Total Electron Content Changes Research Articles

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.

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.

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.

Influence of the October 14, 2023, Ring of Fire Annular Eclipse on the Ionosphere: A Comparison Between GNSS Observations and SAMI3 Model Prediction

AbstractCelestial phenomena such as solar eclipses disrupt the ionosphere's inherent photochemical, dynamic, and electrodynamic processes and can be viewed as a natural experiment that provides a unique opportunity to study ionospheric perturbations. We investigate the spatiotemporal ionospheric response induced by the 14 October 2023 annular eclipse using ground‐based Global Navigation Satellite System (GNSS) receivers located over the continental region of North and South America. The largest total electron content (TEC) change (∼22 TECU decrease) is observed over the path of the eclipse at around 16°–18°N which lies just before the greatest eclipse location. However, the percentage change in TEC here is ∼44% (of the background value), which is less than that observed at midlatitudes around 30°–35°N (∼18.7 TECU, ∼50%). We observed a latitudinal dependency of TEC variation and time delay in ionospheric response to the eclipse with midlatitudes experiencing greater TEC changes and longer time delays compared to low‐latitude and equatorial regions. The SAMI3 model used to simulate the impact of the eclipse, captures the large decrease in VTEC along the eclipse path. Interestingly, increases in the TEC are also observed in several GNSS sites and it varied from 4.5 to 6.5 TECU in the Northern Hemisphere and 7 to 12 TECU in the Southern Hemisphere. The SAMI3 model results also show an enhancement in TEC but significantly less than that observed. Moreover, the SAMI3 model simulations for individual GNSS site coordinates within the conjugate regions failed to predict accurately, the TEC enhancement recorded by the GNSS sites.

The Influence of Different Phases of a Solar Flare on Changes in the Total Electron Content in the Earth’s Ionosphere

Abstract Variations in X-ray and extreme ultraviolet (EUV) irradiance during solar flares lead to a noticeable increase in the electron concentration in the illuminated part of the Earth’s ionosphere. Due to the large amount of experimental data accumulated by global navigation satellite systems, the total electron content (TEC) response to the impulsive phase of a solar flare has been studied quite well. However, recent studies have shown that a large fraction of X-class flares have a second strong peak of warm coronal emission (which is called “EUV late phase”), whose influence on the ionization of ionospheric layers is not yet clear. A combined analysis of successive solar emissions and the caused TEC changes made it possible to numerically estimate the ionospheric response to the impulsive, gradual, and late phases of the X2.9 solar flare that occurred on 2011 November 3 and demonstrate the high geoeffectiveness of the rather weak Fe xv 28.4 nm solar emission during the EUV late phase. It was found that the ionospheric response to the relatively weak emissions of the EUV late phase of the X2.9 solar flare amounted to almost a third of the TEC increase during the impulsive phase.

The effects of the May 2024 Mother’s Day superstorm over the Mediterranean sector: from data to public communication

On 8 May 2024, the solar active region AR13664 started releasing a series of intense solar flares. Those of class X released between 9 and 11 May 2024 gave rise to a chain of fast Coronal Mass Ejections (CMEs) that proved to be geoeffective. The Storm Sudden Commencement (SSC) of the resulting geomagnetic storm was registered on 10 May 2024 and it is, to date, the strongest event since November 2003. The May 2024 storm, named hereafter Mother’s Day storm, peaked with a Dst of –412 nT and stands out as a “standard candle” storm affecting modern era technologies prone to Space Weather threats. Moreover, the recovery phase exhibited almost no substorm signatures, making the Mother’s Day storm as a perfect storm example. Despite the plethora of notable near Earth environment modifications that are still under investigation, in this paper we concentrate on the Space Weather effects over the Mediterranean sector, with a focus on Italy. In fact, the Istituto Nazionale di Geofisica e Vulcanologia (INGV) manages a dense network of GNSS receivers (including scintillation receivers), ionosondes and magnetometers in the Mediterranean area, which facilitated for a detailed characterization of the modifications induced by the storm. Concerning the geomagnetic field, observatories located in Italy recorded a SSC with a rise time of only 3 minutes and a maximum variation of around 600 nT. The most notable ionospheric effect following the arrival of the disturbance was a significant decrease in plasma density on 11 May, resulting in a pronounced negative ionospheric storm registered on both the critical F2-layer frequency (foF2) and the Total Electron Content (TEC). Another negative effect was recorded on 13 May, while no signatures of composition changes and, specifically, to a decrease of the [O]/[N ] ratio. The IRI UP IONORING 2 data-assimilation procedure, recently developed to nowcast foF2 over Italy, proved to be quite reliable during this extreme event, being characterised just by an overestimation during the main phase of the storm, when the electron density and the height of the F region decreased and increased, respectively. Relevant outcomes of the work relate to the Rate Of TEC change Index (ROTI), which shows unusually high spatially distributed values on the nights of 10 and 11 May. The ROTI enhancements on 10 May might be linked to Stable Auroral Red (SAR) arcs and an equatorward displacement of the main ionospheric trough. Instead, the ROTI enhancements on 11 May might be triggered by a joint action of low-latitude plasma pushed poleward by the pre-reversal enhancement (PRE) in the post-sunset hours and wave-like perturbations propagating from the North. Furthermore, the storm generated immediate attention of the general public to Space Weather effects, including mid-latitude visible phenomena like SAR arcs. This paper outlines the report of the Space Weather Monitoring Group (SWMG) of the INGV Environment Department and its effort to disseminate information about this exceptional event.

The Effect of Energy Deposition Hemispherical Asymmetry on Characteristics of LSTIDs During 17 March 2015 Geomagnetic Storm

AbstractThis article presents the effect of energy deposition hemispherical asymmetry on characteristics of large scale traveling ionospheric disturbances (LSTIDs) such as the amplitude and velocity during the geomagnetic storm of 17–18 March 2015. LSTIDs are quantified using total electron content (TEC) derived from Global Navigation Satellite System observations considering a longitude range of 50° over the America, Europe‐Africa, and Asia‐Australia sectors at geomagnetic Conjugate Regions (CR). We applied the cross‐correlation method between time series of change in TEC (ΔTEC) to estimate meridional velocities of LSTIDs in all sectors of both hemispheres. Our results revealed that amplitude of LSTIDs are larger most of the time in the northern hemisphere than the geomagnetic CR in the southern hemisphere which may partly be accounted for by hemispherical energy imbalance during storm time. In addition, on average higher meridional LSTIDs velocities are observed in northern hemisphere in all sectors during 17 March 2015 geomagnetic storm.

Variation in Total Electron Content Over Ethiopia During the Solar Eclipse Events

AbstractThis work studies variations of ionospheric total electron content (TEC) during four distinct solar eclipse events over the Ethiopia region. Dual‐frequency global positioning system (GPS) data obtained from UNAVCO over Addis Ababa (9.036°N, 38.76°E) and Bahir Dar (11.6°N, 37.34°E) stations are used to examine the ionospheric variability during two annular solar eclipses on 15 January 2010 and 1 September 2016, a partial solar eclipse on 4 January 2011, and a hybrid solar eclipse (the eclipse path starts out as annular but later changes to total) on 3 November 2013. The results show a significant decrease in TEC values during the occurrence of the solar eclipses. Specifically, the TEC values are reduced to −20% and −10% during the annular eclipse on 15 January 2010, −33% and −38% during the partial solar eclipse on 4 January 2011, −26% and −24% during the annular solar eclipse on 1 September 2016, over the Addis Ababa and Bahir Dar stations, respectively. There is only minimal change in TEC of −8% and −9% at Addis Ababa and Bahir stations, respectively, during the 3 November 2013 solar eclipse even if the obstruction rate is high over the study area. Furthermore, the study shows that the spatial gradient of TEC reduction varies at different locations, which is attributed to the distinct amount of reduction in solar radiation reaching the Earth's surface, resulting in reduced photo‐ionization. Overall, this study provides insightful information about the behavior of the ionospheric TEC during solar eclipses over Ethiopia and emphasizes the use of dual‐frequency GPS data in tracking the variations of the TEC.

Investigation into potential TEC changes due to 9 seismic tremors of 2021–2022

This research document delves into the analysis of alterations in Total Electron Content (TEC) as identified through GPS observations leading up to nine earthquakes that transpired between the late months of 2021 and 2022. The comprehensive study investigates the TEC variations and provides a detailed examination of heat maps centred around the earthquake epicentres. These heat maps, characterized by diverse latitudes and longitudes, play a pivotal role in validating the precise locations of the earthquake epicentres. Notably, the examination of these maps unveils discernible GPS TEC variations several days before the occurrence of each earthquake. In addition to the TEC analysis, the research sheds light on the fluctuations in solar magnetic parameters. The study elucidates instances where TEC peaks surpassed normal values, particularly during periods characterized by minimal solar radiation effects. This correlation between solar magnetic parameters and TEC fluctuations adds a nuanced layer to the understanding of the complex interplay of factors leading to seismic events. The cumulative findings derived from the investigation point towards a compelling conclusion: GPS TEC observations and the analysis of heat maps serve as indispensable indicators for identifying precursory signs that precede an impending earthquake. This multidimensional approach enhances our comprehension of the temporal and spatial aspects of seismic activity and underscores the potential significance of solar magnetic parameters in influencing such geophysical events.

Forecasting Ionospheric TEC Changes Associated with the December 2019 and June 2020 Solar Eclipses: A Comparative Analysis of OKSM, FFNN, and DeepAR Models

This paper presents forecast and investigation of the variation in ionospheric Total Electron Content (TEC) during the solar eclipses (SEs) of December 2019 and June 2020 using three different methods: Deep Autoregressive model (DeepAR), Feed-Forward Neural Network (FFNN), and Ordinary Kriging-based Surrogate Model (OKSM), and the TEC data predicted by DeepAR, FFNN, and OKSM were compared with the actual TEC during the observation days. The study was conducted based on GPS data taken from the IISC receiver located in Bangalore, India, during the SEs which happened on 26.12.2019 and 21.06.2020. The TEC data were examined to assess the effect of solar eclipses on TEC values. Eighty-day prior TEC data for the IISC station are gathered from IONOLAB servers along with the other parameter data like Dst, Ap, F10.7, and Kp taken from OMNIWEB servers which were used to predict TEC. The reliability of the forecasted results is evaluated using numerical factors like Normalized Root Mean Square Error (NRMSE), Correlation Coefficient (CC), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and R-squared. The study demonstrates the usefulness of combining multiple methods for analyzing TEC variations during SEs and highlights the potential of OKSM, FFNN, and DeepAR models for studying TEC variation in the same context. The findings may be useful for satellite broadcasting and navigational services and for further research into the influence of solar eclipses on the TEC changes.

Latitude Variation of the Post‐Sunset Plasma Density Enhancement During the Minor Geomagnetic Storm on 27 May 2021

AbstractIn this study, multiple instrumental observations including Global Navigation Satellite System total electron content (TEC), plasma drift velocity measured by Sanya (18.3°N, 109.6°E, dip latitude 12.6°N) Incoherent Scatter Radar (SYISR) and F2‐layer peak electron density (NmF2) and peak height (hmF2) from ionosonde and SYISR have been used to investigate ionospheric responses during a minor yet highly geo‐effective geomagnetic storm on 26–27 May 2021. Our findings revealed a significant time delay in the post‐sunset plasma density enhancement peak across different latitudes over East Asia, that is, the lower the geographic latitude, the earlier the peak appeared. The plasma density enhancement was accompanied by a decrease in hmF2 prior to NmF2 peak around sunset. The newly built SYISR measurements around sunset verified that the field‐aligned drift decreased the ionosphere with a notable time delay at latitude, beneficial to electron density enhancements at lower altitudes within the 16–30°N latitudinal band but a small TEC change. While at 30–50°N, it is possible that the competition between storm‐induced equatorward winds and downward field‐aligned drift depressed hmF2 decline and the buildup increased both NmF2 and TEC. The ICON observations suggested that the meridional wind during this minor storm event modulated the direction of plasma transport near sunset, playing a dominant role in post‐sunset plasma density enhancement from low to middle latitudes. These results provide fresh insight into the electrodynamic mechanisms of post‐sunset enhancements at middle and low latitudes over East Asia, and also enhance our understanding of the intricate behaviors within the ionosphere‐thermosphere system in response to a minor storm.

A cyclone formation, eastward plume drag, ion-hydration process, and the consequent ionospheric changes following the 2022 Hunga Tonga-Hunga Ha'apai volcanic eruption

In this paper, we investigate the possible mechanism behind the volcanic plume drag and the massive expansion of the ionospheric Total Electron Content (TEC) along the eastern longitudes by using near-field observations of the 15 January 2022 Hunga Tong-Hunga Ha'apai volcanic eruption. The meteorological omega parameter shows a dominant upward/downward movement of the air parcels along the eastern and southern sides of the volcanic eruption. This dominant air convection under the presence of the heavy moisture of volcanic plume and vector winds indicated the formation of a cyclone over and around the Tonga volcano. This cyclone dragged the heavy volcanic plume towards the eastern longitudes (150oE – 180oE). Moreover, we observe that the volcanic plume injected about 5 DU of Sulfur Dioxide (SO2) and 30 kgm−2 of the water content along the total atmospheric column. The chain reactions of Hunga Tonga volcanic trace gases with the gases present in the neutral atmosphere instigated an air ionization process, while under the presence of the heavy water content, these ions underwent an ion-hydration process that contributed to the electrodynamical changes in the region. These electrodynamical changes can be regarded as Atmospheric Chemical Potential (ACP) in the lower atmosphere. We observe a sharp increase in the ACP of ∼0.26 eV along the eastern longitudes (160–180°E) exactly above the dragged volcanic plume area. The long-term TEC changes that have been observed towards the eastern longitudes are the result of the ion-hydration process at the lower atmosphere. The statistical analysis showed a critical change during the day of the event up to ∼48 TECU with an increasing value of 28 and 18 TECU from the lowest and highest ranges before the event over the crater, respectively. Our main findings showed ionospheric anomalies in the near field started from 6:00 to 16:00 UTC. Based on Swarm satellites, the S4 values have significant changes around 0.3–0.6 lasting for tens of minutes.

Experimental Determination of the Ionospheric Effects and Cycle Slip Phenomena for Galileo and GPS in the Arctic

The ionosphere can impair the accuracy, availability and reliability of satellite-based positioning, navigation and timing. The Arctic region is particularly affected by strong ionospheric gradients and phase scintillation, posing a safety issue for critical infrastructure and operations. Ionospheric warning and impact maps can provide support to Arctic operations, but to produce such maps threshold values have to be determined. This study investigates how such thresholds can be derived from the GPS and Galileo satellite signals. Rapid changes in total electron content (TEC) or scintillation-induced receiver tracking errors could result in cycle slips or even loss of lock. Cycle slips and data outages are used as a measure of impact on the receiver in this paper. For Galileo, 73.6% of the impacts were cycle slips and 26.4% were outages, while for GPS, 29.3% of the impacts were cycle slips and 70.7% were outages. Considering the sum of cycle slips and outages, it is worth noting that the sum of impacts for Galileo signals is larger than for GPS. A range of possible explanations have been examined through hardware-in-the-loop simulations. The simulations showed that the GPS L2 signal was not adequately tracked during rapid TEC changes and TEC changes were underestimated, thus the GPS cycle slips, derived from L1 and L2 derived TEC changes, were not all registered. These results are important in designing threshold values for TEC and for scintillation impact maps as well as for the operation of GNSS equipment in the Arctic. In particular, the results show that ionospheric changes could be underestimated if GPS L1 and L2 were used in isolation from other dual frequency combinations. It is the first time this analysis has been made for Greenland and the first time that the dual frequency derivation of ionospheric delay using GPS L1 and L2 has been shown to underestimate large TEC gradients. This has important implications for informing GNSS operations that rely on GPS to provide reliable estimates of the ionosphere.

A New Approach to the Ionosphere at Middle and Low Latitudes under the Geomagnetic Quiet Time of December 2019 by ICON and GOLD Observations

It has been found that the total electron content (TEC) and the ionospheric electric fields indicated by the geomagnetic data showed inconsistent changes with each other at the mid- and low latitudes in both the American and the Asian–Australian sectors during geomagnetic quiet time (GQT) from 30 November to 8 December 2019 (Kpmax = 1.7). Meanwhile, the effects of thermospheric compositions are still indistinct. In this work, we analyze the mid/low-latitude ionospheric variations during this period, utilizing multi-instrument observations. The vertical drift velocities from the Ionospheric Connection Explorer (ICON) show significant variations and are in line with the changes in TEC at low latitudes in both of the two sectors. The zonal electric fields are supposed to play the main role in the TEC changes. This is also confirmed by the ionospheric F2 layer parameters data from the ionosonde stations at Sanya in the Asian–Australian sectors. The correlation between the variations in the geomagnetic H component (ΔH) and ionospheric F-layer electric fields can be affected by solar activity levels. The geomagnetic data ΔH sometimes may not indicate the magnitude of the electric fields in the F-region ionosphere under geomagnetic quiet conditions. The column density ratio of atomic oxygen (O) to molecular nitrogen (N2) (∑O/N2) from the Global Scale Observations of the Limb and Disk (GOLD) showed a strong enhancement at mid-latitudes in the American sector on 30 November. It is speculated that the neutral compositions should make a minor contribution to the changes in TEC during this event, compared with the electric fields.

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.

Regional Ionospheric Frequency Domain Characterization and Short-term Forecasting Methods

Total electron content (TEC) is an important characteristic parameter of the ionosphere, which has a great impact on applications such as navigation error correction. The paper addresses the need for short- and medium-term forecasting of regional ionospheric TEC. Firstly, we analyze the frequency domain characteristics of ionospheric TEC, and analyze the data in the frequency domain based on the characteristics of trend, periodicity and abruptness of ionospheric TEC changes influenced by solar activity, combined with the Prophet algorithm. Then, according to its frequency domain characteristics, Prophet is used to achieve hour-by-hour prediction. The computer modeling results show that the RMSE of the 7-day forecast is better than 1.262 TECU during the geomagnetically quiet period, and the method is suitable for the short- and medium-term forecasting of ionospheric TEC with good forecast accuracy and time efficiency.

Responses of Total Electron Content to Solar Flares over Low and Mid-Latitude Regions during Sun Halo Day

The responses of total electron content (TEC) to solar flares over low- and mid-latitude regions during the sun halo days were investigated. The research is based on GPS data obtained from Bangalore (13.02117°N, 77.57038°E) on May 24, 2021; Cape Town (-33.918861°N, 18.423300°E) on October 26, 2020; and North Dakota (46.55756°N, -96.472300°E) on December 27, 2021, during the solar halo days. The results of this study demonstrate that the values of TEC increased at Bangalore and Cape Town stations during solar halo days as compared to other days. However, over the North Dakota station, TEC during the sun halo day was greater than that on the days before and after the halo day from around 19:00 UT to 23:59 UT hours. During the sun halo over Bangalore and Cape Town stations, positive relative changes in TEC prevail, suggesting that the action of the interplanetary electric field, the prompt penetration electric field, and the disturbance dynamo electric field lead to higher TEC values. However, during the sun halo over North Dakota station, negative relative changes in TEC prevail, which might be related to the consequence that low solar activity with no earth’s field disturbances (a positive Dst) leads to lower ionospheric TEC values. Stations that have greater relative changes in TEC have shown greater power spectrum and global wave spectrum energy. Cape Town has a greater relative change in TEC, a greater power spectrum, and a larger global wave spectrum than Bangalore and North Dakota stations. The values of TEC over three stations are different due to the latitudinal and longitudinal differences in addition to the universal time effects. Finally, solar flares have a major influence on ionosphere electrodynamics, and the upward drift velocity of ionospheres at the low latitude station is more strongly influenced by solar flares due to the effects of the ExB drifts, which induce TEC disturbances during the halo days over the suggested stations.

On estimating the phase scintillation index using TEC provided by ISM and IGS professional GNSS receivers and machine learning

Amplitude and phase scintillation indexes (S4 and σϕ) provided by Ionospheric Scintillation Monitoring (ISM) receivers are the most used GNSS-based indicators of the signal fluctuations induced by the presence of ionospheric irregularities. These indexes are available only from ISM receivers which are not as abundant as other types of professional GNSS receivers, resulting in limited geographic distribution. This makes the scintillation indexes measurements rare and sparse compared to other types of ionospheric measurements available from GNSS receivers. Total Electron Content (TEC), on the other hand, is an ionospheric parameter available from a wide range of multi-frequency GNSS receivers. Many efforts have worked on establishing scintillation indicators based on TEC, and geodetic receivers in general, introducing various metrics, including the Rate of TEC change (ROT) and ROT Index (ROTI). However, a possible relationship between TEC and its variation, and the corresponding scintillation index that an Ionospheric Scintillation Monitor (ISM) receiver would estimate is not trivial. In principle, TEC can be retrieved from carrier phase measurements of the GNSS receiver, as σϕ. We investigate how to estimate σϕ from time series of TEC and ROT measurements from an ISM in Ny-Ålesund (Svalbard) using Machine Learning (ML). To evaluate its usability to estimate σϕ from geodetic receivers, the model is tested using TEC data provided by a quasi-co-located geodetic receiver belonging to the International GNSS Service (IGS) network. It is shown that the model performance when TEC from the IGS receiver is used gives comparable results to the model performance when TEC from the ISM receiver is utilised. The model's ability to infer the exact value of the scintillation index is bound to Mean Square Error (MSE) = 0.1 radians2 when σϕ≤0.8 radians. For σϕ>0.8 the MSE reaches 0.18 and 0.45 radians2 in operative testing using ISM and IGS measurements, respectively. However, the model’s ability to detect phase scintillation from IGS TEC measurements is comparable to expert visual inspection. Such a model has potential in alerting against phase fluctuations resulting in enhanced σϕ, especially in locations where ISM receivers are not available, but other types of dual-frequency GNSS receivers are present.

Spatiotemporal Prediction of Ionospheric Total Electron Content Based on ED-ConvLSTM

Total electron content (TEC) is a vital parameter for describing the state of the ionosphere, and precise prediction of TEC is of great significance for improving the accuracy of the Global Navigation Satellite System (GNSS). At present, most deep learning prediction models just consider TEC temporal variation, while ignoring the impact of spatial location. In this paper, we propose a TEC prediction model, ED-ConvLSTM, which combines convolutional neural networks with recurrent neural networks to simultaneously consider spatiotemporal features. Our ED-ConvLSTM model is built based on the encoder-decoder architecture, which includes two modules: encoder module and decoder module. Each module is composed of ConvLSTM cells. The encoder module is used to extract the spatiotemporal features from TEC maps, while the decoder module converts spatiotemporal features into predicted TEC maps. We compared the predictive performance of our model with two traditional time series models: LSTM, GRU, a spatiotemporal mode1 ConvGRU, and the TEC daily forecast product C1PG provided by CODE on a total of 135 grid points in East Asia (10°–45°N, 90°–130°E). The experimental results show that the prediction error indicators MAE, RMSE, MAPE, and prediction similarity index SSIM of our model are superior to those of the comparison models in high, normal, and low solar activity years. The paper also analyzed the predictive performance of each model monthly. The experimental results indicate that the predictive performance of each model is influenced by the monthly mean of TEC. The ED-ConvLSTM model proposed in this paper is the least affected and the most stable by the monthly mean of TEC. Additionally, the paper compared the predictive performance of each model during two magnetic storm periods when TEC changes sharply. The results indicate that our ED-ConvLSTM model is least affected during magnetic storms and its predictive performance is superior to those of the comparative models. This paper provides a more stable and high-performance TEC spatiotemporal prediction model.

Kalman Filter, ANN-MLP, LSTM and ACO Methods Showing Anomalous GPS-TEC Variations Concerning Turkey’s Powerful Earthquake (6 February 2023)

On 6 February 2023, at 1:17:34 UTC, a powerful Mw = 7.8 earthquake shook parts of Turkey and Syria. Investigating the behavior of different earthquake precursors around the time and location of this earthquake can facilitate the creation of an earthquake early warning system in the future. Total electron content (TEC) obtained from the measurements of GPS satellites is one of the ionospheric precursors, which in many cases has shown prominent anomalies before the occurrence of strong earthquakes. In this study, five classical and intelligent anomaly detection algorithms, including median, Kalman filter, artificial neural network (ANN)-multilayer perceptron (MLP), long short-term memory (LSTM), and ant colony optimization (ACO), have been used to detect seismo-anomalies in the time series of TEC changes in a period of about 4 months, from 1 November 2022 to 17 February 2023. All these algorithms show outstanding anomalies in the period of 10 days before the earthquake. The median method shows clear TEC anomalies in 1, 2 and, 3 days before the event. Since the behavior of the time series of a TEC parameter is complex and nonlinear, by implementing the Kalman filter method, pre-seismic anomalies were observed in 1, 2, 3, 5, and 10 days prior to the main shock. ANN as an intelligent-method-based machine learning also emphasizes the abnormal behavior of the TEC parameter in 1, 2, 3, 6, and 10 days before the earthquake. As a deep-learning-based predictor, LSTM indicates that the TEC value in the 10 days prior to the event has crossed the defined permissible limits. As an optimization algorithm, the ACO method shows behavior similar to Kalman filter and MLP algorithms by detecting anomalies 3, 7, and 10 days before the earthquake. In a previous paper, the author showed the findings of implementing a fuzzy inference system (FIS), indicating that the magnitude of the mentioned powerful earthquake could be predicted during about 9 to 1 day prior to the event. The results of this study also confirm the findings of another study. Therefore, considering that different lithosphere–atmosphere–ionosphere (LAI) precursors and different predictors show abnormal behavior in the time period before the occurrence of large earthquakes, the necessity of creating an earthquake early warning system based on intelligent monitoring of different precursors in earthquake-prone areas is emphasized.