The Influence of Solar Wind and Geomagnetic Indices on Lower Band Chorus Emissions in the Inner Magnetosphere
The Influence of Solar Wind and Geomagnetic Indices on Lower Band Chorus Emissions in the Inner Magnetosphere
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DOI：
10.1029/2018ja025704
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发表时间：
2018-11
期刊：
影响因子：
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通讯作者：
R. Boynton;Homayon Aryan;S. Walker;V. Krasnoselskikh;M. Balikhin
中科院分区：
文献类型：
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作者：
R. Boynton;Homayon Aryan;S. Walker;V. Krasnoselskikh;M. Balikhin
统计波模型，描述波振幅的分布作为位置，地磁活动和其他参数的函数，需要作为基础来描述辐射带数值模型中的波粒相互作用。在这项研究中，我们扩大了统计波模型的范围，通过调查太阳风参数或地磁指数和它们的时间滞后对低带合唱（LBC）在内磁层波的振幅有最大的影响。太阳风参数或地磁指数与最大的控制波被发现使用的误差减少比（ERR）分析，这在系统识别建模技术中起着关键作用。在这个应用中，LBC的幅度在不同的位置被认为是作为输出数据，而滞后的太阳风参数的输入数据。ERR分析会自动确定一组最具影响力的参数，以解释排放量的变化。线性和非线性应用的ERR分析进行了比较，使用太阳风输入，并表明线性ERR分析可能会产生误导。线性结果表明，行星际磁场（IMF）因子在每个磁地方时（MLT）扇区的影响最大。然而，非线性ERR分析表明，与太阳风速度耦合的IMF因子对LBC波的震级有主要贡献。当地磁指数作为输入与太阳风参数的非线性ERR分析，结果表明，大部分的排放量的变化可能是由于极光电喷流（AE）指数。在00 ~ 12 MLT和5 < L < 7的黎明扇区，AE指数乘以零时滞太阳风速对LBC振幅的影响最大。对于5 < L < 7，具有最高ERR的参数是AE指数乘以在12-16 MLT下具有2小时时滞的太阳风速度，在16-20 MLT下具有2小时时滞的线性AE指数，以及在20-00 MLT下具有零时滞的AE指数乘以IMF因子。对于4 < L < 5，具有最高ERR的参数是AE指数乘以在00-04 MLT时具有零时滞的太阳风动压，AE指数乘以在14和12 MLT之间具有零时滞的太阳风速度，AE指数乘以在12-16 MLT时具有2小时时滞的太阳风速度，在12-16 MLT时，Dst指数有6小时的时间滞后，在20-00 MLT时，AE指数乘以IMF因子，零滞后。
Statistical wave models, describing the distribution of wave amplitudes as a function of location, geomagnetic activity, and other parameters, are needed as the basis to describe the wave‐particle interactions within numerical models of the radiation belts. In this study, we widen the scope of the statistical wave models by investigating which of the solar wind parameters or geomagnetic indices and their time lags have the greatest influence on the amplitudes of lower band chorus (LBC) waves in the inner magnetosphere. The solar wind parameters or geomagnetic indices with the greatest control over the waves were found using the error reduction ratio (ERR) analysis, which plays a key role in system identification modeling techniques. In this application, the LBC magnitudes at different locations are considered as the output data, while the lagged solar wind parameters are the input data. The ERR analysis automatically determines a set of the most influential parameters that explain the variations in the emissions. Both linear and nonlinear applications of the ERR analysis are compared using solar wind inputs and show that the linear ERR analysis can be misleading. The linear results show that the interplanetary magnetic field (IMF) factor has the most influence on at each magnetic local time (MLT) sector. However, the nonlinear ERR analysis shows that the IMF factor coupled with the solar wind velocity has the main contribution to the LBC wave magnitudes. When geomagnetic indices are included as inputs with the solar wind parameters to the nonlinear ERR analysis, the results show that the majority of the variation in emissions may be attributed to the Auroral Electrojet (AE) index. In the dawn sectors between 00 and 12 MLT and 5 < L < 7, the AE index multiplied by the solar wind velocity with zero time lag has the most influence on the amplitudes of LBC. For 5 < L < 7, the parameters with the highest ERR are the AE index multiplied by the solar wind velocity with a 2‐hr time lag at 12–16 MLT, the linear AE index with a 2‐hr time lag at 16–20 MLT, and AE index multiplied by the IMF factor with zero lag at 20–00 MLT. For 4 < L < 5, the parameters with the highest ERR are the AE index multiplied by the solar wind dynamic pressure with zero time lag at 00–04 MLT, the AE index multiplied by the solar wind velocity with zero time lag between 14 and 12 MLT, the AE index multiplied by the solar wind velocity with a 2‐hr time lag at 12–16 MLT, the Dst index with a 6‐hr time lag at 12–16 MLT, and the AE index multiplied by the IMF factor with zero lag at 20–00 MLT.