Ionospheric Total Electron Content Research Articles

Disturbances of Ionospheric Total Electron Content during Great and Severe Geomagnetic Storms above Iraq and Surrounding Areas

Several studies have been made to understand the behavior of Total Electron Content (TEC) in the topside of the ionosphere during two types of geomagnetic storms. The aim of this research is to examine the ionospheric disturbances using the TEC parameter during the great and severe geomagnetic storms that occurred during the period (28-30 October 2003). Theexamination was conducted for the region,includingIraq and surrounding areas that cover the arealatitude 27-39oN; longitude 27-54oE). TEC data were obtained fromthe Defense Meteorological Satellite Program (DMSP), to determine the type of geomagnetic storm, the Disturbance storm time (Dst) index was gathered for the selected days from the website Koyota, Japan WDC.Fromthe results,it wasfound that there was a good relationship between Dst-indexand TEC parameter values. There is a discrepancy in observations recorded about TEC values during the period of geomagnetic storms.There were significant enhancements in the values of TEC during storms; however, there was an anomaly when the storm continued for several hours during the day, there was a highly broad increase in TEC from sunrise to sunset. Moreover, when two types of storms occurred, two peaks or more appeared, and they remained for one event, or even for more than one day. Comparisons between the observed TEC and the predicted values were applied in order to check the validity of the IRI-20 ionospheric model during the storm time above the mid-latitude studiedareas;it found that there is a non-linear correlation between them withi24 hours and three days, so the model wouldneed some modification in the future to be suitable for the Iraq region during storm time.

Mapping the ionosphere with millions of phones.

The ionosphere is a layer of weakly ionized plasma bathed in Earth's geomagnetic field extending about 50-1,500 kilometres above Earth1. The ionospheric total electron content varies in response to Earth's space environment, interfering with Global Satellite Navigation System (GNSS) signals, resulting in one of the largest sources of error for position, navigation and timing services2. Networks of high-quality ground-based GNSS stations provide maps of ionospheric total electron content to correct these errors, but large spatiotemporal gaps in data from these stations mean that these maps may contain errors3. Here we demonstrate that a distributed network of noisy sensors-in the form of millions of Android phones-can fill in many of these gaps and double the measurement coverage, providing an accurate picture of the ionosphere in areas of the world underserved by conventional infrastructure. Using smartphone measurements, we resolve features such as plasma bubbles over India and South America, solar-storm-enhanced density over North America and a mid-latitude ionospheric trough over Europe. We also show that the resulting ionosphere maps can improve location accuracy, which is our primary aim. This work demonstrates the potential of using a large distributed network of smartphones as a powerful scientific instrument for monitoring Earth.

Impact of Tonga volcanic eruption on the ionosphere based on ground-based GNSS and COSMIC occultation data

The eruption of large volcanoes may potentially disturb the upper atmosphere, causing disturbances in ionospheric plasma and triggering irregular changes in the Total Electron Content (TEC). The GNSS ionospheric inversion method has been used to analyze ionospheric anomalies caused by the volcano, while it was limited to the spatial distribution of ground-based GNSS. The joint observation with space-based GNSS can further improve the spatio-temporal distribution of ionospheric monitoring. The Tonga volcano erupted on January 15th, 2022, is the largest volcanic eruption since the beginning of the 21st century and generated intense acoustic, atmospheric gravity waves and ionospheric disturbance. In this paper, we used the GIM products, ground-based GNSS station data around the volcanic eruption point and COSMIC occultation data to analyze the short-term TEC change. The experiments showed that: the average TEC of GIM grid data on eruption day dropped by 7.5 TECu compared to the last day which was substantially larger than other three days. The TEC extracted from nearby tracking stations was smaller than GIM and the difference was inversely proportional to the distance between the station and the volcanic eruption site. The average daily ionospheric TEC obtained from the nearest TONG station was the biggest, as much as 3.44 TECu lower. The peak electron density of COSMIC occultation data gradually diminished and then recovered with TEC about 7.32 TECu lower than GIM products. The experments showed that there was a good consistency in the TEC obtained from space-based and ground-based GNSS, the Tonga volcano produced a huge TEC change over 10 TECu or even bigger. As a global grid product, GIM underestimated ionospheric changes due to its limited spatial resolution, which needs further improvement.

Imaging the May 2024 Extreme Aurora With Ionospheric Total Electron Content

AbstractThe continental United States is well instrumented with facilities for mid‐latitude upper atmosphere research that operate on a continuous basis. In addition, citizen scientists provide a wealth of information when unusual events occur. We combine ionospheric total electron content (TEC) data from distributed arrays of GNSS receivers, magnetometer chains, and auroral observations obtained by citizen scientists, to provide a detailed view of the intense auroral breakup and westward surge occurring at the peak of the 10–11 May 2024 extreme geomagnetic storm. Over a 20‐min interval, vertical TEC (vTEC) increased at unusually low latitude (∼45°) and rapidly expanded azimuthally across the continent. Individual receiver/satellite data sets indicate sharp bursts of greatly elevated of vTEC (∼50 TECu). Intense red aurora was co‐located with the leading edge of the equatorward and westward TEC enhancements, indicating that the large TEC enhancement was created by extremely intense low‐energy precipitation during the rapid substorm breakup.

Ionospheric TEC and its irregularities over Egypt: a comprehensive study of spatial and temporal variations using GOCE satellite data

Abstract This study delves into ionospheric characteristics during solar cycle 24 using data from the Global Positioning System (GPS) and Low Earth Orbit (LEO) satellites, GOCE. The present study fills the gap of not assessing and studying GOCE satellite data before in Egypt. Focusing on spatial and temporal variations of ionospheric Total Electron Content (TEC) over Egypt and the Middle East across a 4-year dataset, monthly average Vertical Total Electron Content (VTEC) variations are scrutinized, emphasizing extremes during heightened and reduced solar and geomagnetic activities. Results TEC typically decreases with latitude’s increase, with peak ionization during equinoxes and troughs during solstices. Notably, VTEC values consistently reach maximum values on days of heightened solar and geomagnetic activities. GOCE data is evaluated against International Reference Ionosphere (IRI) model and NeQuick2 model by selecting 10 days from all data by using a statistical comparison via t-tests. There is not significant difference between them except for two days between GOCE-IRI. The values of Root Mean Square Error (RMSE) are 3.7403 and 4.4655 for GOCE – NeQuick2 model and GOCE – IRI model, respectively. Ionospheric scintillation, signifying rapid electron density fluctuations, is assessed through amplitude scintillation index (S4), S4 proxy, and Rate Of TEC Index (ROTI). A robust correlation between S4 and S4 proxy is noted, thus scintillation can be studied by using S4 proxy instead of S4. Temporal variations indicate heightened scintillation during geomagnetic storms and peak solar activity, contrasting with reduced activity during solar minimum. Throughout the study period the maximum scintillation index values for ROTI, S4, and S4 proxy are 0.3, 0.15, and 0.05, indicating minimal scintillation. This comprehensive analysis, rooted in GOCE data, illuminates spatial and temporal dynamics of ionospheric TEC and elucidates ionospheric scintillation characteristics in the Egypt region. These findings are crucial for refining ionospheric models, improving scintillation prediction, and enhancing satellite communication and navigation systems, especially in the study region.

Galileo and BeiDou AltBOC Signals and Their Perspectives for Ionospheric TEC Studies.

For decades, GNSS code measurements were much noisier than phase ones, limiting their applicability to ionospheric total electron content (TEC) studies. Ultra-wideband AltBOC signals changed the situation. This study revisits the Galileo E5 and BeiDou B2 AltBOC signals and their potential applications in TEC estimation. We found that TEC noises are comparable for the single-frequency AltBOC phase-code combination and those of the dual-frequency legacy BPSK/QPSK phase combination, while single-frequency BPSK/QPSK TEC noises are much higher. A two-week high-rate measurement campaign at the ACRG receiver revealed a mean 100 sec TEC RMS (used as the noise proxy) of 0.26 TECU, 0.15 TECU, and 0.09 TECU for the BeiDou B2(a+b) AltBOC signal and satellite elevations 0-30°, 30-60°, and 60-90°, correspondingly, and 0.22 TECU, 0.14 TECU, and 0.09 TECU for the legacy B1/B3 dual-frequency phase combination. The Galileo E5(a+b) AltBOC signal corresponding values were 0.25 TECU, 0.14 TECU, and 0.09 TECU; for the legacy signals' phase combination, the values were 0.19 TECU, 0.13 TECU, and 0.08 TECU. The AltBOC (for both BeiDou and Galileo) SNR exceeds those of BPSK/QPSK by 7.5 dB-Hz in undisturbed conditions. Radio frequency interference (the 28 August 2022 and 9 May 2024 Solar Radio Burst events in our study) decreased the AltBOC SNR 5 dB-Hz more against QPSK SNR, but, due to the higher initial SNR, the threshold for the loss of the lock was never broken. Today, we have enough BeiDou and Galileo satellites that transmit AltBOC signals for a reliable single-frequency vTEC estimation. This study provides new insights and evidence for using Galileo and BeiDou AltBOC signals in high-precision ionospheric monitoring.

Ionospheric Total Electron Content Response to the Intense Geomagnetic Storm of 10th -11th May 2024 over Low, Mid and High Latitude Regions

In this paper, we investigated the response of ionospheric Total electron content (TEC) to the intense geomagnetic storm of 10th - 11th May 2024 using 6 Global Navigation Satellite System (GNSS) stations: BAKE (Geog. Lat.64.33o N; Geog. Lon. 96.02o W), BELE (Geog. Lat. 1.41o S; Geog. Lon. 48.46o W), MBAR (Geog. Lat. 0.60o S; Geog. Lon. 30.74o E), SUTH (Geog. Lat. 32.38o S; Geog. Lon. 20.81o E), BJFS (Geog. Lat. 39.61o N; Geo. Lon. 115.89o E) and DUND (Geog. Lat. 45.88o S; Geog. Lon.170.60o E) situated over low, mid and high latitude regions. The GPS-TEC data was extracted, processed and used to plot vertical total electron content (VTEC) verses universal time (UT) from 8th to 13th May 2024 for each GNSS receiver station. A contour plot of TEC variation was also plotted for each station from 8th to 13th May 2024. The results showed TEC enhancing significantly at the beginning of the storm, during daytime at the geomagnetic equator, with the exception of MBAR, where TEC increased at night. This was attributed to the effect of prompt penetration of electric field (PPEF). A reduction in TEC was also noted on 11th May 2024, during the recovery phase over all the GNSS receiver stations. This was attributed to the effect of the disturbance dynamo electric field (DDEF) and composition change. The contour plots showed diurnal variation in TEC concentration over each GNSS receiver station. The TEC concentration however reduced during the storm period.

Secondary Gravity Wave Propagation in Tropical Thermospheric Region: Role of Varying Kinematic Viscosity

AbstractThe current study has investigated the thunderstorm induced atmospheric gravity waves (AGWs) over Indian region based on the perturbation signatures in ionospheric total electron content (TEC) measurement. Robust traveling ionospheric disturbance (TID) signature has been identified along the east side of the thunderstorm affected area. Neutral wind was found to have a favorable impact in this aspect for a certain time duration of the day by modulating the vertical wavelength. The role of temperature was analyzed in terms of kinematic viscosity which is a crucial component, especially over tropical region, for wave dissipation and reflection along its propagation path. Ray tracing algorithm is also applied with varying kinematic viscosity and thermal diffusivity for retrieval of possible ray paths and source location of observed waves. A statistical investigation has been carried out to identify the dissipation altitude of observed waves along the ray paths. It has been found that all waves dissipated at almost a constant altitude for a specific kinematic viscosity and above this altitude vertical wavelength was found to decrease. The ray paths interacted at a common point which was located at about 125 km altitude and was very close to the region of maximum lightning activity. It can also be noted that the observed phase velocities can't be achieved by a wave below the turbopause. It indicates that the observed waves were excited from a secondary source and not directly connected to convective system. The study provides an in‐depth analysis of mesoscale system induced gravity wave propagation and dissipation over tropical region.

Data-driven Simulation of Effects of a Solar Flare with Extreme-ultraviolet Late Phase on Ionospheric Electron Density

Effects of the extreme-ultraviolet (EUV) late phase of solar flares on the ionosphere were rarely studied. Here we simulated such effects on the ionospheric electron density using an ionosphere−thermosphere coupled model driven by the realistic solar spectrum observed during the X1.8 flare on 2012 October 23. Global total electron content (TEC) observations and simulations showed that the dayside ionospheric TEC during the EUV late phase increased more than that of the flare’s main phase. We examined the performance of the model for flares with EUV late phase. The results showed that the F-region electron density enhancement and recovery did not vary in the same pace as the temporal variations of the EUV late phase, and the presence of the EUV late phase prolonged the recovery of electron density by ∼9 hr. We also found that the enhancement in electron density was mainly determined by the chemical production, while the recovery of electron density was primarily controlled by the electric field transport effects. This study enhanced understanding of the intricate physical and photochemical processes governing Earth’s space environment and similar planetary systems during solar flare events with EUV late phase.

The April 2023 SYM‐H = −233 nT Geomagnetic Storm: A Classical Event

AbstractThe 23–24 April 2023 double‐peak (SYM‐H intensities of −179 and −233 nT) intense geomagnetic storm was caused by interplanetary magnetic field southward component Bs associated with an interplanetary fast‐forward shock‐preceded sheath (Bs of 25 nT), followed by a magnetic cloud (MC) (Bs of 33 nT), respectively. These interplanetary structures were led by a coronal mass ejection erupted from the Sun in association with an M1.7 X‐ray flare. At the center of the MC, the plasma density exhibited an order of magnitude decrease, leading to a sub‐Alfvénic solar wind interval for ∼2.1 hr. Ionospheric Joule heating accounted for a significant part (∼81%) of the magnetospheric energy dissipation during the storm main phase. Equal amount of Joule heating in the dayside and nightside ionosphere is consistent with the observed intense and global‐scale DP2 (disturbance polar) currents during the storm main phase. The sub‐Alfvénic solar wind is associated with disappearance of substorms, a sharp decrease in Joule heating dissipation, and reduction in electromagnetic ion cyclotron wave amplitude. The shock/sheath compression of the magnetosphere led to relativistic electron flux losses in the outer radiation belt between L* = 3.5 and 5.5. Relativistic electron flux enhancements were detected in the lower L* ≤ 3.5 region during the storm main and recovery phases. Equatorial ionospheric plasma anomaly structures are found to be modulated by the prompt penetration electric fields. Around the anomaly crests, plasma density at ∼470 km altitude and altitude‐integrated ionospheric total electron content are found to increase by ∼60% and ∼80%, with ∼33% and ∼67% increases in their latitudinal extents compared to their quiet‐time values, respectively.

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.

Impact of the October 28, 2021 Solar Flare and the November 4, 2021 Geomagnetic Storm on the Low, Middle, and High-Latitude Ionosphere

This study examines the ionospheric Total Electron Content (TEC) responses to a solar flare on October 28, 2021, and a geomagnetic storm on November 4, 2021, across low, middle, and high latitude regions. We utilized GPS-TEC data from the University NAVSTAR Consortium’s dual-frequency GPS devices at the IFR1, IISC, YIBL, YKRO, KERG, and SVTL stations. While the solar flare on October 28, 2021, triggered the geomagnetic storm on November 4, 2021, our analysis revealed notable TEC changes during the latter event. TEC fluctuations were observed across all stations during the geomagnetic storm, with significant disruptions and variable depletion rates. However, distinct TEC variations were noted at KERG and YIBL stations before the storm, likely due to the preceding solar flare. Continuous wavelet analysis (CWT) showed higher periodicity during the storm compared to the flare, proving CWT to be an effective tool for analyzing TEC variability by revealing periodicity fluctuations at all stations. In conclusion, we found that both solar flares and geomagnetic storms can cause significant positive TEC changes.

First Detections of Ionospheric Plasma Density Irregularities from GOES Geostationary GPS Observations during Geomagnetic Storms

In this study, we present the first results of detecting ionospheric irregularities using non-typical GPS observations recorded onboard the Geostationary Operational Environmental Satellites (GOES) mission operating at ~35,800 km altitude. Sitting above the GPS constellation, GOES can track GPS signals only from GPS transmitters on the opposite side of the Earth in a rather unique geometry. Although GPS receivers onboard GOES are primarily designed for navigation and were not configured for ionospheric soundings, these GPS measurements along links that traverse the Earth’s ionosphere can be used to retrieve information about ionospheric electron density. Using the radio occultation (RO) technique applied to GPS measurements from the GOES–16, we analyzed variations in the ionospheric total electron content (TEC) on the links between the GPS transmitter and geostationary GOES GPS receiver. For case-studies of major geomagnetic storms that occurred in September 2017 and August 2018, we detected and analyzed the signatures of storm-induced ionospheric irregularities in novel and promising geostationary GOES GPS observations. We demonstrated that the presence of ionospheric irregularities near the GOES GPS RO sounding field of view during geomagnetic disturbances was confirmed by ground-based GNSS observations. The use of RO observations from geostationary orbit provides new opportunities for monitoring ionospheric irregularities and ionospheric density.

Prediction of ionospheric TEC during the occurrence of earthquakes in Indonesia using ARMA and CoK models

Predicting ionospheric Total Electron Content (TEC) variations associated with seismic activity is crucial for mitigating potential disruptions in communication networks, particularly during earthquakes. This research investigates applying two modelling techniques, Autoregressive Moving Average (ARMA) and Cokriging (CoK) based models to forecast ionospheric TEC changes linked to seismic events in Indonesia. The study focuses on two significant earthquakes: the December 2004 Sumatra earthquake and the August 2012 Sulawesi earthquake. GPS TEC data from a BAKO station near Indonesia and solar and geomagnetic data were utilized to assess the causes of TEC variations. The December 2004 Sumatra earthquake, registering a magnitude of 9.1–9.3, exhibited notable TEC variations 5 days before the event. Analysis revealed that the TEC variations were weakly linked to solar and geomagnetic activities. Both ARMA and CoK models were employed to predict TEC variations during the Earthquakes. The ARMA model demonstrated a maximum TEC prediction of 50.92 TECU and a Root Mean Square Error (RMSE) value of 6.15, while the CoK model predicted a maximum TEC of 50.68 TECU with an RMSE value of 6.14. The August 2012 Sulawesi earthquake having a magnitude of 6.6, revealed TEC anomalies 6 days before the event. For both the Sumatra and Sulawesi earthquakes, the GPS TEC variations showed weak associations with solar and geomagnetic activities but stronger correlations with the earthquake-induced electric field for the considered two stations. The ARMA model predicted a maximum TEC of 54.43 TECU with an RMSE of 3.05, while the CoK model predicted a maximum TEC of 52.90 TECU with an RMSE of 7.35. Evaluation metrics including RMSE, Mean Absolute Deviation (MAD), Relative Error, and Normalized RMSE (NRMSE) were employed to assess the accuracy and reliability of the prediction models. The results indicated that while both models captured the general trend in TEC variations, nuances emerged in their responses to seismic events. The ARMA model demonstrated heightened sensitivity to seismic disturbances, particularly evident on the day of the earthquake, whereas the CoK model exhibited more consistent performance across pre- and post-earthquake periods.

Ionospheric TEC Prediction in China during Storm Periods Based on Deep Learning: Mixed CNN-BiLSTM Method

Applying deep learning to high-precision ionospheric parameter prediction is a significant and growing field within the realm of space weather research. This paper proposes an improved model, Mixed Convolutional Neural Network (CNN)—Bidirectional Long Short-Term Memory (BiLSTM), for predicting the Total Electron Content (TEC) in China. This model was trained using the longest available Global Ionospheric Maps (GIM)-TEC from 1998 to 2023 in China, and underwent an interpretability analysis and accuracy evaluation. The results indicate that historical TEC maps play the most critical role, followed by Kp, ap, AE, F10.7, and time factor. The contributions of Dst and Disturbance Index (DI) to improving accuracy are relatively small but still essential. In long-term predictions, the contributions of the geomagnetic index, solar activity index, and time factor are higher. In addition, the model performs well in short-term predictions, accurately capturing the occurrence, evolution, and classification of ionospheric storms. However, as the predicted length increases, the accuracy gradually decreases, and some erroneous predictions may occur. The northeast region exhibits lower accuracy but a higher F1 score, which may be attributed to the frequency of ionospheric storm occurrences in different locations. Overall, the model effectively predicts the trends and evolution processes of ionospheric storms.

Supersonic Waves Generated by the 18 November 2023 Starship Flight and Explosions: Unexpected Northward Propagation and a Man‐Made Non‐chemical Depletion

AbstractOn 18 November 2023, SpaceX launched the Starship, the tallest and the most powerful rocket ever built. The Super Heavy engine separated from the Starship spacecraft and exploded at 90 km of altitude, while the main core Starship continued to rise up to 149 km and exploded after ∼8 min of flight. In this work, we used data from ground‐based GNSS receivers and we analyzed total electron content (TEC) response to the Starship flight and the two explosions. For the first time, we observed large‐distance northward propagation of intensive 2,000 km V‐shaped ionospheric disturbances from the rocket trajectory. The observed perturbations, most likely, represent shock waves propagating with the cone angle of ∼14° on the North and ∼7° on the South against the flight track that corresponds to the Mach angle of the shock waves in the lower atmosphere. The Starship explosion also produced a non‐chemical depletion in the ionospheric TEC.

Assessment of Satellite Differential Code Biases and Regional Ionospheric Modeling Using Carrier-Smoothed Code of BDS GEO and IGSO Satellites

The geostationary earth orbit (GEO) represents a distinctive geosynchronous orbit situated in the Earth’s equatorial plane, providing an excellent platform for long-term monitoring of ionospheric total electron content (TEC) at a quasi-invariant ionospheric pierce point (IPP). With GEO satellites having limited dual-frequency coverage, the inclined geosynchronous orbit (IGSO) emerges as a valuable resource for ionospheric modeling across a broad range of latitudes. This article evaluates satellite differential code biases (DCB) of BDS high-orbit satellites (GEO and IGSO) and assesses regional ionospheric modeling utilizing data from international GNSS services through a refined polynomial method. Results from a 48-day observation period show a stability of approximately 2.0 ns in BDS satellite DCBs across various frequency signals, correlating with the available GNSS stations and satellites. A comparative analysis between GEO and IGSO satellites in BDS2 and BDS3 reveals no significant systematic bias in satellite DCB estimations. Furthermore, high-orbit BDS satellites exhibit considerable potential for promptly detecting high-resolution fluctuations in vertical TECs compared to conventional geomagnetic activity indicators like Kp or Dst. This research also offers valuable insights into ionospheric responses over mid-latitude regions during the March 2024 geomagnetic storm, utilizing TEC estimates derived from BDS GEO and IGSO satellites.

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.

Ionospheric and meteorological response to total solar eclipses

Studies concerning solar eclipses have been rising significantly, yet, different circumstances during their occurrence provide uniqueness to every study. This paper studies the ionospheric and meteorological response to the total solar eclipses of August 21, 2017, March 20, 2015 and August 1, 2008. The ionospheric total electron content (TEC) was calculated from the signals beamed by the dual-frequency Global Positioning System (GPS) satellites and accessed from University NAVSTAR Consortium (UNAVCO) data archive. Similarly, the data of meteorological parameters were accessed from the historical climate archive of respective countries. The TEC drop of ∼2–7 TECU with a lag of ∼15–30 min is observed at varying latitudes which correspond with the findings of numerous past research. We analyzed the data of 15 stations under ∼100% obscuration to rule out the varying effects of different obscuration rates. Yet, given the turbulent nature of climate, we found varying changes at observed locations. A good relationship, however, was observed in 8 of the stations, where temperature drop ranged from 0.4 °C to 6.11 °C, and rise in relative humidity ranged from 0 to ∼77%. Wind speed has shown the most turbulent behavior. Their change was largely impacted by the eclipse on 5 of the stations, while the local factor was dominant on the others. In spite of this, the stations under observation showed distinct responses to the ionospheric change during the total solar eclipses, thus demonstrating the relation of meteorological parameters with eclipses.

Prediction of global ionospheric TEC using attention based bidirectional long short-term memory and gated recurrent unit

An accurate prediction of ionospheric total electron content (TEC) at the primary stage is essential for applications related to global navigation satellite systems (GNSS) under varying weather conditions. The previous TEC prediction schemes contribute for each time step that increases the prediction time. The eye contact phenomenon establishes a metaphorical connection which intends to capture and emphasize the attention worthy elements in a sequence. This research introduces a deep learning approach which is a combination of attention-based bidirectional long short-term memory and gated recurrent unit (Bi-LSTM GRU) to predict TEC in the ionosphere. Bidirectional LSTM is the better option for achieving durability when combined with a gated recurrent unit (GRU) to predict TEC in the ionosphere. The proposed approach is evaluated with the existing LSTM approach for root mean square error (RMSE) during training and validation. The RMSE while predicting the global ionospheric delay using the existing LSTM for 20 epochs is seen to be 0.004, whereas the existing approach achieves a training error of 0.003.