A statistical analysis of vertical ionospheric sounding data from the Yakutsk station (62.01° N, 129.43° E, 57.12° MLAT) for the period from 1956 to 2017 encompassing six solar cycles has been carried out to identify long-term changes in the F2 layer of the subauroral ionosphere and their relationship with solar and geomagnetic activity. We examined variations in one of the main parameters of the ionospheric F2 layer, the critical frequency. A high correlation was found between the F2-layer critical frequency and the solar activity index F10.7. It is shown that during six solar cycles (cycles 19–24) there were negative trends in annual average F2-layer critical frequencies both at midday and at midnight. It has been revealed that foF2 trends depend on the season and time of day. Absolute values of the trends are higher in equinoctial and summer seasons. Peak negative trends are observed at midday during equinoctial months, reaching approximately –11 kHz/year.

A statistical analysis of vertical ionospheric sounding data from the Yakutsk station (62.01° N, 129.43° E, 57.12° MLAT) for the period from 1956 to 2017 encompassing six solar cycles has been carried out to identify long-term changes in the F2 layer of the subauroral ionosphere and their relationship with solar and geomagnetic activity. We examined variations in one of the main parameters of the ionospheric F2 layer, the critical frequency. A high correlation was found between the F2-layer critical frequency and the solar activity index F10.7. It is shown that during six solar cycles (cycles 19–24) there were negative trends in annual average F2-layer critical frequencies both at midday and at midnight. It has been revealed that foF2 trends depend on the season and time of day. Absolute values of the trends are higher in equinoctial and summer seasons. Peak negative trends are observed at midday during equinoctial months, reaching approximately –11 kHz/year.

Abstract Ionospheric data assimilation is the art of combining imperfect data with incomplete models to estimate the state of the ionosphere. The three most common data types are ionosonde measurements, Ground‐to‐GNSS (Global Navigation Satellite System), TEC (Total Electron Content) measurements, and RO (Radio Occultation) TEC measurements. Despite the ubiquitous use of these measurement types, scant research exists on the relative merits of each measurement type. This study evaluates the impact of assimilating all possible combinations of these three measurement types. To do this, we simulate representative data for all three measurement types using an electron density truth model, and then ingest all possible combinations of data in separate assimilation runs. Since we assimilate ground TEC in an absolute and relative sense, this yields 11 combinations. The performance of each assimilation run is assessed by how well each analysis replicates the truth model's vertical TEC (vTEC), critical plasma frequency of the F2 layer (foF2), the height at which it occurs (hmF2) and HF propagation metrics. When considering vTEC, fof2, and hmF2, we find that absolute ground TEC data is the most useful for specifying vTEC and that Radio Occultation data is the most useful when specifying foF2 and hmF2. Somewhat surprisingly, we find that adding absolute ground TEC can worsen predictions of foF2 and hmF2. Our analysis of HF propagation shows that ionosonde and RO data are quite valuable, and that ingesting ground TEC in a relative sense is better than absolute, regardless of what additional data (RO, Ionosonde) is present.

BackgroundMenopause and periodontitis can lead to changes in mandibular bone structure. Fractal dimension (FD) and radiomorphometric indices, which are widely are used to assess such changes.ObjectiveThis study aimed to evaluate mandibular trabecular bone using fractal analysis and cortical bone using radiomorphometric indices on panoramic radiographs of individuals with and without periodontitis during the perimenopausal and postmenopausal periods.MethodsThis retrospective study used panoramic radiographs from 60 females, categorized into four groups: perimenopausal and periodontally healthy (PERI-H); perimenopausal with periodontitis (PERI-P); postmenopausal and periodontally healthy (POST-H); postmenopausal with periodontitis (POST-P). Radiomorphometric indices and FD were measured bilaterally on selected condylar (F1, F6) and gonial regions (F2, F5), as well as between the first molar and second premolar teeth (F3, F4) bilaterally.ResultsIn the F3 and F4 regions, the POST-P group exhibited lower FD values compared to the PERI-H group (p = 0.035, p = 0.001, respectively). In the F1 region, significantly lower FD values were observed in the POST-P group versus the PERI-H, PERI-P and POST- H groups (p = 0.017, p = 0.011 and p = 0.017, respectively), and the POST-H group showed significantly lower FD values than the PERI-H group (p = 0.011). Cortical bone classification showed that C1 was most common in the PERI-H group (66.7%), C2 in the POST-H and POST-P groups (60.0%, 66.7%, respectively), and C3 in the POST-P group (26.7%) (p = 0.004).ConclusionsPostmenopausal females exhibited greater bone resorption in the alveolar region and the right condyle, and also showed lower FD values compared to perimenopausal females. Additionally, females with periodontitis exhibited lower fractal dimension values and increased bone porosity compared to the healthy group.

The basic aim of this research is to present an analysis of the relationship between the average ionospheric response of peak electron density height of the ionospheric F2 region (hmF2) and the geomagnetic activity for ionospheric station Rome, Italy. Significantly long data series for both quantities in the period 2002–2022 (including two solar cycles) were used, and the method for determining the relative deviation of the parameters from quiet (median) conditions was applied. To analyze the influence of geomagnetic activity represented by planetary Kp index on the hmF2, correlation analysis was used. The results show that a) the predominant ionospheric response is positive with a delay of about 1–2 hours; b) the influence of geomagnetic activity on hmF2 is maximal during the equinox months, during the night hours and around local noon. The obtained results of the positive correlation coefficient, indicating the presence of a relationship between hmF2 and Kp index (reaching up to 30%), show that the most significant influence on hmF2 is the vertical upward drift caused by the induced electric fields in the ionosphere from the “perturbed dynamo” and prompt penetration electric fields (PPEF). The choice of the selected ionospheric station Rome is related to the fact that this station is located at the geographic latitude coinciding with the latitude of Sofia, which suggests similar characteristics of the ionosphere for the territory of Bulgaria.

Abstract Accurately resolving the ion composition in the upper F2 region and topside ionosphere remains a challenge due to temperature‐composition ambiguity and measurement limitations. This study presents a hybrid fitting algorithm that combines exhaustive dictionary search with gradient descent to improve the estimation of incoherent scatter spectrum parameters, particularly He + and H + fractions, ion temperature ( T i ), and electron temperature ( T e ). The algorithm performs independent height‐based fitting while incorporating constraints on T e and T i to enhance stability. We apply the method to Arecibo incoherent scatter radar coded‐long‐pulse (CLP) data to demonstrate its effectiveness. To improve the fitting results, we employ an adaptive thresholding method to reduce the interference from satellites and space debris and allow negative He + concentration to mitigate fitting biases. With various factors considered, we show that parameter estimations, especially H + and He + fractions, can be significantly improved. Comparisons with multi‐radar autocorrelation function results further validate our improvements in accuracy and consistency. Although the CLP program was initiated at Arecibo decades ago, this is the first time that the data has been used to derive light ion fractions. This study provides a more robust framework for aeronomy studies in the upper F region.

In this contribution, we propose a new short-term forecast machine learning model of Large Scale Travelling Ionospheric Disturbances (LSTIDs) occurrence at specific locations in Europe. The model is trained using as input data time series of LSTIDs drivers and characteristics of LSTIDs detected events. The concept underpinning the selection of the input data is based on the phenomenological scenario that the intensity of the auroral electrojets is regulated by the Lorentz force and the Joule heating generates Atmospheric Gravity Waves (AGWs) in the lower thermosphere and LSTIDs in the ionosphere. Based on this scenario, the Total Electron Content (TEC) gradients and the intensity of the auroral electrojets are representative drivers for LSTIDs occurrence. Detected LSTID events and their characteristics are calculated with the HF Interferometry method (HF-INT) over European Digisonde stations. The method looks for coherent oscillation activity in the Maximum Usable Frequency for a 3000 km radio path via reflection from the F2 layer (MUF(3000)F2) , and sets bounds to time intervals for which such activity occurs into a given region. HF-INT provides the Spectral Energy Contribution (SEC), which is the contribution of the LSTIDs to the total variability for a given time series. These features (drivers and detected characteristics) are exploited for the forecast of LSTID occurrence utilizing an advanced Machine Learning tool, namely, the Temporal Fusion Transformer (TFT) model. The performance of the TFT model is compared with other more traditional classifiers, such as, the k-Nearest Neighbor classifier (k-NN), the Feedforward Neural Networks (FNNs). Several experiments are performed for two distinct scenarios: (a) values of SEC greater than 50% indicating moderate and strong LSTID activity, and (b) values of SEC greater than 70% indicating strong LSTID activity. The classifiers’ performance is assessed through the F1-score metric, which takes values between 0 and 1 (the higher its value, the better the classifier performance). The forecasting accuracy decreases from 0.9 to 0.6 approximately with increasing forecasting horizon up to two hours ahead for TFT, while the FNNs have the next best performance, and k-NN has inferior performance. The qualitative analysis of the TFT results provides evidence for a more direct dependence of the performance of the models from the historical time series of SEC at the Digisonde locations that are closer to the auroral oval, and much weaker dependence for the lower latitude Digisonde locations. This might be the result of the decreasing LSTIDs amplitude as they travel equatorward and could highlight the dissipative nature of the ionospheric medium. The TFT performance analysis leads to the conclusion that the forecast of LSTID occurrence is extremely complex as it involves not only a careful data pre-processing but also consideration of the drivers, of the propagation pattern and of other phenomena, such as the damping effect and the interhemispheric propagation.

Abstract ANCHOR is a novel data assimilation model developed at the U.S. Naval Research Laboratory for nowcasting ionospheric parameters relevant to space weather applications. ANCHOR incorporates electron density observations from ionosondes, Abel inverted radio occultation (RO) data, and ground‐based GNSS receiver data into a PyIRI‐driven model background using the Kalman filter technique. The purpose of this study is to validate the estimated model parameters with parameters derived from electron density observations from incoherent scatter radars (ISR) at various levels of solar activity. Four distinct events were identified from a 6‐year data set spanning from 2018 to 2024 collected from four operating ISRs located at varying latitudes west of the prime meridian: Arecibo, Jicamarca, Millstone Hill, and Poker Flat. These events span a range of solar activity levels, with two events at low solar activity, one at moderate and one at high solar activity, each with data coverage from at least two radars. Parameter extraction is achieved by fitting Epstein functions to the electron density profiles, where the peak density (NmF2), peak altitude (hmF2), and the bottomside and topside thickness parameters are simultaneously optimized to characterize the F2 layer. The ISR‐extracted parameters are used to directly compare with the model outputs using the root mean square error (RMSE) analysis method. Up to 75% improvement relative to the background model for F2, F2, and thickness parameters with consistency across all latitudes is found. Additionally, the ANCHOR assimilative model was compared to PyIRTAM model, showing a good agreement between the performances of both systems.

Abstract The effects of the 23–24 March 2023 geomagnetic storm in the magnetically conjugate low‐latitude ionosphere‐thermosphere along the 72°E ± 5°, 95°E ± 5° and 130°E ± 5° sectors are presented. The Total Electron Content (TEC)/NmF2 data from ground stations as well as satellite data, and model simulations are utilized to investigate the hemispheric and longitudinal variations. For the India‐East Asia region, the most prominent TEC variations manifested only on 24th March. In all the three sectors, the northern VTEC/NmF2 showed a stronger negative effect with maximum depletion over the low‐middle latitude station Dibrugarh (∼75–80%). The TEC map generated from the South–East Asia low latitude network suggested an underdeveloped EIA along 100°E on 24th March. The vertical electron density profiles estimated from COSMIC RO confirmed the hemispheric and longitudinal asymmetry of the F2 layer density. The conjugate hemisphere F2 layer height variation was indicative of equatorward winds in the Southern Hemisphere and poleward winds in the Northern Hemisphere. SWARM B on 24th March not only recorded the hemispheric asymmetry in the topside but also indicated equatorward winds in the southern hemisphere. The Thermosphere‐Ionosphere‐Electrodynamics General Circulation Model and WAM IPE models underestimated, whereas the IRI overestimated the low latitude storm time TEC. The negative effect can be attributed to the Disturbance Dynamo Electric Field, whereas the asymmetric equatorward expansion of the thermospheric composition perturbation and winds may have led to the hemispheric asymmetry of the storm response. Therefore, a conjugate ionospheric response in three close‐by sectors suggests a possible role of inter‐hemispheric meridional winds in equinoctial conditions for the asymmetric effects.

This study presents a theoretical analysis of the proximity effect in superconductor/ferromagnet (S/F) hybrid structures, specifically focusing on NbN/Ho/NbN and NbN/F1/Ho/F2/NbN multilayers. We use self-consistent solutions of the Usadel equations to investigate the energy-resolved density of states (DOS) and superconducting critical temperature (Tc) as functions of ferromagnetic layer thickness, misorientation angles, interfacial roughness, and phase differences between the superconducting NbN layers. The thickness of the ferromagnetic Ho layer is shown to significantly influence superconducting properties, including a reduction in the superconducting gap due to the inverse proximity effect. For thin Ho layers, both DOS and Tc are slightly suppressed, while intermediate thicknesses exhibit non-monotonic behavior, corresponding to complex superconducting-ferromagnetic interactions, and thicker Ho layers lead to diminished superconductivity. The interplay between the magnetization inhomogeneity in Ho and the phase coherence of the superconducting order parameter is emphasized, revealing the appearance of zero-energy peaks (ZEPs) in the DOS. These unique features, such as controllable singlet and triplet states, are sensitive to the phase difference between the NbN electrodes. Additionally, triplet generation is also sensitive to the misalignment angle between the F1, F2, and Ho layers. The phase difference modulates DOS and Tc, with certain alignments favoring triplet pairing, enabling external control over triplet generation similar to a spin valve device. Furthermore, the effect of interfacial roughness is systematically analyzed, with its implications for experimental realizations providing practical insights into optimizing S/F systems for future applications in superconducting spintronics. Our findings offer a comprehensive understanding of the proximity effect in S/F hybrid structures and its potential for tailored superconducting spintronic applications.

Knowledge of ionospheric plasma altitudinal distribution is crucial for the effective operation of radio wave propagation, communication, and navigation systems. High-frequency sounding radars—ionosondes—provide unbiased benchmark measurements of ionospheric plasma density due to a direct relationship between the frequency of sound waves and ionospheric electron density. But ground-based ionosonde observations are limited by the F2 layer peak height and cannot probe the topside ionosphere. GNSS Radio Occultation (RO) onboard Low-Earth-Orbiting satellites can provide measurements of plasma distribution from the lower ionosphere up to satellite orbit altitudes (~500–600 km). The main goal of this study is to investigate opportunities to obtain full observation-based ionospheric electron density profiles (EDPs) by combining advantages of ground-based ionosondes and GNSS RO. We utilized the high-rate Ebre and El Arenosillo ionosonde observations and COSMIC-2 RO EDPs colocated over the ionosonde’s area of operation. Using two types of ionospheric remote sensing techniques, we demonstrated how to create the combined ionospheric EDPs based solely on real high-quality observations from both the bottomside and topside parts of the ionosphere. Such combined EDPs can serve as an analogy for incoherent scatter radar-derived “full profiles”, providing a reference for the altitudinal distribution of ionospheric plasma density. Using the combined reference EDPs, we analyzed the performance of the International Reference Ionosphere model to evaluate model–data discrepancies. Hence, these new profiles can play a significant role in validating empirical models of the ionosphere towards their further improvements.

This study developed machine learning models using different algorithms, including support vector machine (SVM), random forest (RF), and backpropagation neural network (BPNN), to estimate the critical frequency of the F2 layer (foF2) and the maximum usable frequency of the F2 layer for a 3000 km circuit (MUF(3000)F2) based on the total electron content (TEC) observed by global navigation satellite system (GNSS) receivers. The ionospheric dataset used comprised TEC, foF2, and MUF(3000)F2 measurements from 11 stations in China during a solar activity period (2008–2020). The results indicate that all three machine learning models performed better than the IRI-2020 model, with varying levels of accuracy. For foF2 (MUF(3000)F2) estimation, the root mean square error (RMSE) values at Kunming and Xi’an stations were reduced by approximately 38% (26%) and 18% (11%), respectively, compared to IRI-2020. During geomagnetic disturbances, all three models were able to reproduce the variations in both foF2 and MUF(3000)F2 parameters. Nevertheless, the RF model showed significantly better performance in foF2 estimation compared to the SVM and BPNN models.

Abstract The Triple Ionospheric PhotoMeter (TriIPM) carried on the newly launched early morning FY3E meteorological satellite measures the spectral radiances of the Earth's far ultraviolet airglow in atomic oxygen 135.6 nm (OI 135.6 nm) and N2 Lyman‐Birge‐Hopfield (N2 LBH) bands. In this paper, the TriIPM instrument data are used for the first time to record the thermospheric O/N2 ratio variations during geomagnetic storms through the case study of the superstorm on 10–11 May 2024. The variations of the TriIPM O/N2 ratio and the ionospheric peak density at F2 layer (NmF2) derived from the modified IRI2016 model are also compared to explore the relationship between the storm‐time thermospheric and ionospheric responses. Our results show that the TriIPM O/N2 ratio depletion extends down to the equator in the Northern Hemisphere during the main phase of the storm period. The TriIPM O/N2 ratio depletion shows a good quantitative agreement with the NmF2 depletion within the disturbed region at a local time period between 1100 and 1900 LT. The good agreement between the TriIPM O/N2 ratio and NmF2 indicates that the new satellite TriIPM instrument may provide a good opportunity for understanding how the thermosphere‐ionosphere system responds to geomagnetic storms.

Abstract This study investigates the distinctive features of the daytime equatorial E‐region ionosphere using observations of HF radar (18 MHz), ionosonde, and magnetometer at Thumba (8.5°N, 77°E, dip lat = 1.9°N), India, during the extreme geomagnetic storm of May 10–11, 2024 (minimum SYM‐H ∼ −497 nT). The northward component of the Interplanetary Magnetic Field (IMF Bz) exhibited large‐amplitude oscillations throughout the day on May 11 that resulted in sudden changes in the Interplanetary Electric Field, in turn manifesting as Prompt Penetration Electric Field (PPEF) in the ionosphere. The drift measurements of plasma irregularities using the radar indicate that these PPEF modulations effectively mapped onto the equatorial ionosphere in the Indian region. The radar observations of the zonal drift of daytime E‐region plasma irregularities exhibited four notable and significant aspects: (a) enhancements in westward drift velocities during eastward electric field to 500 m/s, exceeding the ion acoustic limit by ∼140 m/s, (b) eastward reversal of drift with speeds ∼200 m/s in response to westward PPEF, (c) occasional disappearance of plasma irregularities, and (d) short‐lived eastward drifting echoes in east beam of radar suggesting a possible localized alteration in electron density profiles. In the absence of PPE15F modulation, one of the significant enhancements in the drift of plasma irregularities coincided with an X5‐class solar flare. Concurrent ionosonde measurements revealed a rise in the ionospheric peak altitude beyond 500 km during local noon, accompanied by the presence of the F3 layer. This alluded to the combined effect of eastward PPEF and equatorward thermospheric wind.

Abstract The semiannual anomaly, characterized by increased peak electron density in the F2 layer (NmF2) at equinoxes compared to solstices, remains incompletely elucidated, especially at high latitudes. The magnetospheric convection pattern introduces additional complexity to its formation mechanisms. This study utilized the NCAR Thermosphere‐Ionosphere‐Electrodynamics General Circulation Model (TIEGCM) to explore the semiannual anomaly at the Zhongshan Station (ZHS), Antarctica, focusing on the processes occurring in the polar upper atmosphere, which are crucial in the formation of this phenomenon. Simulations reveal that the convective electric fields at high latitudes amplify the semiannual variation of NmF2. Specifically, the daytime peak electron density at ZHS is primarily influenced by the availability of ionization sources at middle‐high latitudes for convective transport. During the equinox, this peak is enhanced through transport, due to higher plasma density at middle‐high latitudes, whereas in summer, there is a depletion of ionization sources at lower latitudes, results in a less efficient transport effect. The semiannual variation in ionization sources is attributed to changes in the neutral composition driven by thermospheric circulation and neutral temperature. Additionally, during the equinox, the coupling of neutral winds with ion convection draws air parcels with larger O/N2 from lower latitudes, shaping the distribution of neutral composition into a “neutral tongue,” further intensifying the plasma transport effect. These findings provide new insights into the intricate interactions among magnetospheric, ionospheric, and thermospheric dynamics.

Abstract Juno's highly eccentric polar orbit takes it to perijove distances of 1.06 on each orbit. For the first perijove, this occurred just north of the jovigraphic equator, but has precessed north by about a degree per orbit over the mission. Minimum altitudes vary from 3,200–8,000 km through the mission. The Waves instrument observes a number of plasma wave modes in and near the non‐auroral ionosphere that provide information on the local electron number density, including electron plasma oscillations that occur at the electron plasma frequency and whistler‐mode hiss which has an upper frequency limit of in Jupiter's strongly magnetized inner magnetosphere. The electron plasma frequency provides the electron number density. We present electron densities in the topside ionosphere, similar to Earth's F2 layer, from the 59 perijoves analyzed to date. Peak densities range from 100 to 80,000 at latitudes up to . The density profiles can be highly variable from one perijove to the next. And, there can be deviations from simple smooth variations with altitude within individual ionospheric passes. Spatial variations may be responsible for some of the variability, perhaps related to Jupiter's complex magnetic field. We show the variation in ionospheric density profiles and the distribution of peak densities as a function of latitude and longitude as well as other geometric parameters. In addition to the complex magnetic field, possible factors affecting ionospheric density variations investigated here are ionospheric dynamos analogous to those at Earth and precipitation of energetic particles.

Abstract Ionospheric vertical sounding is a well‐established and widely used ground‐based technique for ionospheric detection. Efficient and accurate automatic scaling of ionograms is crucial for real‐time applications of vertical sounding. However, current scaling methods face challenges in achieving precise trace segmentation due to noise, interference, and various ionospheric disturbances. This paper proposes a deep learning model for ionogram automatic scaling based on generative adversarial network (GAN), which is named IASGAN. The model integrates a 50‐layer residual network (ResNet50) and a feature pyramid network (FPN) as the generator, with a multi‐layer convolutional neural network (CNN) as the discriminator. Given a vertical ionogram, the generator produces segmentation result that closely resemble the corresponding label, while the discriminator provides feedback loss to the generator for adversarial training, thereby enhancing the segmentation performance of the generator. Experimental results demonstrate that the IASGAN model can precisely and effectively autoscale E, F1, and F2 layer traces. Compared to existing scaling methods, the IASGAN model can produce finer trace extraction from ionograms, with a mean maximum critical frequency absolute deviation (D‐MCF) of 0.0803 MHz and a mean minimum virtual height absolute deviation (D‐MEH) of 5.8205 km. This capability can provide technical support for the extraction of characteristic parameters and ionospheric inversion, which is significant for real‐time acquisition of ionospheric characteristics and structure information.

Abstract We investigate ionospheric disturbances over China during the May 2017 geomagnetic storm using an integrated data set, including total electron content (TEC) measurements from BeiDou Navigation Satellite System's (BDS) Geostationary Earth Orbit (GEO) observations, ionosonde data, Swarm satellites, and global navigation satellite system (GNSS) radio occultation (RO) data. TEC anomalies were identified using the Prophet forecasting model, and results were compared with three sliding time window methods, showing consistent outcomes. The significant TEC increase during the storm's main phase was driven by prompt penetration electric fields (PPEF) linked to interplanetary magnetic field (IMF) Bz fluctuations. During the recovery phase, TEC increased on May 29 night, associated with southward IMF Bz turning and westward PPEF. Notably, the TEC negative storm observed at the KUN1 station on the morning of May 29 was likely caused by F‐layer uplift driven by the eastward overshielding electric field (OPEF) during the recovery phase, which induced Rayleigh‐Taylor instability and resulted in the formation of plasma bubbles. Additionally, the electron density (Ne) measured by COSMIC and Swarm satellites, along with ionospheric F2 layer parameters critical frequency (foF2) and peak height (hmF2) showed significant increases during the storm. A negative ionospheric response was observed over China on May 30 from 00:00 to 12:00 UT, likely caused by thermosphere composition changes. This study highlights the efficiency of BDS‐GEO satellites in monitoring ionospheric TEC variations, capturing spatiotemporal characteristics of disturbances, and validating the Prophet model for detecting anomalous TEC fluctuations during geomagnetic storms.

The present study examines the global ionospheric response to the “Mother’s Day Superstorm” (10–11 May 2024), one of the most intense geomagnetic storms since 1957, with a minimum SYM-H index of −436 nT. Constellation Observing System for Meteorology, Ionosphere, and Climate-2 (COSMIC-2) Radio Occultation (RO) data indicated an increase in the F2 layer maximum critical frequency (foF2) over midlatitude dayside regions, which was accompanied by a significant F-region uplift (hmF2 increase) on a global scale, even on the nightside during the main and recovery phases. At the same time, a decrease in foF2 was observed on the nightside. High southeastward and vertical drift velocities were observed in the nightside sector of the northern hemisphere with the dayside sector exhibiting upward and southwestward-to-northwestward drifts during the main and recovery phases of the storm. An intense upward drift (~170 m/s) in the southern hemisphere was registered with the poleward expansion of the Equatorial Ionization Anomaly (EIA) during the main phase. Swarm A data highlighted the EIA expansion from ~45°N to 60°S during the dayside main phase and from ~30°N to 40°S on the nightside during recovery.

Abstract The impacts of the severe geomagnetic storm on 23–25 April 2023 on the low‐latitude ionosphere over the American sector are investigated. Based on the ground‐based and spaceborne instruments, a series of storm‐associated ionospheric disturbances have been observed as follows: (a) The eastward Prompt Penetration Electric Fields intensified the equatorial upward E × B drift, and enhanced the Equatorial Ionization Anomaly (EIA) during the first main phase. (b) The EIA asymmetry in the region east of 70°W and one‐sided F3 layer in the northern hemisphere are observed and are attributed to the strong southward transequatorial winds. (c) The disturbances of electric field and neutral winds raised the low‐latitude and equatorial F‐layer dramatically, and generated post‐midnight Equatorial Plasma Bubbles. These bubbles rapidly extended to the magnetic latitude of 30°, corresponding to a rare Apex altitude of ∼2,600 km at magnetic equator. (d) On 24 April, the thermospheric composition disturbances with high ΣO/N2 were concentrated in the southern hemisphere, and resulted in the positive storm over South America.