SA004-0014
Substorm dynamics in MHD: Statistical validation tests and paths for improvement

Tuesday, 8 December 2020
Poster
John David Haiducek1, Daniel T Welling2, Steven Morley3, Agnit Mukhopadhyay4, Xiangning Chu5, Joseph Helmboldt1, Joseph D Huba6 and Natalia Y Ganushkina4, (1)US Naval Research Laboratory, Washington, DC, United States, (2)University of Texas at Arlington, Arlington, TX, United States, (3)Los Alamos National Laboratory, Los Alamos, NM, United States, (4)University of Michigan Ann Arbor, Ann Arbor, MI, United States, (5)University of California Los Angeles, Department of Atmospheric and Oceanic Sciences, Los Angeles, CA, United States, (6)Syntek Technologies, Fairfax, VA, United States
Abstract:
Magnetohydrodynamic (MHD) models have been used for nearly four decades to study the dynamics of magnetospheric substorms. However, until recently no demonstration has been made that MHD models can consistently reproduce substorm onset times in a statistical sense. To test whether MHD can reproduce observed substorm onset times, we developed a procedure for identifying substorm onsets that can be applied both to observational data and to MHD output. Our substorm identification procedure aims to improve upon existing methods of substorm identification by using multiple types of observations to corroborate each identified substorm. Using this procedure, we identified over 100 substorms from the period 1-31 January 2005. Using this list of substorm onset times, we show that the MHD model has weak, but statistically significant skill in predicting substorm onset times. We explore paths to improving the ability of the MHD model to predict substorm dynamics by testing different configurations of the MHD model.
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