, providing better performances once trained to predict its behaviour. Considering the wide spectrum of the GNSS features and attending to the GNSS User needs, the envisaged IONO ML demonstrator focuses on Ionospheric slant total electron content (STEC) modelling. ML IONO demonstrator is trained using input data from different available public data sources. HARMONY ML-IONO demonstrator is an advanced ML-engine able to model ionosphere behaviour, and is designed following the GNSS user needs, while the development as well as implementation path followed is set by the existing and available data. This paper aims to provide a summary of the implementation and design of HARMONY demonstrator, including the definition of interfaces and the explanation of its main components. Additionally, detailed analysis of the design choices selected for each use as well as the development of the ML models is included. To facilitate the explanation of each component as well as the relations interconnecting them, the ML-demonstrator is referred to and treated as a unified system.

Machine learning based storm time modeling of ionospheric vertical total electron content over Ethiopia

Geomagnetic storms can cause variations in the ionization levels of the ionosphere, which is commonly studied using the total electron content (TEC). TEC is a crucial parameter to identify the possible effects of ionospheric variations on satellite communication and navigation. This paper assesses the performance of light gradient boosting machine (LGB) and deep neural network (DNN) machine learning algorithms in modeling ionospheric vertical TEC (VTEC) during geomagnetic disturbances. GPS VTEC data for years 2011–2016 from 13 dual-frequency receiver stations over Ethiopia was utilized. Input parameters for the models were derived from the factors that influence VTEC, such as time, location, geomagnetic activity, solar activity, solar wind, and the interplanetary magnetic field. The LGB model improved the predictions of the DNN model from root mean squared error (RMSE), mean absolute percentage error (MAPE), and R2 values of 5.45 TECU, 21%, and 0.93 to 4.98 TECU, 18%, and 0.94 on the testing data, respectively. The two machine learning models significantly outperformed the International Reference Ionosphere (IRI 2020) model during the selected geomagnetic storm periods. This study could provide insight into the impacts of ionosphere variations on satellite communication and navigation systems in the low-latitude ionospheric region.

Total Root Electron Content: A New Metric for the Ionosphere Below Low Earth Orbiting Satellites

AbstractPowerful lightning strikes generate broadband electromagnetic signals. At Extremely Low Frequencies (ELF), the signal partly leaks into the ionosphere and produces whistlers that can be detected by satellites. Indeed, the satellites of the European Space Agency (ESA) Swarm Earth Explorer mission can detect those signals during 250 Hz burst‐mode acquisition campaigns of their Absolute Scalar Magnetometers (ASM). The dispersion of these whistlers depends on their propagation path and the distribution of ionization in the ionosphere crossed along that path. In this paper, we introduce a technique to derive a new measure of ionosphere electron content, the Total square‐Root Electron Content (TREC), using the arrival times of two frequencies of the whistler signal. We validate this approach by using data from ionosondes and from in situ measurements of the electron density at Swarm location. This technique brings new opportunities for sounding the ionosphere in regions poorly observed by other techniques.

MAOOA‐Residual‐Attention‐BiConvLSTM: An Automated Deep Learning Framework for Global TEC Map Prediction

AbstractThe high‐precision prediction of total ionospheric electron content (TEC) is of great significance for improving the accuracy of global navigation satellite systems. There are two problems with the current prediction of TEC: (a) The existing TEC prediction models mainly based on stacked structure, which has insufficient predictive ability when the network has fewer layers, and loss of fine‐grained features when there are more layers, resulting in a decrease in predictive performance; (b) The existing research on ionospheric TEC prediction mainly focuses on building deep learning prediction models, while there is little research on optimizing the hyper‐parameters of TEC prediction models. Optimization can help find a better quasi‐optimal hyperparameter combination and improve the performance of the model. This paper proposed an automatic deep learning framework for global TEC map prediction, named MAOOA‐Residual‐Attitude‐BiConvLSTM. This framework includes a TEC prediction model, Residual‐Attention‐BiConvLSTM, which can simultaneously consider both coarse‐grained and fine‐grained spatiotemporal features. It also includes an optimization algorithm, MAOOA, for optimizing the hyper‐parameters of the model. We conducted comparative experiments between our framework and C1PG, ConvLSTM, ConvGRU, and ED‐ConvLSTM during high solar activity years, low solar activity years, and a magnetic storm event. The results indicate that in all cases, the framework proposed in this paper outperforms the comparative models.

Complexity and Nonlinear Dependence of Ionospheric Electron Content and Doppler Frequency Shifts in Propagating HF Radio Signals within Equatorial Regions

Complexity and Nonlinear Dependence of Ionospheric Electron Content and Doppler Frequency Shifts in Propagating HF Radio Signals within Equatorial Regions

Post-seismic Ionosphere Effects after Croatian MW 6.4 Earthquake on December 29, 2020

The main idea of the present investigation is to study the relationship between post- seismic effects as a result of Petrinja, Croatia earthquake which occurred on December 29, 2020 in some ionospheric parameters. Two types of ionospheric data were used: the critical frequency of the F2 region (foF2), obtained from the vertical sounding of the ionosphere and Total Electron Content (TEC) data. The ionospheric response is studied by the relative deviation of both quantities foF2 and TEC. The results of the ionospheric parameter behaviour show the following: a) a positive anomaly is observed in both ionospheric characteristics; b) the most significant positive response is observed at ionospheric stations closer to the epicentre; c) the delay of the observed post-seismic ionospheric effect increases with distance from the epicentre in both data used. The observed anomalies in the behaviour of considered ionospheric parameters could be associated with the considered lithospheric phenomenon because the selected period is with completely quiet geomagnetic conditions. The results of this research gives additional insight into the Lithosphere-Atmosphere-Ionosphere Coupling during Earthquake processes.

Periodic Oscillation of VLF Transmitter Signals Measured in Low and Middle Latitude Regions

AbstractMeasurements of Very Low Frequency (VLF) signals from navy transmitters carry direct information about the D‐region ionosphere and have been widely utilized for detecting the electron density at D‐region altitudes, but not frequently for the atmospheric waves therein. Atmospheric waves have been extensively studied using the total ionospheric electron content, but if and how they are correlated with the D‐region ionosphere and VLF measurements still remains poorly investigated. In this study, we have conducted a comprehensive analysis using 7‐year measurements (2016–2022) of VLF signals from the JJI, NWC, and VTX transmitters as being recorded in Suizhou, China. These three transmitter‐receiver paths are representative and the corresponding observations constitute a valuable data set to investigate the periodicities of VLF data. Different from previous studies, we have utilized the ensemble empirical mode decomposition and Lomb‐Scargle methods to determine the periodicities of these data. By contrasting these paths, prominent periodicities ranging from 2 to 730 days have been found, with clear diurnal variation and suggestive latitudinal/longitudinal dependence. Moreover, we have found that the mesospheric temperature is closely related with the annual oscillation of VLF measurements, while this oscillation has a low correlation with solar Lyman‐α fluxes or geomagnetic activity. The oscillations with relatively shorter periods are likely atmospheric waves such as gravity waves, planetary waves, or harmonics of these waves. Our results suggest that, in addition to the electron density, the subionospheric VLF technique can be potentially utilized to remotely sense atmospheric waves that propagate up to or through the D‐region ionosphere.

A stacked machine learning model for the vertical total electron content forecasting

We design four Artificial Neural Network (ANN) models, namely a Multilayer Perceptron (MLP), a Convolutional Neural Network (CNN), an Extreme Learning Machine network (ELM) and an ensemble model based on stacking (STACKED) to forecast the ionospheric vertical Total Electron Content (vTEC) from 1 to 24 hours in advance on a single station at mid latitude. The time series used, spanning from January 2006 to December 2018, includes vTEC, provided by the Global Navigation Satellite System (GNSS) receiver at Tsukuba (TSKB), Japan (36.06o N, 140.05o E) and suitable external drivers selected among several helio-geophysical parameters. The selection of appropriate external drivers is made by rating their relevance on the vTEC at forecasting timescales (from 1 to 24 hours). The process, based on eleven machine learning models, highlights that the most important external drivers are: the 10.7 cm Solar Flux (F10.7), the magnitude BT of the Interplanetary Magnetic Field (IMF), and the Auroral Electrojet (AE) index. The forecasting performance of the four models (MLP, CNN, ELM, STACKED) is then analysed. The analysis relies on three statistical metrics to compare actual and forecasted vTEC: the coefficient of determination (R2), the Mean Absolute Error (MAE), and the Root Mean Squared Error (RMSE). Additionally, descriptive statistics are presented using box and whisker plots. The four ANN models show a quite satisfactory capability to forecast vTEC when applied to the test dataset which represents 10% of the available data from 2006 to 2018. Furthermore, by conducting a Wilcoxon signed rank test, it is shown that statistically significant improvements are achieved by the STACKED model with regard to MLP, CNN and ELM. On average, by analysing the forecasted (from 1 to 24 hours in advance) vs the actual vTEC, the STACKED model achieves R2=0.816,RMSE=0.426 TECu, and MAE=0.296 TECu (1 TECunit =1016 electrons/m2) whilst MLP, CNN and ELM show respectively R2=0.808,0.812,0.803,RMSE=0.436 TECu, 0.431 TECu, 0.441 TECu and MAE=0.304 TECu, 0.299 TECu, 0.312 TECu.

Physics-informed loss functions for vertical total electron content forecast

We design physics-informed loss functions for training Artificial Neural Network (ANN) models to forecast the ionospheric vertical Total Electron Content (vTEC) from 1 to 24 hours in advance. The ANN models exploit our physics-informed loss functions, data provided by the Global Navigation Satellite Systems (GNSS) receiver installed at Tsukuba (36.06o\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{o}$$\\end{document} N, 140.05o\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{o}$$\\end{document} E), Japan, and external drivers (solar and geomagnetic indices). The time series used span from January 1, 2006, to December 31, 2018, i.e., a full solar cycle. A proper set of external drivers for the ANN models training are selected by ranking their importance in relation to the vTEC dynamics at different forecasting horizons. They result to be the 10.7 cm Solar Flux (F10.7), the magnitude of the Interplanetary Magnetic Field (IMF) BT\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\boldsymbol{B}_{\\boldsymbol{T}}$$\\end{document}, and the Auroral Electrojet (AE) index. Moreover, a second set of indices among those available has been considered as constraints in the design of the physics-informed loss functions. They are the Disturbance Storm Time (Dst) index, the solar wind speed v,BT\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\boldsymbol{v}, \\boldsymbol{B}_{\\boldsymbol{T}}$$\\end{document}, and the By and Bz components of the interplanetary magnetic field. To assess the performance of the resulting ANN models, we use the statistical parameter coefficient of determination (R2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\boldsymbol{R}^{{\ extbf {2}}}$$\\end{document}), the standard deviation (SD), and the Wilcoxon non-parametric signed ranked test. We show that, in the testing period analyzed (from 2017-09-13, at 04:40:00, to 2018-12-31, at 23:55:00), one of our physics-informed loss functions provides a better performance of the ANN with regard to the standard loss function commonly adopted. In particular, when the new loss function is used in the ANN model, the average SD is minimized across all forecasting horizons in the training, validation and test datasets. SD is 0.2560 TECU, 0.3183 TECU and 0.4240 TECU for the training, validation and test dataset respectively, where 1 TECU = 1016\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\ extbf {10}}^{{\ extbf {16}}}$$\\end{document} electrons/m2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\boldsymbol{m}^{{\ extbf {2}}}$$\\end{document}. The ANN model, incorporating the new loss function and applied to the test dataset, shows a significant improvement according to the Wilcoxon signed ranked test. In fact by selecting a significance level α=0.05\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\boldsymbol{\\alpha }~ \\mathbf {= 0.05}$$\\end{document}, the probability to obtain results by chance with the new loss function as compared to the standard loss function is 0.01504 (i.e., <α\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$<\\alpha $$\\end{document}), which implies that the new loss function gives a statistical improvement to the forecasting capability of the ANN model. To the best of our knowledge, this is the first time a physics-informed loss function has been designed for the task of forecasting the ionospheric vTEC.

Characteristics of ionospheric disturbances during the 2021 Typhoon Chanthu based on GPS and GLONASS

Severe typhoons often result in substantial economic and human losses. Therefore, near-real-time monitoring and forecasting of severe typhoons have a significant importance. In this paper, characteristics of ionospheric disturbances during the 2021 Typhoon Chanthu are estimated and investigated from GPS and GLONASS measurements. We calculate high-precision de-trended and filtered ionospheric vertical total electron content (VTEC) data based on GPS and GLONASS data from intensive Global Navigation Satellite System (GNSS) observations at 30-second intervals in Taiwan. Based on the VTEC values, we analyze the characteristics of ionospheric disturbances caused by Typhoon Chanthu, such as the time of appearance of ionospheric disturbances, changes in the magnitude of these disturbances, alterations in two-dimensional disturbances, fluctuations in the speed of disturbance propagation, and shifts in the frequency spectrum. The results reveal that both GPS-TEC and GLONASS-TEC exhibit noticeable disturbances during the specified timeframe. These disturbances are accompanied by the occurrence of “N”-type anomalies in TEC. Notably, at the occurrence of the disturbance, the elevation angle of GPS PRN32 and GLONASS PRN1 satellites is approximately 50°-55°, and their respective distances from the typhoon eye are roughly 350 km and 150 km. The propagation velocity and center frequency of the ionospheric disturbances are located in the range of gravity waves. Specifically, the disturbances propagation speeds of GPS PRN32 and GLONASS PRN1 are approximately 129.36 m/s and 128.33 m/s, respectively, while the center frequencies are about 1.17 mHz and 1.43 mHz. The ionospheric perturbations observed by GPS and GLONASS exhibit certain similarities, which provide valuable insights into the relationship between typhoons and ionospheric disturbances.

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.

Conjugate hemispheric response of Earth’s ionosphere due to 2019 Antarctic minor Sudden Stratospheric Warming (SSW): Comparison with 2013 Arctic major SSW

In this paper, we attempt to compare the Antarctic SSW of 2019 with the Arctic SSW of 2013. Despite being a minor SSW, the rare Antarctic event of September 2019 caused a significant enhancement of stratospheric temperature at high latitudes with more temperature deviation than the Arctic major SSW of 2013. On the basis of the duration and extent of stratospheric zonal mean zonal wind, the 2019 SSW is comparable to major SSWs. An exceptionally strong wave 1 planetary wave was observed during the 2019 SSW. We have investigated the response of the ionospheric total electron content (TEC) during both the SSWs over two low mid-latitude locations Okinawa and Darwin, which form magnetic conjugate pair. The extent of maximum ionospheric deviations during the 2019 SSW is almost comparable to the 2013 SSW in their respective hemispheres. We have observed mostly night time enhancement in TEC during the Arctic SSW, while daytime enhancement is noted during the Antarctic SSW. Hemispheric asymmetry is observed during both the SSWs. Minor geomagnetic storm has been noticed coinciding with each SSW during different phases of the warming. The SSW, solar flux, and storm contribution on the TEC have been calculated during both the SSWs, which shows clear contribution of SSW on the total TEC despite the presence of storm and during moderately high solar activity. We have also investigated the foF2 variations from ionosonde and IRI-16 at the same two locations. The peak electron density from ionosonde responds to the warming in a manner similar to TEC. The foF2 of IRI-16 exhibits overestimation during daytime and underestimation during nighttime on quiet days during low solar activity (2019), while during moderate solar activity (2013), it overestimates during morning hours. However, it can not predict the enhancements of foF2 during SSWs.

Ionospheric compensation in L-band InSAR time-series: Performance evaluation for slow deformation contexts in equatorial regions

Multi-temporal Synthetic Aperture Radar Interferometry (MT-InSAR) is the only geodetic technique allowing to measure ground deformation down to mm/yr over continuous areas. Vegetation cover in equatorial regions favors the use of L-band SAR data to improve interferometric coherence. However, the electron content of ionosphere, affecting the propagation of the SAR signal, shows particularly strong spatio-temporal variations near the equator, while the dispersive nature of the ionosphere makes its effect stronger on low-frequencies, such as L-band signals. To tackle this problem, range split-spectrum method can be implemented to compensate the ionospheric phase contribution. Here, we apply this technique for time-series of ALOS-PALSAR data, and propose optimizations for low-coherence areas. To evaluate the efficiency of this method to retrieve subtle deformation rates in equatorial regions, we compute time-series using four ALOS-PALSAR datasets in contexts of low to medium coherence, showing slow deformation rates (mm/yr to cm/yr). The processed tracks are located in Ecuador, Trinidad and Sumatra, and feature 15 to 19 acquisitions including very high, dominating ionospheric noise, corresponding to equivalent displacements of up to 2 m. The correction method performs well and allows to reduce drastically the noise level due to ionosphere, with significant improvement compared with a simple plane fitting method. This is due to frequent highly non-linear patterns of perturbation, characterizing equatorial TEC distribution. We use semivariograms to quantify the uncertainty of the corrected time-series, highlighting its dependence on spatial distance. Thus, using ALOS-PALSAR-like archive, one can expect a detection threshold on the Line-of-Sight velocity ranging between 3 and 6 mm/yr, depending on the spatial wavelength of the signal to be observed. These values are consistent with the accuracy derived from the comparison of velocities between two tracks in their overlapping area. In the case studies that we processed, the time-series corrected from ionosphere allows to retrieve accurately fault creep and volcanic signal but it is still too noisy for retrieving tiny long-wavelength signals such as slow (mm/yr) interseismic strain accumulation.

Influence of the Neutral Atmosphere Model on the Correctness of Simulation the Electron and Ion Concentrations in the Lower Ionosphere

AbstractThe work is devoted to modeling and verification of the dynamics of charged components of the Earth's lower ionosphere, obtained using different values of the background neutral atmosphere parameters. Long‐term experimental data on temperature and concentration of neutral components from the Aura satellite and Mass Spectrometer Incoherent Scatter Radar (MSIS) model were used as input data. The average relative difference between the electron concentration values obtained using Aura and MSIS data (11 June 2014, 6–18 UT, h = 50–90 km, 41°N 10°E) was 34%. Obtained results were verified with use of very low frequency (VLF) signals propagation data, which were collected on Mikhnevo Geophysical Observatory (GPO) and A118 station from GBZ, GQD, DHO, TBB, and ICV transmitters. Based on the results of the verification, it was found that the response of the lower ionosphere to X‐ray flares, that occurred on 9–11 June 2014, is better described by the Aura satellite data in 4 out of 5 cases. Consequently, they are more preferable for predicting the state of ionospheric parameters below the reflection height of signals of the considered frequencies (∼75 km). It is also shown that taking into account the ion concentration for calculation the amplitude‐phase characteristics of the VLF‐signals makes it possible to describe the dynamics of parameters better.

A simulation study of the Argo program-enhanced global ionospheric modeling

The ionospheric total electron content (TEC) is a critical parameter for space weather and Global Navigation Satellite System (GNSS) applications. Since 1998, the Ionospheric Working Group of the International GNSS Service (IGS) has been generating global ionospheric maps using data from hundreds of GNSS stations. However, the uneven geographical distribution of these observations has resulted in significant variations in TEC map accuracy, particularly in marine areas where observations are scarce or absent. In this study, we introduce Argo data as additional observations to enhance the accuracy of global ionospheric modeling. Argo data consists of measurements collected by more than 1,000 robotic floats deployed across the world's oceans. By utilizing the NeQuick model and considering the geographic coordinates of both IGS stations and Argo buoys, we simulate the ionospheric Slant Total Electron Content (STEC). This approach allows us to examine the impact of integrating Argo data into global ionospheric modeling and evaluate its effectiveness. The results indicate a significant improvement in the overall accuracy of the model after incorporating Argo data. Furthermore, by increasing the model's degree, a better global fit is achieved. It eliminates noticeable overestimation or underestimation of TEC, especially evident at a degree of 30. As the availability and uniform distribution of observations improve, the model demonstrates higher accuracy and enhanced global performance.

Ionospheric Global and Regional Electron Contents in Solar Cycles 23–25

The Earth’s ionosphere experiences forcing from above and below and varies in different periods. We analyzed the dynamics of the ionospheric global and regional electron contents (GEC and REC) in solar cycles 23/24 (SC23/SC24) and the first part of solar cycle 25 (SC25). We considered several methodological issues for GEC calculations and created a tool to compute GEC and made it available through SIMuRG (System for Ionosphere Monitoring and Research from GNSS). The paper shows the asymmetry of GEC dynamics in different solar cycles. The mid-latitude summer evening anomaly disrupted the diurnal REC variation in the Siberian region under solar minima. The mean GEC showed similar dependence on the F10.7 index in SC25 and SC23/SC24. The difference in solar cycles could prevent reliable forecasting for GEC for the next solar cycle. Our model, based on a neural network, could predict GEC dynamics in SC25 accurately when we input the F10.7 index.

Response of the ionosphere to equatorial electrojet at latitudes near the northern EIA crest in the East African longitudinal sector

Equatorial Electrojet (EEJ) and Equatorial Ionization Anomaly (EIA) are two distinct ionospheric phenomena which are caused by the horizontal configuration of the geomagnetic field at the equator. On the day side of the earth, these activities are controlled by a common zonal electric field, and their interactions must be researched for a number of scientific reasons. In this study, we used multiple Global Navigation Satellite System (GNSS) stations at Al Wajh, Sola village, Nama, and Sheba in 2012 to investigate the correlations between the instantaneous and integrated equatorial electrojet (EEJ) and the variations of vertical total electron content (vTEC) at the northern crest of EIA in the East Africa and Middle East longitudinal sector. The equinoctial and solstitial months have the highest and lowest values of the monthly mean EEJ and vTEC, respectively, and as a result, both parameters show a semi-annual variability. The monthly mean vTEC also demonstrates equinoctial and solstitial asymmetries, resulting in vTEC values that are higher at the September equinox and the December solstice than at the March equinox and the June solstice, respectively. These asymmetries have been also observed in the composition of [O]/[N2] ratio. On days with strong counter electrojet (CEJ), EEJ peaks are smaller and occur later than on days with weak CEJ. Additionally, when CEJ is high, EIA is suppressed and vTEC exhibits low values at all crest points. In the early hours (10:00–12:00 Lt), Nama and Sheb showed higher correlation coefficient values with significant levels than Alwj and Sola. According to the correlation analysis, the anomaly crest develops at the lower latitude stations between 10:00 and 12:00 Lt and then moves to the higher latitude stations later. Generally, the correlations are higher and more significant in the equinoxes than the solstices. The correlations between integrated EEJ (IEEJ) and vTEC were also found to be strong and significant at the lower latitude stations throughout all seasons. These findings suggest that both the instantaneous and integrated EEJ can be used as a proxy for the eastward zonal equatorial electric field and must be considered in regional and global models of total electron content of equatorial and low latitude ionosphere.

Global Variations of the Total Electron Content in the Equatorial Ionosphere during the Annular Solar Eclipse of June 21, 2020

Global Variations of the Total Electron Content in the Equatorial Ionosphere during the Annular Solar Eclipse of June 21, 2020