基于经验模态分解的地球同步轨道高能电子通量预报
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摘要
在磁暴恢复相期间,大量相对论(高能)电子从磁层的外辐射带渗透到地球同步轨道区.其中> 2 MeV的高能电子能够穿透卫星表面并聚积在材料内部,导致卫星无法正常运行或完全损坏.磁暴期间的高能电子通量变化的非平稳与非线性特征十分明显.通过实验发现,经验模态分解法能够极大地降低高能电子通量非平稳性问题造成的预报影响.以2008-2009年的数据作为训练集,2010-2013年数据作为测试集.结果表明:2010-2013年的预报率约为0.84;在太阳活动较为复杂的2013年,预报率达到0.81.引入经验模态分解后预报效率得到显著提高.
During the recovery of a magnetic storm,the relativistic electrons with MeV energy diffuse from the outer radiation belt to geosynchronous orbit. The electrons which energy are larger than 2 MeV could penetrate the surface of satellites and accumulate inside them. Such an electron flux effect could cause satellites to be unable to function properly or to fail completely. Relativistic electrons change very rapidly during the magnetic storm and are very non-stationary. These effects are reduced by empirical mode decomposition method. Data in 2008-2009 are used as the training set, and data in 2010-2013 are used as the testing set. The result shows that the average prediction efficiency of the testing set is 0.81. The solar activity is complex in 2013, and the prediction efficiency is up to 0.81. The prediction efficiency of electron flux has been greatly improved by using empirical decomposition method.
During the recovery of a magnetic storm,the relativistic electrons with MeV energy diffuse from the outer radiation belt to geosynchronous orbit. The electrons which energy are larger than 2 MeV could penetrate the surface of satellites and accumulate inside them. Such an electron flux effect could cause satellites to be unable to function properly or to fail completely. Relativistic electrons change very rapidly during the magnetic storm and are very non-stationary. These effects are reduced by empirical mode decomposition method. Data in 2008-2009 are used as the training set, and data in 2010-2013 are used as the testing set. The result shows that the average prediction efficiency of the testing set is 0.81. The solar activity is complex in 2013, and the prediction efficiency is up to 0.81. The prediction efficiency of electron flux has been greatly improved by using empirical decomposition method.
引文
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[10] LI X L, TEMERIN M, BAKER D N, et al. Quantitative prediction of radiation belt electrons at geostationary orbit based on solar wind measurements[J]. Geophys. Res.Lett., 2001, 28(9):1887-1890
[11] LI X. Variations of 0.7-60MeV electrons at geosynchronous orbit ar a function of solar wind[J]. Space Weather, 2004, 2(03):6-15
[12] TURNER D L, LI X L. Quantitative forecast of relativistic electron flux at geosynchronous orbit based on lowenergy electron flux[J]. Space Weather. Int. J. Res., 2008,6(5):620-628
[13] POTAPOV A, RYZHAKOVA L, TSEGMED B. A new approach to predict and estimate enhancements of “killer”electron flux at geosynchronous orbit[J]. Acta Astron.,2016, 126(5):47-51
[14] POTAPOV A, TESGMED B, RYZHAKOVAL. Solar cycle variation of “killer” electrons at geosynchronous orbit and electron flux correlation with the solar wind parameters and ULF waves intensity[J]. Acta Astron., 2014,93(3):55-63
[15] FUTAKA M, TAGUCHI S, OKUZAWA T, et al. Neural network prediction of energetic electrons at geosynchronous orbit during the storm recovery phase:effects of recurring substorms[J]. Ann. Geophys., 2002, 20(7):947-951
[16] LING A G, GINET G P, HILMER R V, et al. A neural network-based geosynchronous relativistic electron flux forecasting model[J]. Space Weather, 2010, 8(09):3-16
[17] XUE Bingsen, YE Zonghai. Method for forecasting relativistic electron enhan-cement events in geosynchronous orbit[J]. Chin.J. Space Sci.,2004, 24(4):283-288(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)
[18] GUO Ce, XUE Bingsen, LIN Zhaoxiang. Geosynchronous orbit high energy electron flux prediction method[J].Chin. J.Space Sci.,2013, 33(4):418-426(郭策,薛炳森,林兆祥.地球同步轨道高能电子通量预报方法研究[J].空间科学学报,2013, 33(4):418-426)
[19] XIAO F L, ZHANG S, SU Z P, et al. Rapid acceleration of radiation belt energetic electrons by Z-mode waves[J].Geophys. Res. Lett., 2012, 39(3):3103
[20] ZHANG S, XIAO F L. Chorus-driven outer radiation belt electron dynamics at different shells[J]. Chin. Phys. Lett., 2010, 27(12):219-222
[21] XU Xiaogang, XU Guanlei, WANG Xiaotong, et al.Empirical Mode Decomposition(EMD)and its applications[J]. Chin. J. Elct., 2009, 37(3):581-585(徐晓刚,徐冠雷,王孝通,等.经验模式分解(EMD)及其应用[J].电子学报,2009, 37(3):581-585)
[22] HAO Huan, WANG Huali, WEI Qin. Theory and application of empirical mode decomposition[J]. Chin. High Technol. Lett.,2016, 26(1):67-80(郝欢,王华力,魏勤.经验模态分解理论及其应用[J].高技术通讯,2016, 26(1):67-80)
[23] REEVES G D, MCADAMS K L, FRIEDEL R H. Accele-ration and loss of relativistic electrons during geomagnetic storms[J]. Geophys. Res. Lett., 2003, 30(10):1529-1532
[24] KELLERMAN A C, SHPRITS Y Y. 2011. On the influence of solar wind conditions on the outer-electron radiation belt[J]. J. Geophys. Res., 2012, 117(A5):5217-5230
[25] YOUSRFI M R, KASMAEI B S, VAHABIE A, et al. Input selection based on information theory for constructing predictor models of solar and geomagnetic activity indices[J]. Sol. Phys., 2009, 258(2):297-318
[26] HUANGN E, SHEN Z, LONG S R. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J]. Proc. A, 1998,454(1971):903-995
[27] SAIN STEPHAN R. The nature of statistical learning theory[J]. Technometric, 1996, 38(4):409
[28] RILLING G, FLANDRIN P, GONCALVES P. On empirical mode decomposition and its algorithms//IEEEEURASIP Workshop on Nonlinear Signal and Image Processing[C]. Grado:IEEE, 2003:8-11
[29] BROWN R G, HWANG P Y. Introduction to Random Signal and Applied Kalman Filtering[M]. Toronto:John Wiley and Sons, 1992
[30] YANG Peicai, ZHOU Xiuji. On non-sataionary behaviors and prediction theory of climate systems[J]. J. Meteor.Res.,2005, 63(5):556-570(杨培才,周秀骥.气候系统的非平稳行为和预测理论[J].气象学报,2005, 63(5):556-570)
[31] KIM K C, LEE D Y, KIM H J, et al. Numerical calculations of relativistic electron drift loss effect[J]. Geophys.Res. Lett., 2008, 113(A9):212-225
*https://ngdc.noaa.gov/
**http://swdcwww.kugi.lQroto-u.ac.jp/wdc/Sec3.html
[2] WRENN G L, RODGERS D J, RYDEXL K A. A solar cycle of spacecraft anomalies due to internal charging[J].Ann. Geophys., 2002, 20:953-956
[3] HE Tian, LIU Siqing, XUE Bingsen, et al. The use of geomagnetic pulsations forecast geostationary orbit relativistic electron flux method[J]. Chin. J. Geophys., 2009,52(10):2419-2427(何甜,刘四清,薛炳森,等.利用地磁脉动预报地球同步轨道相对论电子通量方法的研究[J].地球物理学报,2009, 52(10):2419-2427)
[4] PAULIKAS G A, BLAKE J B. Effects of solar the wind on magnetospheric dynamics:energetic electrons at the synchronous orbit[C]. Quantitative Modeling of Magnetospheric Processes, AGU, 1979:180-202
[5] BAKER D N, MCPHERRON R L, CAYTON T E, et al. Linear prediction filter analysis of relativistic electron properties at 6.6 Re[J]. J. Geophys. Res., 1990,95(A9):15133-15140
[6] LI Sheng, HUANG Wengeng. Dynamic prediction model of relativistic electron differential fluxes at the geosynchronous orbit[J]. Chin. J. Space Sci., 2017, 37(3):298-311(李胜,黄文耿.地球同步轨道相对论电子微分通量的动态预报模型[J]·空间科学学报,2017, 37(3):298-311)
[7] RIGLER E J, BAKER D N, WEIGEL R S, et al. Adaptive liner prediction of radiation belt electrons using the kalman filter[J]. Space Wea.:Int. J. Res., 2004,2(3):3003-3011
[8] SAKGUIC K,MIYOSHI Y,SATIO S, et al. Elative electron flux forecast at geostationary orbit using Kalaman filter based on ultivariate autoregressive model[J]. Space Wea., 2013, 11(2):79-89
[9] HE Tian,LIU Siqing,SHEN Hua, et al. Quantitative prediction of relativistic electron flux at geosynchronous orbit with geomagnetic pulsation parameters[J]. Chin. J.Space Sci.,2013, 33(1):20-27(何甜,刘四清,沈华,等.使用地磁脉动参数定量预报地球同步轨道相对论电子通量的建模研究[J].空间科学学报,2013, 33(1):20-27)
[10] LI X L, TEMERIN M, BAKER D N, et al. Quantitative prediction of radiation belt electrons at geostationary orbit based on solar wind measurements[J]. Geophys. Res.Lett., 2001, 28(9):1887-1890
[11] LI X. Variations of 0.7-60MeV electrons at geosynchronous orbit ar a function of solar wind[J]. Space Weather, 2004, 2(03):6-15
[12] TURNER D L, LI X L. Quantitative forecast of relativistic electron flux at geosynchronous orbit based on lowenergy electron flux[J]. Space Weather. Int. J. Res., 2008,6(5):620-628
[13] POTAPOV A, RYZHAKOVA L, TSEGMED B. A new approach to predict and estimate enhancements of “killer”electron flux at geosynchronous orbit[J]. Acta Astron.,2016, 126(5):47-51
[14] POTAPOV A, TESGMED B, RYZHAKOVAL. Solar cycle variation of “killer” electrons at geosynchronous orbit and electron flux correlation with the solar wind parameters and ULF waves intensity[J]. Acta Astron., 2014,93(3):55-63
[15] FUTAKA M, TAGUCHI S, OKUZAWA T, et al. Neural network prediction of energetic electrons at geosynchronous orbit during the storm recovery phase:effects of recurring substorms[J]. Ann. Geophys., 2002, 20(7):947-951
[16] LING A G, GINET G P, HILMER R V, et al. A neural network-based geosynchronous relativistic electron flux forecasting model[J]. Space Weather, 2010, 8(09):3-16
[17] XUE Bingsen, YE Zonghai. Method for forecasting relativistic electron enhan-cement events in geosynchronous orbit[J]. Chin.J. Space Sci.,2004, 24(4):283-288(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)
[18] GUO Ce, XUE Bingsen, LIN Zhaoxiang. Geosynchronous orbit high energy electron flux prediction method[J].Chin. J.Space Sci.,2013, 33(4):418-426(郭策,薛炳森,林兆祥.地球同步轨道高能电子通量预报方法研究[J].空间科学学报,2013, 33(4):418-426)
[19] XIAO F L, ZHANG S, SU Z P, et al. Rapid acceleration of radiation belt energetic electrons by Z-mode waves[J].Geophys. Res. Lett., 2012, 39(3):3103
[20] ZHANG S, XIAO F L. Chorus-driven outer radiation belt electron dynamics at different shells[J]. Chin. Phys. Lett., 2010, 27(12):219-222
[21] XU Xiaogang, XU Guanlei, WANG Xiaotong, et al.Empirical Mode Decomposition(EMD)and its applications[J]. Chin. J. Elct., 2009, 37(3):581-585(徐晓刚,徐冠雷,王孝通,等.经验模式分解(EMD)及其应用[J].电子学报,2009, 37(3):581-585)
[22] HAO Huan, WANG Huali, WEI Qin. Theory and application of empirical mode decomposition[J]. Chin. High Technol. Lett.,2016, 26(1):67-80(郝欢,王华力,魏勤.经验模态分解理论及其应用[J].高技术通讯,2016, 26(1):67-80)
[23] REEVES G D, MCADAMS K L, FRIEDEL R H. Accele-ration and loss of relativistic electrons during geomagnetic storms[J]. Geophys. Res. Lett., 2003, 30(10):1529-1532
[24] KELLERMAN A C, SHPRITS Y Y. 2011. On the influence of solar wind conditions on the outer-electron radiation belt[J]. J. Geophys. Res., 2012, 117(A5):5217-5230
[25] YOUSRFI M R, KASMAEI B S, VAHABIE A, et al. Input selection based on information theory for constructing predictor models of solar and geomagnetic activity indices[J]. Sol. Phys., 2009, 258(2):297-318
[26] HUANGN E, SHEN Z, LONG S R. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J]. Proc. A, 1998,454(1971):903-995
[27] SAIN STEPHAN R. The nature of statistical learning theory[J]. Technometric, 1996, 38(4):409
[28] RILLING G, FLANDRIN P, GONCALVES P. On empirical mode decomposition and its algorithms//IEEEEURASIP Workshop on Nonlinear Signal and Image Processing[C]. Grado:IEEE, 2003:8-11
[29] BROWN R G, HWANG P Y. Introduction to Random Signal and Applied Kalman Filtering[M]. Toronto:John Wiley and Sons, 1992
[30] YANG Peicai, ZHOU Xiuji. On non-sataionary behaviors and prediction theory of climate systems[J]. J. Meteor.Res.,2005, 63(5):556-570(杨培才,周秀骥.气候系统的非平稳行为和预测理论[J].气象学报,2005, 63(5):556-570)
[31] KIM K C, LEE D Y, KIM H J, et al. Numerical calculations of relativistic electron drift loss effect[J]. Geophys.Res. Lett., 2008, 113(A9):212-225
*https://ngdc.noaa.gov/
**http://swdcwww.kugi.lQroto-u.ac.jp/wdc/Sec3.html