Ionospheric Electron Research Articles

Scintillations in Southern Europe During the Geomagnetic Storm of June 2015

The sensitivity of Global Navigation Satellite System (GNSS) receivers to ionospheric disturbances and their constant growth are nowadays resulting in an increased concern of GNSS users about the impacts of ionospheric disturbances at mid-latitudes. The geomagnetic storm of June 2015 is an example of a rare phenomenon of a spill-over of equatorial plasma bubbles well north from their habitual. We study the occurrence of small- and medium-scale irregularities in the North Atlantic Eastern Mediterranean mid- and low-latitudinal zone by analysing the amplitude of the scintillation index S4 and rate of total electron content index (ROTI) measurements during this storm. In addition, large-scale perturbations of the ionospheric electron density were studied using ground and space-borne instruments, thus characterising a complex perturbation behaviour over the region mentioned above. The involvement of large-scale structures is emphasised by the usage of innovative approaches such as the ground-based gradient ionosphere index (GIX) and electron density and total electron content gradients derived from Swarm satellite data. The multi-source data allow us to characterise the impact of irregularities of different scales to better understand the ionospheric dynamics and stress the importance of proper monitoring of the ionosphere in the studied region.

A deep learning approach for the prediction of ionospheric total electron content (TEC) based on combined prediction and two-step loss fine-tuning

A deep learning approach for the prediction of ionospheric total electron content (TEC) based on combined prediction and two-step loss fine-tuning

Geometric deep learning for ionospheric TEC modeling using a temporal graph convolutional network

Abstract This document proposes a spatiotemporal deep learning model for ionospheric total electron content (TEC) modeling using global navigation satellite systems (GNSSs) observables. Data from dual-frequency GNSS receivers are used to compute the daily GNSS TEC timeseries. Usually, these timeseries are computed independently per GNSS permanent station, and the state-of-the art models proposed in literature exploit temporal characteristics of the timeseries and neglect any spatial dependencies and information from different stations. In our approach, we propose a practical solution for parallel processing of TEC timeseries and additional indicators from various adjacent stations to predict future VTEC values. We face the problem in both spatial and temporal dimensions adopting a graph neural network-based approach from the broader family of geometric deep learning. According to our proposed scheme, the different adjacent GNSS stations are structured in a graph and then, we apply the proposed temporal graph convolutional network called ION_TGNN. Our model predicts future vertical TEC (VTEC) values for all stations in a single run with mae error better than 1.0 TECU. Comparisons with state-of-the art models show the superiority of the proposed method in terms of performance but also in terms of computational cost during training and test phases.

Investigation of the Fresnel Scale From Ionospheric Scintillation Spectra

AbstractTrans‐ionospheric radio signals recorded on the ground exhibit random amplitude and phase fluctuations attributed to irregularities in the ionospheric electron density. Studying the ground‐based measurements of trans‐ionospheric radio signals can contribute to understanding plasma instability mechanisms leading to the development of ionospheric structures. In this regard, radio signals emitted from satellites, making up the Global Positioning System (GPS), and recorded by the Canadian High Arctic Ionospheric Network (CHAIN) GPS receivers, are analyzed to study the physical signatures of both amplitude and phase fluctuations. The current ionospheric scintillation paradigm posits that amplitude fluctuations arise from diffraction caused by Fresnel scale ionospheric structures, while refraction is responsible for signal phase variations. The amplitude power spectrum profile consistently displays a rollover frequency, which is not equal to the Fresnel frequency under the Taylor hypothesis. Phase screen theory is used to investigate this phenomenon further and identify an empirical relation between the rollover and Fresnel frequencies. Notably, we have found that the rollover frequency is consistently smaller than the Fresnel frequency. Furthermore, the Fresnel frequency extracted from two‐component phase spectra tends to be larger than the rollover frequency. Based on our results, we have concluded that the identified Fresnel frequencies are directly linked to the ionospheric irregularities causing scintillation.

Investigating the prediction ability of the ionospheric continuity equation during the geomagnetic storm on May 8, 2016

Abstract Ionospheric electron density (IED) and vertical total electron content (VTEC) are two of the most important characteristics that interpret the ionosphere. Today, satellite geodesy plays an important role in providing these data. Apart from monitoring the ionosphere by means of Global Navigation Satellite System observations, predictions of these ionospheric characteristics have also been taken into consideration. The majority of prediction methods in the ionosphere are focused on VTEC prediction. The continuity equation in the ionosphere is a spatiotemporal partial differential equation that can be used to predict both IED and VTEC. In this study, we address this issue during May 8, 2016, which was a day of high geomagnetic activity. For this purpose, first, high-accuracy IED grids with a spatial resolution of 0.5° × 0.5° in longitude and latitude, 50 km in altitude, and a temporal resolution of 10 min are prepared. These IED grids are called analysis grids and are derived by assimilation of GPS-derived VTECs into the background IED grids provided by international reference ionosphere. Second, the analysis grids can be utilized as the initial and boundary values in the continuity equation to predict IED for the next 3 h with a step of 10 min. The analysis grids are constructed over the Iran region, and the performance of the continuity equation in the ionosphere prediction is investigated during the strongest geomagnetic storm of 2016. Finally, the accuracy of the predictions has been evaluated in two ways. First, analysis grids with high accuracy are available for each epoch in which the prediction is presented. On average, the root mean square (RMS) of the differences between the predicted IEDs and the analysis IEDs is 1.66 × 1010 el/m3 for 1-h predictions and 5.33 × 1010 el/m3 for 3-h predictions. Second, by numerical integration of the predicted grids in the vertical direction, VTECs are estimated and compared with VTEC values of test data that were already known. On average, the RMS of their difference was 1.29 total electron content unit (TECU) for 1-h predictions and 2.57 TECU for 3-h predictions. An acceptable accuracy for both IED and VTEC prediction by a continuity equation has been obtained up to 3 h on the most active ionospheric day.

Impact of Ionospheric Electron Density on Second-Order Ionospheric Error at L5 and S1 Frequencies Using Dual-Frequency NavIC System

Impact of Ionospheric Electron Density on Second-Order Ionospheric Error at L5 and S1 Frequencies Using Dual-Frequency NavIC System

Relationship Between TEC Perturbations and Rayleigh Waves Associated With 2023 Turkey Earthquake Doublet

AbstractAn earthquake doublet occurred in Turkey on 6 February 2023, with propagating Rayleigh waves triggering perturbations in the ionospheric total electron content (TEC) for both the M 7.8 earthquake (EQ7.8) and the M 7.5 earthquake (EQ7.5). A discrepancy between the velocities of TEC perturbations and Rayleigh waves has been noted, but its causes remain unresolved in previous studies. In this study, we calculated the velocities of TEC perturbations and the frequency‐dependent velocities of Rayleigh waves, considering their intrinsic dispersive characteristics. To retrieve TEC, we utilized ground‐based Global Navigation Satellite System (GNSS) data from geostationary Earth orbit (GEO) satellites to mitigate the effects of moving ionospheric pierce points (IPPs) from orbiting satellites. The results reveal that the velocities of TEC perturbations (2.60 km/s for EQ7.8 and 2.77 km/s for EQ7.5) do not align with the velocities of Rayleigh waves across the entire frequency band (2.4–3.0 km/s for EQ7.8 and 2.6–3.5 km/s for EQ7.5). However, they are comparable within specific periods of 10–30 s due to dispersion effects for both EQ7.8 and EQ7.5. The dispersive Rayleigh waves, which exhibit significant amplification in the 10–30 s period range, are identified as the primary source of the pronounced coseismic TEC perturbations, particularly for EQ7.5.

Ground and Space (LEO) Based GNSS Ionospheric Detection Using Chapman Function Constraint

AbstractLimited by the number and location distribution of ground stations, the ground based ionospheric detection using Global Navigation Satellite Systems (GNSS) technology suffers from low accuracy and poor reliability in some regions. To improve the performance of the global ionospheric model, this study combines the Continuously Operating Reference Station and the Low Earth Orbit (LEO) Satellite observation to conduct the ground and space‐based (G/SBased) joint ionospheric detection technique. With reference to the ionospheric radar data of the global ionospheric radio observatory, the performance of the G/SBased GNSS joint ionospheric detection technique was tested in quiet and disturbed geomagnetic environments. Results show that in the ground and space based joint mode, the distribution of ionospheric puncture points (IPPs) is uniform, thus effectively avoiding the problem of IPPs blank in the absence of ground stations. In the quiet geomagnetic environment, the ionospheric detection of the G/SBased joint mode has a remarkable performance improvement compared with the ground GNSS mode, with an average improvement of 55.51%. In the geomagnetically disturbed environment, the G/SBased joint mode still has high consistency with the ionospheric radar results, and the detection performance is better than that of the ground GNSS mode. This research shows that combining with ground GNSS and LEO satellite observation data can substantially provide the performance of GNSS ionospheric total electron content detection and can provide good data support for the construction of a global ionospheric refinement model.

Two‐ and Three‐Dimensional Propagation Characteristics of Co‐Volcanic Ionospheric Disturbances Induced by the 2022 Tonga Volcano Eruption Using Dense GNSS Network Data

AbstractOn 15 January 2022, the submarine volcano in Hunga Tonga‐Hunga Ha’apai (hereinafter Tonga volcano) erupted at 04:14 universal time. This study investigated the two‐ and three‐dimensional co‐volcanic ionospheric disturbances (CVIDs) induced by the strong Tonga volcano. Based on dense Global Navigation Satellite System network data in Australia, the two‐dimensional detrended total electron content maps were first generated by using the Savitzky‐Golay filtering method. Using a compressed sensing‐based computerized ionospheric tomography approach, the three‐dimensional ionospheric electron densities were reconstructed. After the eruption, the distinctive CVIDs began to be mostly observed over southeastern Australia, about 4,000 km from the volcano. Two patterns of observable CVIDs, that is, fast‐mode and slow‐mode CVIDs, traveled outward from the Tonga volcano at speeds of 600–850 and 200–350 m/s, respectively. The slow‐mode CVIDs were related to the Lamb and secondary gravity waves, while the fast‐mode CVIDs were related to acoustic waves. Three‐dimensional reconstruction results demonstrated that as the CVIDs propagated upward, the electron densities fluctuated at most altitude‐longitude slices, and the wave‐like propagating patterns were clearly observed around the peak ionospheric heights of 260–340 km. At the peak ionospheric height, the ionospheric parameters derived from the two ionosondes over Australia also detected similar wave‐like features. Two‐dimensional and three‐dimensional observational evidence of the Tonga‐induced CVIDs enhances research on volcanic eruption effects over the upper ionosphere.

Ionospheric Response to the 24–27 February 2023 Solar Flare and Geomagnetic Storms Over the European Region Using a Machine Learning–Based Tomographic Technique

AbstractTwo solar flares accompanied by coronal mass ejections (CMEs) occurred on 24–25 February (DOY 055–056), 2023, resulting in a large magnetic storm on DOY 058. We reconstructed the ionospheric electron density (IED) in Europe to analyze the spatial distribution of ionosphere and its temporal evolution during this period. Computerized ionospheric tomography based on machine learning (CIT‐ML) was used to predict the IEDs of unobserved voxels. The IEDs were examined using observation data from the Swarm satellite. The CIT‐ML accuracy was 28.3% higher than the improved algebraic reconstruction technique with relaxation factor automatic search technology (IART‐AS), which effectively improved the typical ill‐posed problem of CIT. The first flare generated the Bz component of the interplanetary magnetic field (IMF), which continued southward for 13 hr, causing a small magnetic storm before the second flare occurred, resulting in an increased nighttime IED and nighttime medium‐scale traveling ionospheric disturbance (MSTID). The vertical total electron content (VTEC) and IED declined in the early stages of the main phase of the large magnetic storm, but later increased, indicating that negative‐positive biphasic storms were occurring in the ionosphere that altered the ionospheric daily cycle, resulting in the peak of the ionosphere being advanced by approximately 1.5 hr. The storms also caused nighttime MSTIDs during the main phase (DOY 057 at night) and the recovery phase (DOY 058 at night). To investigate the mechanisms of these results, we conducted a term analysis of the ion continuity equation using the Thermosphere Ionosphere Electrodynamics General Circulation Model (TIEGCM). The analysis showed that ambipolar diffusion driver nighttime MSTIDs during flares, while increased geomagnetic disturbances amplify the effects of neutral wind transport, E × B drift and chemical reactions during magnetic storms. These combined effects offset the alternating positive and negative structures induced by ambipolar diffusion, becoming the main cause of electron density variations during ionospheric storms.

Detection of ionospheric disturbances with a sparse GNSS network in simulated near-real time Mw 7.8 and Mw 7.5 Kahramanmaraş earthquake sequence

On February 6, 2023 the Kahramanmaraş Earthquake Sequence caused significant ground shaking and catastrophic losses across south-central Türkiye and northwest Syria. These seismic events produced ionospheric perturbations detectable in Global Navigation Satellite System (GNSS) total electron content (TEC) measurements. This work aims to develop and incorporate a near-real-time (NRT) ionospheric disturbance detection method into JPL’s GUARDIAN system. Our method uses a Long Short-Term Memory (LSTM) neural network to detect anomalous ionospheric behavior, such as co-seismic ionospheric disturbances among others. Our method detected an anomalous signature after the second Mw 7.5 earthquake at 10:24:48 UTC (13:24 local time) but did not alert after the first Mw 7.8 earthquake at 01:17:34 UTC (04:17 local time), which had a visible disturbance of smaller amplitude likely due to lower ionization levels at night and potentially the multi-source mechanism of the slip.Plain Language Summary Seismic activity, including the destructive Kahramanmaraş Earthquake Sequence on February 6, 2023 in the Republic of Türkiye, result in vertical ground displacement that cause atmospheric waves. These waves propagate upwards to the outer atmosphere, disturbing the ionospheric electron content. This disturbance impacts the signals broadcast by positioning satellites (such as GPS) and received by ground-based receivers. If the receiver position is known, the impact to these signals can be used to measure the electron density disturbance caused by these seismically-induced atmospheric waves. Such studies usually rely on being aware of the event a priori. Using deep learning neural networks, we instead aim to detect anomalous signals automatically. We propose to utilise this method to detect seismically-induced disturbances over a large geographical area. The detection method proposed in this paper successfully detected an anomalous event in the ionosphere approximately ten minutes after the second earthquake in the Kahramanmaraş Earthquake Sequence.

EclipseNB: A Network of Low‐Cost GNSS Receivers to Study the Ionosphere

AbstractThis work aims to demonstrate that dense networks of low‐cost dual‐frequency global navigation satellite systems (GNSS) receivers can be used to retrieve ionospheric electron content with almost the same level of accuracy as scientific‐grade GNSS receivers. A network of 15 GNSS receivers called EclipseNB was designed and installed in New Brunswick, Canada to study ionospheric structure and dynamic behavior, including the response of the ionosphere to the total solar eclipse in April 2024. EclipseNB observations during the solar eclipse and the extreme geomagnetic storm in May 2024 are presented. The status and the future of the network are discussed.

Day‐To‐Day Variability of Ionospheric Electron Temperature Revealed by High‐Resolution SYISR Observations During April–May 2022

AbstractThis study investigated the day‐to‐day variability of ionospheric electron temperature (Te) over Sanya using the newly built Sanya Incoherent Scatter Radar (SYISR; 18.3°N, 109.6°E, dip latitude: 12.8°N). The 24 days of observations with transmitted beams encoded by the alternating code and directed toward the zenith provided Te profiles during April–May 2022. The spatial‐temporal resolution was as precise as 4.5 km and ∼120 s. The day‐to‐day variability of Te over Sanya thus revealed, which was strong in the sunrise Te peak region (above ∼260 km, duration increases from ∼0.5 to ∼3.5 hr with altitude) and the top of the 200–300 km Te bulge (∼260 to ∼300 km, lasting from ∼09:00 LT to ∼18:30 LT). The day‐to‐day variability of Te in the two regions could reach ∼26% and ∼16% of the average Te, respectively. Although the day‐to‐day variability of Te is negatively correlated with the variation of ionospheric electron densities (Ne) in general, a positive correlation still occurred during the daytime, especially at 400–480 km from ∼17:00 LT to ∼18:00 LT. Further Thermosphere Ionosphere Electrodynamics General Circulation Model simulations reproduced the positive correlation, which may result from the internal heat exchange between electrons, ions, and neutrals. The simulations suggested that the variation of solar radiation/geomagnetic activity contributes most to the daytime/sunrise day‐to‐day variability of Te over Sanya.

Оценка электронного содержания плазмосферы и высоты перехода O⁺/H⁺ во время геомагнитной бури в феврале 2022 г. по данным Иркутского радара НР

We study the topside ionosphere above the NmF2 ionization maximum and the transition region between the ionosphere and the plasmasphere. We analyze the interaction between the topside ionosphere and the plasmasphere during a strong geomagnetic storm in February 2022, using data from the Irkutsk Incoherent Scatter Radar (IISR) and total electron content data from global navigation satellite systems. To determine the ionosphere and plasmasphere electron contents, an original technique is employed to calculate the integral content of ion density from IISR data, which takes into account the two-component composition of ionospheric plasma. We compare different functions of approximation of the topside ionosphere. The original technique was adjusted for use with IISR Ne data fitted based on the β-Chapman profile. We compare the plasmasphere electron content during quiet and magnetically disturbed days, as well as the dynamics of the O⁺/H⁺ transition height, which is the upper boundary of the ionosphere and the lower boundary of the plasmasphere.

Estimated plasmasphere electron content and O⁺/H⁺ transition height during the February 2022 geomagnetic storm from Irkutsk IS radar data

We study the topside ionosphere above the NmF2 ionization maximum and the transition region between the ionosphere and the plasmasphere. We analyze the interaction between the topside ionosphere and the plasmasphere during a strong geomagnetic storm in February 2022, using data from the Irkutsk Incoherent Scatter Radar (IISR) and total electron content data from global navigation satellite systems. To determine the ionosphere and plasmasphere electron contents, an original technique is employed to calculate the integral content of ion density from IISR data, which takes into account the two-component composition of ionospheric plasma. We compare different functions of approximation of the topside ionosphere. The original technique was adjusted for use with IISR Ne data fitted based on the β-Chapman profile. We compare the plasmasphere electron content during quiet and magnetically disturbed days, as well as the dynamics of the O⁺/H⁺ transition height, which is the upper boundary of the ionosphere and the lower boundary of the plasmasphere.

Evaluating Ionospheric Total Electron Content (TEC) Variations as Precursors to Seismic Activity: Insights from the 2024 Noto Peninsula and Nichinan Earthquakes of Japan

This study provides a comprehensive investigation into ionospheric perturbations associated with the Mw 7.5 earthquake on the Noto Peninsula in January 2024, utilizing data from the International GNSS Service (IGS) network. Focusing on Total Electron Content (TEC), the analysis incorporates spatial mapping and temporal pattern assessments over a 30-day period before the earthquake. The time series for TEC at the closest station to the epicenter, USUD, reveals a localized decline, with a significant negative anomaly exceeding 5 TECU observed 22 and 23 days before the earthquake, highlighting the potential of TEC variations as seismic precursors. Similar patterns were observed at a nearby station, MIZU, strengthening the case for a seismogenic origin. Positive anomalies were linked to intense space weather episodes, while the most notable negative anomalies occurred under geomagnetically calm conditions, further supporting their seismic association. Using Kriging interpolation, the anomaly zone was shown to closely align with the earthquake’s epicenter. To assess the consistency of TEC anomalies in different seismic events, the study also examines the Mw 7.1 Nichinan earthquake in August 2024. The results reveal a prominent negative anomaly, reinforcing the reliability of TEC depletions in seismic precursor detection. Additionally, spatial correlation analysis of Pearson correlation across both events demonstrates that TEC coherence diminishes with increasing distance, with pronounced correlation decay beyond 1000–1600 km. This spatial decay, consistent with Dobrovolsky’s earthquake preparation area, strengthens the association between TEC anomalies and seismic activity. This research highlights the complex relationship between ionospheric anomalies and seismic events, underscoring the value of TEC analysis as tool for earthquake precursor detection. The findings significantly enhance our understanding of ionospheric dynamics related to seismic events, advocating for a comprehensive, multi-station approach in future earthquake prediction efforts.

Impact of different solar extreme ultraviolet (EUV) proxies and Ap index on hmF2 trend analysis

Abstract. Long-term trend estimation in the peak height of the F2 layer, hmF2, needs the previous filtering of much stronger natural variations such as those linked to the diurnal, seasonal, and solar activity cycles. If not filtered, they need to be included in the model used to estimate the trend. The same happens with the maximum ionospheric electron density that occurs in this layer, NmF2, which is usually analyzed through the F2 layer critical frequency, foF2. While diurnal and seasonal variations can be easily managed, filtering the effects of solar activity presents more challenges, as does the influence of geomagnetic activity. However, recent decades have shown that geomagnetic activity may not significantly impact trend assessments. On the other hand, the choice of solar activity proxies for filtering has been shown to influence trend values in foF2, potentially altering even the trend's sign. This study examines the impact of different solar activity proxies on hmF2 trend estimations using data updated to 2022, including the ascending phase of solar cycle 25, and explores the effect of including the Ap index as a filtering factor. The results obtained based on two mid-latitude stations are also comparatively analyzed to those obtained for foF2. The main findings indicate that the squared correlation coefficient, r2, between hmF2 and solar proxies, regardless of the model used or the inclusion of the Ap index, is consistently lower than in the corresponding foF2 cases. This lower r2 value in hmF2 suggests a greater amount of unexplained variance, indicating that there is significant room for improvement in these models. However, in terms of trend values, foF2 shows greater variability depending on the proxy used, whereas the inclusion or exclusion of the Ap index does not significantly affect these trends. This suggests that foF2 trends are more sensitive to the choice of solar activity proxy. In contrast, hmF2 trends, while generally negative, exhibit greater stability than foF2 trends.

Ionospheric total electron content and electron density response induced by the 8 April 2024 total solar eclipse

Ionospheric total electron content and electron density response induced by the 8 April 2024 total solar eclipse

Super-Intense Geomagnetic Storm on 10-11 May 2024: Possible Mechanisms and Impacts.

One of the most intense geomagnetic storms of recent times occurred on 10-11 May 2024. With a peak negative excursion of Sym-H below -500nT, this storm is the second largest of the space era. Solar wind energy transferred through radiation and mass coupling affected the entire Geospace. Our study revealed that the dayside magnetopause was compressed below the geostationary orbit (6.6 RE) for continuously ∼6hr due to strong Solar Wind Dynamic Pressure (SWDP). Tremendous compression pushed the bow-shock also to below the geostationary orbit for a few minutes. Magnetohydrodynamic models suggest that the magnetopause location could be as low as 3.3RE. We show that a unique combination of high SWDP (≥15nPa) with an intense eastward interplanetary electric field (IEFY≥2.5mV/m) within a super-dense Interplanetary Coronal Mass Ejection lasted for 409min-is the key factor that led to the strong ring current at much closer to the Earth causing such an intense storm. Severe electrodynamic disturbances led to a strong positive ionospheric storm with more than 100% increase in dayside ionospheric Total Electron Content (TEC), affecting GPS positioning/navigation. Further, an HF radio blackout was found to occur in the 2-12MHz frequency band due to strong D- and E-region ionization resulting from a solar flare prior to this storm.

PyIRTAM: A New Module of PyIRI for IRTAM Coefficients

AbstractA novel model called PyIRI was recently developed. It constructs the ionospheric electron density for the entire day and on the entire global grid in one computation, which has a very low computational overhead. PyIRI introduced a novel approach to the computation of the global and diurnal functions and their matrix multiplication with Consultative Committee on International Radio (CCIR) coefficients or the International Union of Radio Science (URSI) coefficients, that enabled this global approach for the density specification. Since the International Reference Ionosphere‐based Real‐Time Assimilative Model (IRTAM) produces coefficients in a similar format as CCIR/URSI coefficients, the PyIRI computational approach was extended to work with IRTAM coefficients. This technical note describes the PyIRTAM software and provides usage examples. The PyIRTAM tool is made publicly available through PyPI and GitHub.