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.

Total electron content (TEC) observations can provide insights into electron density variations in the ionosphere. Such variations are associated with many aspects of ionospheric dynamics, including traveling ionospheric disturbances. In the present work we assimilate observations of TEC and horizontal TEC gradients into the SAMI3 (SAMI3 is Another Model of the Ionosphere 3D) ionosphere model. Assimilation into SAMI3 is accomplished using an ensemble Kalman filter implemented within LightDA, an extensible data assimilation library. Our TEC gradient observations are obtained from the Very Large Array Low-band Ionosphere and Transient Experiment (VLITE) and the TEC measurements are derived from GNSS receiver data. VLITE provides high precision and high spatial resolution TEC gradient observations over a small area, while GNSS observations supplement these with global coverage. By leveraging TEC observations in a physics-based model through data assimilation, we aim to improve our understanding of ionospheric processes and develop tools for improved ionospheric forecasting capabilities.