利用H_p分量预测空间环境地磁活动指数K_p  

Forecasting Geomagnetic Activity Kp Index of Space Environment With H_p

作　　者：宋丹[1] 薛炳森[2] 程国生[1] 

机构地区：[1]南京信息工程大学数学与统计学院,南京211044 [2]国家卫星气象中心,北京100081

出　　处：《空间科学学报》2013年第2期151-157,共7页Chinese Journal of Space Science

基　　金：公益性气象行业专项资助(GYHY200906013)

摘　　要：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K_p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H_p数据,分析了地磁Kp指数与H_p分量波动幅度间的统计关系,结果显示,H_p分量的变化与K_p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H_p分量的变化,计算出相同时段的K_p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K_p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.Geomagnetic disturbance can cause a series of changes for near-Earth space environment, including the ionosphere and magnetosphere, and Kp index is the important reference index for dis- turbance of space weather. Hp component data monitored by GOES-8 satellite of geosynchronous orbit was used in this paper. By analyzing statistical relation between Kp index and Hp component width of fluctuation, it is shown that the change of Hp component and Kp index have good corre- lation. Based on the change of Hp component, Kp index in the same time interval is calculated, and then Kp index forecast model is established by means of regression analysis and RBF neural networks. Monitoring results show that the prediction method has certain validity and practicability, especially that Kp index calculated by artificial neural networks is quite consistent with its measured values. By using this method, it is able to forecast geomagnetic disturbance quickly, and to provide reference in time for space weather guarantee not relying on the data from geomagnetic diction. In addition, China is going to launch FY-4 satellite, on which a magnetic field detector will be carried. Hence, this research will provide the foundation for application using FY-4 data in the future.

关 键 词：地磁场 HP 分量 KP指数 预测 

分 类 号：P318[天文地球—固体地球物理学]