Research on the ionospheric Spread-F (SF) phenomenon holds significant value in both fields as ionospheric electrodynamic research and enhanced operational applications in radio-based technologies (e.g., communication and navigation). To date, the classification of Spread-F remains largely reliant on the manual interpretation of ionograms by experts, suffering from inefficiency (~10 seconds per figure) and subjectivity. There has been no publicly available ionogram dataset classifying Frequency/Range/Mix/Strong Range SF (FSF/RSF/MSF/SSF) by either human labor or machine processing. To address this problem, we introduce the first open, expert-guided ionogram dataset that is simultaneously the most comprehensive in terms of class coverage, the largest in volume, and the most extensive in temporal span. This collection encompasses 150,000 ionograms (30,000 per class, including a “non-SF” group) spanning 14 years from 2002 to 2016, thereby capturing a diverse range of solar and geomagnetic conditions. The attached classification SA-ResNet50 model based on this dataset could be applied to further data.

Gamma-ray bursts (GRBs) have long been proposed to perturb Earth's ionosphere, with occasional reports of disruptions in ultra- and extremely-low-frequency radio signals. The exceptionally bright GRB 221009A was recently claimed to induce multi-altitude ionospheric responses, including perturbations in satellite electric fields, regional total electron content (TEC), and the equatorial electrojet (EEJ). These claims have renewed interest in the potential near-Earth impacts of astrophysical transients. Here we perform an independent reassessment using expanded datasets spanning multiple altitudes. We find no coherent, burst-like TEC enhancement, show that the reported electric-field anomalies recur under specific illumination conditions each orbit, and demonstrate that the EEJ fluctuations preceded the burst and coincide with solar-wind variability. Together, these results indicate that the reported GRB-induced ionospheric responses are fully attributable to other natural geophysical processes and instrumental artefacts, thereby resolving a high-profile controversy and clarifying the true limits of GRBs'ionospheric effects.

Using the multiple ground-based/space-based observations, an abnormal response of ionospheric irregularities in the eastern Pacific region during the recovery phase of the great magnetic storm on 24 April 2023 was analyzed. The ground-based GPS observations exhibited ionospheric irregularities occurred from night to day, lasting for over ten hours, with the longest exceeding 14 h, especially near the magnetic equatorial region. SWARM satellites detected significant plasma bubble/disturbance structures near sunset and in the morning when crossing the low latitude and equatorial region from the western Americas to the eastern Pacific. The satellites (C2E1-C2E5) of COSMIC-2 detected the strong ionospheric bubbles and their quasi periodic structures when crossing the low latitude region from the eastern Pacific to the western Americas (-180 degrees to-60 degrees), which occurred from sunset to daytime, lasting up to 10-14 h. The enhanced eastward electric field and the seed disturbance maybe main factors for the occurrence of ionospheric irregularities in the eastern Pacific region during the recovery phase of magnetic storm on 24th. (c) 2026 COSPAR. Published by Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

Abstract The auroral‐like sporadic E‐layer is typically observed at altitudes between 100 and 150 km within the South American Magnetic Anomaly region. However, the occurrence of an unusual layer event detected above this altitude range on 18 July 2017 has raised intriguing questions regarding the physical processes responsible for the substantial vertical displacement of this layer. The interplanetary medium conditions during the event exhibited characteristics consistent with the recovery phase of a geomagnetic storm driven by a complex solar wind structure, occurring after substorm activity. To address this atypical layer occurrence, we examined the magnetospheric conditions using the BATS‐R‐US model with two experiments: one including the actual geomagnetic dipole inclination and another omitting it. These configurations allow for a discussion of the energy deposition processes under the prevailing interplanetary medium conditions. Also, the dynamics of the inner radiation belt were investigated through analysis of the magnetic field power spectral density and low‐energy electron flux measurements from the Van Allen Probes mission to verify electron precipitation. Plasma wave observations and their characterization also revealed atypical behavior, in which a non‐standard confinement of hiss waves (200 and 500 Hz) occurred simultaneously with an electron flux decrease (<1 keV), an integrated ionization rate between 250 and 300 km, and the formation of a peculiar layer. Finally, although this magnetic storm was moderate, we demonstrate that particle precipitation significantly impacted the atmosphere, as evidenced by pronounced ozone depletion in the mesosphere with magnitudes substantially stronger when compared to the extreme storm of May 2024.

The Digisonde Portable Sounder (DPS) ionosonde at Hainan Station (19.5°N, 109.1°E; magnetic latitude: 11°N) has been monitoring ionospheric conditions since 2002, routinely recording ionospheric plasma profiles, sporadic E layers, and Spread-F structures through ionograms. A Spatial Group-wise Enhanced ConvTransformer (SGE-ConvTransformer) is proposed in this study for spatiotemporal ionospheric prediction at the Hainan station, with emphasis on Spread-F, enabling a one-hour lead time with a 15-minute sampling resolution. The SGE module optimizes semantic feature extraction from the global spatial context, dynamically recalibrating attention to prioritize information-rich regions, such as F-layer traces, over background noise. To further improve visual clarity, a super-resolution Enhanced Deep Super-Resolution (EDSR) module is integrated to sharpen the predicted ionograms. Leveraging DPS ionosonde data from 2002 to 2015, we constructed a large-scale ionogram sequence dataset comprising 36,240 Spread-F instances and 396,931 non-Spread-F instances, which were further categorized into five distinct classes. On the 2016 test set, our model achieved an average Spread-F classification accuracy of 90.05% and a correlation coefficient of 0.8115 for the predicted F-trace. Demonstrating superior robustness under disturbance conditions, the model maintained high performance during six representative geomagnetically disturbed intervals (2023–2024), achieving a classification accuracy of up to 95.69%. Furthermore, the model's generalizability was examined by applying pre-trained weights to data from low-latitude (Brazil, Peru), mid-latitude (Irkutsk), and high-latitude (Zhigansk) stations. Quantitative Spread-F Classification Accuracy (SFCA) metrics at low latitudes and qualitative visual assessments across all regions demonstrate the morphological transferability of our approach across diverse geospatial environments.

A complex active region in the Sun’s photosphere from 8 May 2024, produced seven halo-type Coronal Mass Ejections (CMEs) following extreme solar flares. These events generated Solar Energetic Particles (SEPs) that propagated toward Earth, culminating in an extreme geomagnetic storm (SYM-H = −497 nT) from May 10 to 13 May 2024. This study analyzes the Sun’s photosphere, interplanetary medium, inner radiation belt, and the space weather impacts on the neutral atmosphere and E and F ionospheric layers over the South Atlantic Magnetic Anomaly (SAMA) during the storm’s main phase. The first and second Interplanetary CMEs (ICMEs) reached Earth’s bow shock at 15:00 UT and 17:00 UT on May 10, respectively. The second ICME, associated with a shock, caused a significant displacement of the dayside magnetopause (∼6 Earth radii, RE) and the first solar wind Poynting flux transfer into the magnetosphere (Akasofu parameter, Epsilon ∼ 1 × 1013 W). At 18:00 UT, the third ICME and its associated shock pushed the magnetopause further to ∼5 RE and added energy to the magnetospheric budget (Epsilon ∼2.5 × 1013 W). Between 19:00 and 21:00 UT, the solar wind proton density (>40 cm-3) peaked at Earth’s bow shock, but no energy input to the magnetosphere occurred (Epsilon ∼0 W). Low-energy electron/ion fluxes vanished in the inner radiation belt. Epsilon gradually increased between 21:00 and 22:30 UT, coinciding with the onset of low-energy electron/ion injections into the inner radiation belt and substorm activity. These injections persisted after 22:30 UT, albeit limited to specific energy levels. Enhanced energetic particle precipitation (EPP) and local particle acceleration caused significant variability in electron/ion fluxes in the inner radiation belt. Increased scattering by plasma waves precipitated particles into the SAMA atmosphere, raising ionization rates and depleting ozone in the mesosphere and stratosphere. Extra ionization in the E ionospheric region further indicated auroral-like effects in this low-latitude region during the storm’s main phase.

The SMILE ground segment comprises the Chinese Academy of Sciences (CAS) ground segment and the European Space Agency (ESA) ground segment, which collaborate closely on this mission. The Ground Support System (GSS) and the Science and Application System (SAS) are two important components of the CAS ground segment. Development of these systems began in 2016, focusing on requirements for addressing the significant challenges associated with the SMILE mission. The GSS is primarily responsible for data reception, mission operations, data processing, data management, and data services. It has established an operational platform based on a “common platform + mission-specific plug-ins” model, enabling support for the SMILE mission through the development of tailored plugins. The SAS functions as a dedicated scientific research center for the SMILE mission within CAS, managing science operations, processing scientific data, and conducting scientific data analysis. Its establishment was driven by the unique requirements of the SMILE mission. Additionally, the SAS is tasked with fostering collaboration between CAS and ESA, designing effective frameworks to coordinate scientists in planning SMILE science operations. This paper provides a brief overview of the design of the GSS and SAS, as well as SMILE mission operations. We anticipate that these two systems will effectively support the SMILE mission in the future.

Ionograms are radar echo graphs that depict vertical ionospheric density profiles, structures, fluctuations, and irregularities, with the F region represented by F‐trace and Spread‐F features in the graphs. In this paper, IonoGAN, an enhanced neural network based on the Generative Adversarial Network architecture, is proposed for direct prediction of ionograms and the variation of these ionospheric conditions. This estimation is based on the trends of density profiles and the waves/structures presented in the ionogram sequence. The IonoGAN extends the spatiotemporal information‐preserving and perception‐augmented (STIP) ability by incorporating a Local‐Global discriminator to focus on the F region in ionograms. In addition, two scientific characteristics of ionospheric natural phenomena are extracted and used as constraints in the modeling: Spread‐F Classification Accuracy (SFCA) and Absolute Value of the Correlation Coefficient for the F trace (AVCC‐F). For training, ionograms from Hainan Fuke station (19.5°N, 109.1°E, magnetic 11°N) during 2002–2015 were processed into 36,435 sequences with Spread‐F phenomena and 147,147 sequences without. To strengthen their features, Spread‐F phenomena were further classified into types of frequency, range, mix, and strong range. After the parameter training, the IonoGAN achieved SFCA and AVCC‐F converging to their optimal values: on the 2016 test set, SFCA = 90.92%, AVCC‐F = 0.6917. This modification enables the network to effectively capture the distinct features of the ionospheric F trace and the Spread‐F phenomenon during both quiet and disturbed periods.

An intelligent high-definition and short-term prediction of ionograms with/without Spread-F for the observation at Hainan (19.5 degrees N, 109.1 degrees E, magnetic 11 degrees N) is presented in this paper, which comprises a spatio-temporal ConvGRU network and a super-resolution EDSR network. Our prediction is based on spatio-temporal features in the ionogram graph only. There are 469,227 ionograms classified into 5 categories, that is, frequency/range/mix/strong range/no Spread F, over a solar cycle (14 years) labeled manually by the research group, and we process these ionograms into two data sets for training the two networks mentioned above. A series of comprehensive experiments have been designed and conducted to determine the optimal super-parameters. Our method inputs 8 consecutive authentic ionograms (lasting 2 hr) and generates the next 2 figures (next 30 min). Remarkably, all predicted figures achieve a high accuracy rate of over 94% in predicting the occurrence of Spread-F. Since the clear trace indicates the ionospheric electron density background and the Spread-F indicates disturbances/irregularities, the prediction of both ionograms with/without Spread-F is important to the research and application. There are currently no well-established neural networks specifically designed to predict the extensive information encompassed within ionograms, particularly the intricate characteristics of Spread-F. In this paper, we approach the short-term prediction (next 30 min) of ionograms with/without Spread-F. To achieve this, we employ the ConvGRU network to generate ionograms blurred but still captured the primary spatio-temporal features of various types of Spread-F (FSF/RSF/MSF/SSF) in ionogram sequences, as well as features without Spread-F. Subsequently, we refine these rough images by EDSR network to obtain clear and detailed predictions. Our treatment to blurred prediction figures and our focus on the Spread-F key area are innovative. Through our work, the auto-prediction for ionosonde observation benefits ionosphere research and monitoring. Rather than numerical prediction methods, our prediction is based on spatio-temporal features only Predicting high-definition ionograms by combining ConvGRU and EDSR on the basis of establishing two ionogram data sets Achieving a high accuracy of 94.28% for Spread-F prediction

Based on the Chinese Meridian Project (CMP), the International Meridian Circle Program (IMCP) organizes a comprehensive ground-based monitoring network along the 120°E -60°W Great Meridian Circle to track the propagation and evolution of space weather events.IMCP has more than ten ionosonde along the Meridian Circle at different latitudes to monitor the ionosphere, supporting Chinese researchers in studies on ionospheric statistical characteristic and events.The DPS-4D Ionosonde at Hainan Fuke station (19.5°N, 109.1°E) has been observing the ionosphere for 20 years, with this data we manually classified the SF as 4 types and studied the variation features.Furthermore, with deep learning method, we made a detection (accuracy≥95%) & classification (average accuracy=95%) model and a shortimpending prediction model for the SF in the ionogram over this station.The models are based on image characteristics, no matter what the ionogram data formats or the auto-models in ionosonde.

AbstractAn intelligent Spread‐F image detection and classification method is presented in this paper based on an ionogram image set using deep learning models. The ionogram images from the Hainan station, spanning from 2002 to 2015, have been manually labeled into five categories, resulting in a unique ionogram image set for supervised learning models. To balance the number of different types, simulated noises were added to these images. Based on 80,000 samples with Spread‐F and 20,000 samples without, numerous experiments have been conducted to train VGG, ResNet, EfficientNet, ViT, MobileNet, and other networks. The results on the test set indicate that these models except VGG have a good ability of exacting features of different types, leading to a high level of accuracy in detecting Spread‐F and a relatively accurate classification of it. The ionogram images in 2016 are then employed as another test set to further examine the performance of the trained models. Both quantitative and qualitative analyses have demonstrated the results obtained by deep learning models are highly consistent with manual identification.

Data measured by the Digisonde at the low-latitude station Hainan from 2003 to 2016 are statistically analyzed to specify the diurnal average variations of the bottom-side F region ionospheric plasma velocity vector V. This is the first comprehensive analysis of Digisonde measurements of low latitude F region plasma velocities in the East Asian sector that use a database covering more than one solar cycle. The velocity components V-N (Northward), V-E (Eastward), and V-Z (Upward) are analyzed for two levels of solar flux and two levels of geomagnetic activity, respectively. The diurnal variations of the average V-Z show three positive peaks near the prereversal enhancement (PRE) period, pre-midnight, and before sunrise, respectively, and a prominent valley in the early morning. The averaged V-Z significantly increased with solar flux in the period of PRE during equinoxes, but it was only slightly affected by Kp. The V-E component was westward in daytime and eastward in nighttime. The average eastward V-E increased significantly with solar flux but decreased with Kp, whereas the average westward V-E exhibited only a small variation with solar flux and Kp. The average V-N was almost southward independent of solar flux and Kp. The plasma velocities over the Hainan station were mainly caused by the electric field and neutral wind. Our results show that the features of the vertical and meridional velocities over the Hainan station in the morning are associated with the formation of the equatorial ionization anomaly (EIA).

The interaction of the solar wind with the Earth’s magnetosphere can result in various disturbance phenomena inside the magnetosphere, such as the density, occurrence, and distribution of the field-aligned currents (FACs) in the magnetosphere-ionosphere system. The FACs take an important role in the magnetosphere - ionosphere coupling and have been investigated for many decades. However, it’s response to the solar wind and IMF still needs to be further studied. In this study, we statistically investigate the influence of the solar wind dynamic pressure (SW Pdyn) on the FACs in the magnetotail based on 1492 FAC cases occurring from July to October in 2001 and 2004, which covers 117 Cluster spacecraft crossings of plasma sheet boundary layer (PSBL). The FAC density in the magnetotail is derived from the magnetic field data with the four-point measurement of Cluster and the SW Pdyn is derived from ACE data. It was confirmed that the magnetotail FAC density depended on the solar wind dynamic pressure during both magnetic storm and non-storm time, and the FAC density was much stronger with intense SW Pdyn during storm time. The statistical results show that the FAC occurrence increases monotonically with SW Pdyn in the three levels (weak: SW Pdyn<2 nPa; medium: 2 nPadyn<5 nPa; strong: SW Pdyn>5 nPa). The FAC density increases with increasing SW Pdyn, while its footprint (invariant latitude, ILAT) in the polar region decreases with increasing SW Pdyn. Also, the FAC in the magnetotail response to SW Pdyn has a north-south hemispheric asymmetry. The correlation of FAC density with SW Pdyn is better in the Northern Hemisphere, while the correlation of the footprint position with SW Pdyn is better in the Southern Hemisphere. Furthermore, the influences of the solar wind core angle and clock angle on the FAC density, occurrence, and distribution are also shown in this presentation. The concerned mechanisms for our results are analyzed and discussed.

针对东亚地区地磁低纬度南北半球Vanimo台站(地理2.7°S,141.3°E;地磁11.2°S,146.2°W)和海南台站(地理19.5°N,109.1°E;地磁9.1°N,179.1°W)上空的3个电离层等离子体块与等离子体泡相关联的事件,利用地面台站的电离层测高仪连续观测数据,研究等离子体泡演化期间的电离层虚高变化.结果表明:以往提出的等离子体块出现约2 h之前等离子体垂直漂移速度从向上(东向电场)转为向下(西向电场)的观点,本文的3个事例均与之不符,或者距反转时间很远(约6 h),或者由向下转为向上.在等离子体块形成时间点前的1 h内,均有突发的等离子体堆积的运动趋势,或是下降运动速度突然变慢,或是从下降转为向上运动,或是上升运动速度突然加快.这一堆积现象与等离子体块现象相关性更好,也不仅限于漂移速度从向上转变为向下.

利用海南台站和东南亚地区的多种地基和天基观测手段，对2014年7月28日夜间观测到的东亚低纬F区不规则体事件的时空变化及其物理过程进行分析。结果表明，海南台站观测到了罕见的长时间持续的F区电离层不规则体，不同手段观测到的电离层不规则体存在明显的形态差异。不同台站观测到的电离层不规则体活动存在明显的差异。海南台站经度区南北异常峰附近的TEC起伏活动在日落后至午夜附近明显增强，在午夜后明显减弱。C/NOFS卫星轨迹午夜后逐渐接近于磁赤道，且处于较低高度上，几乎总会观测到弱等离子体扰动/泡的发生，与该区域地基观测的弱电离层不规则体活动存在明显的联系。SWARM卫星在黎明海南台站附近经度区仍观测到较强的赤道异常双峰结构，且西侧异常峰区附近仍存在明显的等离子体密度耗空/泡结构。海南台站西侧磁赤道区附近（中南半岛）强对流活动（MCC）激发的重力波种子扰动对东亚低纬区等离子体泡及准周期结构的产生发挥了重要作用。

利用Cluster四颗卫星的磁场探测数据计算磁尾场向电流并投影到极区电离层，研究其投影位置在南北半球的分布规律，统计过程中去除了强磁暴（磁暴主相Dst＜–100 nT）期间的场向电流事件。结果显示：磁尾场向电流事件在极区投影位置的纬度分布具有明显的南北半球不对称性，北半球为单峰结构，南半球为双峰结构。在北半球投影到较低纬度（＜64°）的场向电流事件数目明显多于南半球，并且所能达到的最低纬度更低；在南半球投影到较高纬度（＞74°）的场向电流事件数目明显多于北半球，并且所能达到的最高纬度更高。地磁平静条件下（|AL|＜100 nT），磁尾场向电流密度随磁地方时（MLT）呈递增趋势，这一结果与低高度卫星在极区对I区场向电流的探测结果符合很好。研究结果表明，磁尾场向电流投影位置的纬度分布呈现出明显的南北不对称性，这与南北半球磁尾场向电流的空间分布以及磁层中磁场结构具有密切关系。

The nonlinear waves exists everywhere in space plasmas, such as solar wind, planetary magnetosphere, ionosphere, and so on. The nonlinear structure could be complex particle density structure and electric field structure. In this study, the nonlinear waves observed in space plasmas are reviewed. A fully nonlinear theoretical model for the observed ion nonlinear structure in low $\beta$ plasma is introduced. The Model results show that the electrostatic linear ion-cyclotron wave and ion acoustic wave could be excited and developed to nonlinear structures, such as the density periodical nonlinear wave, hump soliton, dip soliton and shock, as well as the nonlinear E-field structures. The plasma condition to excite each type of the nonlinear structures are given, and the condition of the E-filed nonlinear waves are analyzed and given, too. With concrete parameters, the model results could be consistent with observation in the nonlinear waveform and size, both for nonlinear density waves and E-field waves.