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.      

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.      

Forecasting of DST variations from polar cap indices using neural networks

The 15-min averaged polar cap (PC) index was used as an input parameter for the Dst variation forecasting. The PC index is known to describe well the principal features of the solar wind as well as the total energy input to the magnetosphere. This allowed us to design a neural network able to forecast the Dst variations from 1 to 4 h ahead. 1998 PC and Dst data sets were used for training and testing and 1997 data sets was used for validation proposes. From the 15 moderate and strong geomagnetic storms observed during 1997, nine were successfully forecasted. In three cases the observed minimum Dst value was less than the predicted one, and only in three cases the neural network was not able to reproduce the features of the geomagnetic storm.     

Interplanetary shocks and geomagnetic activity during solar maximum (2000) and solar minimum (1995–1996)

Plasma and magnetic field parameter variations through fast forward interplanetary shocks were correlated with the peak geomagnetic activity index Dst in a period from 0 to 3 days after the shock, during solar maximum (2000) and solar minimum (1995–1996). Solar wind speed (V) and total magnetic field (B_t) were the parameters with higher correlations with peak Dst index. The correlation coefficients were higher during solar minimum (r² = 56% for V and 39% for B_t) than during solar maximum (r² = 15% for V and 12% for B_t). A statistical distribution of geomagnetic activity levels following interplanetary shocks was obtained. It was observed that during solar maximum, 36% and 28% of interplanetary shocks were followed by intense (Dst  −100 nT) and moderate (−50  Dst < −100 nT) geomagnetic activity, whereas during solar minimum 13% and 33% of the shocks were followed by intense and moderate geomagnetic activity. It can be concluded that the upstream/downstream variations of V and B_t through the shocks were the parameters better correlated with geomagnetic activity level, and during solar maximum a higher relative number of interplanetary shocks can be followed by intense geomagnetic activity than during solar minimum. One can extrapolate, for forecasting goals, that during a whole solar cycle a shock has a probability of around 50% to be followed by intense/moderate geomagnetic activity.     

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     

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   

The Earth’s magnetosphere response to interplanetary medium conditions on January 21–22, 2005 and on December 14–15, 2006

The Earth’s magnetosphere response to interplanetary medium conditions on January 21–22, 2005 and on December 14–15, 2006 has been studied. The analysis of solar wind parameters measured by ACE spacecraft, of geomagnetic indices variations, of geomagnetic field measured by GOES 11, 12 satellites, and of energetic particle fluxes measured by POES 15, 16, 17 satellites was performed together with magnetospheric modeling based in terms of A2000 paraboloid model. We found the similar dynamics of three particle populations (trapped, quasi-trapped, and precipitating) during storms of different intensities developed under different external conditions: the maximal values of particle fluxes and the latitudinal positions of the isotropic boundaries were approximately the same. The main sources caused RC build-up have been determined for both magnetic storms. Global magnetospheric convection controlled by IMF and substorm activity driven magnetic storm on December 14–15, 2006. Extreme solar wind pressure pulse was mainly responsible for RC particle injection and unusual January 21, 2005 magnetic storm development under northward IMF during the main phase.     

利用人工神经网络预报大磁暴

本文采用阈值预报的策略和人工神经网络BP模型,以13个太阳风参量和地磁AE,Dst指数作为输入,以0或1作为输出,提前4h预报大磁暴主相发生的时刻．结果表明,采用神经网络方法的阈值预报可以对灾害性磁暴的发生提前数小时做出比较准确的预报．     

Statistical analysis of the occurrence rate of geomagnetic storms during solar cycles 20–24

This study examines the occurrences rate of geomagnetic storms during the solar cycles (SCs) 20–24. It also investigates the solar sources at SCs 23 and 24. The Disturbed storm time (Dst) and Sunspot Number (SSN) data were used in the study. The study establishes that the magnitude of the rate of occurrences of geomagnetic storms is higher (lower) at the descending phases (minimum phases) of solar cycle. It as well reveals that severe and extreme geomagnetic storms (Dst < -250 nT) seldom occur at low solar activity but at very high solar activity and are mostly associated with coronal mass ejections (CMEs) when occurred. Storms caused by CME + CH-HSSW are more prominent during the descending phase than any other phase of the solar cycle. Solar minimum features more CH-HSSW- associated storms than any other phase. It was also revealed that all high intensity geomagnetic storms (strong, severe and extreme) are mostly associated with CMEs. However, CH-HSSW can occasionally generate strong storms during solar minimum. The results have proven that CMEs are the leading cause of geomagnetic storms at the ascending, maximum and the descending phases of the cycles 23 and 24 followed by CME + CH-HSSW. The results from this study indicate that the rate of occurrence of geomagnetic storms could be predicted in SC phases.     

Storm-time atmospheric density modeling using neural networks and its application in orbit propagation

Upper atmospheric densities during geomagnetic storms are usually poorly estimated due to a lack of clear understanding of coupling mechanisms between the thermosphere and magnetosphere. Consequently, the orbit determination and propagation for low-Earth-orbit objects during geomagnetic storms have large uncertainties. Artificial neural networks are often used to identify nonlinear systems in the absence of rigorous theory. In the present study, an attempt has been made to model the storm-time atmospheric density using neural networks. Considering the debate over the representative of geomagnetic storm effect, i.e. the geomagnetic indices ap and Dst, three neural network models (NNM) are developed with ap, Dst and a combination of ap and Dst respectively. The density data used for training the NNMs are derived from the measurements of the satellites CHAMP and GRACE. The NNMs are evaluated by looking at: (a) the mean residuals and the standard deviations with respect to the density data that are not used in training process, and (b) the accuracy of reconstructing the orbits of selected objects during storms employing each model. This empirical modeling technique and the comparisons with the models NRLMSIS-00 and Jacchia-Bowman 2008 reveal (1) the capability of neural networks to model the relationship between solar and geomagnetic activities, and density variations; and (2) the merits and demerits of ap and Dst when it comes to characterizing density variations during storms.     

Evolution of Dst index,cosmic rays and global temperature during solar cycles 20–23

We have studied conditions in interplanetary space, which can have an influence on galactic cosmic ray (CR) and climate change. In this connection the solar wind and interplanetary magnetic field parameters and cosmic ray variations have been compared with geomagnetic activity represented by the equatorial Dst index from the beginning 1965 to the end of 2012. Dst index is commonly used as the solar wind–magnetosphere–ionosphere interaction characteristic. The important drivers in interplanetary medium which have effect on cosmic rays as CMEs (coronal mass ejections) and CIRs (corotating interaction regions) undergo very strong changes during their propagation to the Earth. Because of this CMEs, coronal holes and the solar spot numbers (SSN) do not adequately reflect peculiarities concerned with the solar wind arrival to 1 AU. Therefore, the geomagnetic indices have some inestimable advantage as continuous series other the irregular solar wind measurements. We have compared the yearly average variations of Dst index and the solar wind parameters with cosmic ray data from Moscow, Climax, and Haleakala neutron monitors during the solar cycles 20–23. The descending phases of these solar cycles (CSs) had the long-lasting solar wind high speed streams occurred frequently and were the primary contributors to the recurrent Dst variations. They also had effects on cosmic rays variations. We show that long-term Dst variations in these solar cycles were correlated with the cosmic ray count rate and can be used for study of CR variations. Global temperature variations in connection with evolution of Dst index and CR variations is discussed.     

Advances in space research nonlinear dependence of Dst and AE indices on the electric field of magnetic clouds

Using the Dst and AE geomagnetic index values and parameters of interplanetary magnetic field and solar wind we have examined the geoeffectiveness of transient ejections in the solar wind, namely, magnetic clouds and high-speed streams. It is found that for magnetic clouds the dependences of indices on the solar wind electric field are nonlinear of different kind. In contrast to magnetic clouds, the dependence of Dst and AE geomagnetic index values on the solar wind electric field agrees closely with the linear one for high-speed streams. We suggest approximating formulas to describe dependences obtained taking into account the relation of the electric field transpolar potential to the electric field and dynamic pressure of the solar wind. We suppose that the interplanetary magnetic field fluctuations also contribute to these dependences.     

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.     

Forecasting the geomagnetic activity of the Dst index using multiscale radial basis function networks

The Dst index is a key parameter which characterises the disturbance of the geomagnetic field in magnetic storms. Modelling of the Dst index is thus very important for the analysis of the geomagnetic field. A data-based modelling approach, aimed at obtaining efficient models from limited input–output observational data, provides a powerful tool for analysing and forecasting geomagnetic activities including the prediction of the Dst index. In this study, the process of the Dst index is treated to be a structure-unknown system, where the solar wind parameter (VBs) and the solar wind dynamic pressure (P) are the system inputs, and the Dst index is the system output. A novel multiscale RBF (MSRBF) network is introduced to represent such a two-input and single-output system, where the Dst index is related to the solar wind parameter and the dynamic pressure, via a hybrid network model consisting of two submodels: a linear part that reflects the linear relationship between the output and the inputs, and a nonlinear part that captures the effect of the interacting contribution of past observations of the inputs and the output, on the current output. The proposed MSRBF network can easily be converted into a linear-in-the-parameters form and the training of the linear network model can easily be implemented using a forward orthogonal regression (FOR) algorithm. One advantage of the new MSRBF network, compared with traditional single scale RBF networks, is that the new network is more flexible for describing complex nonlinear dynamical systems.     

Effects of geomagnetic storms on the bottomside ionospheric F region

The geomagnetic storm is a complex process of solar wind/magnetospheric origin. The variability of the ionospheric parameters increases substantially during geomagnetic storms initiated by solar disturbances. Various features of geomagnetic storm act at various altitudes in the ionosphere and neutral atmosphere. The paper deals with variability of the electron density of the ionospheric bottomside F region at every 10 km of altitude during intense geomagnetic storms with attention paid mainly to the distribution of the F1 region daytime ionisation. We have analysed all available electron density profiles from some European middle latitude stations (Chilton, Pruhonice, Ebro, Arenosillo, Athens) for 36 events that occurred in different seasons and under different levels of solar activity (1995–2003). Selected events consist of both depletion and increase of the F2 region electron density. For European higher middle and middle latitude the F1 region response to geomagnetic storm was found to be negative (decrease of electron density) independent on the storm effect on the F2 region. For lower middle latitude the F1 response is weaker and less regular. Results of the analysis also show that the maximum of the storm effect may sometimes occur below the height of the maximum of electron density (NmF2).     

行星际日冕物质抛射地磁效应研究的支持向量机方法初步研究

行星际日冕物质抛射（Interplanetary Coronal Mass Ejection,ICME）与地球磁层相互作用并带来地磁暴等地磁扰动.从Richardson和Cane提供的近地球ICME列表中筛选出ICME事件集,基于ICME扰动期间的行星际等离子体与磁场数据提取出特征.通过计算各特征的费舍尔分值（Fisher Score）,对这些特征进行选择,发现行星际磁场南北向分量持续时间小于-10nT且激波等扰动所带来的ICME扰动开始时,太阳风速度的增量等特征与ICME事件的地磁效应密切相关.这与现有的传统统计研究结果一致.以这些特征为基础,训练得到的径向基函数支持向量机能够以0.78±0.08的准确率判断ICME事件是否会产生中等及以上强度的地磁暴（Dst ≤-50nT）.      

1998年4月下旬至5月上旬地磁暴的日地事件认证

综合运用SOHO／LASCO、SOHO／EIT关于CME的观测结果和WIND飞船关于太阳风的观测记录，识别了1998年4月下旬至5月上旬发生的磁暴的CME源，分析了与5月初强磁暴群相联系的日地事件。结果表明，所用日地扰动事件关系认证的方法是可行的，本文就上述日地事件所涉及的磁暴群与活动区的关系、CME地磁效应的日面东西不对称性以及磁云与高速流的作用等问题进行了讨论。     

利用行星际监测数据进行地磁暴预报

利用全连接神经网络方法应用于地磁Dst指数的预报中.对ACE卫星探测的太阳风和行星际磁场及其变化对未来几小时的Dst指数的影响进行了统计分析,发现在这些行星际实测参数中,对Dst指数作用较为明显的是太阳风速度、太阳风质子密度和行星际磁场南向分量,同时,当前Dst指数实测值对今后几小时的Dst指数已有很强的制约作用.在统计分析的基础上,建立了全连接神经网络预报模型.由于采用了全连接神经网络结构,模式能够反映出太阳风、行星际磁场等参数与地磁Dst指数参数的复杂联系,可以自动建立输入参量的最佳组合方式,提高了预报精度.通过利用大量实测数据对神经网络模式进行训练,最终建立了利用优选的ACE卫星行星际监测数据提前2 h对Dst指数进行预报.通过检测,预报的误差为14.3%.