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第23太阳活动周期太阳风参数及地磁指数的统计分析
Alternative TitleA statistical analysis of solar wind parameters and geomagnetic indices for the Solar Cycle 23
沈晓飞; 倪彬彬; 顾旭东; 周晨; 刘勇; 项正; 赵正予; 倪彬彬,bbni@whu.edu.cn
Department空间科学部
Source Publication地球物理学报
2015
Volume58Issue:2Pages:362-370
ISSN0001-5733
Language中文
Keyword太阳风 日冕物质抛射 共转相互作用区 地磁暴 统计分析
Abstract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Bz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 Bz、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,Nsw/Pdyn(Nsw表示太阳风质子密度,Pdyn表示太阳风动压)线性拟合斜率一般为正;对于CIR事件,Nsw/Pdyn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,相较于CIR事件,CME事件有更大的南向IMF Bz分量、太阳风动压Pdyn、AE指数、Kp指数以及更小的Dstmin.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.
Other AbstractCoronal 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 13,, 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 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.
Indexed BySCI ; EI ; CSCD
Citation statistics
Document Type期刊论文
Identifierhttp://ir.nssc.ac.cn/handle/122/4699
Collection空间科学部
Corresponding Author倪彬彬,bbni@whu.edu.cn
Recommended Citation
GB/T 7714
沈晓飞,倪彬彬,顾旭东,等. 第23太阳活动周期太阳风参数及地磁指数的统计分析[J]. 地球物理学报,2015,58(2):362-370.
APA 沈晓飞.,倪彬彬.,顾旭东.,周晨.,刘勇.,...&倪彬彬,bbni@whu.edu.cn.(2015).第23太阳活动周期太阳风参数及地磁指数的统计分析.地球物理学报,58(2),362-370.
MLA 沈晓飞,et al."第23太阳活动周期太阳风参数及地磁指数的统计分析".地球物理学报 58.2(2015):362-370.
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