Data Assimilation Into a Machine Learning‐Based Emulator of a Global MHD Simulation for Analyzing the Polar Ionosphere

The temporal evolution of the polar ionosphere's electric field distribution can be simulated by a magnetohydrodynamic (MHD) model of the magnetosphere–ionosphere system. The emulator SMRAI2 mimics the ionospheric state calculated with an MHD model, REPPU, under given solar wind conditions. We derive an updated version, SMRAI2.1, which predicts the electric field distribution in the polar ionosphere as a smooth spatial function. This enables us to easily obtain the plasma drift velocity for comparison with radar observations. We then compare the output of SMRAI2.1 with SuperDARN line‐of‐sight velocity data and confirm that the emulator can predict the observed line‐of‐sight velocity better than a naive prediction assuming zero drift velocity and that it well predicts the direction of the line‐of‐sight velocity. In addition, we propose a method using a data assimilation technique for incorporating the SuperDARN data into the emulator. The results of the assimilation shows that the electric field in the polar ionosphere is more variable than the emulator predicts. This suggests that the emulator prediction could be less variable than the real ionosphere and that it is effective to combine real observations and the emulator for reproducing the polar ionospheric environment.