论文标题
从地球层成像观测中预测地球上冠状质量弹出的到期时间
Predicting the Time-of-Arrival of Coronal Mass Ejections at Earth From Heliospheric Imaging Observations
论文作者
论文摘要
地球上冠状质量弹出(CME)的到达时间(TOA)是一个关键参数,因为与CME到达相关的太空天气现象,例如激烈的地磁风暴。尽管使用新仪器和新方法的发展,但TOA估计的错误平均仍在10小时以上。在这里,我们使用地球层成像仪的观测值(即,从Heliopentric距离的观测值高于现有冠状动脉覆盖的距离。为了执行这项工作,我们分析了Heliospheric Imager hi-1在船上观察到的14个CME,以确定其前部位置和速度。运动参数是根据基于椭圆转换(ELCON)方法的新技术得出的,该方法使用立体声的两个观点同时观察。在仪器的视野之外,我们假设CME演化的动力学由空气动力阻力控制,即是由与背景太阳风中的粒子相互作用引起的力。为了建模阻力力,我们使用一个物理模型,该物理模型使我们能够得出其参数,而无需依赖于经验得出的阻力系数。我们发现CME TOA的平均误差为$ 1.6 \ pm8.0 $小时TOA和一组14个事件的平均绝对错误$ 6.9 \ pm3.9 $小时。结果表明,从HI-1的观察结果导致估计值与冠状动脉的观察相似。
The Time-of-Arrival (ToA) of coronal mass ejections (CME) at Earth is a key parameter due to the space weather phenomena associated with the CME arrival, such as intense geomagnetic storms. Despite the incremental use of new instrumentation and the development of novel methodologies, ToA estimated errors remain above 10 hours on average. Here, we investigate the prediction of the ToA of CMEs using observations from heliospheric imagers, i.e., from heliocentric distances higher than those covered by the existent coronagraphs. In order to perform this work we analyse 14 CMEs observed by the heliospheric imagers HI-1 onboard the twin STEREO spacecraft to determine their front location and speed. The kinematic parameters are derived with a new technique based on the Elliptical Conversion (ElCon) method, which uses simultaneous observations from the two viewpoints from STEREO. Outside the field of view of the instruments, we assume that the dynamics of the CME evolution is controlled by aerodynamic drag, i.e., a force resulting from the interaction with particles from the background solar wind. To model the drag force we use a physical model that allows us to derive its parameters without the need to rely on drag coefficients derived empirically. We found a CME ToA mean error of $1.6\pm8.0$ hours ToA and a mean absolute error of $6.9\pm3.9$ hours for a set of 14 events. The results suggest that observations from HI-1 lead to estimates with similar errors to observations from coronagraphs.