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The Growth and Decay of Intense GNSS Amplitude and Phase Scintillation During Non-Storm Conditions.
IF 3.8 2区 地球科学 Q2 ASTRONOMY & ASTROPHYSICS Pub Date : 2024-12-01 Epub Date: 2024-11-30 DOI: 10.1029/2024SW004108
Mahith Madhanakumar, Andres Spicher, Juha Vierinen, Kjellmar Oksavik, Anthea J Coster, Devin Ray Huyghebaert, Carley J Martin, Ingemar Häggström, Larry J Paxton

A multi-instrument study is conducted at the dayside polar ionosphere to investigate the spatio-temporal evolution of scintillation in Global Navigation Satellite System (GNSS) signals during non-storm conditions. Bursts of intense amplitude and phase scintillation started to occur at 9 MLT and persisted for more than 1 hour implying the simultaneous existence of Fresnel and large-scale sized irregularities of significant strength in the pre-noon sector. Measurements from the EISCAT radar in Svalbard (ESR) revealed the presence of dense plasma structures with significant gradients in regions of strong Joule heating/fast flows and soft precipitation when scintillation was enhanced. Plasma structuring down to Fresnel scales were observed both in the auroral oval as well as inside the polar cap with the associated amplitude scintillation exhibiting similar strengths regardless of whether the density structures were in regions of active auroral dynamics or not. The observations are placed within the context of different sources of free energy, providing insights into the important mechanisms that generate irregularities capable of perturbing GNSS signal properties in the dayside ionosphere. Furthermore, a strong negative excursion in the interplanetary magnetic field (IMF) B y component during the northward turning of B z led to the transport of a depleted region of plasma density into the post-noon sector that significantly weakened both amplitude and phase scintillation.

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引用次数: 0
Super-Intense Geomagnetic Storm on 10-11 May 2024: Possible Mechanisms and Impacts.
IF 3.8 2区 地球科学 Q2 ASTRONOMY & ASTROPHYSICS Pub Date : 2024-12-01 Epub Date: 2024-11-27 DOI: 10.1029/2024SW004126
S Tulasi Ram, B Veenadhari, A P Dimri, J Bulusu, M Bagiya, S Gurubaran, N Parihar, B Remya, G Seemala, Rajesh Singh, S Sripathi, S V Singh, G Vichare

One of the most intense geomagnetic storms of recent times occurred on 10-11 May 2024. With a peak negative excursion of Sym-H below -500 nT, this storm is the second largest of the space era. Solar wind energy transferred through radiation and mass coupling affected the entire Geospace. Our study revealed that the dayside magnetopause was compressed below the geostationary orbit (6.6 RE) for continuously ∼6 hr due to strong Solar Wind Dynamic Pressure (SWDP). Tremendous compression pushed the bow-shock also to below the geostationary orbit for a few minutes. Magnetohydrodynamic models suggest that the magnetopause location could be as low as 3.3RE. We show that a unique combination of high SWDP (≥15 nPa) with an intense eastward interplanetary electric field (IEFY ≥ 2.5 mV/m) within a super-dense Interplanetary Coronal Mass Ejection lasted for 409 min-is the key factor that led to the strong ring current at much closer to the Earth causing such an intense storm. Severe electrodynamic disturbances led to a strong positive ionospheric storm with more than 100% increase in dayside ionospheric Total Electron Content (TEC), affecting GPS positioning/navigation. Further, an HF radio blackout was found to occur in the 2-12 MHz frequency band due to strong D- and E-region ionization resulting from a solar flare prior to this storm.

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引用次数: 0
Influence of Solar Wind High-Speed Streams on the Brazilian Low-Latitude Ionosphere During the Descending Phase of Solar Cycle 24.
IF 3.8 2区 地球科学 Q2 ASTRONOMY & ASTROPHYSICS Pub Date : 2024-12-01 Epub Date: 2024-11-28 DOI: 10.1029/2024SW003873
S P Moraes-Santos, C M N Cândido, F Becker-Guedes, B Nava, V Klausner, C Borries, F S Chingarandi, T O Osanyin

This study investigates the Brazilian low-latitude ionospheric response to CIR/HSS-driven geomagnetic storms during the declining phase of solar cycle 24, from 2016 to 2017. In this period the geomagnetic storms were mostly moderate, SymHmin ≈ -72 nT, AEmax ≈ 1580 nT, Vswmax ≈ 690 km/s and lasted, on average, for 6 days. We analyze the variations in Vertical Total Electron Content (VTEC) at three representative regions: bele, over the equatorial region; boav and cuib, at the northern and southern crests of the Equatorial Ionization Anomaly. Our findings reveal the role of High-Speed Solar Wind Streams and Corotating Interaction Region-driven geomagnetic storms. The VTEC intensifications were up to 30 TECu, during the daytime and nighttime. Additionally, three categories of nighttime enhancements were observed and analyzed with distinct characteristics and levels of pre-reversal strengthening; Depletions up to 20 TECu also occurred during the day and nighttime. The delay between the storm commencement and the positive and negative variations were, on average, 7 and 20 hours, respectively. We discuss the Prompt Penetration Electric Fields and Disturbance Dynamo Electric Fields following the magnetic reconnection between Earth's and interplanetary magnetic field, using observational data and modeling. Furthermore, this study presents catalogs of low-latitude ionospheric storms, providing detailed information for space weather applications and ionospheric modeling.

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引用次数: 0
Interhemispheric Asymmetry in the High-Latitude Neutral Density Variations During the 13-14 March 2022 Storm. 2022 年 3 月 13-14 日风暴期间高纬度中性密度变化的半球间不对称性。
IF 3.8 2区 地球科学 Q2 ASTRONOMY & ASTROPHYSICS Pub Date : 2024-11-01 Epub Date: 2024-11-26 DOI: 10.1029/2024SW004084
Qingyu Zhu, Gang Lu, Sarah Vines, Marc Hairston

During the second recovery phase of the 13-14 March 2022 storm, intense high-latitude neutral mass density spikes are detected by satellites at ∼500 km in both hemispheres. These density spikes, accurately modeled by the Global Ionospheric Thermosphere Model (GITM), are identified as high-latitude neutral mass density anomalies (HDAs). The GITM simulation indicates that these HDAs, which extends over the polar region, are important features in high-latitude neutral density. Furthermore, GITM reveals that these HDAs are manifestations of transpolar traveling atmospheric disturbances triggered on the dawn side. Moreover, GITM also reveals significant interhemispheric asymmetries (IHAs) in the magnitude, propagation speed, and propagation direction of HDAs, which are linked to the IHAs in the distribution and magnitude of Joule heating deposited as well as the thermospheric background conditions. This study presents a dynamic perspective on the IHA of storm-time high-latitude neutral density variations that is particularly helpful to the proper interpretation of satellite observations.

在 2022 年 3 月 13-14 日风暴的第二个恢复阶段,两个半球 500 公里处的卫星都探测到了强烈的高纬度中性质量密度尖峰。全球电离层热层模型(GITM)对这些密度尖峰进行了精确建模,并将其确定为高纬度中性质量密度异常(HDAs)。全球电离层热层模型模拟结果表明,这些延伸到极区的 HDA 是高纬度中性密度的重要特征。此外,GITM 还揭示了这些 HDA 是在黎明侧引发的跨极地巡回大气扰动的表现形式。此外,GITM 还揭示了 HDA 在幅度、传播速度和传播方向上的显著半球间不对称性(IHAs),这与沉积的焦耳热分布和幅度以及热层背景条件的 IHAs 有关。本研究从动态角度介绍了风暴时高纬度中性密度变化的 IHA,特别有助于正确解释卫星观测结果。
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引用次数: 0
Forecasting Ionospheric foF2 Using Bidirectional LSTM and Attention Mechanism 利用双向LSTM和关注机制预测电离层of2
2区 地球科学 Q2 ASTRONOMY & ASTROPHYSICS Pub Date : 2023-11-01 DOI: 10.1029/2023sw003508
Jun Tang, Dengpan Yang, Mingfei Ding
Abstract The critical frequency of ionospheric F2 layer (foF2) is an important ionospheric characteristic parameter. In this paper, a deep learning model based on Bidirectional long short‐term memory (BiLSTM) and attention mechanism is implemented for predicting the foF2 parameter. The inputs of models are the foF2 of globally available ionospheric ionosonde stations, geographic longitude and latitude, world time (UT), geomagnetic activity index, and solar activity index from 2015 to 2017. The superiority of the model is analyzed from different latitudes, seasons, and geomagnetic conditions. The results show that the prediction performance of the Bidirectional long short‐term memory model based on attention mechanism (BiLSTM‐Attention) is better than other models. The performance of the prediction model is optimal at high latitudes. The root mean square error (RMSE) and correlation coefficient (R) of the BiLSTM‐Attention model are 0.539 MHZ and 0.908 MHz at high latitudes, respectively. In terms of RMSE, it is 25.243%, 18.209%, and 11.203% lower than those of the international reference ionosphere (IRI), LSTM, and BiLSTM models, respectively. The prediction results of the four seasons show that the models are more applicable in winter. Compared with the IRI model, the RMSE of the BiLSTM‐Attention model in spring, summer, autumn, and winter is reduced by 24.344%, 21.181%, 25.058%, and 30.948%, respectively. The prediction effect of the BiLSTM‐Attention model is improved in the magnetic quiet period, the magnetic moderate period and the magnetic storm period. Also, the improvement effect is more obvious in the magnetostatic day, and the RMSE is reduced by 27.462% compared with the IRI model.
电离层F2层临界频率(foF2)是电离层重要的特征参数。本文提出了一种基于双向长短期记忆(BiLSTM)和注意机制的深度学习模型,用于预测foF2参数。模型输入2015 - 2017年全球电离层电离层探空站foF2、地理经纬度、世界时间(UT)、地磁活动指数和太阳活动指数。在不同纬度、季节和地磁条件下分析了模型的优越性。结果表明,基于注意机制的双向长短期记忆模型(BiLSTM‐attention)的预测性能优于其他模型。该模型在高纬度地区的预报效果最佳。在高纬度地区,BiLSTM‐Attention模型的均方根误差(RMSE)和相关系数(R)分别为0.539 MHZ和0.908 MHZ。RMSE分别比国际参考电离层(IRI)、LSTM和BiLSTM模式低25.243%、18.209%和11.203%。四季预报结果表明,该模型在冬季更适用。与IRI模型相比,BiLSTM‐Attention模型在春季、夏季、秋季和冬季的RMSE分别降低了24.344%、21.181%、25.058%和30.948%。在磁平静期、磁温和期和磁暴期,BiLSTM‐Attention模型的预报效果得到了提高。在静磁日,改进效果更为明显,RMSE较IRI模型降低了27.462%。
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引用次数: 0
Automatic Detection and Classification of Spread‐F From Ionosonde at Hainan With Image‐Based Deep Learning Method 基于图像深度学习的海南电离探空仪扩散F自动检测与分类
2区 地球科学 Q2 ASTRONOMY & ASTROPHYSICS Pub Date : 2023-11-01 DOI: 10.1029/2023sw003498
Zheng Wang, Meiyi Zhan, Pengdong Gao, Guojun Wang, Chu Qiu, Quan Qi, Jiankui Shi, Xiao Wang
Abstract An 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.
摘要提出了一种基于离子图图像集的基于深度学习模型的智能Spread - F图像检测与分类方法。海南台站2002年至2015年的离子图图像被人工标记为五类,形成了一个独特的离子图图像集,用于监督学习模型。为了平衡不同类型的数量,模拟噪声被添加到这些图像中。基于8万个带有Spread‐F的样本和2万个没有Spread‐F的样本,我们进行了大量的实验来训练VGG、ResNet、EfficientNet、ViT、MobileNet和其他网络。在测试集上的结果表明,除VGG外,这些模型对不同类型的特征都具有较好的精确提取能力,从而对Spread‐F的检测具有较高的准确性,对其进行了相对准确的分类。然后将2016年的离子图图像用作另一个测试集,以进一步检查训练模型的性能。定量和定性分析都表明,深度学习模型获得的结果与人工识别高度一致。
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引用次数: 0
Forecasting Geomagnetic Storm Disturbances and Their Uncertainties Using Deep Learning 利用深度学习预测地磁风暴扰动及其不确定性
2区 地球科学 Q2 ASTRONOMY & ASTROPHYSICS Pub Date : 2023-11-01 DOI: 10.1029/2023sw003474
D. Conde, F. L. Castillo, C. Escobar, C. García, J. E. García, V. Sanz, B. Zaldívar, J. J. Curto, S. Marsal, J. M. Torta
Abstract Severe space weather produced by disturbed conditions on the Sun results in harmful effects both for humans in space and in high‐latitude flights, and for technological systems such as spacecraft or communications. Also, geomagnetically induced currents (GICs) flowing on long ground‐based conductors, such as power networks, potentially threaten critical infrastructures on Earth. The first step in developing an alarm system against GICs is to forecast them. This is a challenging task given the highly non‐linear dependencies of the response of the magnetosphere to these perturbations. In the last few years, modern machine‐learning models have shown to be very good at predicting magnetic activity indices. However, such complex models are on the one hand difficult to tune, and on the other hand they are known to bring along potentially large prediction uncertainties which are generally difficult to estimate. In this work we aim at predicting the SYM‐H index characterizing geomagnetic storms multiple‐hour ahead, using public interplanetary magnetic field (IMF) data from the Sun‐Earth L1 Lagrange point and SYM‐H data. We implement a type of machine‐learning model called long short‐term memory (LSTM) network. Our scope is to estimate the prediction uncertainties coming from a deep‐learning model in the context of forecasting the SYM‐H index. These uncertainties will be essential to set reliable alarm thresholds. The resulting uncertainties turn out to be sizable at the critical stages of the geomagnetic storms. Our methodology includes as well an efficient optimization of important hyper‐parameters of the LSTM network and robustness tests.
太阳扰动条件产生的恶劣空间天气对空间和高纬度飞行中的人类以及航天器或通信等技术系统都造成有害影响。此外,地磁感应电流(gic)在长接地导体(如电网)上流动,可能会威胁到地球上的关键基础设施。开发针对gic的警报系统的第一步是预测它们。考虑到磁层对这些扰动的响应是高度非线性的,这是一项具有挑战性的任务。在过去的几年里,现代机器学习模型在预测地磁活动指数方面表现得非常出色。然而,这种复杂的模型一方面难以调整,另一方面,众所周知,它们可能带来很大的预测不确定性,这些不确定性通常难以估计。在这项工作中,我们的目标是利用来自太阳-地球L1拉格朗日点的公共行星际磁场(IMF)数据和SYM - H数据,提前数小时预测表征地磁风暴的SYM - H指数。我们实现了一种称为长短期记忆(LSTM)网络的机器学习模型。我们的范围是在预测SYM - H指数的背景下,估计来自深度学习模型的预测不确定性。这些不确定性对于设置可靠的警报阈值至关重要。由此产生的不确定性在地磁风暴的关键阶段是相当大的。我们的方法还包括LSTM网络重要超参数的有效优化和鲁棒性测试。
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引用次数: 0
A Series of Advances in Analytic Interplanetary CME Modeling 解析行星际CME模型的一系列进展
2区 地球科学 Q2 ASTRONOMY & ASTROPHYSICS Pub Date : 2023-10-31 DOI: 10.1029/2023sw003647
C. Kay, T. Nieves‐Chinchilla, S. J. Hofmeister, E. Palmerio, V. E. Ledvina
Abstract Coronal mass ejections (CMEs) and high speed streams (HSSs) are large‐scale transient structures that routinely propagate away from the Sun. Individually, they can cause space weather effects at the Earth, or elsewhere in space, but many of the largest events occur when these structures interact during their interplanetary propagation. We present the initial coupling of Open Solar Physics Rapid Ensemble Information (OSPREI), a model for CME evolution, with Mostly Empirical Operational Wind with a High Speed Stream, a time‐dependent HSS model that can serve as a background for the OSPREI CME. We present several improvements made to OSPREI in order to take advantage of the new time‐dependent, higher‐dimension background. This includes an update in the drag calculation and the ability to determine the rotation of a yaw‐like angle. We present several theoretical case studies, describing the difference in the CME behavior between a HSS background and a quiescent one. This behavior includes interplanetary CME propagation, expansion, deformation, and rotation, as well as the formation of a CME‐driven sheath. We also determine how the CME behavior changes with the HSS size and initial front distance. Generally, for a fast CME, we see that the drag is greatly reduced within the HSS, leading to faster CMEs and shorter travel times. The drag reappears stronger if the CME reaches the stream interaction region or upstream solar wind, leading to a stronger shock with more compression until the CME sufficiently decelerates. We model a CME–HSS interaction event observed by Parker Solar Probe in January 2022. The model improvements create a better match to the observed in situ profiles.
日冕物质抛射(cme)和高速流(hss)是一种大规模的瞬态结构,通常会从太阳向外传播。单独来说,它们可以在地球或太空中的其他地方造成空间天气影响,但当这些结构在星际传播过程中相互作用时,就会发生许多最大的事件。我们提出了开放太阳物理快速集合信息(OSPREI)的初始耦合,一个CME演化模型,与高速流的主要经验操作风,一个时间相关的HSS模型,可以作为OSPREI CME的背景。为了利用新的时间相关的高维背景,我们提出了对OSPREI进行的几项改进。这包括在拖动计算中的更新和确定偏航角旋转的能力。我们提出了几个理论案例研究,描述了在HSS背景和静止背景下CME行为的差异。这种行为包括行星际抛射的传播、膨胀、变形和旋转,以及抛射驱动鞘层的形成。我们还确定了CME的行为如何随着HSS大小和初始锋面距离的变化而变化。一般来说,对于一个快速日冕抛射,我们看到在高通量内的阻力大大减少,导致日冕抛射更快,旅行时间更短。如果日冕物质抛射到达流相互作用区域或上游太阳风,阻力会再次变得更强,导致更强的激波和更多的压缩,直到日冕物质抛射充分减速。我们模拟了帕克太阳探测器在2022年1月观测到的CME-HSS相互作用事件。模型的改进使其与观测到的原位剖面更好地匹配。
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引用次数: 0
Ensemble Forecasts of Solar Wind Connectivity to 1 Rs Using ADAPT‐WSA 利用ADAPT - WSA对1rs太阳风连通性的集合预报
2区 地球科学 Q2 ASTRONOMY & ASTROPHYSICS Pub Date : 2023-10-01 DOI: 10.1029/2023sw003554
D. E. da Silva, S. Wallace, C. N. Arge, S. Jones
Abstract The solar wind which arrives at any location in the solar system is, in principle, relatable to the outflow of solar plasma from a single source location. This source location, itself usually being part of a larger coronal hole, is traceable to 1 R S along the Sun's magnetic field, in which the entire path from 1 R S to a location in the heliosphere is referred to as the solar wind connectivity. While not directly measurable, the connectivity between the near‐Earth solar wind is of particular importance to space weather. The solar wind solar source region can be obtained by leveraging near‐sun magnetic field models and a model of the interplanetary solar wind. In this article, we present a method for making an ensemble forecast of the connectivity presented as a probability distribution obtained from a weighted collection of individual forecasts from the combined Air Force Data Assimilative Photospheric Flux Transport‐Wang Sheeley Arge (ADAPT‐WSA) model. The ADAPT model derives the photospheric magnetic field from synchronic magnetogram data, using flux transport physics and ongoing data assimilation processes. The WSA model uses a coupled set of potential field type models to derive the coronal magnetic field, and an empirical relationship to derive the terminal solar wind speed observed at Earth. Our method produces an arbitrary 2D probability distribution capable of reflecting complex source configurations with minimal assumptions about the distribution structure, prepared in a computationally efficient manner.
到达太阳系中任何位置的太阳风,原则上都与太阳等离子体从单一源位置流出有关。这个源位置,本身通常是一个更大的日冕洞的一部分,沿着太阳磁场可以追溯到1rs,其中从1rs到日球层某个位置的整个路径被称为太阳风连系。虽然不能直接测量,但近地太阳风之间的连通性对空间天气特别重要。利用近太阳磁场模型和行星际太阳风模型可以得到太阳风的太阳源区域。在本文中,我们提出了一种对连通性进行整体预测的方法,该方法以概率分布的形式呈现,该概率分布是由空军数据同化光球通量输送-王希利大(ADAPT - WSA)联合模型的单个预测的加权集合获得的。ADAPT模型利用通量输运物理和持续数据同化过程,从同步磁图数据推导出光球磁场。WSA模型使用一组耦合的势场型模型来推导日冕磁场,并使用经验关系来推导在地球观测到的终端太阳风速度。我们的方法产生了一个任意的二维概率分布,能够反映复杂的源配置,对分布结构的假设最少,以计算效率高的方式制备。
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引用次数: 0
Resolving Moving Heliospheric Structures Using Interplanetary Scintillation Observations With the Murchison Widefield Array 利用默奇森宽场阵列的行星际闪烁观测来解析移动的日球层结构
2区 地球科学 Q2 ASTRONOMY & ASTROPHYSICS Pub Date : 2023-10-01 DOI: 10.1029/2023sw003570
A. Waszewski, J. S. Morgan, R. Chhetri, R. Ekers, M. C. M. Cheung, N. D. R. Bhat, M. Johnston‐Hollitt
Abstract We have conducted a blind search in 49 consecutive days of interplanetary scintillation observations made by the Murchison Widefield Array from mid‐2019, with overlapping daily observations approximately East and South‐East of the Sun at an elongation of ∼30° and a field of view of 30°. These observations detect an unprecedented density of sources. In spite of these observations being taken at sunspot minimum, this search has revealed several interesting transitory features characterized by elevated scintillation levels. One solar wind enhancement is captured in two observations several hours apart, allowing its radial movement away from the Sun to be measured. We present here a methodology for measuring the plane‐of‐sky velocity for the moving heliospheric structure. The plane‐of‐sky velocity was inferred as 0.66 ± 0.147 hr −1 , or 480 ± 106 kms −1 assuming a distance of 1AU. After cross‐referencing our observed structure with multiple catalogs of heliospheric events, we propose that the likely source of our observed structure is a stream‐interaction region originating from a low‐latitude coronal hole. This work demonstrates the power of widefield interplanetary scintillation observations to capture detailed features in the heliosphere which are otherwise unresolvable and go undetected.
从2019年中期开始,我们对默奇森宽场阵列(Murchison Widefield Array)连续49天的行星际闪烁观测进行了盲搜,每天的观测重叠在太阳的东侧和东南侧,延伸约30°,视场为30°。这些观测发现了前所未有的辐射源密度。尽管这些观测是在太阳黑子最小的时候进行的,但这次搜索已经揭示了几个有趣的瞬变特征,其特征是闪烁水平升高。一次太阳风增强是在相隔几小时的两次观测中捕捉到的,从而可以测量其远离太阳的径向运动。我们在这里提出了一种测量运动日球层结构的天空平面速度的方法。假设距离为1AU,平面速度推断为0.66±0.147小时−1,或480±106公里−1。在将我们观测到的结构与多个日球层事件表交叉对照后,我们提出我们观测到的结构的可能来源是一个起源于低纬度日冕洞的流相互作用区。这项工作证明了宽视场行星际闪烁观测在捕捉日球层的详细特征方面的力量,否则这些特征是无法分辨和未被发现的。
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引用次数: 0
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Space Weather-The International Journal of Research and Applications
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