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UsingMultiple Signatures to Improve Accuracy of Substorm Identification

机译:UsingMultiple签名来提高精度亚暴识别

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摘要

We have developed a new procedure for combining lists of substorm onset times from multiple sources.We apply this procedure to observational data and to magnetohydrodynamic (MHD) model output from 1-31 January 2005.We show that this procedure is capable of rejecting false positive identifications and filling data gaps that appear in individual lists. The resulting combined onset lists produce a waiting time distribution that is comparable to previously published results, and superposed epoch analyses of the solar wind driving conditions and magnetospheric response during the resulting onset times are also comparable to previous results. Comparison of the substorm onset list from the MHD model to that obtained from observational data reveals that the MHD model reproduces many of the characteristic features of the observed substorms, in terms of solar wind driving, magnetospheric response, and waiting time distribution. Heidke skill scores show that the MHD model has statistically significant skill in predicting substorm onset times.
机译:我们已经开发出一种新程序结合从多个列表的亚暴出现倍来源。数据和磁流体动力(磁流体动力)模型输出从2005年1月1日至31日。过程能够拒绝假阳性识别和填充数据差距出现在单独的列表。列出生产等待时间分布与以前公布的结果,太阳风的叠加分析时代驾驶条件和磁性层的响应结果发病时期也与之前的结果。亚暴出现列表从磁流体动力模型从观测数据显示,获得磁流体动力模型再现了的许多特点的特点,观察亚暴,太阳风开车,磁性层的反应,等待时间分布。表明磁流体动力模型统计预测亚暴发病的重要技能次了。

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