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Machine learning emulator for physics-based prediction of ionospheric response to solar wind variations
  • Ryuho Kataoka,
  • Shinya Nakano,
  • Shigeru Fujita
Ryuho Kataoka
National Institute of Polar Research

Corresponding Author:[email protected]

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Shinya Nakano
The Institute of Statistical Mathematics
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Shigeru Fujita
the Joint Support-Center for Data Science Research
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Physics-based simulations are important for elucidating the fundamental mechanisms behind the time-varying complex ionospheric conditions, such as field-aligned currents (FACs) and plasma convection patterns, against unprecedented solar wind variations incidents in the Earth’s magnetosphere. However, to perform a huge parameter survey for understanding the nonlinear solar wind density dependence of the FAC and convection patterns, for example, a large-scale cluster computer is not fast enough to run state-of-the-art global magnetohydrodynamic (MHD) simulations. Here we report the impressive performance of a machine-learning based surrogate model for the ionospheric outputs of a global MHD simulation, using the reservoir computing technique called echo state network (ESN). The trained ESN-based emulator is exceptionally fast to perform the parameter survey, suggesting a missing solar wind density dependence of the ionospheric polar cap potential. We discuss future directions including the promising application for the space weather forecast.
11 Jan 2023Submitted to ESS Open Archive
17 Jan 2023Published in ESS Open Archive