发表机构
Laboratory for Atmospheric and Space Physics, University of Colorado Boulder; Haystack Observatory, Massachusetts Institute of Technology; Department of Atmospheric and Oceanic Sciences, University of California, Los Angeles; Space Science Department, Southwest Research Institute; Department of Physics and Astronomy, University of Texas at San Antonio; Stochastic Research Institute; University of New Mexico; Astrophysical and Planetary Sciences Department, University of Colorado, Boulder（科罗拉多大学博尔德分校大气与空间物理实验室; 麻省理工学院海斯塔克天文台; 加州大学洛杉矶分校大气与海洋科学系; 西南研究所空间科学部; 得克萨斯大学圣安东尼奥分校物理与天文学系; 随机性研究所; 新墨西哥大学; 科罗拉多大学博尔德分校天体物理与行星科学系）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究通过统计2010-2024年75次磁暴数据，发现电离层SED羽流与等离子体层羽流在时间上同步发生，并利用机器学习诊断揭示其与太阳风及地磁活动峰值相关，为磁层-电离层耦合提供新见解。
AI 中文摘要
我们提出了磁暴期间电离层风暴增强密度（SED）与等离子体层羽流之间存在时间相关性的统计证据。我们在2010-2024年间识别出75次具有足够总电子含量（TEC）覆盖的磁暴中的SED羽流。我们还利用基于机器学习的DEN3D模型导出的等离子体层不对称（PPA）指数，在这些磁暴期间识别了等离子体层羽流。我们发现所有SED羽流均与等离子体层羽流同时发生。两种现象的开始和结束时间在半小时间隔内表现出接近零的滞后，证实了它们的同步演化。首次通过叠加历元分析揭示，羽流爆发与太阳风驱动和地磁活动相一致，包括电场、耦合函数、极光电射流指数和非对称环电流指数的峰值，所有这些都促进了羽流的形成。这项工作为磁层-电离层耦合过程提供了及时的新见解，并展示了基于机器学习的诊断工具在空间物理/天气中的实用性。
英文摘要
We present a statistical evidence of a temporal correlation between ionospheric storm-enhanced density (SED) and plasmaspheric plumes during magnetic storms. We identified SED plumes in 75 storms between 2010-2024 with sufficient total electron content (TEC) coverage. We also identified plasmaspheric plumes during these storms using a plasmapause asymmetry (PPA) index derived from the machine-learning-based DEN3D model. We found that all SED plumes coincided with plasmaspheric plumes. The start and end times of both phenomena exhibit near-zero lag within a half-hour delay, confirming their synchronized evolution. For the first time, superposed epoch analyses reveal that plume onsets align with solar wind driving and geomagnetic activity, including peaks in the electric field, coupling function, auroral electrojet indices, and the asymmetric ring current index, all of which facilitate plume formation. This work provides timely new insights into magnetosphere-ionosphere coupling processes and demonstrates the utility of machine-learning-based diagnostics for space physics/weather.
