4:15 PM - 4:30 PM
[PEM09-21] Mapping of ionospheric electric potential with data assimilation into an emulator of global MHD simulation
Keywords:SuperDARN, ionospheric convection, emulator, data assimilation
The problem with the simulation approach is its computational cost. The realistic MHD model is too computationally expensive to apply data assimilation. In this study, we employ a machine learning-based emulator of the global MHD model to resolve the problem of the computational cost. This emulator is based on an echo state network model, and it efficiently mimics the MHD model to reproduce an ionospheric potential pattern under a give solar wind condition. We can therefore assimilate the SuperDARN data into this emulator to obtain the global potential map. We will demonstrate the electric potential maps as a result of data assimilation into the emulator.