Space Weather最新文献

Quantifying Extreme Values in Geomagnetic Perturbations Using Ground Magnetic Records 利用地面磁场记录量化地磁扰动的极端值

Space Weather Pub Date : 2024-07-01 DOI: 10.1029/2024sw003991

J. Rodríguez-Zuluaga, J. Gjerloev, S. Ohtani, Y. Zou, B. Anderson

{"title":"Quantifying Extreme Values in Geomagnetic Perturbations Using Ground Magnetic Records","authors":"J. Rodríguez-Zuluaga, J. Gjerloev, S. Ohtani, Y. Zou, B. Anderson","doi":"10.1029/2024sw003991","DOIUrl":"https://doi.org/10.1029/2024sw003991","url":null,"abstract":"We comprehensively analyzed geomagnetic perturbations using ground magnetic records from over 400 stations spanning four solar cycles, from 1976 to 2023. We assess the perturbations in the three magnetic components separately. Our study covers low, middle, and high magnetic latitudes in the northern magnetic hemisphere, with the primary objective of quantifying extreme values and evaluating their variability on magnetic latitude, local time, and solar cycle phases “minimum, ascending, maximum, and declining.” Our findings reveal spatial patterns to be less discernible as perturbations intensify, with distinct responses at middle and high latitudes. The extreme values, defined as percentiles 0 and 100, were observed to be localized and randomly distributed in local time, especially in the east magnetic component. Additionally, we observed dusk‐dawn asymmetries in the magnitude of perturbations related to the auroral electrojets, indicating complex interactions between the magnetosphere and ionosphere. Furthermore, the results reveal a preference for the most significant extreme values to occur in the declining phase of the solar cycle. These insights deepen our understanding of geomagnetic perturbations and their variability, contributing to space weather forecasting and mitigation strategies.","PeriodicalId":510519,"journal":{"name":"Space Weather","volume":"77 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141845090","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Comparing Information Theory Analysis With Cross‐Correlation and Minimum Variance Analysis of the Solar Wind Structures 将信息论分析与太阳风结构的交叉相关分析和最小方差分析进行比较

Space Weather Pub Date : 2024-07-01 DOI: 10.1029/2024sw003870

K. M. Holland, H. Nykyri, X. Ma, S. Wing

{"title":"Comparing Information Theory Analysis With Cross‐Correlation and Minimum Variance Analysis of the Solar Wind Structures","authors":"K. M. Holland, H. Nykyri, X. Ma, S. Wing","doi":"10.1029/2024sw003870","DOIUrl":"https://doi.org/10.1029/2024sw003870","url":null,"abstract":"The space weather effects at the Earth's magnetosphere are mostly driven by the solar wind that carries the interplanetary magnetic field (IMF). In this paper, we use 2 years of data in the solar wind from lunar orbiting ARTEMIS and MMS spacecraft upstream of the Earth's bow shock to study the structure of the IMF. We determine the lag times of IMF structures and their dependence on spacecraft positions by conducting an information theory analysis and comparing it with two traditional analysis methods: cross‐correlation (CC) analysis and minimum variance of magnetic field analysis (MVAB). For the events with long time intervals (i.e., >4 hr) and with small‐spatial separation between the MMS and ARTEMIS along the yGSM‐direction (i.e., <40Re, where Re is the Earth's radius), the lag times based on the CC and the mutual information (MI) analyses statistically agree with each other, with p‐values of 1.675 × 10−7 and 4.833 × 10−9, with the confidence of 95%. Both the results based on MI and CC have a large deviation from the results from MVAB. For some of the events, such a deviation could be improved by taking the fast mode speed into account; however, p‐tests showed that they were not statistically significant to the 95% confidence level.","PeriodicalId":510519,"journal":{"name":"Space Weather","volume":"57 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141845526","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Modeling GIC in the Southern South Island of Aotearoa New Zealand Using Magnetotelluric Data 利用磁位测量数据模拟新西兰奥特亚罗瓦南岛的 GIC

Space Weather Pub Date : 2024-07-01 DOI: 10.1029/2024sw003907

K. Pratscher, M. Ingham, D. H. Mac Manus, M. Kruglyakov, W. Heise, C. Rodger, T. Divett, E. Bertrand, M. Dalzell, J. Brundell

{"title":"Modeling GIC in the Southern South Island of Aotearoa New Zealand Using Magnetotelluric Data","authors":"K. Pratscher, M. Ingham, D. H. Mac Manus, M. Kruglyakov, W. Heise, C. Rodger, T. Divett, E. Bertrand, M. Dalzell, J. Brundell","doi":"10.1029/2024sw003907","DOIUrl":"https://doi.org/10.1029/2024sw003907","url":null,"abstract":"Magnetotelluric (MT) impedances from 62 sites in southern South Island of Aotearoa New Zealand have been used to model geomagnetically induced currents (GIC) in four transformers during two solar storms. Induced electric fields during the storms are calculated from the MT impedances using the magnetic fields measured at the Eyrewell (EYR) geomagnetic observatory, approximately 200 km north of the study area. Calculated GIC during the sudden storm commencements (SSC) give a generally good match to GIC measured by the network operator, Transpower New Zealand. Long period GIC (periods longer than about 10,000 s) are less well modeled. Calculations based on thin‐sheet modeling, which has restrictions on the shortest period of variation which can be modeled, perform less well for the GIC associated with SSC, but are equally good, if not better, at modeling longer period GIC. Consistent underestimation of large GIC at one transformer (HWBT4) near Dunedin are likely to be the result of uncertainty in the assumed values of line, transformer, and earthing resistances. The assumption of a spatially uniform magnetic field across the study area, which is implied by use of the magnetic field measured at EYR as a basis for calculation, may also lead to incorrect calculation of GIC. For one storm use of magnetic field data from a magnetometer within the study area leads to much improved modeling of the observed GIC. This study compares modeled and measured GIC using specifically measured MT impedance data.","PeriodicalId":510519,"journal":{"name":"Space Weather","volume":"14 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141695649","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

China's Ground‐Based Space Environment Monitoring Network—Chinese Meridian Project (CMP) 中国地基空间环境监测网络--子午工程（CMP）

Space Weather Pub Date : 2024-07-01 DOI: 10.1029/2024sw003972

Chi Wang, Jiyao Xu, Zhiqing Chen, Hui Li, Xueshang Feng, Zhao-Yan Huang, Jiangyan Wang

{"title":"China's Ground‐Based Space Environment Monitoring Network—Chinese Meridian Project (CMP)","authors":"Chi Wang, Jiyao Xu, Zhiqing Chen, Hui Li, Xueshang Feng, Zhao-Yan Huang, Jiangyan Wang","doi":"10.1029/2024sw003972","DOIUrl":"https://doi.org/10.1029/2024sw003972","url":null,"abstract":"Monitoring and investigation of the solar‐terrestrial space environment is a huge challenge for humans in space age. To this end, China has established the Ground‐based Space Environment Monitoring Network, namely Chinese Meridian Project (CMP). The project comprises three major systems: the Space Environment Monitoring System, Data and Communication System, and Scientific Application System. The Space Environment Monitoring System adopts a well‐designed monitoring architecture, known as “One Chain, Three Networks, and Four Focuses,” to achieve stereoscopic and comprehensive monitoring of the entire solar‐terrestrial space. The “One‐Chain” component utilizes optical, radio, interplanetary scintillation, cosmic ray instruments to cover the causal chain of space weather disturbances from the solar surface to near‐Earth space. For the ionosphere, middle and upper atmosphere, and magnetic field, instruments are deployed along longitudes of 120° and 100°E, and latitudes of 30° and 40°N, forming the “Three Networks.” Furthermore, more powerful monitoring facilities or large‐scale instruments have been deployed in four key regions: the high‐latitude polar region, mid‐latitude region in northern China, low‐latitude region at Hainan Island, and the Tibet region. These four regions are crucial for disturbances propagation and evolution, or possess unique geographical and topographical characteristics. The Data and Communication System and Scientific Application System are designed for data collecting, processing, storage, mining, and providing user service based on data acquired by the Space Environment Monitoring System. The data obtained by CMP will be shared with the global scientific community, facilitating enhanced collaboration on space weather and space physics research.","PeriodicalId":510519,"journal":{"name":"Space Weather","volume":"5 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141705200","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Empirical Model of Equatorial ElectroJet (EEJ) Using Long‐Term Observations From the Indian Sector 利用印度扇区的长期观测数据建立赤道电射流（EEJ）的经验模型

Space Weather Pub Date : 2024-07-01 DOI: 10.1029/2024sw003988

S. Tulasi Ram, M. Ankita, B. Nilam, S. Gurubaran, Manoj Nair, Gopi K Seemala, P. S. Brahmanandam, A. Dimri

{"title":"Empirical Model of Equatorial ElectroJet (EEJ) Using Long‐Term Observations From the Indian Sector","authors":"S. Tulasi Ram, M. Ankita, B. Nilam, S. Gurubaran, Manoj Nair, Gopi K Seemala, P. S. Brahmanandam, A. Dimri","doi":"10.1029/2024sw003988","DOIUrl":"https://doi.org/10.1029/2024sw003988","url":null,"abstract":"The Equatorial Electrojet (EEJ) is one of the important near‐earth space weather phenomena which exhibits significant diurnal, seasonal and solar activity variations. This paper investigates the EEJ variations at diurnal, seasonal and solar cycle time scales from the Indian sector and portrays a new empirical EEJ field model developed using the observations spanning over nearly two solar cycles. The Method of Naturally Orthogonal Components (MNOC), also known as Principal Component Analysis (PCA), was employed to extract the dominant patterns of principal diurnal, semi‐diurnal, and ter‐diurnal components contributing to the EEJ variation. The amplitudes of these diurnal, semi‐diurnal, and ter‐diurnal components in EEJ are found to vary significantly with the season and solar activity. The seasonal and solar activity dependencies of these principal components are modeled using suitable bimodal distribution functions. Finally, the empirical model for EEJ field was built by combining the principal components with their corresponding modeled amplitudes. This model accurately reproduces the diurnal, seasonal and solar activity variations of EEJ. The modeled monthly mean variations of EEJ field at ground exhibit excellent correlation of 0.96 with the observations with the root mean square error <5 nT. It also successfully captures the seasonal and solar activity variations of Counter Electrojet (CEJ). Finally, this model named “Indian Equatorial Electrojet (IEEJ) Model” is made publicly available for interested scientific users (https://iigm.res.in/system/files/IEEJ_model.html).","PeriodicalId":510519,"journal":{"name":"Space Weather","volume":"17 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141848719","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep Learning‐Based Prediction of Global Ionospheric TEC During Storm Periods: Mixed CNN‐BiLSTM Method 基于深度学习的风暴期全球电离层 TEC 预测：混合 CNN-BiLSTM 方法

Space Weather Pub Date : 2024-07-01 DOI: 10.1029/2024sw003877

Xiaochen Ren, Biqiang Zhao, Zhipeng Ren, Yan Wang, Bo Xiong

{"title":"Deep Learning‐Based Prediction of Global Ionospheric TEC During Storm Periods: Mixed CNN‐BiLSTM Method","authors":"Xiaochen Ren, Biqiang Zhao, Zhipeng Ren, Yan Wang, Bo Xiong","doi":"10.1029/2024sw003877","DOIUrl":"https://doi.org/10.1029/2024sw003877","url":null,"abstract":"The application of deep learning in high‐precision ionospheric parameter prediction has become one of the focus in space weather research. In this study, an improved model called Mixed Convolutional Neural Networks (CNN)—Bi‐Long Short Term Memory is proposed for predicting future ionospheric Total Electron Content (TEC). The model is trained using the longest available (25 years) Global Ionospheric Maps‐TEC and evaluated the accuracy of ionospheric storm predictions. The results indicate that using historical TEC in the solar‐geographical reference frame as input driving data achieves higher prediction accuracy compared to that in the geocentric coordinate system. Additionally, by comparing different input parameters, it is found that incorporating the Kp, ap, and Dst indices as inputs to the model effectively improves its accuracy, especially in long‐term forecasting where R2 increased by 3.49% and Root Mean Square Error decreased by 13.48%. Compared with BiLSTM‐Deep Neural Networks (DNN) and CNN‐BiLSTM, the Mixed CNN‐BiLSTM model has the highest prediction accuracy. It suggests that the utilization of CNN modules for processing spatial information, along with the incorporation of DNN modules to incorporate geomagnetic indices for result correction. Moreover, in short‐term predictions, the model accurately forecasts the evolution process of ionospheric storms. When extending the predicted length, although there are cases of prediction errors, the model still captures the entire process of ionospheric storms. Furthermore, the predicted results are significantly influenced by longitude, magnetic latitude, and local time.","PeriodicalId":510519,"journal":{"name":"Space Weather","volume":"25 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141712134","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Inner Radiation Belt Simulations During the Successive Geomagnetic Storm Event of February 2022 2022 年 2 月连续地磁暴事件期间的内辐射带模拟

Space Weather Pub Date : 2024-07-01 DOI: 10.1029/2023sw003789

Kirolosse M. Girgis, Tohru Hada, Akimasa Yoshikawa, S. Matsukiyo, Abraham C.‐L. Chian, Ezequiel Echer

引用次数: 0

Investigation of the Contribution of Five Broadcast Ionospheric Models (GPSK, NTCMG, NEQG, BDGIM, and BDSK) and IRTG to GNSS Positioning During Different Solar Activities in Solar Cycle 25 调查五个广播电离层模型（GPSK、NTCMG、NEQG、BDGIM 和 BDSK）和 IRTG 在太阳周期 25 不同太阳活动期间对全球导航卫星系统定位的贡献

Space Weather Pub Date : 2024-07-01 DOI: 10.1029/2023sw003829

Min Li, Yunbin Yuan, Ting Zhang, Hanying Xu, X. Huo, Wenyao Zhang

{"title":"Investigation of the Contribution of Five Broadcast Ionospheric Models (GPSK, NTCMG, NEQG, BDGIM, and BDSK) and IRTG to GNSS Positioning During Different Solar Activities in Solar Cycle 25","authors":"Min Li, Yunbin Yuan, Ting Zhang, Hanying Xu, X. Huo, Wenyao Zhang","doi":"10.1029/2023sw003829","DOIUrl":"https://doi.org/10.1029/2023sw003829","url":null,"abstract":"Additional ionospheric information is essential for mitigating errors in single‐frequency (SF) Global Navigation Satellite Systems (GNSS) positioning. The increasing number of low‐cost dual‐frequency (DF) receiver users faces limitations in tracking DF observables compared to traditional geodetic receivers. Consequently, ionospheric correction algorithms (ICAs) are also essential for low‐cost DF devices in hybrid‐frequency positioning. To evaluate the performance of commonly used ICAs during solar cycle 25, our study presents a global statistical investigation of the contribution of five broadcast ionospheric models (BIMs) and the International GNSS Service (IGS) combined real‐time global ionospheric maps (IRTG) to the positioning domain, covering both quiet and perturbed ionospheric conditions. The BIMs investigated include the GPS Klobuchar (GPSK), Galileo NequickG (NEQG), NTCM‐GlAzpar (NTCMG), BDS‐2 Klobuchar (BDSK), and BeiDou Global Ionospheric delay correction Model (BDGIM). Experimental results from standard point positioning indicate that IRTG demonstrates superior overall accuracy compared to all BIMs, with a mean 3D root mean squared (RMS) of 2.76 m during perturbed period. Specifically, GPSK, NTCMG, NEQG, BDGIM, and BDSK exhibit RMS values of 2.03, 1.67, 1.72, 1.62, and 2.36 m during quiet conditions, and 4.02, 3.17, 2.86, 3.14, and 4.44 m during perturbed conditions, respectively. Among the BIMs, NEQG demonstrates superior performance at middle and high latitudes but exhibits lower accuracy than NTCMG and BDGIM at low latitudes during daytime under quiet conditions. BDGIM performs slightly better than NTCMG at low latitudes but slightly worse at high latitudes. BDSK shows notable improvement for high‐ and mid‐latitude regions since 3 June 2020.","PeriodicalId":510519,"journal":{"name":"Space Weather","volume":"136 37","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141713785","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MAOOA‐Residual‐Attention‐BiConvLSTM: An Automated Deep Learning Framework for Global TEC Map Prediction MAOOA-Residual-Attention-BiConvLSTM：用于全球 TEC 地图预测的自动深度学习框架

Space Weather Pub Date : 2024-07-01 DOI: 10.1029/2024sw003954

Haoran Wang, Haijun Liu, Jing Yuan, H. Le, Weifeng Shan, Liangchao Li

{"title":"MAOOA‐Residual‐Attention‐BiConvLSTM: An Automated Deep Learning Framework for Global TEC Map Prediction","authors":"Haoran Wang, Haijun Liu, Jing Yuan, H. Le, Weifeng Shan, Liangchao Li","doi":"10.1029/2024sw003954","DOIUrl":"https://doi.org/10.1029/2024sw003954","url":null,"abstract":"The high‐precision prediction of total ionospheric electron content (TEC) is of great significance for improving the accuracy of global navigation satellite systems. There are two problems with the current prediction of TEC: (a) The existing TEC prediction models mainly based on stacked structure, which has insufficient predictive ability when the network has fewer layers, and loss of fine‐grained features when there are more layers, resulting in a decrease in predictive performance; (b) The existing research on ionospheric TEC prediction mainly focuses on building deep learning prediction models, while there is little research on optimizing the hyper‐parameters of TEC prediction models. Optimization can help find a better quasi‐optimal hyperparameter combination and improve the performance of the model. This paper proposed an automatic deep learning framework for global TEC map prediction, named MAOOA‐Residual‐Attitude‐BiConvLSTM. This framework includes a TEC prediction model, Residual‐Attention‐BiConvLSTM, which can simultaneously consider both coarse‐grained and fine‐grained spatiotemporal features. It also includes an optimization algorithm, MAOOA, for optimizing the hyper‐parameters of the model. We conducted comparative experiments between our framework and C1PG, ConvLSTM, ConvGRU, and ED‐ConvLSTM during high solar activity years, low solar activity years, and a magnetic storm event. The results indicate that in all cases, the framework proposed in this paper outperforms the comparative models.","PeriodicalId":510519,"journal":{"name":"Space Weather","volume":"30 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141697993","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhanced Sporadic E Layer and Its Perturbations During the 2022 Hunga Volcanic Eruption 2022 年洪加火山喷发期间增强的零星 E 层及其扰动

Space Weather Pub Date : 2024-07-01 DOI: 10.1029/2023sw003837

Lihui Qiu, Huixin Liu, Yifan Qi, F. Poblet

{"title":"Enhanced Sporadic E Layer and Its Perturbations During the 2022 Hunga Volcanic Eruption","authors":"Lihui Qiu, Huixin Liu, Yifan Qi, F. Poblet","doi":"10.1029/2023sw003837","DOIUrl":"https://doi.org/10.1029/2023sw003837","url":null,"abstract":"Sporadic E (Es) layers are plasma irregularities significantly affecting radio communication and navigation systems. And, their dominant formation mechanism at mid‐latitudes, known as the wind shear theory, suggests that they serve as indicators of the atmosphere‐ionosphere coupling processes in the mesosphere and lower thermosphere region. On 15 January 2022, the Hunga Tonga‐Hunga Ha'api submarine volcanic eruption provided a unique opportunity to investigate the Es layer responses to lower atmospheric perturbations. Using the FORMOSAT‐7/COSMIC‐2 radio occultation and ground‐based ionosonde observations, this study reveals the spatial‐temporal behaviors of the Es layers after the Hunga volcanic eruption. The results show that significant Es layer perturbations occurred over the northwest of the epicenter ∼4 hr after the eruption and lasted for approximately ∼22 hr. We also calculated the geographical distribution of the vertical ion convergence (VIC) using neutral winds obtained from the Michelson Interferometer for Global High‐resolution Thermospheric Imaging on the Ionospheric Connection Explorer (ICON) satellite. A comparison of the geographical distribution of positive VIC and Es layer perturbations shows a good agreement, which indicates that the enhanced Es layers are caused by strong VIC associated with the atmospheric perturbations due to the eruption. This study presents observational evidence for coupling between the Es layer and lower atmospheric perturbations, which can be helpful for understanding the occasionality and variability of Es layer occurrence.","PeriodicalId":510519,"journal":{"name":"Space Weather","volume":"32 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141702321","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0