Data assimilation into a machine learning-based emulator of a global MHD simulation for analyzing the polar ionosphere

The ionospheric condition can be predicted by a machine-learning-based model that mimics a numerical simulation model of the magnetosphere–ionosphere system. We assess this machine-learning-based model based on radar observations in the polar ionosphere. We have also developed a method to estimate the state of the polar ionosphere by combining the model prediction and radar observations. The results reproduced the variability of the polar ionosphere. The proposed method is a promising approach for investigating the polar ionospheric phenomena.

Recommended citation: Nakano, S., Reddy, S. A., Kataoka, R., Nakamizo, A., Fujita, S., and Yukimatu, A. S. (2025). "Data assimilation into a machine learning-based emulator of a global MHD simulation for analyzing the polar ionosphere" Space Weather,23, e2025SW004488 https://doi.org/10.1029/2025SW004488