Abstract
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.
Highlights
Auroral electrojets are azimuthal electric currents localized in the auroral region
We examine the responses of the AU and AL indices to solar-wind inputs by putting various artificial inputs into the trained echo state network (ESN) model and identify the properties of the auroral electrojets
As the solar-wind speed increases, AUNeff increases and ALNeff decreases. This suggests that the solar-wind density effect on the auroral electrojets is not independent of the solar-wind speed effect but that the solar-wind density contributes to the auroral electrojet intensity more effectively under high solar-wind speed conditions
Summary
Auroral electrojets are azimuthal electric currents localized in the auroral region. A westward auroral electrojet is mostly observed in pre-midnight to early morning local time, and an eastward electrojet is mostly observed in evening time (Allen and Kroehl, 1975). It has been demonstrated that the AU, AL, and AE indices can be predicted well with feed-forward neural networks using time histories of solar-wind parameters as inputs (e.g., Gleisner and Lundstedy, 1997; Takalo and Timonen, 1997; Pallocchia et al, 2008; Bala and Reiff, 2012). Blunier et al (2021) have identified solar-wind parameters which affect the value of geomagnetic indices by putting perturbed inputs into a trained neural network. We employ an echo state network (ESN) model (Lukoševicius and Jaeger, 2009; Jaeger and Haas, 2004) to describe the relationship between various solarwind parameters and the auroral electrojet indices AU and AL. We examine the responses of the AU and AL indices to solar-wind inputs by putting various artificial inputs into the trained ESN model and identify the properties of the auroral electrojets
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