Total Electron Content Data Sets Research Articles

Topside Ionosphere Sounding From the CHAMP, GRACE, and GRACE‐FO Missions

AbstractSatellites in Low Earth Orbit (LEO) are essential for sounding the topside ionosphere. In this work, we present and validate a data set of Total Electron Content (TEC) and in situ electron density observations from the Gravity Recovery And Climate Experiment (GRACE) and GRACE‐Follow‐On missions as well as a TEC data set from the CHAllenging Minisatellite Payload mission. Concerning TEC, special emphasis is put to ensure optimal consistency to the already existing Swarm and Gravity field and steady‐state ocean circulation explorer (GOCE) TEC data sets. The newly processed satellite missions allow covering two full solar cycles with LEO slant TEC. Furthermore, the twin satellite missions GRACE and GRACE‐FO equipped with inter‐satellite K‐band ranging allows to derive the horizontal TEC and, due to the small inter‐satellite distance of the satellite pairs, an approximation for local electron density. However, the derived value of electron density is relative and requires calibration using external information. In this work, the calibration is performed using the IRI‐2016 model. Radar observations, as well as in situ electron density observations available from Swarm B Langmuir probes, are used for validation. Conjunctions between satellites are used to validate the TEC time series. The newly derived data set is shown to be highly consistent with the already existing data sets with standard deviations below 3 TECU for TEC (even 1 TECU was reached for low solar flux) and an offset below 7 × 1010 m−3 with a standard deviation near 1 × 1011 m−3 for the electron density.

RETRACTED: An unsupervised anomaly detection approach for pre-seismic ionospheric total electron content

This paper focuses on the anomaly detection for ionospheric total electron content (TEC) before earthquakes. In this paper, a novel unsupervised approach is proposed. First, interval-based method is employed to granulate the TEC series. Justifiable granularity principle is utilized to construct interval information granules (IGs) for representing TEC series. Second, high-order difference method is introduced to construct rectangle IGs and cube IGs for obtaining the new representation of TEC. Third, corresponding similarity measurement method is designed to calculate the anomaly score of each IG, which is the evaluation criterion for detecting the anomalies. Finally, experimental results using real TEC datasets validate the effectiveness of the proposed approach. Compared with the existing major approaches, because the proposed approach can capture more morphological details and variation trend of TEC series, it can achieve a higher detection accuracy.

Using Deep Learning to Map Ionospheric Total Electron Content over Brazil

The low-latitude ionosphere has an active behavior causing the total electron content (TEC) to vary spatially and temporally very dynamically. The solar activity and the geomagnetic field have a strong influence over the spatiotemporal distribution of TEC. These facts make it a challenge to attempt modeling the ionization response. Single frequency GNSS users are particularly vulnerable due to these ionospheric variations that cause degradation of positioning performance. Motivated by recent applications of machine learning, temporal series of TEC available in map formats were employed to build an independent TEC estimator model for low-latitude environments. A TEC dataset was applied along with geophysical indices of solar flux and magnetic activity to train a feedforward artificial neural network based on a multilayer perceptron (MLP) approach. The forecast for the next 24 h was made relying on TEC maps over the Brazilian region using data collected on the previous 5 days. The performance of this approach was evaluated and compared with real data. The accuracy of the model was evaluated taking into account seasonality, spatial coverage and dependence on solar flux and geomagnetic activity indices. The results of the analysis show that the developed model has a superior capacity describing the TEC behavior across Brazil, when compared to global ionosphere maps and the NeQuick G model. TEC predictions were applied in single point positioning. The achieved errors were 27% and 33% lower when compared to the results obtained using the NeQuick G and global ionosphere maps, respectively, showing success in estimating TEC with small recent datasets using MLP.

A Neural Network-Based TEC Model Capable of Reproducing Nighttime Winter Anomaly

With the availability of fast computing machines, as well as the advancement of machine learning techniques and Big Data algorithms, the development of a more sophisticated total electron content (TEC) model featuring the Nighttime Winter Anomaly (NWA) and other effects is possible and is presented here. The NWA is visible in the Northern Hemisphere for the American sector and in the Southern Hemisphere for the Asian longitude sector under solar minimum conditions. During the NWA, the mean ionization level is found to be higher in the winter nights compared to the summer nights. The approach proposed here is a fully connected neural network (NN) model trained with Global Ionosphere Maps (GIMs) data from the last two solar cycles. The day of year, universal time, geographic longitude, geomagnetic latitude, solar zenith angle, and solar activity proxy, F10.7, were used as the input parameters for the model. The model was tested with independent TEC datasets from the years 2015 and 2020, representing high solar activity (HSA) and low solar activity (LSA) conditions. Our investigation shows that the root mean squared (RMS) deviations are in the order of 6 and 2.5 TEC units during HSA and LSA period, respectively. Additionally, NN model results were compared with another model, the Neustrelitz TEC Model (NTCM). We found that the neural network model outperformed the NTCM by approximately 1 TEC unit. More importantly, the NN model can reproduce the evolution of the NWA effect during low solar activity, whereas the NTCM model cannot reproduce such effect in the TEC variation.

Detecting the Ionospheric Disturbances in Japan Using the Three‐Dimensional Computerized Tomography

AbstractIn this study, a three‐dimensional (3‐D) ionospheric tomography was developed in Japan using the constraint least‐square fitting algorithm, a model‐free method. Moreover, to make enough room for electron density (Ne) variations in the horizontal direction, only constraints on height were adopted. Slant total electron content data sets detected by 800 Global Positioning System (GPS) ground‐based receivers in 25°–50°N, 125°–150°E were selected. The spatial resolution was 1°, 1°, and 30 km in latitude, longitude, and altitude, respectively. The reconstructed performances concerning the quiet ionosphere, large‐scale traveling ionospheric disturbances on July 27, 2004, and nighttime medium‐scale traveling ionospheric disturbances on June 23, 2012, were verified successively. By comparing with Ne distributions from the International Reference Ionosphere model and GPS‐TEC mapping, the reconstructed results in both horizontal and vertical directions show good performance in imaging the ionospheric background and disturbed characteristics. The finding strongly indicates that retrieving Ne distributions by the improved constraint least‐square algorithm is promising for ionospheric studies and practical applications.

Implementation of storm‐time ionospheric forecasting algorithm using SSA–ANN model

Forecasting of total electron content (TEC)/global positioning system (GPS) signal delays in storm conditions is considered the most challenging task for accurate position estimation, especially in critical applications. Therefore, a storm-time ionospheric model is proposed to forecast TEC based on the artificial neural network (ANN) using singular spectrum analysis (SSA). The study area covers four Global Navigation Satellite System (GNSS) stations located in the low-latitude and two GNSS stations located in the mid-latitude ionosphere. The geographical area extends between 11–43°N latitude and 77–93°E longitude. The selection of GPS–TEC data is based on the storm criterion of Dst ≤ −50 nT, and the storm day data sets from 2009 to 2017 period are used for model development. The proposed algorithm is tested with the GPS–TEC data sets of three geomagnetic storm days (i) severe, (ii) moderate storm and (iii) strong at low and mid-latitudes. The average precision and mean absolute error of the proposed SSA-ANN model is 1.41 and 1.06 TECU (strong storm), respectively. The prediction performance of the proposed SSA-ANN model is compared with the standard principal component analysis-ANN model. The improvement factor of the SSA-ANN is improved by 43.82%.

Comparison of Global Vertical Total Electron Content from Various Global Data Centers

In this study, the inter-comparison of various global vertical total electron contents are derived from Global Positioning System (GPS) networks worldwide. Based on observation data obtained from global network of dual frequency, the ionospheric variability on one full year, 2008 is studied through the vertical electron conten distribution GPS. The comparisons are aimed at comparability of the different vertical total electron content data sets in terms of absolute magnitude, capturing diurnal, seasonal variability globally. Total electron content (TEC) values were compared by computing the TEC differences among different stations. Most of the data sets exhibit expected diurnal variability with some differences on the absolute magnitude of vertical total electron content moreover, seasonally, the variability is also comparable. In this observation, highest vertical electron contents are observed on the Jet prolusion laboratory. It is followed by International global service, vertical total electron content and the least is observed on the Polytechnical University data sets.

Modeling of local ionospheric time varying characteristics based on singular value decomposition over low-latitude GPS stations

Singular Value Decomposition (SVD) model is implemented to recognize the Total Electron Content (TEC) time series of daily, temporal as well as seasonal characteristics throughout the 24th solar cycle period of the year 2015 in the study. The Vertical (vTEC) analysis has been carried out with Global Positioning System (GPS) data sets collected from five stations from India namely GNT, Guntur (16.44∘ N, 80.62∘ E), and IISC, Bangalore (12.97∘ N, 77.59∘ E), LCK2, Lucknow (26.76∘ N, 80.88∘ E), one station from Thailand namely AITB, Bangkok (14.07∘ N, 100.61∘ E), and one station from South Andaman Island namely PBR, Port Blair (11.43∘ N, 92.43∘ E), located in low latitude region. The first five singular value modes constitute about 98% of the total variance, which are linearly transformed from the observed TEC data sets. So it is viable to decrease the number of modeling parameters. The Fourier Series Analysis (FSA) is carried out to characterize the solar-cycle, annual and semi-annual dependences through modulating the first three singular values by the solar (F10.7) and geomagnetic (Ap) indices. The positive correlation coefficient (0.75) of daily averaged GPS–TEC with daily averaged F10.7 strongly supports the temporal variations of the ionospheric features depends on the solar activity. Further, the significance and reliability of the SVD model is evaluated by comparing it with GPS–TEC data and the standard global model (Standard Plasma-Spherical Ionospheric Model, SPIM and International Reference Ionosphere, IRI 2016).

Atmosphere-ionosphere coupling from convectively generated gravity waves

Ionospheric variability impacts operational performances of a variety of technological systems, such as HF communication, Global Positioning System (GPS) navigation, and radar surveillance. The ionosphere is not only perturbed by geomagnetic inputs but is also influenced by atmospheric tides and other wave disturbances propagating from the troposphere to high altitudes. Atmospheric Gravity Waves (AGWs) excited by meteorological sources are one of the largest sources of mesoscale variability in the ionosphere. In this paper, Total Electron Content (TEC) data from networks of GPS receivers in the United States are analyzed to investigate AGWs in the ionosphere generated by convective thunderstorms. Two case studies of convectively generated gravity waves are presented. On April 4, 2014 two distinct large convective systems in Texas and Arkansas generated two sets of concentric AGWs that were observed in the ionosphere as Traveling Ionospheric Disturbances (TIDs). The period of the observed TIDs was 20.8min, the horizontal wavelength was 182.4km, and the horizontal phase speed was 146.4m/s. The second case study shows TIDs generated from an extended squall line on December 23, 2015 stretching from the Gulf of Mexico to the Great Lakes in North America. Unlike the concentric wave features seen in the first case study, the extended squall line generated TIDs, which exhibited almost plane-parallel phase fronts. The TID period was 20.1min, its horizontal wavelength was 209.6km, and the horizontal phase speed was 180.1m/s. The AGWs generated by both of these meteorological events have large vertical wavelength (>100km), which are larger than the F2 layer thickness, thus allowing them to be discernible in the TEC dataset.

Tomography Reconstruction of Ionospheric Electron Density with Empirical Orthonormal Functions Using Korea GNSS Network

In South Korea, there are about 80 Global Positioning System (GPS) monitoring stations providing total electron content (TEC) every 10 min, which can be accessed through Korea Astronomy and Space Science Institute (KASI) for scientific use. We applied the computerized ionospheric tomography (CIT) algorithm to the TEC dataset from this GPS network for monitoring the regional ionosphere over South Korea. The algorithm utilizes multiplicative algebraic reconstruction technique (MART) with an initial condition of the latest International Reference Ionosphere-2016 model (IRI-2016). In order to reduce the number of unknown variables, the vertical profiles of electron density are expressed with a linear combination of empirical orthonormal functions (EOFs) that were derived from the IRI empirical profiles. Although the number of receiver sites is much smaller than that of Japan, the CIT algorithm yielded reasonable structure of the ionosphere over South Korea. We verified the CIT results with NmF2 from ionosondes in Icheon and Jeju and also with GPS TEC at the center of South Korea. In addition, the total time required for CIT calculation was only about 5 min, enabling the exploration of the vertical ionospheric structure in near real time.

Spatial and temporal analysis of the total electron content over China during 2011–2014

In the present work we investigate variations of ionospheric total electron content (TEC) with empirical orthogonal function (EOF) analysis, the four-year TEC data are derived from ∼250GPS observations of the crustal movement observation network of China (CMONOC) over East Asian area (30–55°N, 70–140°E) during the period from 2011, January to 2014, December. The first two EOF components together account for ∼93.78% of total variance of the original TEC data set, and it is found that the first EOF component represents a spatial variability of semi-annual variation and the second EOF component exhibits pronounced east–west longitudinal difference with respect to zero valued geomagnetic declination line. In addition, climatology of the vertical plasma drift velocity vdz induced by HWM zonal wind field (∼300km) are studied in the paper. Results shows vdz displays significant east–west longitudinal difference at 10:00LT and 20:00LT, and its daytime temporal variation is consistent with the second EOF principal component, which suggests that the east–west longitudinal variability is partly caused by the thermospheric zonal wind and geomagnetic declination. It is expected that with this dense GPS network, local ionospheric variability can be described more accurately and a more realistic ionospheric model can be constructed and used for the satellite navigation and radio propagation.

Characterization of ionospheric variability in TEC using EOF and wavelets over low-latitude GNSS stations

Investigation of ionospheric variability is essential for improving the daily ionospheric modeling and forecasting services of Global Navigation Satellite System (GNSS) applications. As India is a low-latitude region, more care has to be taken here to characterize the ionosphere due to irregularities and Equatorial Ionization Anomaly (EIA) conditions. Therefore, an appropriate method is required to diagnose the ionospheric variations during geomagnetic, solar and other disturbances. In this paper, the temporal ionospheric time delay variations were studied based on the Empirical Orthogonal Function (EOF) analysis and wavelet transforms (WT).These analyses were carried out with Total Electron Content (TEC) datasets obtained from three GNSS stations located in low-latitude regions. EOF analysis was performed on the TEC datasets, which were decomposed into a time series of orthogonal eigen values (or base functions) and associated coefficients. EOF base functions and their associated coefficients signify the hourly time variations and the day of the year variations. The results reveal that the first few EOFs represented the majority of TEC variability pertaining to the physical processes of the ionosphere. The accuracy of the EOF model was validated by the evaluation of observational TEC data with International Reference Ionosphere (IRI) 2012 models. The EOF model coefficients for each GNSS station showed a strong correlation with the IRI models and also described the correlation between the impacts of the level of geomagnetic activity on the ionosphere. The correlation coefficients for the first three EOFs were more than 0.95. The phase relationship of ionospheric TEC anomalies, with respect to the geomagnetic indices (Dst), were analyzed by wavelet transforms.

Modelling total electron content during geomagnetic storm conditions using empirical orthogonal functions and neural networks

AbstractIt has been shown in ionospheric research that modelling total electron content (TEC) during storm conditions is a big challenge. In this study, TEC modelling was performed over Sutherland (32.38°S, 20.81°E, 41.09°S geomagnetic), South Africa, during storm conditions, using a combination of empirical orthogonal function (EOF) and regression analyses techniques. The neural network (NN) technique was also applied to the same TEC data set, and its output was compared with TEC modeled using the EOF model. TEC was derived from GPS observations, and a geomagnetic storm was defined for Dst≤−50 nT. The hour of the day and the day number of the year, F10.7p and A indices, were chosen as inputs for the modeling techniques to take into account diurnal and seasonal variation of TEC, solar, and geomagnetic activities, respectively. Both EOF and NN models were developed using GPS TEC data for storm days counted from 1999 to 2013 and tested on different storms. For interpolation, the EOF and NN models were validated on storms that occurred during high and low solar activity periods (storms of 2000 and 2006), while for extrapolation the validation was done for the storms of 2014 and 2015, identified based on the provisional Dst index data. A comparison of the modeled TEC with the observed TEC showed that both EOF and NN models perform well for storms with nonsignificant ionospheric TEC response and storms that occurred during period of low solar activity. For storms with significant TEC response, TEC magnitude is well captured during the nighttime and early morning, but short‐term features, TEC enhancement, and depression are not sufficiently captured by the models. Statistically, the NN model performs 12.79% better than the EOF model on average, over all storm periods considered. Furthermore, it has been shown that the EOF and NN models developed for a specific station can be used to estimate TEC over other locations within a latitudinal and longitudinal coverage of 8.7° and 10.6°, respectively. This is an important result as it reduces the data dimensionality problem for computational purposes.

Ionospheric symmetry caused by geomagnetic declination over North America

We describe variations in total electron content (TEC) in the North American sector exhibiting pronounced longitudinal progression and symmetry with respect to zero magnetic declination. Patterns were uncovered by applying an empirical orthogonal function (EOF) decomposition procedure to a 12 year ground‐based American longitude sector GPS TEC data set. The first EOF mode describes overall average TEC, while the strong influence of geomagnetic declination on the midlatitude ionosphere is found in the second EOF mode (or the second most significant component). We find a high degree of correlation between spatial variations in the second EOF mode and vertical drifts driven by thermospheric zonal winds, along with well‐organized temporal variation. Results strongly suggest a causative mechanism involving varying declination with longitude along with varying zonal wind climatology with local time, season, and solar cycle. This study highlights the efficiency and key role played by the geomagnetic field effect in influencing mesoscale ionospheric structures over a broad midlatitude range.

Storms in the ionosphere: Patterns and processes for total electron content

The ionosphere's total electron content (TEC) is a parameter widely used in studies of the near‐Earth plasma environment. The scientific use of TEC appeared early in the artificial satellite era, and among its many contributions were fundamental insights into how the ionosphere responds to geomagnetic storms. While many excellent reviews of solar‐terrestrial disturbances exist in the literature, none have concentrated on the TEC parameter per se. With new TEC data sets increasingly available from the Global Positioning System (GPS), a comprehensive summary of pre‐GPS storm studies is needed to set the base for progress in the GPS era. This review summarizes past case studies, describes statistical occurrence pattern, and identifies responsible mechanisms validated via modeling. It presents a new set of results of TEC disturbance patterns during 180 geomagnetic storms to describe seasonal and solar cycle effects. It concludes with a set of open questions that require additional study.

Longitude structure of ionospheric total electron content at low latitudes measured by the TOPEX/Poseidon satellite

The longitude structure of ionospheric total electron content (TEC) at low latitudes has been evaluated using the NASA/Centre Nationale d'Etudes Spatiales TOPEX/Poseidon satellite. The TEC data set is given by the ionospheric range correction, which is computed from TOPEX dual‐frequency altimeter measurements. The satellite's orbit allows analysis of vertically measured TEC values at approximately 30° intervals of longitude across the world at local time differences of only 6–12 min. Patterns of longitudinal dependence of the equatorial anomaly were observed during the equinoxes, summers, and winters of 1993, 1994, and 1995. TOPEX observations reveal occurrence of relative maximum anomaly TEC values in the Indian/Asian longitude sector. This dominance in TEC is seen most consistently in the Asian Southern Hemisphere. Also, a relative decrease in anomaly TEC values is evident in the western American region, which is observed primarily during equinox and winter. This configuration of the equatorial anomaly TEC is observed on a day‐to‐day basis at particular periods of local time. Global theoretical ionospheric model results are presented in an attempt to reproduce the distinctive longitude structure. Variability in E × B vertical drift velocity within specific longitude sectors is shown to be a primary factor in the longitude dependence of equatorial anomaly TEC.

Longitude structure of the ionosphere F region from TOPEX/Poseidon and ground‐based data during January 20–30, 1993, including the quasi 2‐day oscillation

The global structure of the F region ionosphere during January 20–30, 1993, is examined with emphasis on longitudinal variations. The database consists of total electron content (TEC) measurements from the TOPEX/Poseidon dual‐frequency altimeter experiment from −66° to +66° geographic latitude for prenoon and premidnight conditions, and hourly ƒoF2 and hmF2 values from eight ionosonde stations between −42° and +26° geographic latitude in the Pacific sector. The 10‐day averaged daytime TEC data reveal latitude versus longitude structures whose salient features are captured (within ∼10 TEC units) by the 1990 International Reference Ionosphere (IRI‐90), and which can be explained by dynamically induced neutral composition changes due to a combination of solar and magnetically driven circulation systems. The premidnight TEC data at low latitudes reveal the double‐banded structure of the equatorial ionization anomaly with, however, pronounced variations in amplitude and position as a function of geomagnetic longitude. None of these features, which probably reflect some combination of electric field and neutral wind effects, are captured by IRI‐90. Time series analyses of the ƒoF2 and hmF2 data, and space‐time Fourier spectra of the TEC data, reveal the presence of a quasi 2‐day (QTD) oscillation in the ionosphere which is associated with QTD wind and temperature oscillations in the mesosphere and lower thermosphere previously documented for this time period. The satellite‐based QTD results are subject to significant caveats described herein. Nevertheless, the TOPEX/Poseidon TEC data set clearly represents an important resource for global studies of ionospheric structure and plasma‐neutral interactions.