[P-EM09] Space Weather and Space Climate

[PEM09-21] Mapping of ionospheric electric potential with data assimilation into an 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:SuperDARN, ionospheric convection, emulator, data assimilation

The SuperDARN (Super Dual Auroral Radar Network) provides the valuable information on global plasma flow in the polar ionosphere. However, since there are some wide gaps in the spatial coverage of the SuperDARN, it is not an easy task to retrieve a global convection map from the SuperDARN data. One useful way to obtain the global convection map is to use a numerical simulation, Indeed, recent global magneto-hydrodynamic (MHD) models of the magnetosphere successfully simulate the ionospheric potential pattern. If ionospheric observation data could be assimilated into the MHD model, we could obtain a reliable global potential map.

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.