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地球同步轨道高能电子增强事件预报方法
引用本文:薛炳森,叶宗海.地球同步轨道高能电子增强事件预报方法[J].空间科学学报,2004,24(4):283-288.
作者姓名:薛炳森  叶宗海
作者单位:中国科学院空间科学与应用研究中心,北京,100080
摘    要:分析了地球同步轨道高能电子通量增强事件的发生规律及其与太阳风和行星际磁场参数的关系,并在此基础上建立了基于人工神经网络的高能电子增强事件模式,经实测数据检验,预报模式可以对未来1天的高能电子通量进行预报,误差为8.2%,达到了较高水平.

关 键 词:地球同步轨道  高能电子增强事件  空间预报  太阳风  发生规律
修稿时间:2003年10月30

FORECAST OF THE ENHANCEMENT OF RELATIVISTIC ELECTRON AT THE GEO-SYNCHRONOUS ORBIT
XUE Bingsen YE Zonghai Center for Space Sciences and Applied Research,The Chinese Academy of Sciences,Beijing.FORECAST OF THE ENHANCEMENT OF RELATIVISTIC ELECTRON AT THE GEO-SYNCHRONOUS ORBIT[J].Chinese Journal of Space Science,2004,24(4):283-288.
Authors:XUE Bingsen YE Zonghai Center for Space Sciences and Applied Research  The Chinese Academy of Sciences  Beijing
Institution:XUE Bingsen YE Zonghai Center for Space Sciences and Applied Research,The Chinese Academy of Sciences,Beijing 100080
Abstract:The high flux of energetic electrons on geo-synchronous orbit can induce many kinds of malfunction of the satellite there, within which the bulk charging is the most significant that several broadcast satellite failures were confirmed to be due to this effect. The electron flux on geo-synchronous orbit varies in a large range even up to three orders accompanied the passage of interplanetary magnetic cloud and the following geomagnetic disturbances. Upon the investigation of electron flux, interplanetary solar wind data, and geomagnetic data as well, we found that, (1) The enhancement of energetic flux on the geo-synchronous orbit exhibits periodic recurrence of 27 days; (2) significant increase of electron flux relates to interplanetary index and characters of their distribution; (3) the electron flux also has relation to solar activity index. In our research work, artificial neural net was employed and constructed according to the job. The artificial neural network was employed in this paper to build the relationship between the electron flux and interplanetary parameters. The input parameter is: the solar wind velocity in km/s; the density of the plasma density; the total strength of interplanetary magnetic field. All of them were daily average value. To represent the influence of the variation of the interplanetary parameters influence, the ratio with those of the previous day of the mentioned parameters were also input. Through the training, the neural network can adjust the internal weight and the value of the nods to build the relationship between the electron flux and the interplanetary parameters and their variation. Preliminary result showed that the accuracy forecast of electron flux 1 day ahead can reach 80%. Through the effort in the prediction of energetic electron fluxes enhancement with the solar wind and interplanetary magnetic field parameters, we realized that the mechanism of the electron event is far too complex to be described with simple formulas. Large database is needed for statistic work to make the prediction more accurate. Fundamentally, great measured and advance research work has to be done to investigate the source energetic electron and acceleration mechanism.
Keywords:Energetic  Electron  Solar wind  Interplanetary field  Artificial neuron network
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