帮助 关于我们

返回检索结果

第23太阳活动周期太阳风参数及地磁指数的统计分析
A statistical analysis of solar wind parameters and geomagnetic indices for the Solar Cycle 23

查看参考文献10篇

沈晓飞 1   倪彬彬 2 *   顾旭东 2   周晨 2   刘勇 3   项正 2   赵正予 2  
文摘 日冕物质抛射(Coronal Mass Ejection,简称CME)和共转相互作用区(Corotating Interaction Region,简称CIR)是造成日地空间行星际扰动和地磁扰动的两个主要原因,提供了地球磁暴的主要驱动力,进而显著影响地球空间环境.为深入研究太阳风活动及受其主导影响的地磁活动的时间分布特征,本文对大量太阳风参数及地磁活动指数的数据进行了详细分析.首先,采用由NASA OMNIWeb提供的太阳风参数及地磁活动指数的公开数据,通过自主编写matlab程序对第23太阳活动周期(1996-01-01-2008-12-31)的数据包括行星际磁场B_z分量、太阳风速度、太阳风质子密度、太阳风动压等重要太阳风参数及Dst指数、AE指数、Kp指数等主要的地磁指数进行统计分析,建立了包括269个CME事件和456个CIR事件列表的数据库.采用事例分析法和时间序列叠加法分别对两类太阳活动的四个重要太阳风参数(IMF B_z、太阳风速度、太阳风质子密度、太阳风动压)和三个主要地磁指数(Dst、AE、Kp)进行统计分析,并研究了其统计特征.其次,根据Dst指数最小值确定了第23太阳活动周期内的355个孤立地磁暴事件,并以Dst指数最小值为标准将这些磁暴进一步分类为145个弱磁暴、123个中等磁暴、70个强磁暴、12个剧烈磁暴和5个巨大磁暴.最后,采用时间序列叠加法对不同强度磁暴的太阳风参数和地磁指数进行统计分析.统计分析表明,对于CME事件,N_(sw)/P_(dyn)(N_(sw)表示太阳风质子密度,P_(dyn)表示太阳风动压)线性拟合斜率一般为正;对于CIR事件,N_(sw)/P_(dyn)线性拟合斜率一般为负,这可作为辨别CME和CIR事件的一种有效方法.从平均意义上讲,相皎于CIR事件,CME事件有更大的南向IMF B_z分量、太阳风动压P_(dyn)、AE指数、Kp指数以及更小的Dst_(min).一般情况下,CME事件有更大的可能性驱动极强地磁暴.总体而言,对于不同强度的地磁暴,Dst指数的变化呈现出一定的相似性,但随着地磁暴强度的增强,Dst指数衰减的速度变快.CME和CIR事件以及其各自驱动的地磁暴事件有着很多不同,因此,需要将CME事件驱动的磁暴及CIR事件驱动的磁暴分开研究.建立CME、CIR事件及地磁暴的数据库以及获取的统计分析结果,将为深入研究地球磁层等离子体片、辐射带及环电流对太阳活动的响应特征提供有利的帮助.
其他语种文摘 Coronal mass ejections (CMEs) and corotating interaction regions (CIRs) are two significant contributors to interplanetary disturbances and geomagnetic disturbances, which also play as major drivers of geomagnetic storms to modulate the geo-space environment. In order to comprehensively investigate the characteristic temporal features of the solar wind activity and associated geomagnetic activity, a large amount of solar wind data and geomagnetic activity indices are analyzed in detail. Firstly, using the public data of solar wind parameters and geomagnetic activity indices provided by the NASA OMNIWeb, the MATLAB codes are developed to deal with a number of key parameters including IMF B_z, solar wind velocity, solar wind proton density, solar wind dynamic pressure, Dst, AE,and Kp for the entire Solar Cycle 23 from 1996 to 2008. The complete database with a full list of 269 CME events and 456 CIR events is identified. Case event studies and superposed epoch analyses are implemented to carefully investigate the statistical features of four important solar wind parameters (IMF B_z, solar wind speed, solar wind proton density, and solar wind dynamic pressure) and three major geomagnetic indices (Dst, AE, and Kp) associated with the two types of solar disturbances. Secondly, the minimum of Dst index is utilized to differentiate 355 isolated geomagnetic storms occurring during the Solar Cycle 23. These storms are further categorized according to the magnitude of Dst minimum into 145 weak storms, 123 moderate storms, 70 strong storms, 12 severe storms, and 5 extreme storms. Finally,superposed epoch analysis is applied to evaluate the statistics of solar wind parameters and geomagnetic indices corresponding to magnetic storms with different intensities. It is found that in general the linearly fitted slope of N_(sw)/P_(dyn) (where N_(sw) is the solar wind proton density and P_(dyn) the dynamic pressure) with respect to epoch time remains positive for CME events but negative for CIR events, which can act as a feasible means to distinguish CME and CIR events. On average, compared to CIR events, CME events have larger magnitudes of southward IMF B_z, solar wind dynamic pressure, AE and Kp indices but smaller Dst_(min). In principle, CMEs bear higher possibility to drive extremely intense (i. e.,super) geomagnetic storms. The overall variations of Dst tend to be similar to some extent for different levels of geomagnetic storms, however,Dst decreases faster for stronger storms. There are a large number of differences between CME and CIR events and their driven geomagnetic storms as well. Therefore, CME-driven storms and CIR-driven storms should be studied separately. The established database of CME and CIR events and geomagnetic storms and the quantitative statistical information in combination can provide a useful aid for better understanding the responses of Earth's plasma sheet,radiation belts,and ring current to various solar activities.
来源 地球物理学报 ,2015,58(2):362-370 【核心库】
DOI 10.6038/cjg20150202
关键词 太阳风 ; 日冕物质抛射 ; 共转相互作用区 ; 地磁暴 ; 统计分析
地址

1. 武汉大学物理科学与技术学院, 武汉, 430072  

2. 武汉大学电子信息学院空间物理系, 武汉, 430072  

3. 中国科学院空间天气国家重点实验室, 中国科学院空间天气国家重点实验室, 北京, 100190

语种 中文
文献类型 研究性论文
ISSN 0001-5733
学科 地球物理学
基金 中国博士后科学基金 ;  中央高校自主科研项目 ;  国家自然科学基金
文献收藏号 CSCD:5375261

参考文献 共 10 共1页

1.  Borovsky J E. Differences between CME-driven storms and CIR-driven storms. Journal of Geophysical Research:Space Physics,2006,111(A7) 被引 11    
2.  Cane H V. Interplanetary coronal mass ejections in the near-Earth solar wind during 1996-2002. Journal of Geophysical Research:Space Physics,2003,108(A4) 被引 14    
3.  Chen G M. A comparison of the effects of CIR-and CME-induced geomagnetic activity on thermospheric densities and spacecraft orbits:Case studies. Journal of Geophysical Research:Space Physics,2012,117(A8) 被引 1    
4.  Denton M H. Geomagnetic storms driven by ICME-and CIR-dominated solar wind. Journal of Geophysical Research:Space Physics,2006,111(A7) 被引 3    
5.  Heber B. Corotating interaction regions. Advances in Space Research,1999,23(3):567-579 被引 1    
6.  Loewe C A. Classification and mean behavior of magnetic storms. Journal of Geophysical Research:Space Physics,1997,102(A7):14209-14213 被引 3    
7.  Reeves G D. Acceleration and loss of relativistic electrons during geomagnetic storms. Geophysical Research Letters,2003,30(10):1529-1533 被引 28    
8.  Turner N E. Geoefficiency and energy partitioning in CIR-driven and CME-driven storms. Journal of Atmospheric and Solar-Terrestrial Physics,2009,71(10/11):1023-1031 被引 2    
9.  Zhang Y. Statistical analysis of corotating interaction regions and their geoeffectiveness during solar cycle 23. Journal of Geophysical Research:Space Physics,2008,113(A8) 被引 2    
10.  苑顺周. 地磁活动相关问题研究[硕士论文],2011 被引 1    
引证文献 13

1 陈春 第23太阳活动周强磁暴行星际源的统计分析 电波科学学报,2016,31(4):670-675
被引 0 次

2 明勇 太阳风速度信息数据快速优化检测研究 计算机仿真,2016,33(10):425-428
被引 0 次

显示所有13篇文献

论文科学数据集
PlumX Metrics
相关文献

 作者相关
 关键词相关
 参考文献相关

版权所有 ©2008 中国科学院文献情报中心 制作维护:中国科学院文献情报中心
地址:北京中关村北四环西路33号 邮政编码:100190 联系电话:(010)82627496 E-mail:cscd@mail.las.ac.cn 京ICP备05002861号-4 | 京公网安备11010802043238号