[M-GI26] Data assimilation: A fundamental approach in geosciences

[MGI26-P14] Data assimilation into a machine learning-based emulator of global MHD simulation

*Shin ya Nakano^1,2,4, Ryuho Kataoka^3,4, Shigeru Fujita^1,2 (1.The Institute of Statistical Mathematics, 2.Joint Support Center for Data Science Research, 3.National Institute of Polar Research, 4.SOKENDAI)

Keywords:data assimilation, magnetosphere-ionosphere system, machine learning

The ionospheric electric field is one of fundamental factors controlling the dynamics of the polar ionosphere. The electric potential distribution in the polar ionosphere is imposed as a result of physical processes in the magnetosphere. Hence, the modeling of the magnetospheric process is essential to reproduce the ionospheric dynamics. Recent magneto-hydrodynamic (MHD) models of the magnetosphere enable to successfully simulate the ionospheric potential pattern. However, it is computationally demanding to run the MHD models. In addition, assimilation of the ionospheric observations is desired for obtaining reliable results.

To overcome the problem of the computational cost, we have developed a machine learning-based emulator of the MHD model of the magnetosphere. Our emulator, which is based on an echo state network model, allows us to efficiently predict the ionospheric potential distribution under a given solar wind condition. We then assimilate the SuperDARN (Super Dual Auroral Radar Network) data, which provides the information on ionospheric plasma flow, into the emulator. This approach is promising for real-time monitoring of the ionospheric state.