Vertical Total Electron Content Data Research Articles

Statistical Analysis of Ionospheric Correlation for Shortwave System

AbstractStatistical analysis of the temporal and spatial variations in the ionosphere is necessary to improve the shortwave system. Based on the standardized Euclidean distance algorithm, multisource ionospheric assimilation data, International GNSS Service vertical total electron content data, and ionosonde data are used to statistically analyze the ionospheric correlation distance, and the variation of ionospheric correlation distances with local times, magnetic latitudes, and seasons are obtained. The statistical analysis results show that the zonal ionospheric correlation distance presents minima in the equatorial ionospheric anomaly crest regions. Additionally, the meridional correlation distance in middle magnetic latitudes is greater than that in other regions. The zonal ionospheric correlation distance presents obvious local‐time variability. The variation trends of the meridional and zonal correlation distance during spring and autumn are similar. The patterns of the ionospheric correlation range variation with local times, magnetic latitudes, and seasons can be used to select the optimal locations or spacing for ionosonde stations, shortwave multi‐station systems, and shortwave radio beacons.

Modeling Ionospheric TEC Using Gradient Boosting Based and Stacking Machine Learning Techniques

AbstractAccurately predicting and modeling the ionospheric total electron content (TEC) can greatly improve the accuracy of satellite navigation and positioning and help to correct ionospheric delay. This study assesses the effectiveness of four different machine learning (ML) models in predicting hourly vertical TEC (VTEC) data for a single‐station study over Ethiopia. The models employed include gradient boosting machine (GBM), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM) algorithms, and a stacked combination of these algorithms with a linear regression algorithm. The models relied on input variables that represent solar activity, geomagnetic activity, season, time of the day, interplanetary magnetic field, and solar wind. The models were trained using the VTEC data from January 2011 to December 2018, excluding the testing data. The testing data comprised the data for the year 2015 and the initial 6 months of 2017. The RandomizedSearchCV algorithm was used to determine the optimal hyperparameters of the models. The predicted VTEC values of the four ML models were strongly correlated with the GPS VTEC, with a correlation coefficient of ∼0.96, which is significantly higher than the corresponding value of the International Reference Ionosphere (IRI 2020) model, which is 0.87. Comparing the GPS VTEC values with the predicted VTEC values based on diurnal and seasonal characteristics showed that the predictions of the developed models were generally in good agreement and outperformed the IRI 2020 model. Overall, the ML models used in this study demonstrated promising potential for accurate single‐station VTEC prediction over Ethiopia.

Global Ionosphere Modeling Based on GNSS, Satellite Altimetry, Radio Occultation, and DORIS Data Considering Ionospheric Variation

AbstractThe accuracy of ionospheric models estimated by ground‐based multiple global navigation satellite system ionospheric data over regions with sparse tracking stations is not ideal. To improve the accuracy of the estimated ionospheric model, different types of ionospheric data with different combinations were employed for previous studies. However, the ionospheric observational ranges for different types of ionospheric data are not the same. In this study, the accuracy of ionospheric maps generated by ground‐based ionospheric data (ground‐based strategy) and ground‐based ionospheric data combined with data provided by other geodetic measurements normalized by the single‐layer normalization method (multi‐source strategy) were studied. The results showed that the main differences between the ionospheric models estimated by the two strategies occur for data taken over the ocean, which mainly range from −1 to 0 total electron content unit (TECU). When assessed using Jason‐3 vertical total electron content data, the mean root mean square (RMS) value of the ionospheric model estimated by the multi‐source strategy was 5.03 TECU, which is approximately 15% smaller than that estimated by the ground‐based strategy. The maximum reduction in results using the multisource strategy was approximately 25% over different latitudes compared with that of the ground‐based strategy. Furthermore, the self‐consistency evaluation method was employed for evaluation. The results showed that the RMS of the ionospheric model estimated by the multi‐source strategy was 2.41 TECU, which is 3.60% better than that of the ground‐based strategy. The maximum reduction was 15% on different days.

Regional ionospheric electron density modeling by assimilation of GPS-derived TEC into IRI-provided grids on May 8, 2016

Retrieved vertical total electron content (VTEC) via dual-frequency GPS measurements is a valuable observation for regional VTEC modeling of the ionosphere (3D), and also ionospheric electron density (IED) modeling (4D). In the case of 4D modeling, spatial and temporal changes in IED are imaged. 4D reconstruction of IED in different ionosphere layers is not possible only by retrieved VTECs. In all IED modeling methods, in addition to VTEC observations, a priori knowledge of how electron density is distributed in different layers is required, which is often taken from global empirical models. One of these methods is data assimilation, in which the information of a global model is considered as background and both observations and background are used optimally for IED modeling. Data assimilation has more prominent importance in regions where the observations are sparse. In this study, a grid-based data assimilation method is proposed for IED modeling. The optimum interpolation method (OI) is presented with a modification to find the optimal variance factor related to the background covariance matrix. The covariance matrix of the background during the data assimilation interval is estimated by an optimization process. The method is utilized for regional assimilative IED modeling over Iran with sparse VTEC data from a low number of permanent GPS stations on May 8, 2016 which is the most active ionospheric day in that year. International reference ionosphere (IRI) is considered as background, and although it has a significant bias in the study region, the proposed method resulted in an accurate regional IED model. The model is evaluated in two ways. First, by measuring RMS of the differences between parts of VTECs that are excluded from observations as test data and the obtained VTECs from the model. Second, by examining the accuracy of ionospheric delay estimation via single-frequency positioning at a control station in Tehran. Despite the use of sparse data on an active ionospheric day and poor background accuracy in the study region, the constructed regional assimilative model is accurate both in RMS with test data and ionospheric delay estimation.

Analysis of Ionosphere Day to Day Variability using Parametric Methods on GPS TEC

The development of global positioning systems (GPS) has cleared the way for a better understanding of the globe and the systems surrounding the earth. The GPS signals that are sent out and received by GPS receivers are affected by severe space weather (in the ionosphere) as well as atmospheric weather and lithospheric disturbances. These disturbances not only result in inaccuracies in GPS location but also make it difficult to make the necessary corrections to those faults. The study of these perturbations is helpful in gaining an understanding of the phenomenal changes that occur in the ionosphere as a result of the Sun and its solar effects, which include solar flares, sunspot numbers, solar winds, geomagnetic fields, and natural disasters such as earthquakes and thunderstorms, among other things. In this study, an effort is made to highlight the distinctions between geomagnetic calm days and disturbed days that occurred in January 2022. The research is carried out by using parametric approaches to the GPS vertical total electron content (VTEC) at Hyderabad (17.4°N, 78.5°E), which is a station with a low height. An examination of the power spectral densities (PSDs) of both the tranquil and the disturbed conditions is carried out. The improvement in VTEC data was noted on two days in January 2022 that had low geomagnetic activity; these days were the 6th and 13th. On the sixth of January 2022, enhancements can be seen at the normalised frequencies of 0.4966 and 0.7683, with a power spectral density (PSD) of -10.14dB and -12.94dB, respectively. On the thirteenth of January 2022, enhancements can be seen at the normalised frequencies of 0.5005 and 0.956, with PSDs of -9.264dB and -8.392dB. When compared to the preceding records, it can be seen very clearly that the PSDs of both geomagnetically tranquil and disturbed days are rather comparable to one another.

Variation of Total Electron Content in the Quiet and Disturbed Period and Their Correlation with Solar Wind Parameters

Total electron content and electron density are the fundamental parameters that determine the main properties of the ionosphere. We have observed variation of Global Positioning System (GPS) derived Vertical Total Electron Content (VTEC) on three different geomagnetic events. The observed VTEC data is recorded from four GPS stations at different locations (87.26°E, 26.48°N); (85.79°E, 27.87°N); (84.57°E, 28.17°N), and (86.70°E, 27.81°N). To determine the severity of storms, we analysed the north-south component of interplanetary magnetic field (IMF-Bz), solar wind parameters- solar wind speed (Vsw) and solar wind dynamic pressure (Psw) ,and geomagnetic indices- Dst index, Kp index and Auroral Electrojet (AE). For all the studied event days, we observed intensified VTEC on geomagnetically disturbed days over quiet days. The VTEC enhancement was significantly high on the severely disturbed day, followed by the moderate storm and the minor storm.The study made to observe association of VTEC with different interplanetary and geomagnetic indices shows that during all studied event days, VTEC enhancement is positively correlated with Vsw, Psw, AE and Kp with cross-correlation coefficient above 0.8 at zero time lag and strong negative correlation with Dst index. In contrast, the correlation of IMF-Bz vary with the intensity of storm. Our finding show a significant variation in VTEC during the geomagnetic disturbances, supporting previous studies on ionospheric responses to geomagnetic storms as well as theoretical assumptions.

Enhanced neural network model for regional ionospheric modeling and evaluation under different solar-geomagnetic conditions

Monitoring spatiotemporal variations of ionospheric vertical total electron content (VTEC) is crucial for space weather and satellite positioning. In the present study, an enhanced neural network (ENN) model is proposed to capture the changing characteristics of ionospheric VTEC and compared with the traditional mathematical models, i.e. the POLYnomial (POLY) model, generalized trigonometric series function and spherical harmonic function (SHF) model. The ionospheric VTEC data obtained from 31 permanent global positioning system stations in the southwest region of China on 26 August and 8 September, 2017, were used to test the performance of the mentioned models under different Solar-geomagnetic conditions. The ENN model is derived from the ensemble learning method, and the disadvantage that simple backpropagation neural network learners that are not robust enough is weakened by the ENN model. After statistical analysis and single-frequency precise point positioning (SF-PPP) experiments, it is demonstrated that the ENN model is superior to the above three mathematical models, regardless of the solar-geomagnetic conditions. In terms of mean absolute error, root mean square error, standard deviation, and mean absolute percentage error, the ENN model outperforms the SHF model, which is the best mathematical model in the analysis, by 40.7%, 30.20%, 29.88%, 38.04% under quiet solar-geomagnetic conditions, and by 37.66%, 29.93%, 30.96%, 32.01% under active solar-geomagnetic conditions. In addition, the accuracy of the SF-PPP is greatly affected by the error caused by ionosphere. In the static SF-PPP experiment of this study, the ENN model can better correct ionospheric error. Under quiet and active solar-geomagnetic conditions, the SF-PPP accuracy can be improved by 85.1% and 85.2% with the ionosphere delay correction from the ENN model.

New Lightning‐Derived Vertical Total Electron Content Data Provide Unique Global Ionospheric Measurements

AbstractA newly released, novel ionospheric data set of global gridded vertical total electron content (VTEC) is introduced in this paper. This VTEC data set, provided by Los Alamos National Laboratory (LANL), is derived from very high frequency (VHF; defined as 30–300 MHz) broadband radio‐frequency (RF) measurements of lightning made by U.S. Department of Defense sensing systems on board Global Positioning System (GPS) satellites. This paper presents the new data set (LANL VTEC), discusses the errors inherent in VHF TEC estimation due to ionospheric dispersion, and compares the LANL VTEC to two community standard VTEC gridded products: Jet Propulsion Laboratory's Global Ionospheric Model (JPL GIM) and the CEDAR community's Open Madrigal VTEC gridded measurements of L‐band GNSS (global navigation satellite systems) TEC. We find that the LANL VTEC data have an offset of 3 TECU from CEDAR Madrigal GNSS VTEC and a full‐width‐half‐maximum (FWHM) of 6 TECU. In comparison, the offset between LANL VTEC and the JPL GIM model is −3 TECU, but with a FWHM of 5 TECU. We also compare to Jason‐3 VTEC measurements over the ocean, finding an offset of less than 0.5 TECU and an FWHM of <5 TECU. Because this technique uses a completely different methodology to determine TEC, the sources of errors are distinct from the typical ground‐based GNSS L‐band (GHz) TEC measurements. Also, because it is derived from RF lightning signals, this data set provides measurements in regions that are not well covered by ground‐based GPS measurements, such as over oceans and over central Africa.

The First Evidence for the Detection of CIDs Masked by Equatorial Plasma Bubbles From GPS‐TEC Data

AbstractThe strong influence of equatorial plasma bubbles (EPBs) appears to mask co‐seismic ionospheric disturbances (CIDs) for the earthquakes occurring in the equatorial low‐latitudes during the post‐sunset time. Based on GPS‐TEC data, we attempted to unveil the CIDs for the two great seismic events that are the 2005 M 8.6 Nias and 2017 M 8.2 Mexico earthquakes. In the case of the Nias earthquake, the CIDs have been identified in the bandpass filtered vertical total electron content data obtained from selective GPS satellite‐receiver pairs. For the Mexico earthquake, the EPB anomalies are dominant, making the appearance of CIDs hazy. However, the N‐shaped vertical total electron content anomalies masked by the plasma bubble anomalies in the bandpass filtered data are clear after applying the frequency domain singular spectrum analysis (SSA). Furthermore, the frequency domain SSA indicates the acoustic and surface waves of CIDs for both earthquakes. It also shows the signature of propagating gravity waves for the Mexico earthquake that has generated a moderate tsunami. Thus, the present study illustrates that the CIDs concealed by the non‐stationary EPB anomalies for earthquakes occurring in low latitude equatorial regions during post‐sunset become visible.

Correcting the FRA Systematic Error in VTEC Maps From SMOS Radiometric Data

The Faraday Rotation (FR) is a non-negligible effect at the L-band, which is the operation frequency of the Soil Moisture and Ocean Salinity (SMOS) mission. This effect introduces a rotation in the electromagnetic field polarization when propagating through the ionosphere that must be compensated. Recently, a methodology was developed in order to retrieve the Vertical Total Electron Content (VTEC) from SMOS radiometric data with the aim to better correct the Faraday rotation effect [1]. In that work, systematic patterns in the retrieved Faraday Rotation Angle (FRA) were detected. In this paper, these systematic patterns are characterized and corrected to improve the quality of the retrieved VTEC maps. These maps can be then re-used in the SMOS level 2 processor for the correction of the FRA in the mission. The impact of using the SMOS-derived VTEC maps instead of the VTEC data from GPS measurements on the ocean brightness temperature measurement has also been analyzed. Results of this analysis show that the usage of those maps allows a significant enhancement in the quality of the brightness temperatures, which will lead to an improvement on salinity retrievals.

Ionospheric vertical total electron content prediction model in low-latitude regions based on long short-term memory neural network

Ionosphere delay is one of the main sources of noise affecting global navigation satellite systems, operation of radio detection and ranging systems and very-long-baseline-interferometry. One of the most important and common methods to reduce this phase delay is to establish accurate nowcasting and forecasting ionospheric total electron content models. For forecasting models, compared to mid-to-high latitudes, at low latitudes, an active ionosphere leads to extreme differences between long-term prediction models and the actual state of the ionosphere. To solve the problem of low accuracy for long-term prediction models at low latitudes, this article provides a low-latitude, long-term ionospheric prediction model based on a multi-input-multi-output, long-short-term memory neural network. To verify the feasibility of the model, we first made predictions of the vertical total electron content data 24 and 48 hours in advance for each day of July 2020 and then compared both the predictions corresponding to a given day, for all days. Furthermore, in the model modification part, we selected historical data from June 2020 for the validation set, determined a large offset from the results that were predicted to be active, and used the ratio of the mean absolute error of the detected results to that of the predicted results as a correction coefficient to modify our multi-input-multi-output long short-term memory model. The average root mean square error of the 24-hour-advance predictions of our modified model was 4.4 TECU, which was lower and better than 5.1 TECU of the multi-input-multi-output, long short-term memory model and 5.9 TECU of the IRI-2016 model.

DETECTION OF IONOSPHERIC DISTURBANCES OVER MAIDANAK USING GPS DATA ON SEPTEMBER 24, 2021

In this study we investigated the effects of ionosphere on variations of Total Electron Content (TEC), and consequently deviations on regional models of Vertical TEC (VTEC), as well as variations in ionospheric GPS stations was analyzed using PRN 1 and PRN 32 codes. The estimation algorithm is applied to the computed VTEC data for Maidanak GPS station on 24 September 2021.

Prediction of Total Electron Content (TEC) using Neural Network over Anomaly Crest Region Bhopal

In the present work neural network has been developed for the prediction of Vertical Total Electron Content (VTEC) over Bhopal (23.2°N, 77.4°E & MLAT 14.2°N), an equatorial anomaly crest region. The study is based on the VTEC data recorded by a multi-frequency multi-constellation GNSS receiver installed at Bhopal during May 2016 to August 2017. Neural Network (NN) consists of artificial neurons that are used to learn data patterns adjusting weights between input, hidden and output layers by computational modelling. In order to obtain the optimal number of neurons in the hidden layer, the performance of four systems of neural network have been tested. Out of which, the fourth neural network has been used to predict VTEC with 7 input neurons in the present study. The number of hidden layer neurons is a factor that affects the performance of the trained networks. The input layer of the network includes the year, day of the year, hour of the day, geographic latitude, geographic longitude, SSN and IRI NmF2. We have compared the TEC obtained from the neural network prediction (NN TEC), GPS derived TEC (GPS TEC) and TEC from IRI-2016 model (IRI TEC) and analysis has been made. We observed that the Root Mean Square Error (RMSE) decreases with increase in the number of hidden layer neurons. The RMSE comes out to be lowest (1.96 TECU), when the number of hidden layer neurons are 41.The TEC predicted from the NN model and estimated from GPS receiver have same trend at 01:00 and 07:00 UT. The modelled NN TEC values are in good agreement with GPS TEC values for all seasons and 0.99 correlation coefficient was calculated for summer months, but IRI TEC always underestimates GPS TEC in all seasons. The NN model excellently predicted the TEC values of August 2017 by putting the credentials of August 2016 and only 0.55–32.16% relative errors were estimated between them whereas it reaches up to 93.86% with IRI TEC. Thus, the result shows that the proposed NN model can predict the diurnal and seasonal GPS TEC more accurate than IRI over anomaly crest region Bhopal. However, the inclusion of IRI-NmF2 as input layer neuron increases the network performance.

Operational Multi‐GNSS Global Ionosphere Maps at GFZ Derived From Uncombined Code and Phase Observations

AbstractGlobal navigation satellite system (GNSS) networks with multi‐frequency data can be used to monitor the activity of the Earth's ionosphere and to generate global maps of the vertical total electron content (VTEC). This article introduces and evaluates operational GFZ VTEC maps. The processing is based on a rigorous least squares approach using uncombined code and phase observations, and does not entail leveling techniques. A single‐layer model with a spherical harmonic VTEC representation is used. The solutions are generated in a daily post‐processing mode with GPS, GLONASS, and Galileo data, and are provided for the period since the beginning of 2000. A comparison of the GFZ VTEC maps with the final combined International GNSS Service (IGS) product shows a high consistency with the solutions of the IGS analysis centers. A validation with about four years of Jason‐3 altimetry‐derived VTEC data is provided, in which the GFZ solution has the smallest bias of 1.2 TEC units compared to the solutions of the IGS analysis centers, and with 3.0 TEC units one of the smallest standard deviations.

Ordered Subsets-Constrained ART Algorithm for Ionospheric Tomography by Combining VTEC Data

Computerized ionospheric tomography is an important technique for ionosphere investigation. However, it is an ill-posed problem owing to an insufficient amount of available data, because of which the distributions of ionospheric electron density (IED) cannot be reconstructed accurately. In light of this, the ordered subsets-constrained algebraic reconstruction technique (OS_CART) is developed here using vertical total electron content (VTEC) data to solve this problem, where the VTEC derived from the slant total electron content (STEC) of Global Navigation Satellite System (GNSS) signal paths is used to compensate for the lack of data provided by GNSS observations in inversion regions, and the OS_CART is also used to improve the spatial resolution and inversion efficiency. The proposed method was validated by conducting numerical experiments using GNSS and independent ionosonde data in both quiescent and disturbed ionospheric conditions. In contrast to classical methods of ionospheric tomography, the proposed method exhibited significantly higher reconstruction accuracy. While delivering a comparable accuracy to that of traditional methods in terms of self-consistency validation using STEC data and without overfitting, the proposed method yielded a more than 90% improvement over the self-consistency validation using VTEC data. In addition, a better daily description of the ionosphere was obtained using the proposed method, where an increase in the peak height and irregular changes to the IED, associated with variations in the number of epochs and the occurrence of magnetic storms, were observed. Overall, the results reveal that the proposed method is a useful tool for research on space weather.

Prediction of GPS TEC during the X9.3 Solar Flare for DGAR low latitude station by using OKSM

The effect of solar flares on the ionospheric regions of earth, which in turn affects signals of the various Global Navigation Satellite Systems, is a very important criterion to be considered in satellite communication. In this paper, we are investigating the prediction capability of the Kriging based Model and its effect on calculating the signal delay of GPS system on 6.9.2017during which NASA have observed a solar flare which have recorded X9.3 on solar storm scale. The GPS data used in this paper for prediction of TEC is taken from the DGAR Island station. The Vertical Total Electron Content data for GPS is predicted from 3rd September 2017 to 7th September 2017 by using the previous collected 6 days of TEC data of the low latitude DGAR station from IONOLAB and also by using the input parameters like Kp index, SSN, Ap index and Dst index. The predicted results are validated by comparing them with IRI 2016 and IRI PLAS 2017 model collected during the same dates.

Hemispheric Asymmetries in the Mid‐latitude Ionosphere During the September 7–8, 2017 Storm: Multi‐instrument Observations

AbstractHemispheric asymmetries of the Vertical Total Electron Content (VTEC) were observed during the first recovery phase of the geomagnetic storm on September 7–8, 2017. These asymmetries occurred at the mid latitudes at two different local times simultaneously: In the European‐African sector (early morning), the storm time VTEC in the southern/northern hemisphere was higher/lower than the quiet time value, suggesting the southern/northern hemisphere entered the positive/negative phase (N−S+). In the East Asian‐Australian sector (afternoon), the storm time VTEC change was positive in the northern hemisphere, but negative in the southern hemisphere (N+S−). The electron density profiles from digisondes demonstrated that the asymmetries appeared in the F region density as well. The plasma drifts data from digisondes, the column‐integrated [O]/[N2] ratio from GUVI onboard the TIMED satellite, and the detrended VTEC were utilized to study the drivers of the asymmetries. Traveling Ionospheric Disturbance (TID) signatures were identified in the digisonde drift and detrended VTEC data before the appearance of the asymmetry. The magnitude of TIDs was larger in the hemisphere where the negative phase occurred later. The storm time [O]/[N2] ratio change was positive in Africa (S+) and negative in Europe (N−). However, the [O]/[N2] measurements were not available in the East Asian‐Australian sector during the focused period. The hemispheric differences in the vertical drifts were also observed in both sectors. Therefore, the observed hemispheric asymmetries in both sectors are suggested to be due to the hemispheric asymmetries in the thermospheric composition change, vertical drift, and TID activity.

Variation on Solar Wind Parameters and Total Electron Content Over Middle‐ to Low‐Latitude Regions During Intense Geomagnetic Storms

AbstractStreams of the particle ejected from the Sun and the extreme space weather conditions like storms, high‐speed streamers (HSSs), interplanetary coronal mass ejections (ICMEs), corotating interaction regions (CIRs), and interplanetary shocks (IS) termed as geomagnetic storms have massive influence in the climate and components of the Earth's upper atmosphere such as total electron content (TEC). The study of TEC helps to understand variations in ionospheric electron density during geomagnetic storms. Global ionospheric maps of TEC are a real‐time mapping of GPS observations produced by ground‐based stations. In this paper, we have analyzed three intense geomagnetic storms of the year 2015: during 16–21 March 2015 (the St. Patrick's Day storm), 21–24 June 2015, and 18–22 December 2015. We present the variations of IMF‐Bz, solar wind parameters (Vsw, Nsw, and Psw), and geomagnetic indices (AE and SYM‐H) and the variations of vertical total electron content (VTEC) using simultaneous VTEC data from 12 GPS‐TEC stations over the Indian, Australian, Brazilian, and South African regions. We describe contrast in TEC throughout the globe using global ionospheric maps at a regular 2 hr interval of UT during the three intense geomagnetic storms. Moreover, we observed that heavily TEC‐influenced areas were found to be transposing through the equatorial plane starting from eastern sectors to the western sectors. The Indian Ocean, Atlantic Ocean, and South Pacific Ocean sectors were affected flowingly. Global ionospheric maps evince that Indian and Brazilian sectors were affected heavily explaining the traveling ionospheric disturbances (TID) and equatorial anomaly as seen in those areas. The equatorial and low‐latitude regions have been mainly affected by geomagnetic storms. All these results suggested that the acute disruption of global winds (surging toward the equator from higher latitudes) and electric fields commenced from magnetosphere‐ionosphere interaction causing the severe modification in the equatorial, low‐latitude region. We also checked the cross correlation of VTEC of LCK3 station and various other stations during the period of high solar and geomagnetic activities; the correlation gradually increased with the nearby stations by latitudes in most of the cases which was another intriguing result. Thus, these results suggested that the storms were affected globally, which is why we believe that variation of TEC over various stations of the globe could turn out to be very helpful in predicting solar wind coupling with the magnetosphere‐ionosphere.

Examination of seismo-ionospheric anomalies before earthquakes of Mw ≥ 5.1 for the period 2008–2015 in Oaxaca, Mexico using GPS-TEC

Earthquakes are a major danger in a constantly growing society due to their imminent impact and power of destruction. Therefore, the idea of successfully forecasting an earthquake continues to motivate the multidisciplinary study of phenomena proposed as possible earthquake precursors such as ionospheric anomalies. In that sense, total electron content (TEC) has demonstrated to be an efficient parameter for investigating the state of the ionosphere by making use of the Global Positioning System receivers. In the present study, raw vertical TEC data obtained from the standard RINEX files of the GPS constellation are used to examine the state of the ionosphere during the occurrence of light to moderate earthquakes in Mexico from years 2008 to 2015 with the aim of search for possible ionospheric anomalies related to seismic activity. In order to evaluate the impact at the geomagnetic and ionospheric environments, the Geomagnetic Equatorial Dst index, which is considered to have a great influence on TEC during geomagnetic storm period, and solar activity parameters, have been considered. The results indicated that 17 (74%) of the studied events presented not quiet geomagnetic conditions for the days before the earthquake. Thus, the changes in VTEC are most likely related to geomagnetic anomalies which rules out its possible seismic origin. Contrariwise, 3 (13%) of the events presented geomagnetic anomalies the days after the earthquake. For the remaining 3 (13%) events, these presented particular characteristics, such as: not quiet geomagnetic condition for the earthquake day, geomagnetic anomalies throughout the period and the opposite.

Nighttime enhancements in the VTEC at anomaly crest region Bhopal: Solar and magnetic activity effects

Nighttime enhancement in the Vertical Total Electron Content (VTEC) of the ionosphere near equatorial ionization anomaly crest region in the Indian ionospheric sector has been studied during May 2016 to August 2017, a solar minimum period. VTEC data is obtained from a multi-frequency multi-constellation global navigation satellite system (GNSS) receiver installed at Bhopal (23.2°N, 77.4°E & MLAT 14.2°N). Only those enhancements which show VTEC enhancement ≥ 10% compared to the background has been considered in the study. Out of the total 105 cases of enhancement recorded during this period, 86 events occurred during pre-midnight and 19 during post-midnight hours. Enhancements in VTEC occurred in all seasons; more frequent however during summer than winter and equinox months. Some of the nighttime enhancements in VTEC resulted in the scintillations in the satellite signals as well. Peak amplitude of the enhancement maximizes in summer whereas its half amplitude duration maximizes during equinox month. The number of occurrences of nighttime enhancement in VTEC shows negative correlation with peak amplitude while they are positively correlated with half amplitude duration. We have found that the peak amplitude of nighttime enhancements in VTEC shows strong positive correlation while percentage occurrence shows strong negative correlation with solar and magnetic activities respectively. Other characteristics like half amplitude duration, time of peak and occurrence time of nighttime enhancement in VTEC show positive correlation with solar activity while they are insignificant with magnetic activity. The peak amplitude of pre-midnight enhancements in VTEC is highly correlated with the strength of equatorial electrojet strength. The results are compared with the earlier observations and possible mechanism is also discussed.