Journal of Atmospheric and Solar-Terrestrial Physics最新文献

{"title":"Winter noctilucent clouds following sudden stratospheric warming: First observations","authors":"Oleg S. Ugolnikov","doi":"10.1016/j.jastp.2025.106507","DOIUrl":"10.1016/j.jastp.2025.106507","url":null,"abstract":"<div><div>Mesospheric structures identical to summer noctilucent clouds were observed during the nights of December 17–19, 2024 in Siberian Russia. Based on the available photo data, the mean altitude of the clouds 70.1 ± 1.5 km was measured by umbral colorimetric method. This coincided spatially and temporary with a deep temperature minimum below 160K in mesosphere, following the polar vortex displacement and the warming of stratosphere below the clouds. The satellite data on temperature and water vapor is used to study the nature of this unexpected event.</div></div>","PeriodicalId":15096,"journal":{"name":"Journal of Atmospheric and Solar-Terrestrial Physics","volume":"270 ","pages":"Article 106507"},"PeriodicalIF":1.8,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143817501","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating growth rates of sprite streamers","authors":"George V. Naidis","doi":"10.1016/j.jastp.2025.106506","DOIUrl":"10.1016/j.jastp.2025.106506","url":null,"abstract":"<div><div>Downward-propagating positive sprite streamers are modeled. It is shown that, in agreement with recent high-speed video observations, at the initial stage of streamer propagation the streamer velocity increases with time with a nearly constant growth rate, varying almost proportionally to the streamer length. An approximate expression for the growth rate is presented as a function of the altitude, the driving electric field at this altitude and the peak electric field in the streamer head.</div></div>","PeriodicalId":15096,"journal":{"name":"Journal of Atmospheric and Solar-Terrestrial Physics","volume":"270 ","pages":"Article 106506"},"PeriodicalIF":1.8,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143817502","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automatic detection and classification of Spread-F in ionograms using convolutional neural network","authors":"Moheb Yacoub ,&nbsp;Edgardo E. Pacheco ,&nbsp;Moataz Abdelwahab ,&nbsp;Cesar De La Jara ,&nbsp;Ayman Mahrous","doi":"10.1016/j.jastp.2025.106504","DOIUrl":"10.1016/j.jastp.2025.106504","url":null,"abstract":"<div><div>Equatorial spread-F (ESF) is an irregularity caused by plasma instabilities on the night side that causes signal degradation and disruptions to the GNSS signals. Ionosondes could detect ESF as it appears as a diffused echo in the ionogram images. This study proposes a Convolutional Neural Network (CNN) model that can automatically detect ESF within the ionogram images and classify its type. The model has been trained using 2646 manually labeled ionograms from the Low Latitude Ionospheric Sensor Network (LISN) VIPIR Ionosondes in South America. The data used to train the model was measured from 2019 to 2024. The model was able to classify the testing images into six categories: Clear class, frequency spread-F (FSF), range spread-F (RSF), mixed spread-F (MSF), strong spread-F (SSF), and Unidentified class. It demonstrated high classification accuracy within the extracted test subset and a further random test, showcasing robustness and consistency in detection accuracy across all classes. Furthermore, the model performance has been evaluated and compared with other baseline models: VGG16, VGG19, ResNet18, and Inception-V3 in the same environment. Additionally, a comparison with published models is provided. Our model showed a higher consistency in classification accuracy across all classes compared to the mentioned models.</div></div>","PeriodicalId":15096,"journal":{"name":"Journal of Atmospheric and Solar-Terrestrial Physics","volume":"270 ","pages":"Article 106504"},"PeriodicalIF":1.8,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143735046","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Simultaneous evaluation of solar activity proxies during geomagnetic storms using principal component analysis: Case study of the African low and mid-latitude regions","authors":"Jean Claude Uwamahoro ,&nbsp;John Bosco Habarulema ,&nbsp;Dalia Buresova ,&nbsp;Nigussie Mezgebe Giday ,&nbsp;Valence Habyarimana ,&nbsp;Kateryna Aksonova ,&nbsp;Joseph Ntahompagaze ,&nbsp;Theogene Ndacyayisenga ,&nbsp;Ange Cynthia Umuhire","doi":"10.1016/j.jastp.2025.106477","DOIUrl":"10.1016/j.jastp.2025.106477","url":null,"abstract":"&lt;div&gt;&lt;div&gt;We simultaneously evaluate the contributions of the mostly used solar activity indices to the modelling of geomagnetic storms using principal component analysis (PCA). The selected indices are the sunspot number (SSN), solar radio flux at a wavelength of 10.7 cm (&lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mi&gt;F&lt;/mi&gt;&lt;mn&gt;10&lt;/mn&gt;&lt;mo&gt;.&lt;/mo&gt;&lt;mn&gt;7&lt;/mn&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt;), 12-month running average of SSN (&lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mi&gt;R&lt;/mi&gt;&lt;mn&gt;12&lt;/mn&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt;), 81-day running average of &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mi&gt;F&lt;/mi&gt;&lt;mn&gt;10&lt;/mn&gt;&lt;mo&gt;.&lt;/mo&gt;&lt;mn&gt;7&lt;/mn&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt; (&lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mi&gt;F&lt;/mi&gt;&lt;mn&gt;10&lt;/mn&gt;&lt;mo&gt;.&lt;/mo&gt;&lt;msub&gt;&lt;mrow&gt;&lt;mn&gt;7&lt;/mn&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mn&gt;81&lt;/mn&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt;), and the modified &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mi&gt;F&lt;/mi&gt;&lt;mn&gt;10&lt;/mn&gt;&lt;mo&gt;.&lt;/mo&gt;&lt;mn&gt;7&lt;/mn&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt; index herein referred to as &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mi&gt;F&lt;/mi&gt;&lt;mn&gt;10&lt;/mn&gt;&lt;mo&gt;.&lt;/mo&gt;&lt;mn&gt;7&lt;/mn&gt;&lt;mi&gt;p&lt;/mi&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt;. The assessment of these indices was accomplished by first developing five storm-time empirical models of the ionosphere with ionospheric total electron content (TEC) as dependent variable, and each of the five solar proxies as the independent variable. As the energy from the Sun differs from one latitudinal region to another on Earth, two locations at different latitudes were considered for the analysis. Based on their long data coverage periods, Hartebeesthoek (HRAO, geographic coordinates: 25.89° S, 27.69° E; geomagnetic coordinates: 36.32° S, 94.69° E), South Africa; and Mbarara (MBAR, geographic coordinates: &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mn&gt;0&lt;/mn&gt;&lt;mo&gt;.&lt;/mo&gt;&lt;mn&gt;60&lt;/mn&gt;&lt;mo&gt;°&lt;/mo&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt; S and &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mn&gt;30&lt;/mn&gt;&lt;mo&gt;.&lt;/mo&gt;&lt;mn&gt;74&lt;/mn&gt;&lt;mo&gt;°&lt;/mo&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt; E, geomagnetic coordinates: &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mn&gt;10&lt;/mn&gt;&lt;mo&gt;.&lt;/mo&gt;&lt;mn&gt;22&lt;/mn&gt;&lt;mo&gt;°&lt;/mo&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt; S and &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mn&gt;102&lt;/mn&gt;&lt;mo&gt;.&lt;/mo&gt;&lt;mn&gt;36&lt;/mn&gt;&lt;mo&gt;°&lt;/mo&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt; E), Uganda, were chosen to represent the middle and low latitude ionospheric regions, respectively. Their data coverage periods are 27 September 1996 to 30 March 2024 (HRAO) and 17 July 2001 to 30 March 2024 (MBAR) and only storm-time TEC data within these periods selected based on the criterion &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mi&gt;D&lt;/mi&gt;&lt;mi&gt;s&lt;/mi&gt;&lt;mi&gt;t&lt;/mi&gt;&lt;mo&gt;⩽&lt;/mo&gt;&lt;mo&gt;−&lt;/mo&gt;&lt;mn&gt;50&lt;/mn&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt; nT or &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;msub&gt;&lt;mrow&gt;&lt;mi&gt;K&lt;/mi&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mi&gt;p&lt;/mi&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;mo&gt;⩾&lt;/mo&gt;&lt;mn&gt;4&lt;/mn&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt; were considered for the statistical analysis. Through PCA decomposition, TEC data were broken up into a matrix of principal directions of the maximum variances in the dataset (or matrix of eigenvectors of the covariance matrix) and a matrix of principal components (PCs) which represent the projection of data onto the principal directions. For each model, PCs were thereafter modelled in terms of the corresponding solar activity index and the modelled quantities were further combined with the original PC vectors to get the reconstructed TEC for the entire period of the stu","PeriodicalId":15096,"journal":{"name":"Journal of Atmospheric and Solar-Terrestrial Physics","volume":"270 ","pages":"Article 106477"},"PeriodicalIF":1.8,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143704494","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A decline of linear relation between outgoing longwave radiation and temperature during geomagnetic disturbances","authors":"A.A. Karakhanyan,&nbsp;S.I. Molodykh","doi":"10.1016/j.jastp.2025.106503","DOIUrl":"10.1016/j.jastp.2025.106503","url":null,"abstract":"<div><div>Climate modeling is the main instrument to predict future climate changes. Despite the recent advances in this field, there is still high uncertainty concerning the contribution of natural (including solar/geomagnetic activity) and anthropogenic factors to the current climate changes. Based on the observational data, we studied the linear relation between Outgoing Longwave Radiation (OLR) and Near-Surface Temperature (NST) under quiet and disturbed geomagnetic conditions 1979 through 2022. Water vapor (due to its optical properties) was established to be the main factor to cause a linear OLR-NST relation. The OLR-NST correlation in the optically thin atmosphere above 30° corresponds to quiet geomagnetic conditions and so does the anticorrelation between the above parameters in the optically thick low-latitude atmosphere. The winter ocean regions of the OLR-NST anticorrelation up to 60° in the both hemispheres under quiet geomagnetic conditions related to the clouds. We found the geomagnetic disturbances lead to decrease in the OLR response to the NST variations in the optically thin atmosphere within the mid- and high latitudes, particularly during spring. The considerable changes of linear OLR-NST relation are observed in the optically thick low-latitude atmosphere during geomagnetic disturbances.</div></div>","PeriodicalId":15096,"journal":{"name":"Journal of Atmospheric and Solar-Terrestrial Physics","volume":"270 ","pages":"Article 106503"},"PeriodicalIF":1.8,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143704427","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of Total Column Ozone derived from CAMS against AIRS datasets across the Indian subcontinent","authors":"Sachin Budakoti","doi":"10.1016/j.jastp.2025.106502","DOIUrl":"10.1016/j.jastp.2025.106502","url":null,"abstract":"<div><div>Present work examined the spatio-temporal variability of total column ozone (TCO) using the Copernicus Atmospheric Monitoring Service (CAMS) reanalysis dataset (2005–2020) over the Indian subcontinent. TCO data from CAMS is evaluated against the TCO from the Atmospheric Infrared Sounder (AIRS) satellite dataset using descriptive statistical analysis. CAMS TCO shows better agreement with AIRS TCO with a correlation coefficient ranging from 0.72 to 0.83 across different regions of Indian subcontinent. From the seasonal variation of TCO, an increasing shift of TCO is observed from low (Southern India) to high (North-West &amp; Indo-Gangetic Plains) latitudinal regions during pre-monsoon and monsoon seasons ranging from 280 to 310 DU. From the seasonal evaluation of CAMS TCO against AIRS TCO a strong correlation ranges from 0.85 to 0.92 is observed during the winter (DJF) and pre-monsoon (MAM) seasons, followed by the monsoon (JJAS) and post-monsoon (ON) seasons. Monthly evaluation of CAMS TCO with AIRS TCO, shows that CAMS TCO underestimates in comparison to AIRS TCO during all the monsoon and post-monsoon months over the Indian sub-continent.</div></div>","PeriodicalId":15096,"journal":{"name":"Journal of Atmospheric and Solar-Terrestrial Physics","volume":"270 ","pages":"Article 106502"},"PeriodicalIF":1.8,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143704039","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dayside dynamics of Alfvén waves during geomagnetic storms, including the initial phase","authors":"Andreas Keiling","doi":"10.1016/j.jastp.2025.106493","DOIUrl":"10.1016/j.jastp.2025.106493","url":null,"abstract":"<div><div>In this study, we investigated the dynamics of Alfvén waves as a global phenomenon in the solar wind-magnetosphere coupling as it occurs at the entry point to the dayside auroral acceleration region. It was found that total Alfvén wave power over the dayside auroral zone increases from 1.3 to 5.5 GW with increasing -<em>Dst</em> value, or alternatively, from minor, to moderate, and to major storm. As the storm intensity increases, the Alfvénic “band” (Alfvénic oval) expands to lower latitude with a peak at 76° invariant latitude (ILT) during the initial phase and a peak at 72° ILT during the storm phase of moderate to strong storms. In comparison with the nightside Alfvénic activity, the most striking result was that during the initial phase, enhanced Alfvén wave power mostly occurred on the dayside, which is in contrast to the storm phase which shows strong enhancement on both dayside and nightside. The different sources of initial phase and storm phase suggest different underlying Alfvén wave generation mechanisms in the dayside. Furthermore, similarity in distribution to the dayside storm aurora during the initial phase suggests contributions of Alfvénic auroral acceleration.</div></div>","PeriodicalId":15096,"journal":{"name":"Journal of Atmospheric and Solar-Terrestrial Physics","volume":"270 ","pages":"Article 106493"},"PeriodicalIF":1.8,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143686432","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatial structure, distribution, and fractal analysis of lightning waiting times in three global hotspots","authors":"Samuel T. Ogunjo ,&nbsp;Bolarinwa J. Adekoya ,&nbsp;Ayomide O. Olabode","doi":"10.1016/j.jastp.2025.106489","DOIUrl":"10.1016/j.jastp.2025.106489","url":null,"abstract":"<div><div>Statistical analysis of inter-event times has been employed to gain information about drivers and classification of events such as neuron spikes and earthquake events. This study extends the concept of inter-event statistics to the space time analysis of lightning events across three global hotspot. Using WWLLN Global Lightning Climatology data spanning 11 years (2010 - 2021), lightning waiting time was evaluated using burstiness, probability distribution, and fractal analysis. Results obtained in this study showed that lightning events at hot spot regions are driven by Poisson processes while the surrounding regions with low lightning activities are bursty in nature. Attempts were made to fit lightning event times to three probability distribution functions - Lognormal, Gamma, and Normal distribution. Both Lognormal and Gamma distributions were found to be good fit for lightning waiting times, while the Normal distribution showed poor performance. Complex analysis of lightning waiting times were also considered using entropy and fractal analysis. Persistence was observed in lightning waiting times across the three hotspot regions considered.</div></div>","PeriodicalId":15096,"journal":{"name":"Journal of Atmospheric and Solar-Terrestrial Physics","volume":"270 ","pages":"Article 106489"},"PeriodicalIF":1.8,"publicationDate":"2025-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143637774","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Latitudinal range of geospace currents inferred from correlation matrix analysis of ground magnetic variations during magnetic storms","authors":"V. Pitsis ,&nbsp;D. Vassiliadis ,&nbsp;A.Z. Boutsi ,&nbsp;G. Balasis ,&nbsp;I.A. Daglis","doi":"10.1016/j.jastp.2025.106488","DOIUrl":"10.1016/j.jastp.2025.106488","url":null,"abstract":"<div><div>In recent years, remote sensing of magnetospheric and ionospheric currents using magnetometer arrays has improved considerably in geographic coverage, precision, and time resolution. Magnetic measurements have been used to detect and interpret a wide range of processes such as convection, solar wind (SW) compressions, substorms, magnetic storms (MSs), and more localized effects such as traveling ionospheric vortices, waves, etc. It is important to develop time- and frequency-domain methods that take advantage of these new observational capabilities. Here we apply a basic, but powerful correlation-based method for measuring the location and width of geospace current over a wide geomagnetic-latitude range. We compute the correlation matrix of the horizontal component of the magnetic field and use the structure of the matrix to measure the spatial extent of three current types. The method is applied to two recent, well-known magnetic-storm intervals (March 2015 and August 2018; 27 days each) as well as pre-storm activity intervals; but can be adapted to shorter time intervals by using higher time-resolution field data. We find that the correlation matrix is divided into three blocks, or geomagnetic latitudinal ranges, which are readily understood in terms of the footprint of the ring current, auroral electrojets, and polar convection intensifications. The matrix structure changes depending on intensity and pattern of geomagnetic activity (before, during and after the storm), and magnetic local time (MLT) range, with direct physical interpretation. The high correlations in each region are a quantitative measure of magnetospheric coherence. The results show how the correlation-matrix analysis can be used in quantitative remote sensing of spatial and temporal features of geospace activity.</div></div>","PeriodicalId":15096,"journal":{"name":"Journal of Atmospheric and Solar-Terrestrial Physics","volume":"270 ","pages":"Article 106488"},"PeriodicalIF":1.8,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143686431","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advanced detection methods and machine learning analysis of temporal and spatial patterns of equatorial plasma bubble depth","authors":"Ifeoluwa Adawa ,&nbsp;Yuichi Otsuka ,&nbsp;Moataz Abdelwahab ,&nbsp;Ayman Mahrous","doi":"10.1016/j.jastp.2025.106495","DOIUrl":"10.1016/j.jastp.2025.106495","url":null,"abstract":"<div><div>Strong plasma density depletions, particularly across broader longitudinal areas with consecutive depletion trains, are challenging to analyse using simple polynomial fitting or moving average techniques for detrending. This study presents a double threshold approach, combining the “Bubble Index” and the “Rolling Barrel Technique,” to detect the depth of plasma irregularities. The proposed method is promising in EPB characterization and depth estimation. We validated the result from our method by analysing the temporal, seasonal, and longitudinal characteristics of Equatorial Plasma Bubbles (EPBs) using data for solar cycle 24 from the Communication/Navigation Outage Forecasting System (C/NOFS), covering about 8 years (August 2008–November 2015). Our Machine Learning (ML) prediction results show that XGBoost outperforms other approaches (Random Forest and LSTM), achieving skill, association, and root mean square error scores of 0.78, 0.88, and 0.14, respectively. Climatological verifications of our analysis show that most EPB events are concentrated between −70° and −30° longitude in the South Atlantic Anomaly (SAA). The depth magnitude of EPBs is inversely proportional to altitude, with shallower depths observed during solar minimum and deeper depths during periods of moderate solar activity. Postsunset bubbles are quite deeper, especially between 19:00 LT and 21:00 LT, having significantly deeper magnitude during equinoxes and shallowest in summer, particularly over South America. As the satellite's apex lowered, the depths of postsunset and postmidnight EPB became nearly equal, highlighting the importance of observation altitude. Analysing EPB depth variation with location and time will enhance understanding of EPB dynamics by identifying how seasonal and solar activity influences EPB formation patterns. These findings will further aid in developing improved ionospheric models for space weather forecasting.</div></div>","PeriodicalId":15096,"journal":{"name":"Journal of Atmospheric and Solar-Terrestrial Physics","volume":"270 ","pages":"Article 106495"},"PeriodicalIF":1.8,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143704502","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}