磁鞘等离子体密度统计建模

陈凯文; 来鹏; 赵凯; 蒋勇

doi:10.11728/cjss2017.06.690

磁鞘等离子体密度统计建模

doi: 10.11728/cjss2017.06.690 cstr: 32142.14.cjss2017.06.690

1. 南京信息工程大学数学与统计学院南京 210044;
2. 南京信息工程大学空间天气研究所南京 210044

基金项目:

国家自然科学基金项目（41604134）和江苏省自然科学基金项目（BK20161530）共同资助

详细信息

作者简介:

来鹏,laipeng@amss.ac.cn

中图分类号: P354.4
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出版历程
- 收稿日期: 2016-11-30
- 修回日期: 2017-06-20
- 刊出日期: 2017-11-15

Statistical Modeling Research on the Magnetosheath Plasma Density

1. School of Mathematics and Statistics, Nanjing University of Information Science and Technology, Nanjing 210044;
2. Institute of Space Weather, Nanjing University of Information Science and Technology, Nanjing 210044

摘要

摘要: 利用理想磁流体LFM模型的模拟数据，基于非参数统计方法对2004年11月14日03：00UT-07：00UT磁暴恢复相期间磁鞘等离子体平均密度进行建模.分析磁鞘等离子体平均密度与上游太阳风参数、行星际磁场参数及地磁扰动参数的统计关系，建立基于数据降维的经验模型.结果表明，电离层扰动强度因子、太阳风-磁层耦合强度因子和日地空间因果链耦合强度因子是影响磁鞘等离子体平均密度的三个主要方面.磁暴恢复相期间电离层上行离子是磁层环电流和磁尾等离子体的重要离子来源.建模分析过程表明，利用经验模型对空间物理过程开展建模，数据的严重多重共线性通常会导致模型的精度较差.而利用SIR和LPR建立的磁鞘等离子体平均密度随相关参数变化的经验模型可以有效解决该问题，具有较好的预测精度，统计特征显著.
- 磁鞘等离子体密度 /
- 切片逆回归(SIR) /
- 局部多项式回归(LPR) /
- MHD模拟
Abstract: By using the simulation data of the ideal MHD LFM model, the model of the mean plasma density of the magnetosheath from 03:00UT to 07:00UT on November 14, 2004 is established based on the Sliced Inverse Regression (SIR) and Local Polynomial Regression (LPR) method. The statistical relationship between the mean plasma density and the upstream solar wind parameters, the interplanetary magnetic field, and the geomagnetic disturbance indices are analyzed. The results show that the ionospheric disturbance intensity factor, the solar wind-magnetosphere coupling intensity factor, and the Sun-Earth coupling intensity factor are the three main factors which influence the plasma density of the magnetosheath. The up-flowing ions are the important source for magnetosphere circular current and magnetotail plasma during the recovery phase. The analyses show that, as modeling the process of space physics by the empirical regression method, the multi-collinearity problem usually leads to the poor accuracy of the model. However, the statistical models built by the non-parametric statistical method of SIP and LPR can efficiently solve the problems above.
- Plasma density of the magnetosheath /
- Sliced Inverse Regression (SIR) /
- Local Polynomial Regression (LPR) /
- MHD simulation

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参考文献(22)

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