电离层总电子含量(total electron content,TEC)区域重构常采用克里金空间插值方法,该方法基于电离层TEC数据的位置关系及相关性等特征,同时满足空间加权估计的无偏性和最优性特征,因此被广泛应用.但克里金空间插值方法拟合变异函数时,...电离层总电子含量(total electron content,TEC)区域重构常采用克里金空间插值方法,该方法基于电离层TEC数据的位置关系及相关性等特征,同时满足空间加权估计的无偏性和最优性特征,因此被广泛应用.但克里金空间插值方法拟合变异函数时,传统理论变异函数模型面临函数曲线固定、空间细节变化无法反映以及模型选取人为主观等问题.为解决上述问题,文中提出一种可选的电离层TEC区域重构方法,从TEC数据的空间变化特征出发,采用最小二乘支持向量机(least squares support vector machine,LS-SVM)拟合实验变异函数,能够更精确地刻画变异函数,反映电离层TEC空间变化趋势.为验证本方法的准确性,选用中国陆态网地基GNSS台站三组不同时刻穿刺点垂直TEC(vertical TEC,VTEC)值作为实测数据,同时选用普通克里金空间插值方法中的指数理论变异函数模型、球状理论变异函数模型以及LS-SVM理论变异函数模型进行实验对比.三组实验结果表明,本文方法计算的均方根误差(root mean squared error,RMSE)分别为1.54 TECU、1.76 TECU和2.45TECU,平均绝对误差(mean absolute error,MAE)分别为1.21 TECU、1.23 TECU和1.62 TECU,误差均小于其他两种理论变异函数模型,重构效果最好,为电离层TEC区域重构提供了一种可选的思路.展开更多
赤道电离异常(Equatorial Ionization Anomaly,EIA)是低纬电离层中的一个重要现象.本文基于IGS台网提供的2001-2008年期间的电离层总电子含量(Total Electron Content,TEC)数据,分析了120°E区的EIA强度和磁南北半球不对称性在磁平...赤道电离异常(Equatorial Ionization Anomaly,EIA)是低纬电离层中的一个重要现象.本文基于IGS台网提供的2001-2008年期间的电离层总电子含量(Total Electron Content,TEC)数据,分析了120°E区的EIA强度和磁南北半球不对称性在磁平静时期的变化特征,包括对地方时、季节和太阳活动的依赖.本文结果表明,(1)EIA强度表现出显著的随地方时和季节的变化特征.EIA强度在0200LT和2000LT附近分别出现一个极值,且2000LT附近的EIA强度更大;EIA强度通常在春/秋季较大,在夏/冬季较小,且冬季要大于夏季.(2)EIA南北半球不对称也表现出随地方时和季节变化特征.EIA半球不对称在0200LT和2000-2200LT附近分别出现一个极值;EIA半球不对称的季节变化特征还依赖于太阳活动,太阳活动高年期间,EIA半球不对称通常在春/秋季更显著;太阳活动低年期间,EIA半球不对称通常在冬季更显著.(3)EIA强度和半球不对称性的逐日变化和月变化表现出对太阳活动存在一定的依赖,但依赖性并不显著.2000LT(0200LT)附近的EIA强度的月变化与太阳活动整体呈正(负)相关,而2200LT(0200LT)附近的EIA半球不对称的月变化与太阳活动整体呈负(负)相关.(4)影响EIA强度变化的主要因素可归于纬圈电场和中性风场;影响EIA半球不对称变化的主要因素可归因为子午中性风场.展开更多
The Graz Ionospheric Flux Tube Simulations （GIFTS） has been improved. The improved GIFTS model was used to numerically investigate the energy particle precipitation on the distribution of electron density in the ion...The Graz Ionospheric Flux Tube Simulations （GIFTS） has been improved. The improved GIFTS model was used to numerically investigate the energy particle precipitation on the distribution of electron density in the ionospheric cusp foot-point region under conditions of large plasma convection during magnetic storm. After including the effects of low energy incident particles, the ionospheric electron densities increase remarkably above altitude of -250 km, showing a peak at about 350 km. The percent enhancements of electron densities increase gradually with altitude, exceed- ing 60% near the upper boundary of the calculation. The calculated ionospheric F2-peak was remarkably enhanced and lifted up by the incident low energy electrons.展开更多
SYM-H is one of the important indices for space weather. It indicates the intensity of magnetic storm, similarly to Dst index but with much higher time-resolution. In this paper an artificial neural network (ANN) of N...SYM-H is one of the important indices for space weather. It indicates the intensity of magnetic storm, similarly to Dst index but with much higher time-resolution. In this paper an artificial neural network (ANN) of Nonlinear Auto Regressive with eXogenous inputs (NARX) has been developed to predict SYM-H index from solar wind and IMF data. In comparison with usual BP and Elman network, the new NRAX model shows much better prediction capability. For 15 testing great storms including 5 super-storms of Min. SYM-H < -200 nT, the cross-correlation of SYM-H indices between NARX network predicted and really observed is 0.91 as a whole. For the 5 individual super-storms, the lowest coefficients is 0.91 relating to the super-storm of March 2001 with Min.SYM-H of -434 nT; while for the two super-storms with Min. SYM-H ranging from -300 nT to -400 nT, the correlations reach as high as 0.93 and 0.96 respectively. The remarkable improvement of the model performance can be attributed to such a key feedback from the network output of SYM-H with a suitable length (about 120 min) to the input, which implies that some information on the quasi real-time ring currents with a proper length of history does its work in the prediction. It tells us that, in addition to the direct driving by solar wind and IMF, the own status of the ring current plays an important role in its evolution especially for recovery phase and must properly be considered in storm-time SYM-H prediction by ANN. The neural network model of NARX developed in this paper provides an effective way to achieve it.展开更多
文摘赤道电离异常(Equatorial Ionization Anomaly,EIA)是低纬电离层中的一个重要现象.本文基于IGS台网提供的2001-2008年期间的电离层总电子含量(Total Electron Content,TEC)数据,分析了120°E区的EIA强度和磁南北半球不对称性在磁平静时期的变化特征,包括对地方时、季节和太阳活动的依赖.本文结果表明,(1)EIA强度表现出显著的随地方时和季节的变化特征.EIA强度在0200LT和2000LT附近分别出现一个极值,且2000LT附近的EIA强度更大;EIA强度通常在春/秋季较大,在夏/冬季较小,且冬季要大于夏季.(2)EIA南北半球不对称也表现出随地方时和季节变化特征.EIA半球不对称在0200LT和2000-2200LT附近分别出现一个极值;EIA半球不对称的季节变化特征还依赖于太阳活动,太阳活动高年期间,EIA半球不对称通常在春/秋季更显著;太阳活动低年期间,EIA半球不对称通常在冬季更显著.(3)EIA强度和半球不对称性的逐日变化和月变化表现出对太阳活动存在一定的依赖,但依赖性并不显著.2000LT(0200LT)附近的EIA强度的月变化与太阳活动整体呈正(负)相关,而2200LT(0200LT)附近的EIA半球不对称的月变化与太阳活动整体呈负(负)相关.(4)影响EIA强度变化的主要因素可归于纬圈电场和中性风场;影响EIA半球不对称变化的主要因素可归因为子午中性风场.
基金the National Natural Science Foundation o China(40404015, 40390150)the Open Foundation of the Key Laboratory o Geospace Environment and Geodesy of Ministry of Education
文摘The Graz Ionospheric Flux Tube Simulations （GIFTS） has been improved. The improved GIFTS model was used to numerically investigate the energy particle precipitation on the distribution of electron density in the ionospheric cusp foot-point region under conditions of large plasma convection during magnetic storm. After including the effects of low energy incident particles, the ionospheric electron densities increase remarkably above altitude of -250 km, showing a peak at about 350 km. The percent enhancements of electron densities increase gradually with altitude, exceed- ing 60% near the upper boundary of the calculation. The calculated ionospheric F2-peak was remarkably enhanced and lifted up by the incident low energy electrons.
基金Supported by Doctoral Fund of Ministry of Education of China (Grant No. 200804860012)
文摘SYM-H is one of the important indices for space weather. It indicates the intensity of magnetic storm, similarly to Dst index but with much higher time-resolution. In this paper an artificial neural network (ANN) of Nonlinear Auto Regressive with eXogenous inputs (NARX) has been developed to predict SYM-H index from solar wind and IMF data. In comparison with usual BP and Elman network, the new NRAX model shows much better prediction capability. For 15 testing great storms including 5 super-storms of Min. SYM-H < -200 nT, the cross-correlation of SYM-H indices between NARX network predicted and really observed is 0.91 as a whole. For the 5 individual super-storms, the lowest coefficients is 0.91 relating to the super-storm of March 2001 with Min.SYM-H of -434 nT; while for the two super-storms with Min. SYM-H ranging from -300 nT to -400 nT, the correlations reach as high as 0.93 and 0.96 respectively. The remarkable improvement of the model performance can be attributed to such a key feedback from the network output of SYM-H with a suitable length (about 120 min) to the input, which implies that some information on the quasi real-time ring currents with a proper length of history does its work in the prediction. It tells us that, in addition to the direct driving by solar wind and IMF, the own status of the ring current plays an important role in its evolution especially for recovery phase and must properly be considered in storm-time SYM-H prediction by ANN. The neural network model of NARX developed in this paper provides an effective way to achieve it.
