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Using multiple signatures to improve accuracy of substorm identification
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  • John D Haiducek,
  • Daniel T Welling,
  • Steven K. Morley,
  • Natalia Yu Ganushkina,
  • Xiangning Chu
John D Haiducek
Naval Research Laboratory, Naval Research Laboratory

Corresponding Author:jhaiduce@umich.edu

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Daniel T Welling
University of Texas at Arlington, University of Texas at Arlington
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Steven K. Morley
Los Alamos National Laboratory (DOE), Los Alamos National Laboratory (DOE)
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Natalia Yu Ganushkina
Finnish Meteorological Institute, Finnish Meteorological Institute
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Xiangning Chu
Dept. Atmospheric and Oceanic Science, Dept. Atmospheric and Oceanic Science
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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.
Apr 2020Published in Journal of Geophysical Research: Space Physics volume 125 issue 4. 10.1029/2019JA027559