Abstract. The properties of the auroral electrojets are examined on the basis
of a trained machine-learning model.
The relationships between solar-wind parameters and
the AU and AL indices are modeled with an echo state network (ESN),
a kind of recurrent neural network.
We can consider this trained ESN model to represent nonlinear effects
of the solar-wind inputs on the auroral electrojets.
To identify the properties of auroral electrojets,
we obtain various synthetic AU and AL data by
using various artificial inputs with the trained ESN.
The analyses of various synthetic data show that the AU and AL
indices are mainly controlled by the solar-wind speed
in addition to Bz of the interplanetary magnetic field (IMF)
as suggested by the literature.
The results also indicate that the solar-wind density effect is
emphasized when solar-wind speed is high and when IMF Bz is near zero.
This suggests some nonlinear effects of the solar-wind density.
Echo state network model for analyzing solar-wind effects on the AU and AL indices
