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Machine learning-based emulator for the physics-based simulation of auroral current system
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  • Ryuho Kataoka,
  • Aoi Nakamizo,
  • Shin'ya Nakano,
  • Shigeru Fujita
Ryuho Kataoka
National Institute of Polar Research

Corresponding Author:[email protected]

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Aoi Nakamizo
National Institute of Information and Communications Technology
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Shin'ya Nakano
The Institute of Statistical Mathematics
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Shigeru Fujita
the Joint Support-Center for Data Science Research
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Using a machine learning technique called echo state network (ESN), we have developed an emulator to model the physics-based global magnetohydrodynamic (MHD) simulation results of REPPU (REProduce Plasma Universe) code. The inputs are the solar wind time series with date and time, and the outputs are the time series of the ionospheric auroral current system in the form of two-dimensional (2D) patterns of field-aligned current, potential, and conductivity. We mediated a principal component analysis for a dimensionality reduction of the 2D map time series. In this study, we report the latest upgraded Surrogate Model for REPPU Auroral Ionosphere version 2 (SMRAI2) with significantly improved resolutions in time and space (5 min in time, ~1 degrees in latitude, and 4.5 degrees in longitude), where the dipole tilt angle is also newly added as one of the input parameters to reproduce the seasonal dependence. The fundamental dependencies of the steady-state potential and field-aligned current patterns on the interplanetary magnetic field (IMF) directions are consistent with those obtained from empirical models. Further, we show that the ESN-based emulator can output the AE index so that we can evaluate the performance of the dynamically changing results, comparing with the observed AE index. Since the ESN-based emulator runs a million times faster than the REPPU simulation, it is promising that we can utilize the emulator for the real-time space weather forecast of the auroral current system as well as to obtain large-number ensembles to achieve future data assimilation-based forecast.
19 Sep 2023Submitted to ESS Open Archive
17 Oct 2023Published in ESS Open Archive