Vertical Total Electron Content Research Articles

Long-term equatorial ionosphere variations over Ethiopia

This paper focuses on the investigation of the long-term behavior of the equatorial ionosphere in Ethiopia. To explore the extended ionospheric variations, we analyze Total Electron Content (TEC) fluctuations in conjunction with solar activity indices, including F10.7 radio flux, EUV radiation, and Sunspot Number (SSN). The dataset used encompasses GPS-derived TEC records spanning 11 consecutive years from 2007 to 2017, corresponding to the period of solar cycle 24. Our analysis shows that the highest TEC levels generally occur during the post-noon hours, while the lowest values are observed around post-midnight or early morning. Notably, exceptions to this pattern are found in 2011 and 2013 when the highest TEC levels are observed during the December solstice. Otherwise, the seasonal TEC variation exhibits a semi-annual pattern, with peak TEC values occurring during the equinoxes and the lowest values during the solstices. Further examination demonstrates that in both solar activity phases, TEC magnitude is higher during the March Equinox compared to the September Equinox, and slightly higher during the December Solstice compared to the June Solstice. This reveals an asymmetry in the seasonal variations, encompassing the equinoxes, solstices, and the ascending and descending phases of the solar cycle. Our study confirms that long-term fluctuations in ionospheric TEC closely correspond to variations in solar activity indices. Correlation analysis indicates a strong link between all solar activity indices and TEC. Notably, while a negative correlation is observed in 2007, the correlation in 2011 is remarkably stronger. Additionally, we find that the long-term TEC variation exhibits stronger correlations with EUV and F10.7 radio flux than with SSN, with correlation coefficients of 0.86, 0.85, and 0.78, respectively. Lastly, our study extends previous research by identifying the long-term trends of vertical TEC (vTEC) in relation to various driving factors such as F10.7, SSN, and EUV.

Tracking tsunami propagation and Island’s collapse after the Hunga Tonga Hunga Ha’apai 2022 volcanic eruption from multi-space observations

The quantity and accuracy of satellite-geodetic measurements have increased over time, revolutionizing the monitoring of tectonic processes. Global Navigation Satellite System (GNSS) and satellite radar signals provide observations beyond ground deformation, including how earthquake and tsunami processes affect variations in the ionosphere. Here, we study the Hunga Tonga Hunga Ha’apai (HTHH) volcanic eruption 2022 and its associated tsunami propagation with the analysis GNSS derived Total Electron Content (TEC), Synthetic Aperture Radar (SAR) Sentinel-1 data, complemented with tide gauge observations. We utilize GNSS sites data within a ~ 5000 km radius from the volcanic eruption for estimating the ionospheric perturbation as Vertical TEC. We give evidence on the detection of acoustic gravity, internal gravity, and atmospheric Lamb waves signatures in the TEC perturbation. In particular, the internal gravity waves that concentrated in the southwest of Tonga, directly correlates with the observed tsunami propagation direction as accounted by the tide gauge measurements. However, the acoustic gravity wave signature in the TEC is dominant in the north direction suggesting a surface deformation, which could be verified using Sentinel-1A SAR amplitude data. The analysis presented herein shows that within 5 h of the volcanic eruption, the central part of the HTHH island landscape disappeared with the biggest explosion. The unprecedented detail resolved by integrating satellite data yields previously unknown details of the deformation of the 2022 HTHH volcano eruption.

Effects of Topside Ionosphere Modeling Parameters on Differential Code Bias (DCB) Estimation Using LEO Satellite Observations

Given the potential of low-earth orbit (LEO) satellites in terms of navigation enhancement, accurately estimating the differential code bias (DCB) of GNSS satellites and LEO satellites is an important research topic. In this study, to obtain accurate DCB estimates, the effects of vertical total electron content (VTEC) modeling parameters of the topside ionosphere on DCB estimation were investigated using LEO observations for the first time. Different modeling parameters were set in the DCB estimations, encompassing modeling spacing in the dynamic temporal mode and degree and order (D&O) in spherical harmonic modeling. The DCB precisions were then evaluated, and the impacts were analyzed. Thus, a number of crucial and beneficial conclusions are drawn: (1) The maximum differences in the GPS DCB estimates after adopting different modeling spacings and different D&Os exhibit that the different modeling spacings or D&Os both affect the GPS DCB estimates and their root-mean square (RMS), and the effects of the two are at the same level. (2) The maximum differences in receiver DCBs using different modeling spacings indicate that the modeling spacing has a significant impact on the receiver DCBs, compared with GPS DCBs. Whereas, the maximum differences in receiver DCBs with different modeling D&Os are inferior to the differences in the GPS DCBs. That is, the modeling spacing has a greater impact on the LEO DCBs than those of the modeling D&O. (3) The experimental results indicate that the GPS DCB estimates using a modeling spacing of 12H have higher precisions than the others, whereas LEO receiver DCBs using a spacing of 4H or 6H obtain optimal STD. In terms of modeling D&O, adopting 8D&O in the LEO-based VTEC modeling can attain superior estimates and precisions for both GPS and LEO DCBs. The research conclusions can provide references for LEO-augmented DCB estimation.

Ionospheric response and the impact on GPS positioning accuracy during the Tonga volcano eruption on 15 January 2022

The characteristics of ionospheric disturbances and GPS positioning accuracy during the eruption of the Hunga Tonga-Hunga Ha'apai (Abbreviated as 'Tonga' in this paper) on January 15, 2022 are investigated using International GNSS Service (IGS) and Swarm data. Firstly, the comparison of ionospheric responses between on January 15, 2022 and November 5, 2021 is conducted. The result shows that the number of the rate of TEC index (ROTI) values more than 0.5 TECU/min during Tonga Volcano eruption was significantly larger under the same moderate geomagnetic storms. Moreover, the values of Detrend Vertical Total Electron Content (DVTEC) above IGS stations were increased to 0.3–1 TECU on January 15, 2022. An interesting finding is shown that there was a linear corrleation between the DVTEC increment and the distance from the stations to Tonga Volcano. On the contrary, the DVTEC values on November 5, 2021 were all less than 0.1. Secondly, GPS positioning accuracy on January 15, 2022 and November 5, 2021 is analyzed, respectively. The GPS positioning accuracy was declined on January 15, 2022. Even, the GPS positioning errors of some stations were larger than 3 m. By comparision, the GPS positioning errors of the studied IGS stations on November 5, 2021 were all less than 0.2 m. The above comparison result indicates that the Tonga Volcano eruption on January 15, 2022 could affect the GPS positioning accuracy. At the same time, the GPS cycle slips of the studied IGS stations are obviously enhanced during the Tonga Volcano eruption. Finally, the interaction mechanism between the Tonga volcano eruption, ionospheric disturbances and GPS positioning accuracy is discussed and explored.

Investigating the contribution of BDS-3 observations to multi-GNSS single-frequency precise point positioning with different ionospheric models

Due to the advantages of low-cost and high precision, single-frequency precise point positioning (SF-PPP) plays a vital role in the booming market of the SF global navigation satellite system (GNSS) receivers. With the full completion of the BeiDou global navigation satellite system (BDS-3), its contribution to single-GNSS SF-PPP is worthy of comprehensive exploration. In this study, three SF-PPP models including ionosphere-corrected (IC), ionosphere-free (IF), and ionosphere-weighted (IW) strategies are analyzed, and four ionospheric models including the GPS Klobuchar, BDGIM (BeiDou global ionospheric delay correction model), CNES real-time VTEC (vertical total electron content) and GIM (global ionospheric maps) are used for conducting SF-PPP. As to GPS/GLONASS/Galileo-only IC SF-PPP, its 3D positioning accuracy can be improved by 6.9–11.6 % with the adoption of BDS-3 observations. Similarly, for the IF SF-PPP solution, the corresponding positioning accuracy and convergence time can be improved by at least 17.3 and 38.3 %, respectively. In IW SF-PPP, the convergence performance of the CNES-VTEC-constrained SF-PPP is much better than that of both GPS Klobuchar- and BDGIM-constrained solutions. When using the BDGIM constraints, the horizontal/vertical convergence time of GPS/Galileo + BDS-3 solutions can be shortened by at least 16.5/27.9 % in comparison with GPS/Galileo-only solutions. While for GIM-constrained SF-PPP, the convergence improvement is only shown in the vertical component. In summary, the GIM-constrained SF-PPP with quad-system observations has the best performance, compared to IF solution, its convergence time can be shortened by 82.8 % to 5.5 min in horizontal and 29.7 % to 26.0 min in vertical when converging to 0.3 m. Note that the corresponding improvements in the BDGIM-constrained solution are relatively limited with only 5 %. At present, the quad-system IW SF-PPP has the ability to achieve cm-level fast positioning, with an RMS of 0.06 m in horizontal, showing promising applications in engineering and scientific fields.

GNSS/AQUA Fusion Study of Atmospheric Response Characteristics and Interaction Mechanisms during the 2022 Tonga Volcanic Eruption

A large-scale underwater volcanic eruption occurred at the volcano of Hunga Tonga-Hunga Ha’apai (HTHH) on 15 January 2022. At present, there is no consensus on the ionospheric response characteristics and interaction mechanism during volcanic eruptions. Based on the Global Navigation Satellite System (GNSS), AQUA satellite’s Atmospheric Infrared Sounder (AIRS), the experiment studies the response characteristics of the ionosphere and gravity waves during the eruption of the volcano and their interaction mechanisms and the International Real-Time Geomagnetic Observation Network (INTERMAGNET). First, a geomagnetic anomaly was detected before the eruption, which caused variations in the ionospheric VTEC (Vertical Total Electron Content) by about 15 TECU. Based on the IGS (International GNSS Service) observations, the VTEC distribution between 60° north and south latitudes was retrieved. The results show that before and after the eruption of Tonga Volcano, significant ionospheric anomalies were observed to the south, northwest and southwest of the volcano, with a maximum anomaly of 15 TECU. The study indicates that the geomagnetic anomaly disturbance is one of the precursors of volcanic eruption and has a certain degree of impact on the ionosphere. A correlation between geomagnetic anomalies and ionospheric anomalies was found to exist. The vast impact from the volcanic eruption excites gravity waves over the surface, which then propagate longitudinally, further perturbing the ionosphere. It is also detected that the ionospheric anomaly perturbation has a high coincidence effect with the gravity wave anomaly. Therefore, the gravity waves generated by atmospheric variations are used to explain the ionospheric perturbation phenomenon caused by volcanic eruptions.

A Simple Approach to Determine Single-Receiver Differential Code Bias Using Precise Point Positioning.

In this study, a precise single-receiver differential code bias (DCB) estimation method using the precise point positioning (PPP) model is presented. The first step is to extract the high-precision ionospheric observations, including DCBs, based on the PPP model. Then, the satellite DCBs are corrected using International GNSS Service (IGS) products. Lastly, the algorithm for the minimization of the standard deviation of vertical total electron content (VTECmstd) is employed to determine the value of receiver DCB. To check the method, GNSS data from more than 200 IGS stations around the globe on four days with various geomagnetic and solar activity circumstances are processed. The receiver DCBs are compared to those obtained using previous carried-to-code level (CCL) models. The experimental results show that, compared to the CCL model, the values of VTECmstd for most stations are significantly reduced, the mean number of stations with negative ionospheric measurements is reduced by 40% after correcting the receiver DCBs, and the mean error of estimated receiver DCBs is reduced by approximately 0.6 ns using the PPP model. These results suggest that this method can provide more high-precision receiver DCB estimation.

IonosphericTotal Electron Content Changes during the 15 February 2018 and 30 April 2022 Solar Eclipses over South America and Antarctica

This is one of the first papers to study the ionospheric effects of two solar eclipses that occurred in South America and Antarctica under geomagnetic activity in different seasons (summer and autumn) and their impact on the equatorial ionization anomaly (EIA). The changes in total electron content (TEC) during the 15 February 2018 and 30 April 2022 partial solar eclipses will be analyzed. The study is based on more than 390 GPS stations, Swarm-A, and DMSP F18 satellite measurements, such as TEC, electron density, and electron temperature. The ionospheric behaviors over the two-fifth days on both sides of each eclipse were used as a reference for estimating TEC changes. Regional TEC maps were created for the analysis. Background TEC levels were significantly higher during the 2022 eclipse than during the 2018 eclipse because ionospheric levels depend on solar index parameters. On the days of the 2018 and 2022 eclipses, the ionospheric enhancement was noticeable due to levels of geomagnetic activity. Although geomagnetic forcing impacted the ionosphere, both eclipses had evident depletions under the penumbra, wherein differential vertical TEC (DVTEC) reached values <−40%. The duration of the ionospheric effects persisted after 24 UT. Also, while a noticeable TEC depletion (DVTEC ∼−50%) of the southern EIA crest was observed during the 2018 eclipse (hemisphere summer), an evident TEC enhancement (DVTEC > 30%) at the same crest was seen during the eclipse of 2022 (hemisphere autumn). Swarm-A and DMSP F18 satellite measurements and analysis of other solar eclipses in the sector under quiet conditions supported the ionospheric behavior.

Uncertainty Quantification for Machine Learning‐Based Ionosphere and Space Weather Forecasting: Ensemble, Bayesian Neural Network, and Quantile Gradient Boosting

AbstractMachine learning (ML) has been increasingly applied to space weather and ionosphere problems in recent years, with the goal of improving modeling and forecasting capabilities through a data‐driven modeling approach of nonlinear relationships. However, little work has been done to quantify the uncertainty of the results, lacking an indication of how confident and reliable the results of an ML system are. In this paper, we implement and analyze several uncertainty quantification approaches for an ML‐based model to forecast Vertical Total Electron Content (VTEC) 1‐day ahead and corresponding uncertainties with 95% confidence intervals (CI): (a) Super‐Ensemble of ML‐based VTEC models (SE), (b) Gradient Tree Boosting with quantile loss function (Quantile Gradient Boosting, QGB), (c) Bayesian neural network (BNN), and (d) BNN including data uncertainty (BNN + D). Techniques that consider only model parameter uncertainties (a and c) predict narrow CI and over‐optimistic results, whereas accounting for both model parameter and data uncertainties with the BNN + D approach leads to a wider CI and the most realistic uncertainties quantification of VTEC forecast. However, the BNN + D approach suffers from a high computational burden, while the QGB approach is the most computationally efficient solution with slightly less realistic uncertainties. The QGB CI are determined to a large extent from space weather indices, as revealed by the feature analysis. They exhibit variations related to daytime/nightime, solar irradiance, geomagnetic activity, and post‐sunset low‐latitude ionosphere enhancement.

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.

Performance evaluation for vertical TEC predictions over the East Africa and South America: IRI-2016 and IRI-2020 versions

In this paper, we evaluate and report the International Reference Ionosphere (IRI) model's vertical Total Electron Content (TEC) regional profile. Diurnal, monthly, seasonal, and storm-time characteristics of IRI estimates over the equatorial region's ionosphere are validated. We compared the vertical TEC derived from IRI-2020 and its predecessor, IRI-2016, with the GPS-TEC measurements. Results show that IRI (both versions) agrees with observed TEC during solar cycle maximum periods. Exceptionally, over Turkwel station, IRI-2020 produced double peak profiles on the March equinox, June solstice, and September equinox and overestimated with larger discrepancies. Over the other three stations, both IRI versions reproduced the seasonal averaged observed TEC with only slight time lags and discrepancies. During geomagnetically perturbed times, IRI-2020 better indicated positively enhanced GPS-TEC than IRI-2016. However, during the March 17, 2015 storm, IRI-2020 underestimated the storm-enhanced TEC over Bahir Dar station at all phases of the storm. IRI-2016 shows good performances for negative storms where lower TEC measurements are recorded.

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

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

Investigating the Performance of IGS Real-Time Global Ionospheric Maps under Different Solar Conditions

In recent years, real-time global ionospheric map (RT-GIM) products have been actively developed by the international global navigation satellite system (GNSS) service (IGS) and its ionosphere associate analysis centers (IAACs) along with the increase of RT-GNSS multi-frequency and multi-constellation observations. In this study, the accuracy and consistency of three RT-GIM products from the Chinese Academy of Sciences (CAS), Wuhan University (WHU), and IGS are evaluated and analyzed utilizing three validation methods, namely, comparison with JASON-3 vertical total electron content (VTEC), the difference of slant total electron content (dSTEC), and IGS combined final GIM (IGSG) data. The test period was from 1 January 2019 to 31 December 2022, including the different solar activities. First, the comparison with JASON-3 data illustrates that the quality of the three RT-GIM products over oceans is in great consistency with that of the IGSG during different levels of solar activity and the daily mean bias (MEAN) values in low and high solar activities are approximately 5 and 10 TECU, respectively. The root mean square (RMS) values under low and high solar activities can be up to 7 and 12 TECU. Furthermore, the dSTEC validation results present that the MEAN values of RT-GIM products from different IAACs at high- and mid-latitude stations are about 0.5 TECU, which is smaller than those at low-latitude stations at about 1 TECU over continental regions. The standard deviation (STD) and RMS values for various RT-GIM products are within 3 and 4 TECU at low latitudes, respectively. In terms of the comparison with IGSG, the result shows that IGS combined RT-GIM (IRTG) presents better consistency than CAS RT-GIM (CRTG) and WHU RT-GIM (WRTG) in 2021 and 2022, with average annual STD and RMS values of 2.56 and 2.78 TECU, respectively. The daily biases of the RT-GIM products relative to IGSG can reach 4 TECU in high solar activities and the daily STD and RMS values are mainly within the 5 to 6 TECU range, respectively.

Comparative Analysis of the H2PT Ionosphere Model

The ionosphere stands in the path of signals emitted by Global Navigation Satellite System (GNSS) satellites to receivers located on the Earth’s surface. Many factors affect the accuracy of satellite positioning, but error due to ionospheric refraction is the largest among them. For this reason, it is important to minimize the impact of ionospheric refraction, and ionospheric models are one of the methods used. As the intensity of the processes taking place in the ionosphere is variable because of solar activity, the influence on satellite observations is also not constant; it varies by location and time of day and year. Therefore, models focusing on the region of interest to users are especially useful in precise GNSS applications. In this research, the H2PT model covering the region of Europe was examined at a temporal resolution of 15 min and two spatial resolutions (latitude × longitude) of 1° × 1° and 5° × 5°. This study aimed to compare the H2PT model with the solution obtained from the International GNSS Service (IGS) in the context of vertical total electron content (VTEC). The H2PT values in high-latitude regions turned out to be overestimated compared to IGS VTEC maps, while, in low-latitude regions, the situation was the opposite. Although the differences between the analyzed maps were usually a few TECUs, it was observed that, during the course of a day, they could increase to several dozen TECUs. Furthermore, the data from selected days characterized by high or low activity of the ionosphere were subjected to a detailed analysis (in relation to quiet days, as well as to the median). The data available with a 15-min interval allowed the identification of short-term disturbances appearing in the ionosphere. The analyzed model, which is of a regional nature and has a relatively high resolution, allows improvement to be made to the quality of the determined ionospheric correction in GNSS positioning.

Bi-LSTM based vertical total electron content prediction at low-latitude equatorial ionization anomaly region of South India

In this study, we consider Bi-directional Long Short Term Memory (Bi-LSTM) model based Vertical Total Electron Content (VTEC) prediction over Thanjavur (Geographic 10.72˚ N, 79.01˚ E, Geomagnetic 2.34˚ N, 152.19˚ E) Global Positioning System (GPS) station. This station is located at low latitude Equatorial Ionization Anomaly (EIA) region of 2˚ geomagnetic dip latitude and has unique ionospheric dynamics. In this region, the VTEC prediction is crucial and challenging for space weather and the Sixth Generation (6G) Internet of Space (IoS) application to support early warning systems and future spatial data transmissions. A Deep Learning (DL) model based on Bi-LSTM was developed and trained for F10.7 and Dst index for predicting the VTEC. This study highlights the prediction of VTEC for any day that includes solstice and equinox time frames. The Bi-LSTM has an improvement of 28 % in mean absolute error (MAE), 48% in mean square error (MSE) and 24% in root mean square error (RMSE) as compared to the conventional Long Short Term Memory (LSTM) network. Hence, this Bi-LSTM model can be helpful to predict the VTEC in the EIA region and may be helpful to extrapolate over the unmeasured grid region of ocean and land.

Numerical Modeling and GNSS Observations of Ionospheric Depletions Due To a Small‐Lift Launch Vehicle

AbstractSpace launches produce ionospheric disturbances which can be observed through measurements such as Global Navigation Satellite System signal delays. Here we report observations and numerical simulations of the ionospheric depletion due to a Small‐Lift Launch Vehicle. The case examined was the launch of a Rocket Lab Electron at 22:30 UTC on 22 March 2021. Despite the very small launch vehicle, ground stations in the Chatham Islands measured decreases in slant total electron content for navigation satellite signals following the launch. Global Ionosphere Thermosphere Model results indicated ionospheric depletions which were comparable with these measurements. Measurements indicated a maximum decrease of 2.7 TECU in vertical total electron content, compared with a simulated decrease of 2.6 TECU. Advection of the exhaust plume due to its initial velocity and subsequent effects of neutral winds are identified as some remaining challenges for this form of modeling.

Applying support vector machine (SVM) using GPS-TEC and Space Weather parameters to distinguish ionospheric disturbances possibly related to earthquakes

The effort to identify and comprehend potential earthquake-related phenomena shares a common goal: successful earthquake forecasting. Advancements in science and technology have made this goal multidisciplinary. Currently, possibly earthquake-related anomalies in the Vertical Total Electron Content (VTEC) of the Earth’s ionosphere are being investigated. Global Navigation Satellite Systems (GNSS) can be used to calculate this ionospheric parameter. In this research work, GPS VTEC was calculated for periods between the years 2015 and 2019. The selection of this periods considered both seismically active and non-seismically areas in Mexico. The Mw ≥ 5 earthquakes under study were registered by the National Seismological Service. Moreover, different geomagnetic storm and solar activity parameters, such as the geomagnetic equatorial Dst index and the F10.7 index, were analyzed. Additionally, the daily average and monthly mean number of sunspots (R, SSN, respectively) were included as a direct, long-term record of the development of the solar cycle. To the periods under study different statistical methods were applied, such as Mean-Square Error (MSE) and cross-correlation. The above aims to apply a machine learning technique capable of classifying between periods with seismic and non-seismic activity. The features were constructed using statistical data and results from the implemented analysis. Furthermore, Principal Component Analysis (PCA) was applied to reduce the feature vector dimensions, and accuracy scores were compute using k-fold cross-validation. The results from the Support Vector Machine (SVM) model indicated an accuracy of 88.9% for the training set, and an accuracy of 80% was obtained for the test set. One of the limitations of the current study was the sample size. However, the present initial approach for classifying seismic events from non-seismic periods using SVM demonstrated promising results when considering the indicated parameters and the days under study.

Variation of Total Electron Content over Nepal during Geomagnetic Storms: GPS Observations

Geomagnetic storms have very profound effects on the Total Electron Content (TEC) of the ionosphere. In order to investigate the equatorial and low-latitude ionospheric response to the geomagnetic storms of varying intensities, a detailed study of vertical TEC (VTEC) variations resulting from Global Positioning System (GPS) data acquired at four GPS stations in Nepal along 80°–90° E longitude and 26°–30° N latitude sector has been carried out in the present work. The results were analyzed with other favorable inducing factors (solar wind parameters and geomagnetic indices) affecting TEC to constrain the causative factor. Positive phases are observed for all the storms studied. During the severe geomagnetic activity, the deviation was ~18 TECU, while it was recorded ~12 TECU and ~8 TECU during moderate and minor geomagnetic activity, respectively. The Detrended Cross-Correlation Analysis (DXA) illustrates that the value of the hourly average VTEC of the BESI station was found to have a strong positive correlation with other stations in all types of storm events, indicating a similar response of all stations towards the space weather events. In addition, the correlation of VTEC with solar wind parameters and geomagnetic indices illustrated that the VTEC shows a strong positive association with solar wind velocity (Vsw) in all three geomagnetic events. In contrast, the correlation of plasma density (Nsw), interplanetary magnetic field (IMF-Bz), the symmetric horizontal component of geomagnetic field (SYM-H), and Geomagnetic Auroral Electrojet (AE) index with VTEC vary with the intensity of the storm. Overall results of the study have revealed the characteristic features of TEC variation over Nepal regions during magnetic storms, which validates earlier research on ionospheric responses to geomagnetic storms and theoretical assumptions.

Machine-Learning-Based Lithosphere-Atmosphere-Ionosphere Coupling Associated with Mw > 6 Earthquakes in America

The identification of atmospheric and ionospheric variations through multiple remote sensing and global navigation satellite systems (GNSSs) has contributed substantially to the development of the lithosphere-atmosphere-ionosphere coupling (LAIC) phenomenon over earthquake (EQ) epicenters. This study presents an approach for investigating the Petrolia EQ (Mw 6.2; dated 20 December 2021) and the Monte Cristo Range EQ (Mw 6.5; dated 15 May 2020) through several parameters to observe the precursory signals of various natures. These parameters include Land Surface Temperature (LST), Air Temperature (AT), Relative Humidity (RH), Air Pressure (AP), Outgoing Longwave Radiations (OLRs), and vertical Total Electron Content (TEC), and these are used to contribute to the development of LAIC in the temporal window of 30 days before and 15 days after the main shock. We observed a sharp increase in the LST in both the daytime and nighttime of the Petrolia EQ, but only an enhancement in the daytime LST for the Monte Cristo Range EQ within 3–7 days before the main shock. Similarly, a negative peak was observed in RH along with an increment in the OLR 5–7 days prior to both impending EQs. Furthermore, the Monte Cristo Range EQ also exhibited synchronized ionospheric variation with other atmospheric parameters, but no such co-located and synchronized anomalies were observed for the Petrolia EQ. We also applied machine learning (ML) methods to confirm these abrupt variations as anomalies to further aid certain efforts in the development of the LAIC in order to forecast EQs in the future. The ML methods also make prominent the variation in the different data.

Performance of short-terms prediction methods of vertical total electron content using nonlinear autoregressive neuronal network and stochastic autoregressive model

In this contribution the performance of global short-term predictions methods of vertical total electron content (vTEC) is analyzed during high solar activity. Two kind of predicted global vTEC maps value every 1 h, one-day-ahead, are used. They are C1PG, produced by the Center for Orbit Determination in Europe (CODE), based on the extrapolation of Spherical Harmonic coefficient using Least-squares collocation and the M1PG, proposed in this work, based on multi-step Nonlinear Autoregressive Neural Network (NAR-NN).Global vTEC maps from CODE (CODG) along the year 2015, are used as reference data. The results are obtained for quiet and disturbed conditions, based on geomagnetic and ionospheric planetary indexes.The performance of the forecasting approach is extensively tested under different geospatial conditions. The testing results are very similar in terms of RMSE, as it has been found to range between 1.7 and 7 TECu. RMSE depend on the latitude sectors and geomagnetic conditions, in terms of Mean Forecast Error (MFE) the C1PG shows a clear systematic behavior being negative at Southern Hemisphere and positive at Northern Hemisphere. According to the Mean Absolute Percentage Error (MAPE) values, the relative behavior of vTEC prediction is better for M1PG than for C1PG specially during quiet days and at mid-high latitudes. In general, both models are less accuracy in the equatorial ionization anomaly region and the Southern Hemisphere.Other important contribution of this manuscript is the definition of a planetary ionospheric disturbance index, Wd, based on W-index. This parameter is useful to more complete definition in quiet and disturbed days selection, and it is shown that the dependence of the RMSE according to the latitudinal bands, it is strongly related with the respective value of Wd.