第23太阳活动周中等地磁暴行星际源的统计分析

统计了第23太阳活动周(1996--2006年)发生的183次中等强度地磁暴(-100 nT < Dst ≤ -50 nT)的行星际源,分析了中等磁暴的年分布状况以及引起中等磁暴的不同行星际结构在太阳活动周中的分布特征,同时,与强磁暴行星际源的分布状况做了对比分析,主要的统计分析结果如下. (1)共转相互作用区CIR与行星际日冕物质抛射ICME在中等磁暴中具有同等重要的作用,且在ICME中,具有磁云结构和非磁云结构的ICME在引起中等磁暴的能力方面也基本相同,但带有鞘层结构的ICME在引起中等磁暴中具有更重要的作用. (2)中等磁暴在极大年(2001年)和下降年(2003年)发生次数最多,与地磁活动的双峰年对应,在极小年(1996和2006年)发生次数最少,与地磁活动低年对应,在其他年份分布较平均. (3)中等磁暴在太阳活动极大年主要由ICME引起,在上升年和下降年CIR在其中起主要作用,且下降年基本是上升年的两倍,而对于强磁暴而言,ICME始终是最重要的行星际源.      

第23太阳活动周太阳极紫外辐射与磁暴相关性分析

统计第23个太阳活动周内中等及以上强度（Dst_min<-50nT）的磁暴事件,线性拟合分析磁暴主相DDst_min和达到DDst_min前一个表征太阳极紫外辐射强度的F_10.7之间的相关性.结果表明:随着太阳极紫外辐射增强,DDst_min<-50nT的磁暴出现的总数增多,在弱、中等和强太阳极紫外辐射条件下,其数量分别为56,84和85;随着太阳极紫外辐射增强,强磁暴（-200nT ≤ Dst_min<-100nT）和大磁暴（Dst_min<-200nT）发生的数量和相对发生率呈增长趋势,尤其是大磁暴数目（1,4,12）和相对发生率（1.79%,4.76%,14.12%）明显呈增长趋势;大磁暴（|Dst_min|）与太阳极紫外辐射（F_10.7）之间存在中度正相关关系,其相关系数为0.532,并且主要体现在大磁暴（|Dst_min|）与强太阳极紫外辐射（F_10.7）之间的中度正相关性,其相关系数为0.582.大磁暴与强太阳极紫外辐射之间的相关性可为空间天气预报提供参考依据.      

行星际扰动与不同级别磁暴强度关系的研究

利用1997-2004年间ACE卫星太阳风观测的时均值数据和相应的Dst指数,针对Dstmin≤-50 nT的磁暴,分析了行星际参数(Bz,Ey,v,Pk,|B|,ε'=vxB2zsin4(θ/2))与Dst指数的相关关系.验证了Ey,Bz与Dst指数的良好相关性;按磁暴强度的不同,发现磁暴强度越大,行星际参数与磁暴强度(Dstmin)的相关性就越好.对于中等磁暴(-100 nT＜Dstmin≤-50 nT),行星际参数与磁暴强度的相关系数不高.如果把磁暴分为两个档次,即-150 nT＜Dstmin≤-50 nT的磁暴和Dstmin≤-150 nT的磁暴,计算结果表明,ε'与Dst指数的相关性是最好的.在诸多行星际参数中,就单一因素来说,Ey对磁暴强度影响最大,Bz对磁暴强度影响次之.      

利用支持向量机预测大磁暴期间Dst指数的变化

利用支持向量机（SVM）模型对大磁暴期间Dst指数进行预报研究.以1995-2014年期间的80次大磁暴（Dst≤-100nT）事件共2662组观测数据为研究对象,以对应时间的太阳风参数为模型输入参数,同时建立了神经网络模型和线性机模型进行对比,并利用交叉验证提高预测结果的可靠性.为比较不同模型的预测效果,选用相关系数（CC）、均方根误差（RMS）、磁暴期间Dst指数最小值预测结果的平均绝对误差以及Dst指数最小值出现时间预测结果的平均绝对误差等统计量作为对比参数.结果显示SVM模型的预测效果最好,其中相关系数为0.89,均方根误差为24.27nT,所有磁暴事件的最小Dst值预测平均绝对误差为17.35nT,最小Dst值出现时间的预测平均绝对误差为3.2h.为进一步检验模型对不同活动水平磁暴预报效果的可能差异,将所有磁暴事件分为大磁暴（-200 相似文献   

中等磁暴太阳周分布的统计研究

依据实际观测的中等磁暴数据,统计分析了中等磁暴的太阳周分布.分析结果表明,在一个太阳活动周内,每年中等磁暴随时间的变化出现多个峰值,其中,最大峰值均出现在太阳活动周的下降段,即中等磁暴的峰值比太阳黑子数平滑年均值的峰值要滞后,滞后的时间为2～3年.超过70%的中等磁暴出现在太阳活动周的下降段,这表明绝大多数中等磁暴出现在太阳活动周的下降段.通过对中等磁暴平滑月均值与太阳黑子数平滑月均值相位差的计算分析发现,中等磁暴峰值出现的时间比太阳黑子数峰值出现的时间要滞后,不同太阳活动周中等磁暴峰值出现的时间与太阳黑子数峰值时间滞后的程度不同.      

影响卫星故障的空间天气分析

基于美国国家地球物理数据中心(NGDC) 2384例和中国19颗卫星的263例卫星故障信息, 结合1963-2012年小时平均的多种空间环境数据, 定量分析了三种卫星故障发生期间的空间要素特征, 探讨单粒子锁定(SEU)、表面充电致静电放电(ESD)和内部深层充电所致电子引起的电磁脉冲(ECEMP)与空间天气事件的可能联系, 得出以下主要结论. (1)大部分SEU和ECEMP发生于空间天气平静时, 但在其前后3日内地磁活动达到了磁暴水平, 相对来说比例最大的发生在Dst_min之后第3日 (48～72h). (2) ESD受地磁活动和高能电子通量影响明显. 与磁暴、相对论电子通量增强事件的季节性相对应, 两分点附近ESD和ECEMP的发生率高; 93.6% 的 ESD发生前后72h内地磁活动达到磁暴水平, 故障发生时间均匀分布在 Dst_min前0～48h 和后0～24h; 54.9%的ESD 发生时处于地磁暴期(Dst <-30nT), 以-50～-30nT的小磁暴水平居多; 40.6%的ESD发生于高能电子通量高水平期(≥ 103pfu, 1pfu =1cm-2·s-1·sr-1), 81.9%的ESD发生前后72h 内高能电子通量峰值≥ 103pfu, 发生率最高时段为电子通量峰值前 48～72h. (3)高能电子对中国同步轨道卫星的SEU影响明显, 42.5% 故障发生 时高能电子通量≥ 103pfu, 故障在峰值前48～72h和峰值后48～72h 的发生概率相当, 约为23.0%. (4)同步轨道卫星SEU受太阳质子事件的影响相对较大, 22.5%的中国同步轨道卫星故障发生前后72h内发生了太阳质子事件, 季节性不明显.      

基于多台站观测的中国区域地磁指数

利用中国14个地磁台站和全球23个地磁台站的H分量分钟值数据,分析单台站小时幅度指数r_H的时空分布特征,在此基础上结合台站之间r_H指数的相似度度量(残差指数Ra),采用K均值聚类算法将中国14个地磁台站划分为7个区域,根据加权法计算各区域的区域指数Rr.结果表明,r_H指数具有27天太阳自转周变化,季节变化不显著,但仍存在春秋季大而冬夏季小的特征;在空间变化上,r_H随纬度的增高而增大,并且在磁暴期间r_H指数的幅值和形态均表现出明显的经度差异,随地方时呈现晨-昏不对称现象;与Dst指数、SYM-H指数、Kp指数及各区域内台站的H分量观测数据对比分析发现,区域指数Rr能有效反映区域地磁扰动.      

冕洞特征参数与地磁暴强度及发生时间统计

地磁暴是空间天气预报的重要对象.在太阳活动周下降年和低年,冕洞发出的高速流经过三天左右行星际传输到达地球并引发的地磁暴占主导地位.目前地磁暴的预报通常依赖于1AU处卫星就位监测的太阳风参数,预报提前量只有1h左右.为了增加地磁暴预报提前量,需要从高速流和地磁暴的源头即太阳出发,建立冕洞特征参数与地磁暴的定量关系.分析了2010年5月到2016年12月的152个冕洞-地磁暴事件,利用SDO/AIA太阳极紫外图像提取了两类冕洞特征参数,分析了其与地磁暴期间ap,Dst和AE三种地磁指数的统计关系,给出冕洞特征参数与地磁暴强度以及发生时间的统计特征,为基于冕洞成像观测提前1～3天预报地磁暴提供了依据.      

海南地区强磁暴期间强区域扩展F事件研究

利用2004年海南DPS-4数字测高仪观测到的强区域扩展F(SSF)数据,分析研究了5个强磁暴(Dst＜-100 nT)事件期间海南SSF的响应特征.结果发现,在海南地区,5个强磁暴事件中有3个磁暴Dst最小值位于2200-0200 LT之间,在磁暴主相及恢复相初期均出现了SSF现象,这种触发作用可能源于磁层直接渗透电场的作用,而另两个磁暴Dst最小值均发生在白天,一个SSF现象出现在磁暴的恢复相晚间,另一个SSF现象出现在超强磁暴的初相晚间,后者可能由该超强磁暴的急始造成的直接渗透电场所触发;5个强磁暴期间发生的SSF现象或者仅出现在午夜前,或者先出现在午夜前并持续到午夜后;同时,还就这些观测结果与Dabas等人有关磁暴对ESF影响的结论进行对比和讨论.      

近地尾向流和磁场——TC-1卫星观测结果

2004年10月12日,在01:30—04:30 UT期间,位于向阳侧磁层顶附近的Geotail卫星探测到行星际磁场为持续南向.此太阳风条件驱动了一个小磁暴,Sym-H指数在04:12 UT达到最小值-33 nT.在磁暴主相期间,AE指数维持在较高的水平,其最大值达400 nT.02:00—03:00 UT期间,TC-1卫星在近地磁尾(-10.6,3.2,-0.1)R_e处观测到明显的亚暴膨胀相特征和磁场偶极化过程.在偶极化前1 min,有较强的(v_x<-100 km/s)持续时间超过3 min的尾向流发生.分析发现该尾向流具有低温、高密度和沿磁场流动的特点,这说明尾向流具有来源于电离层风的特征.尾向流期间,TC-1观测的磁场分量B_x和总的磁场强度增加,磁倾角减小,磁场结构变成非偶极型,说明尾向流对磁场结构有一定的影响,文中尝试给出了相应的物理解释.观测表明,该事例中的近地磁尾尾向流可能对磁场偶极化过程的发生有重要意义.     

Large geomagnetic storms of extreme solar event periods in solar cycle 23

During extreme solar events such as big flares or/and energetic coronal mass ejections (CMEs) high energy particles are accelerated by the shocks formed in front of fast interplanetary coronal mass ejections (ICMEs). The ICMEs (and their sheaths) also give rise to large geomagnetic storms which have significant effects on the Earth’s environment and human life. Around 14 solar cosmic ray ground level enhancement (GLE) events in solar cycle 23 we examined the cosmic ray variation, solar wind speed, ions density, interplanetary magnetic field, and geomagnetic disturbance storm time index (D_st). We found that all but one of GLEs are always followed by a geomagnetic storm with D_st  −50 nT within 1–5 days later. Most(10/14) geomagnetic storms have D_st index  −100  nT therefore generally belong to strong geomagnetic storms. This suggests that GLE event prediction of geomagnetic storms is 93% for moderate storms and 71% for large storms when geomagnetic storms preceded by GLEs. All D_st depressions are associated with cosmic ray decreases which occur nearly simultaneously with geomagnetic storms. We also investigated the interplanetary plasma features. Most geomagnetic storm correspond significant periods of southward B_z and in close to 80% of the cases that the B_z was first northward then turning southward after storm sudden commencement (SSC). Plasma flow speed, ion number density and interplanetary plasma temperature near 1 AU also have a peak at interplanetary shock arrival. Solar cause and energetic particle signatures of large geomagnetic storms and a possible prediction scheme are discussed.     

Classification of solar wind structures and intense geomagnetic storm alarms with self-organizing maps

Intense geomagnetic storms (D_st < −100 nT) usually occur when a large interplanetary duskward-electric field (with Ey > 5 mV m⁻¹) lasts for more than 3 h. In this article, a self-organizing map (SOM) neural network is used to recognize different patterns in the temporal variation of hourly averaged Ey data and to predict intense storms. The input parameters of SOM are the hourly averaged Ey data over 3 h. The output layer of the SOM has a total of 400 neurons. The hourly Ey data are calculated from solar wind data, which are provided by NSSDC OMNIWeb and ACE spacecraft and contain information on 143 intense storms and a fair number of moderate storms, weak storms and quiet periods between September 3, 1966 and June 30, 2002. Our results show that SOM is able to classify solar wind structures and therefore to give timely intense storm alarms. In our SOM, 21 neurons out of 400 are identified to be closely associated with the intense storms and they successfully predict 134 intense storms out of the 143 ones selected. In particular, there are 14 neurons for which, if one or more of them are present, the occurrence probability of intense storms is about 90%. In addition, several of these 14 neurons can predict big magnetic storm (D_st  −180 nT). In summary, our method achieves high accuracy in predicting intense geomagnetic storms and could be applied in space environment prediction.     

Geomagnetic storm effects on the occurrences of ionospheric irregularities over the African equatorial/low-latitude region

The study investigated the effects of intense geomagnetic storms of 2015 on the occurrences of large scale ionospheric irregularities over the African equatorial/low-latitude region. Four major/intense geomagnetic storms of 2015 were analyzed for this study. These storms occurred on 17th March 2015 (?229?nT), 22nd June 2015 (?204?nT), 7th October 2015 (?124?nT), and 20th December 2015 (?170?nT). Total Electron Content (TEC) data obtained from five African Global Navigation Satellite Systems (GNSS) stations, grouped into eastern and western sectors were used to derive the ionospheric irregularities proxy indices, e.g., rate of change of TEC (ROT), ROT index (ROTI) and ROTI daily average (ROTI_AVE). These indices were characterized alongside with the disturbance storm time (Dst), the Y component of the Interplanetary Electric Field (IEFy), polar cap (PC) index and the H component of the Earth’s magnetic field from ground-based magnetometers. Irregularities manifested in the form of fluctuations in TEC. Prompt penetration of electric field (PPEF) and disturbance dynamo electric field (DDEF) modulated the behaviour of irregularities during the main and recovery phases of the geomagnetic storms. The effect of electric field over both sectors depends on the local time of southward turning of IMF Bz. Consequently, westward electric field inhibited irregularities during the main phase of March and October 2015 geomagnetic storms, while for the June 2015 storm, eastward electric field triggered weak irregularities over the eastern sector. The effect of electric field on irregularities during December 2015 storm was insignificant. During the recovery phase of the storms, westward DDEF suppressed irregularities.     

行星际南向磁场事件与强磁暴

利用１９７８－１９８８年期间的太阳风和地磁资料对行星际磁场（ＩＭＦ）南向分量Ｂｓ事件（即Ｂｓ〉１０ｎＴ及其所驱动的错向电场ＶＢｓ〉５ｍＶ／ｍ、持续时间△Ｔ〉３ｈ的事件）与弱磁暴（Ｄｓｔ≤－１００ｎＴ）关系进行了分析。结果表明,１００％的Ｂｓ事件能能引起磁暴的发生,但其中只有８４％为强磁暴;强磁暴的发生都与较强的ＩＭＦ Ｂｓ活动密切相关,但只有６８％的强磁共伴随Ｂｓ事件而发生;Ｂｓ事件与强磁暴并不是     

Solar and interplanetary precursors of geomagnetic storms in solar cycle 23

Estimating the magnetic storm effectiveness of solar and associated interplanetary phenomena is of practical importance for space weather modelling and prediction. This article presents results of a qualitative and quantitative analysis of the probable causes of geomagnetic storms during the 11-year period of solar cycle 23: 1996–2006. Potential solar causes of 229 magnetic storms (Dst ? −50 nT) were investigated with a particular focus on halo coronal mass ejections (CMEs). A 5-day time window prior to the storm onset was considered to track backward the Sun’s eruptions of halo CMEs using the SOHO/LASCO CMEs catalogue list. Solar and interplanetary (IP) properties associated with halo CMEs were investigated and correlated to the resulting geomagnetic storms (GMS). In addition, a comparative analysis between full and partial halo CME-driven storms is established. The results obtained show that about 83% of intense storms (Dst ? −100 nT) were associated with halo CMEs. For moderate storms (−100 nT < Dst ? −50 nT), only 54% had halo CME background, while the remaining 46% were assumed to be associated with corotating interaction regions (CIRs) or undetected frontside CMEs. It was observed in this study that intense storms were mostly associated with full halo CMEs, while partial halo CMEs were generally followed by moderate storms. This analysis indicates that up to 86% of intense storms were associated with interplanetary coronal mass ejections (ICMEs) at 1 AU, as compared to moderate storms with only 44% of ICME association. Many other quantitative results are presented in this paper, providing an estimate of solar and IP precursor properties of GMS within an average 11-year solar activity cycle. The results of this study constitute a key step towards improving space weather modelling and prediction.