行星际激波传播至1AU的渡越时间预测

考虑了激波爆发源角宽度、能量、驱动时间、激波速度及其与背景太阳风之间的相互作用,利用流体力学扰动方程建立起一个激波扰动传播模型,用于研究激波从太阳传播到地球轨道附近(1 AU处)所需要的时间(渡越时间)问题.为印证扰动传播模型的适用性,利用1979-1989年间的27个激波事件,以及1997年2月到2000年1月间的68个激波事件,对激波到达地球轨道附近的渡越时间进行了预测,并将结果与STOA和ISPM预报模型结果进行了比较.实验表明,该模型在所有95个事件中,渡越时间相对误差小于10%的事件数占总事件数的25.26%;相对误差小于20%的占总事件数的50.53%;相对误差小于30%的占总事件的65.26%.      

基于CE/SE法的翼型流场激波捕捉计算

在二维四边形结构网格下,针对时空守恒元和解元(CE/SE, space-time Conservation Element and Solution Element)方法捕捉激波需要双倍空间网格点的问题,重新对守恒元和解元进行了定义,计算点同时包含单元中心和网格节点,并推导得到了新的CE/SE方法的计算公式.以此为基础,结合非敏感克朗数(CNIS, Courant Number Insensitive Scheme)计算格式和当地时间步长方法,对激波翼型流场进行数值模拟,并与原方法及AGARD报告进行对比.结果表明,对原CE/SE方法的改进是有效的,可明显提高激波分辨能力,并且激波前后无明显的数值震荡发生,新方法适合应用于翼型流场中捕捉激波.     

联合太阳和行星际物理参数预测行星际激波能否到达地球

选取第23太阳活动周（1997—2006年）期间542例由太阳爆发活动驱动的行星际激波事件,分析确定了太阳源头和行星际空间中影响行星际激波能否到达地球轨道的关键物理参数;在此基础上,建立了预测行星际激波能否到达地球的新预报模型（EdEaSPM）. 回溯预报结果表明,EdEaSPM模型的预报成功率约为66%,略高于国际一流预报模型的预报成功率;EdEaSPM模型的虚报率未超过50%,改善了当前国际主流模型虚报率较大的情况;对于偏度指标,虽然当前所有模型的偏度值均大于1,但EdEaSPM模型的偏度值最接近于1且明显小于其他模型的偏度值;EdEaSPM模型的其他评价指标也都高于国际主流模型的相应指标. 此外,选取2012年期间的激波事件对EdEaSPM模型进行了预报检验,预测结果与实际情况吻合. EdEaSPM模型不仅能够提前约1～3天进行预报,而且预报效果与国际一流模型具有可比性,尤其是在提高预报成功率及降低虚报率方面具有一定优势.      

二维翼型绕流的CE/SE方法

采用时空守恒元和解元CE/SE(space-time Conservation Element and Solution Element method)法,求解二维Euler方程,开展了翼型绕流的无粘数值模拟研究.用非敏感克朗数计算格式消除克朗数过小引起的数值耗散对解的污染,结合当地时间步长法,解决网格不均匀引起的当地克朗数变化跨度大的问题.对NACA0012翼型的无激波流场进行了二维数值模拟,并与AGARD算例做了对比.结果表明:CE/SE方法的计算结果与AGARD结果吻合得很好,为该数值计算方法对翼型绕流数值模拟的进一步应用奠定了基础.      

CME地磁扰动事件的模糊预报方法

以1997-2003年期间的73个日冕物质抛射(CME)激波扰动事件和模糊数学为基础,提出了一种预报地磁扰动的方法.该方法以CME事件爆发的日面经纬度、相关地磁扰动事件的渡越时间、地磁扰动指数、IPS观测的太阳风速度跃变量为基础,建立了预报CME地磁扰动事件的μθ,μφ,μT,μM,μ△v从属函数,考虑了CME初始速度对激波到达时间的影响.以这5个从属函数为基础并利用模糊数学对1996-2004年期间73个经行星际闪烁(IPS)观测认证的CME激波引起的地磁扰动事件进行了预报实验.实验结果表明,磁扰开始时间预报的相对误差,△Tpre/Tobs≤30%的事件占总事件数的91.78%,而△Tpre/Tobs＞30%的事件占总事件数的12.33%;磁扰幅度(∑Kp)大小的预报,其相对误差△∑Kp/∑Kpobs≤30%的事件占总事件数的60.27%,相对误差≥50%的事件占总事件数的12.33%.这表明该预报方法对空间灾害性事件地磁扰动的定量预报具有很大应用潜力.      

从属函数在地磁扰动预报研究中的初步应用

根据1966-1982年期间有关太阳耀斑、行星际激波和地磁扰动的观测资料而建立的从属函数,对1984-1985年间的行星际闪烁观测中能证认出的耀斑-激波所引起的地磁扰动作了预报试验。结果表明:(1)磁扰开始时间预报的相对误差,δT/T≤10%的事件数为20个,占总事件数的50%,δT/T≤20%的事件占总事件数的70%以上;(2)磁扰幅度(ΣKp)大小的预报,其相对误差δΣKp/ΣKp≤30%的事件数为32个,占总事件数的80%,而δΣKp/ΣKp≥60%仅占15%.本文方法显示了一定潜力,有待从聚类分析方面进一步深入。      

太阳活动高年期间(1978—1981)耀斑-行星际激波传播的三维平均特性——日本行星际闪烁(IPS)观测的初步分析

文中通过对行星际激波传播的动力学效应的考虑,根据日本的IPS观测资料,对太阳活动高年期间85个耀斑-IPS激波事件进行了统计研究.结果表明:(1)激波的传播相对耀斑法线方向是非对称传播.传播最快的方向,就经度而言趋向行星际螺旋形磁场方向;就纬度而言很接近在此事件期间日球电流片的平均纬度.(2)传播的纬度范围(-60°— +40°)远比传播的经度范围(<—90°—+90°)小.(3)激波的能量分布有明显的东-西、南-北不对称性,它决定了激波传播的非对称特性.所研究的85个耀斑-激波的平均能量～2.7×1031尔格(单位立体角).上述结果与我们分析美国圣地亚哥IPS观测所得结果基本一致[3].      

影响地球环境的太阳质子事件的时间过程

利用1966年以来的大量太阳耀斑以及相应质子事件的资料,分析研究了质子事件到达时间和极大时间同耀斑经度位置的统计关系.结果表明当耀斑位置处于经过地球的行星际大尺度场磁力线足点位置附近时,上述两种时间过程最短.这个结果支持了太阳耀斑粒子经日冕传播再向行星际空间传播的二阶段传播模型.      

无振荡、无自由参数格式在三维太阳风流动数值模拟中的应用

从快速、有效地进行空间天气数值预报的需要出发,针对1998年5月份行星际太阳风暴事件,采用全三维流体力学(HD)模型进行数值试验,考察了无振荡、无自由参数(NND)格式在三维太阳风流动数值模拟中的应用.计算中内边界的密度分布由观测K日冕数据来确定,由此得到的源表面密度分布具有和源表面电流片相似的结构.数值试验表明: 虽然该格式不需要加入人工粘性,但是具有很好的计算稳定性.在扰动计算时激波间断耗散小,在三维任何方向上间断所占的网格数比较少,没有数值色散现象.      

地球弓激波的三维模拟

利用磁流体动力学(MHD)全球模拟结果,根据弓激波的跃变特性确定出弓激波位置,建立了一个新的综合考虑了快磁声马赫数、太阳风动压、行星际磁场强度以及磁层顶曲率半径的弓激波三维位型模型.将新模型与以往模型的模拟结果进行比较发现,新的弓激波全球模型结果可靠,解决了部分现有模型不能描述弓激波三维位型的问题.研究结果表明,在行星际磁场北向时,随着快磁声马赫数的增大,弓激波日下点距离减小,但是在行星际磁场南向时,快磁声马赫数的变化对弓激波日下点距离影响不大;弓激波位型在赤道面与子午面上存在明显的不对称性,而且随着行星际磁场的转向,这种非对称性也会发生相应改变;行星际磁场南向,B_z值较小时,子午面内弓激波位型已经不是简单的抛物线,出现了明显的类似于极尖区磁层顶的凹陷变化区.      

An empirical model to predict the 1-AU arrival of interplanetary shocks

We extend the empirical coronal mass ejection (CME) arrival model of Gopalswamy et al. [Gopalswamy, N. et al. Predicting the 1-AU arrival times of coronal mass ejections, J. Geophys. Res. 106, 29207, 2001] to predict the 1-AU arrival of interplanetary (IP) shocks. A set of 29 IP shocks and the associated magnetic clouds observed by the Wind spacecraft are used for this study. The primary input to this empirical shock arrival model is the initial speed of white-light CMEs obtained using coronagraphs. We use the gas dynamic piston–shock relationship to derive the ESA model which provides a simple means of obtaining the 1-AU speed and arrival times of interplanetary shocks using CME speeds.     

Forecasting space weather: Predicting interplanetary shocks using neural networks

We are developing a system to predict the arrival of interplanetary (IP) shocks at the Earth. These events are routinely detected by the Electron, Proton, and Alpha Monitor (EPAM) instrument aboard NASA’s ACE spacecraft, which is positioned at Lagrange Point 1 (L1). In this work, we use historical EPAM data to train an IP shock forecasting algorithm. Our approach centers on the observation that these shocks are often preceded by identifiable signatures in the energetic particle intensity data. Using EPAM data, we trained an artificial neural network to predict the time remaining until the shock arrival. After training this algorithm on 37 events, it was able to forecast the arrival time for 19 previously unseen events. The average uncertainty in the prediction 24 h in advance was 8.9 h, while the uncertainty improved to 4.6 h when the event was 12 h away. This system is accessible online, where it provides predictions of shock arrival times using real-time EPAM data.     

不确定理论下竞争失效系统的可靠性分析

针对有新研发产品，故障数据较少的复杂系统，提出了不确定环境下自然退化和外部冲击相互独立的竞争失效模型。考虑了系统同时遭受自然退化和外部冲击，连续的自然退化用一个不确定过程刻画，冲击到达的时间间隔和每次冲击对系统造成的损坏量分别用2个不同的不确定变量来刻画。运用不确定理论，分别在极端冲击模型、累积冲击模型、δ冲击模型下，研究了系统的确信可靠度，结果表明:在有新研发的产品、故障数据较少的复杂系统，用不确定理论的方法来描述模型更合适，并通过数值分析显示了模型的有效性。      

Relative fluxes of shock and prompt peaks in SEP event time profiles

Peak fluxes are an important property of gradual solar energetic particle (SEP) event time profiles from both astro/heliophysical and applications perspectives. However, the peak flux in an event may occur at the event onset, or at the time of the interplanetary shock arrival (the ESP or energetic storm particles). This makes an important difference in the interpretation of the peak flux, and in any attempts to characterize or model it. This paper describes a study of SEP data sets from ACE, IMP-8 and GOES toward determining the relative properties of these peak fluxes for protons with energies near 1, 10, and 50 MeV. The results suggest that for gradual events with both peaks, the ESP peak often dominates at 1 MeV energies and is dominant about half the time at 10 MeV. Moreover, the prompt peak fluxes can be used to estimate the shock peak (ESP event) up to days ahead, especially in the lower energy range.     

Solar wind low-energy energetic ion enhancements: A tool to forecast large geomagnetic storms

Predicting the occurrence of large geomagnetic storms more than an hour in advance is an important, yet difficult task. Energetic ion data show enhancements in flux that herald the approach of interplanetary shocks, usually for many hours before the shock arrival. We present a technique for predicting large geomagnetic storms (Kp  7) following the arrival of interplanetary shocks at 1 AU, using low-energy energetic ions (47–65 keV) and solar wind data measured at the L1 libration point. It is based on a study of the relationship between energetic ion enhancements (EIEs) and large geomagnetic storms by Smith et al. [Smith, Z., Murtagh, W., Smithtro, C. Relationship between solar wind low-energy energetic ion enhancements and large geomagnetic storms. J. Geophys. Res. 109, A01110, 2004. doi:10.1029/ 2003JA010044] using data in the rise and maximum of solar cycle 23 (February 1998–December 2000). An excellent correlation was found between storms with Kp  7 and the peak flux of large energetic ion enhancements that almost always (93% of time in our time period) accompany the arrival of interplanetary shocks at L1. However, as there are many more large EIEs than large geomagnetic storms, other characteristics were investigated to help determine which EIEs are likely to be followed by large storms. An additional parameter, the magnitude of the post-shock total magnetic field at the L1 Lagrangian point, is introduced here. This improves the identification of the EIEs that are likely to be followed by large storms. A forecasting technique is developed and tested on the time period of the original study (the training data set). The lead times, defined as the times from the arrival of the shock to the start of the 3-h interval of maximum Kp, are also presented. They range from minutes to more than a day; the average for large storms is 7 h. These times do not include the extra warning time given when the EI flux cross the high thresholds ahead of the shock. Because the data-stream used in the original study is no longer available, we extended the original study (1998–2000) to 2001, in order to: (a) investigate EIEs in 2001; (b) present a validation of the technique on an independent data set; (c) compare the results based on the original (P1) energy channel to those of the replacement (P1′) and (d), determine new EIE thresholds for forecasting geomagnetic storms using P1′ data. The verification of this P1′ training data set is also presented, together with lead times.     

Rapid and Asymmetric Response of the Earth's Bow Shock:Multipoint Observations

We analyze observations of three bow shock crossings which occurred during 2007, using upstream data from STEREO A/B, ACE and WIND, combined with multi-point THEMIS and Cluster data, and TC-1 data located near noon. During the crossing of 7 May 2007, we find that following a rapid reduction in solar wind ram pressure and subsequent pressure pulse seen by ACE and WIND upstream, the bow shock responds asymmetrically from dawn to dusk. Cluster data on the dawn-side suggest the bow shock is significantly flared and responds rapidly to the pulse arrival, while TC-1 at noon, and THEMIS on the dusk-side, are well matched to the model bow shock, but show a delayed response. The crossings observed on 21 May and 2 June show contrasting response matching the model boundary for northward Interplanetary Magnetic Field (IMF). The IMF and solar wind plasma data suggest that the bow shock crossing at dawn-dusk side and subsolar point were mainly caused by large and smaller scale features of the solar wind ram pressure rise rather than the influence of IMF.