利用Hp分量预测空间环境地磁活动指数Kp

地磁场扰动可以引起近地空间环境(包括电离层和磁层)一系列变化,地磁Kp指数是空间天气扰动的重要参考指标.采用地球同步轨道GOES-8卫星监测到的垂直于同步卫星轨道平面的地磁分量Hp数据,分析了地磁Kp指数与Hp分量波动幅度间的统计关系,结果显示,Hp分量的变化与Kp指数具有很好的相关性.利用回归分析和RBF神经网络方法,建立了Kp指数现报模型,根据地球同步轨道地磁场Hp分量的变化,计算出相同时段的Kp指数.监测结果表明,预报方法具有一定的有效性和实用性,特别是人工神经网络模式计算的Kp指数与实测结果吻合很好.利用此方法能够在不依赖于地面地磁探测数据的情况下,快速预报地磁扰动,及时为空间天气保障提供参考.同时,鉴于中国即将发射的风云四号搭载有地磁场探测仪,本项研究可为自主数据的应用奠定基础.     

一种改进的Kp指数现报模式

在评估国际常用Kp指数现报模式Takahashi及其应用于中国地磁台站效果的基础上,提出了一种改进的地磁Kp指数现报模式,其可以有效识别地磁规则日变化的逐日变化特性,反映地磁扰动的季节效应和地方时效应,从而提升了Kp指数现报的准确性.采用Takahashi模式开发时所使用的台站数据进行对比,新模式将单站地磁Kp指数现报效率由0.77提升至0.84,多站联合Kp指数现报效率由0.88提升至0.92;采用2000—2006年北京十三陵台站(BMT,磁纬29°N)数据进行评估,Kp指数现报效率由0.70提升至0.80.对Kp指数现报结果的误差分析发现,现报误差存在明显的地方时差异和一定的季节差异,误差随扰动强度变化并在中强磁扰时最大.利用SuperMAG的指数分析表明,Kp台站的经度不均匀分布会对现报效果造成一定影响.     

1998年10月18——-19日磁暴主相的行星际源分析

采用高时间分辨率的地磁指数SYM-H, 同时考虑日地连线引力平衡点(L1点)太阳风地磁效应的滞后性, 精确分析了1998年10月18---19日大磁暴主相的行星际源. 分析结果表明, 磁暴主相的行星际源仅为行星际激波和行星际日冕物质抛射之间的太阳风(Sheath), 磁云对磁暴主相没有贡献. 这个磁暴事例的研究表明, 行星际磁场南向分量与太阳风动压的乘积是影响磁暴主相发展的关键参数.      

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%.      

GEO轨道相对论电子日积分通量预报统计建模

基于辐射带相对论电子哨声波局地加速理论,将地磁AE指数作为源电子通量和通量各向异性的指标,将地磁Dst指数作为损失机制的指标,利用滑动窗口线性滤波器方法,建立了一个地球静止轨道大于2MeV相对论电子预报模型.利用该模型开展了2000-2009年地球静止轨道相对论电子通量预报试验.研究发现,这10年总预报效率为0.818,2003年的预报效率（0.633）最低,2009年的预报效率（0.856）最高.模型预报效果与持续模型相比有很大提高,略低于利用太阳风参数作为输入的同类预报模型的预报效果.这说明即使在缺少太阳风参数的情况下,该模型利用地磁扰动参数也能取得较好的预报效果.当模型输入参数增加了太阳风速度时,即综合考虑了行星际扰动和磁层扰动对辐射带粒子加速过程的影响,模型逐年的预报效率进一步提升.其中,2005年的预报效率提升了9.5%,这10年的总预报效率增加到0.848,预报值与实测值之间的线性相关系数为0.918,均方根误差为0.422.      

冕洞特征参数与重现型地磁暴关系的统计研究

在提取冕洞特征参数的基础上,利用1996年到2005年8月近十年来对地磁扰动有影响的356个冕洞事例,定量分析了冕洞特征参数(包括冕洞的面积比、经纬度跨度等)与冕洞高速流特征、重现型地磁扰动特征(包括扰动大小和持续时间等)之间的相关性,研究发现,从引起地磁扰动的冕洞在整个太阳活动周的分布来看,在地磁扰动峰年中冕洞影响同样具有重要的贡献;冕洞高速流太阳风速度与地磁扰动强度之间存在较强的相关性,而高速流中太阳风速度与冕洞面积比关系不大,与冕洞亮度存在一定相关性;冕洞的经度跨度与地磁扰动持续时间存在很强的正相关性.      

太阳风和磁层能量耦合系数的计算研究

本文利用1972—1974年期间十八次事例的太阳风和地磁资料,通过回归分析法计算了各次事例的太阳风和磁层能量的磁耦合参量α值.结果表明,不同事例的α值各不相同,而且显得十分离散.同时,对一些参数进行调节修正后,也不能从本质上改变α取值分散的特点.可见,对不同事例,能量传输机制有可能是不相同的,而电磁能量传输过程可能是最重要的,但不是唯一的.      

太阳风和地球磁层相互作用的两种可能类型

本文对太阳活动20周不同活动期间的太阳风参数与地磁活动性指数分别进行了相关分析,并进一步对太阳活动极大和极小年分别对B_z和太阳风参数V、T、N的时均值日方差作了分析比较。结果指出,除目前普遍认为的IMF与地磁场重联导致的磁扰外,还有一类与B_z无关,而是由高温、高速、热不均匀太阳风等离子体导致的地磁扰动类型。      

地球磁场对太阳风扰动响应的研究

分别对行星际激波、太阳风动压增大事件和减小事件的地球磁场响应进行了比较. 分析结果表明, 同步轨道磁场对太阳风扰动在向阳面产生较强的正响应, 在背阳面 响应较弱且有时会出现负响应, 地磁指数SYM-H对太阳风扰动的响应为正响应. 同时还得出, 向阳侧同步轨道磁场响应幅度d Bz与地磁指数响应幅度d SYM-H、上下游动压均方差均具有较好的相关性. 地磁指数响应幅度与同步轨道磁场响应幅度相关关系在激波和动压增大事件中具有一致性, 动压减小事件出 现明显差异, 这说明激波和动压增大事件在影响地球磁场方面具有某种共性.      

太阳风扰动对极光电急流和环电流指数的影响

利用WIND卫星的太阳风观测数据和地磁活动指数, 研究了太阳风扰动对环电流SYM-H指数, 西向极光电急流AL指数和东向极光电急流AU指数的影响. 结果表明, 太阳风动压增长和减少能够同步或延迟引起地磁活动指数的强烈扰动, 其包括环电流指数的上升, 西向极光电急流指数的下降和东向极光电急流指数的上升. 太阳风动压的突然剧烈增加还能够触发超级亚暴和大磁暴. 太阳风动压脉冲引起的地磁效应具有复杂的表现形式, 这说明太阳风动压脉冲的地磁效应不仅与太阳风动压脉冲大小和持续时间有关, 还与磁层本身所处的状态有关. 时间尺度较长, 消耗能量较大的磁暴只有大的持续时间较长的太阳风动压脉冲才能激发.      

Forecast of recurrent geomagnetic storms

Long-term forecast of space weather allows in achieving a longer lead time for taking the necessary precautions against disturbances. Hence, there is a need for long-term forecasting of space weather. We studied the possibility for a long-term forecast of recurrent geomagnetic storms. Geomagnetic storms recur with an approximate 27-day period during the declining phase of a solar cycle. These disturbances are caused by the passage of corotating interaction regions, which are formed by interactions between the background slow-speed solar wind and high-speed solar wind streams from a coronal hole. In this study, we report on the performance of 27-day-ahead forecasts of the recurrent geomagnetic disturbances using Kp index. The methods of the forecasts are on the basis of persistence, autoregressive model, and categorical forecast using occurrence probability. The forecasts show better performance during the declining phase of a solar cycle than other phases. The categorical forecast shows the probability of detection (POD) more than 0.5 during the declining phase. Transition of the performance occurs sharply among the declining phases and other phases.     

On statistical relationship of solar, geomagnetic and human activities.

Data of galactic cosmic rays, solar and geomagnetic activities and solar wind parameters on the one side and car accident events (CAE) in Poland on the other have been analyzed in order to reveal the statistical relationships among them for the period of 1990-2001. Cross correlation and cross spectrum of the galactic cosmic ray intensity, the solar wind (SW) velocity, Kp index of geomagnetic activity and CAE in Poland have been carried out. It is shown that in some epochs of the above-mentioned period there is found a reliable relationship between CAE and solar and geomagnetic activities parameters in the range of the different periodicities, especially, 7 days. The periodicity of 7 days revealed in the data of the CAE has the maximum on Friday without any exception for the minimum and maximum epochs of solar activity. However, the periodicity of 7 days is reliably revealed in other parameters characterizing galactic cosmic rays, SW, solar and geomagnetic activities, especially for the minimum epoch of solar activity. The periodicity of 3.5 days found in the series of CAE data more or less can be completely ascribed to the social effects, while the periodicity of 7 days can be ascribed to the social effect or/to the processes on the Sun, in the interplanetary space and in the Earth's magnetosphere and atmosphere.     

冕洞相关地磁Ap指数中短期预报方法研究

地磁Ap指数是描述全球地磁活动水平的重要指数, 过去许多参考大气模式中都用Ap指数来表述地磁活动状态, 大气模式的运行需要输入地磁Ap指数, 因此, 地磁Ap指数的预报一直是空间环境预报中一个非常重要的内容. 针对太阳活动低年冕洞引起的地磁扰动具有明显27天重现的特性, 利用修正的自回归方法, 对地磁Ap指数进行了提前27天的预报; 采用从SOHO/EIT观测资料发展出来的描述冕洞特性的Pch因子, 进行了提前三天的地磁Ap指数预报. 结果显示, 将统计方法与物理分析相结合, 进行地磁Ap指数的中短期数值预报, 可以得到较好的预报效果.      

磁扰期间的三维漂移壳分离

基于磁层粒子动力学理论,首先对比了计算漂移壳分离的引导中心法和磁力线追踪法,计算表明两种方法的计算结果一致.然后分别采用T89c和T96磁层磁场模式,用磁力线追踪法数值计算了不同初始位置(≤9Re)、不同初始投掷角、不同Kp指数和不同太阳风压力下,带电粒子的漂移壳分离.计算结果揭示了漂移壳分离随初始位置、投掷角、Kp指数和太阳风压力的变化.其具体特征如下. (1)随着径向距离的增大,漂移壳分离效应愈加显著,由正午出发的粒子将被稳定捕获,而午夜出发的径向距离≥7Re的部分大投掷角粒子将沿磁层顶逃逸. (2)正午出发的粒子,漂移到午夜时其漂移壳随投掷角减小向外排列;午夜出发的粒子,漂移到正午时其漂移壳随投掷角增大排列; 90°投掷角粒子在磁赤道面的漂移壳沿着磁场等值线排列. (3)漂移壳分离随Kp指数和太阳风压力增大变得显著,且随这两种扰动参数的变化特征和趋势是基本相似的.      

Neural network development for the forecasting of upper atmosphere parameter distributions

This paper presents a neural network modeling approach to forecast electron concentration distributions in the 150–600 km altitude range above Arecibo, Puerto Rico. The neural network was trained using incoherent scatter radar data collected at the Arecibo Observatory during the past two decades, as well as the Kp geomagnetic index provided by the National Space Science Data Center. The data set covered nearly two solar cycles, allowing the neural network to model daily, seasonal, and solar cycle variations of upper atmospheric parameter distributions. Two types of neural network architectures, feedforward and Elman recurrent, are used in this study. Topics discussed include the network design, training strategy, data analysis, as well as preliminary testing results of the networks on electron concentration distributions.     

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.     

AL index dependence on the solar wind input revisited

Estimates of the geomagnetic indices made with real-time solar wind measurements form the basis of many space weather forecast techniques. We analyze 20 years of hourly AL and OMNI solar wind data to determine geomagnetic importance of various solar wind and IMF parameters. Besides the solar wind driver of primary importance (VB_s), the first-order contributions, significantly increasing the quality of the model are: solar wind velocity, 2 h of solar wind prehistory and 1 h of AL history. The factors of secondary importance, marginally contributing to overall statistical quality, are IMF B_y, solar wind density, and IMF fluctuations. The dynamic pressure is geomagnetically effective only if the pressure is lower than the average. Modelling of the same data set with an artificial neural network (ANN) confirmed our selection of important factors. Statistically the ANN model was just marginally better than our analytic expression . The AU index dependence is principally different from AL in several respects; therefore modelling of the AE composite index is physically misleading.