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.

Thunderstorms could cause the irregularities of electron density distributions in the ionosphere by exciting gravity waves and modifying ambient electric field (E-field). By comparing the DPS-4D ionosonde observation at 5-min resolution at Fuke Station in Hainan, China with the lightning detection data, we studied the F-layer responses to a thunderstorm on 16 August 2016. The results show that the variation in the F-layer electron density corresponded, with similar to 5-min delay, to the time-resolved lightning occurrence; the observed temporal delay likely reflects the E-field modifications associated with charge separation within thunderclouds. After the peak lightning activity, a weak spread-F appeared alongside sudden rises in plasma drift velocities. These features suggest that lightning disruptions affect ionospheric E-fields, drive E & times; B drifts, and cause irregularities in F-layer electron density via Rayleigh-Taylor and E & times; B instabilities. It is the first high-resolution ionosonde observation of thunderstorm-induced F-layer disturbances at low latitudes, providing more insights into the troposphere-ionosphere coupling.

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.

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.

Previous research has established that in geospace, the total electron content (TEC) in the ionosphere can be modulated efficiently by ultralow frequency (ULF) waves in high-latitude regions. However, the correlations between TEC variations and ULF waves in middle-latitude and low-latitude regions remain inadequately explored. In this study, using ground-based magnetometer data from the Chinese Meridian Project, we identified ULF wave events within Pc4 frequency bands in the midlatitude region. During the period from 1 July to 30 December 2023, we identified 642 distinct ULF wave events in the Pc4 band, thereby creating a comprehensive ULF wave database. Statistical analysis indicated that Pc4-band ULF wave events predominantly occurred on the night side in midlatitude regions. Notably, on 24 August 2023, simultaneous observations of geomagnetic disturbances and TEC disturbances at a similar frequency were recorded, suggesting a potential correlation between Pc4 ULF waves and TEC variations at midlatitudes. Through quantitative analysis, we infer that ionospheric TEC variations were triggered by Pc4 ULF waves during this event and the mechanism underlying this may be the horizonal plasma density gradient. This result provides direct observational evidence of the modulation of the TEC by Pc4 ULF waves in the midlatitude region. Furthermore, multiple additional TEC modulation events associated with ULF waves were identified, substantially reducing the likelihood of coincidental occurrence and reinforcing the validity of our findings. These findings broaden our understanding of the coupling between the solar wind-magnetosphere-ionosphere in midlatitude regions, and may be significant for evaluating the effect of space weather of this coupling process.

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

The global navigation satellite system (GNSS) ionospheric total electron content (TEC) and scintillation monitors in the Phase II of Chinese Meridian Project network provide domestic measurements. After 3 consecutive days of testing, we used PolaRx5 data with the same antenna as a reference to evaluate the quality of the prototype data. For scientific research, the continuity and effectiveness of data, as well as the accuracy of vertical TEC, amplitude scintillation index, and phase scintillation index values, are the most important indicators. In this study, we designed data comparison standards for these aspects based on scientific research scenarios, evaluated the data quality of the prototype, and analyzed the reasons for the characteristics of the detection results of the two devices. These findings provide a reference for the evaluation of data accuracy.

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).

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。

Using the ground-based multi instruments observations in Hainan and Southeast Asia, as well as space-based observations, the long lasting events of ionospheric irregularities occurred in the equatorial region of East Asia on the night of March 31, 2014 are studied. Concurrent observations at Hainan station indicate that ionospheric irregularities measured with different instruments showed distinct spatial structures and temporal variation, mainly from sunset to midnight (19-23LT) and even to post midnight. The 3-m-scale irregularities detected by VHF radar first attenuated and disappeared at 01LT, then the 400-meter-scale irregularities (GPS scintillations) vanished at 03LT, and finally the larger-scale irregularities (spread F) ended at 05LT. It provides a directly observational evidence that the larger the scale is, the latter the irregularities decay after midnight. Ionospheric irregularities near the magnetic equator (ME) and the equatorial abnormal peaks (EAPs) mainly occurred from after sunset to midnight (19-01LT), but there were distinct morphological differences after midnight. The irregularities near ME were attenuated and disappeared rapidly, while irregularities near EAPs weakened obviously, but lasted until dawn. The plasma depletions observed by the SWARM satellite in the latter half of the night were clearly related to the TEC fluctuations at JOG2 site, the ionospheric scintillations and spread F at Hainan station. The quasiperiodic structures of plasma bubbles observed by C/NOFS satellite were clearly related to the occurrence of ionospheric irregularities in Hainan and Southeast Asia, which indicated that the seeding of atmospheric gravity waves may play an important role in the generation of ESF/EPB irregularities, even in the latter half of the night.

The previous studies show that scintillations under 150 km height shown by radio occultation (RO) of COSMIC are considered to be associated with ionospheric sporadic E (Es) layer. In this paper, we perform a statistical study on daytime Es observed by ground-ionosonde at five stations in magnetic Equator region in 2010, and make a comparison between foEs and S4 index from RO-COSMIC within 6 degree of each station. The results show that the foEs and S4 index had similar distributions. The agreement between scintillation occurrence and extreme Es cases showed that the Es had a significant contribution to the S4 index in daytime. It implies the satellite RO data could be used to study ionospheric structures/irregularities. The results also showed that the Es had more contribution at Sao Luis/Ilorin than that at Jicamarca/Kwajalein. However, the Es contribution to the S4 index during daytime need to be further studied in detail.

With simultaneous ionospheric measurements from ROCSAT‐1 satellite and ground ionosondes/GPS receivers, three cases of concurrent plasma blobs and bubbles in the same magnetic meridian were observed around 22:30 LT in Asian‐Oceanian sector during solar maximum. Two cases were observed: equatorial spread F (ESF) over Vanimo station (geog. 2.7°S, 141.3°E; geom. 11.2°S, 146.2°W) and plasma blobs around 8.0°S (geom.) on 1 June and 6 October 2003. The other case observed equatorial spread F over Hainan station (geog. 19.5°N, 109.1°E; geom. 9.1°N, 179.1°W) and plasma blob near the dip equator on 8 March 2004. Plasma blobs were all observed near 600‐km height near the equator. Equatorial spread F and amplitude scintillations from the ground stations were observed near the same magnetic meridian, indicating the existence of bubbles. Considering that both plasma bubbles and blobs are field‐aligned elongated structures, magnetic field line mapping shows that in the two cases at Vanimo, blobs were above bubbles, providing direct observational evidence for blob formation in the intermediate stage of plasma bubble evolution; in the case at Hainan, the blob and bubble were likely at similar height, and it could be generated by gravity wave.

利用2003-2016年期间子午工程海南站（19.5°N，109.1°E）数字测高仪观测到的电离层等离子体漂移数据，分析了高低两种太阳活动条件下纬向和垂直向漂移对近磁静、中等磁扰和强磁扰三种地磁活动水平的响应特性.结果表明：日间纬向漂移各季节均以西向为主，随地磁活动无明显变化，白天日出附近和夜间漂移在各季节均以东向为主，随地磁活动增强而减弱，减弱程度在分季最大，在夏季最小；日间垂直漂移在零值附近变化，且不受地磁活动和季节影响，日落附近漂移仅在分季受到地磁活动的抑制，午夜前垂直漂移在分季受到抑制，在冬季因强磁扰而反向，夏季无明显规律，子夜至日出后垂直漂移在各季节随地磁活动增强而减小.与赤道区Jicamarca相比，两地漂移对地磁活动的响应相近，但在幅度和相位上存在差异，这可能是两地区的地理位置、背景电场和风场结构等不同造成的.

利用海南台站（19.5°N，109.1°E，dip：13.6°N）和磁赤道区的多种地基和天基观测数据，对2011年11月20日观测到的电离层不规则体事件进行了分析.海南台站VHF雷达、电离层闪烁和数字测高仪的综合观测结果表明，当天日落附近发生了强的电离层不规则体事件，主要表现为雷达羽和强闪烁的形态.结合磁赤道区GPS和C/NOFS卫星观测结果进行分析可知，海南台站日落附近出现的雷达羽和强闪烁与南海磁赤道区产生的主等离子体泡存在明显联系.