Substorms are a phenomenon in the magnetosphere–ionosphere system, which are characterised by brightening of an auroral arc and enhancement of electric currents in the polar ionosphere. Since substorms are difficult to predict, this study treats a substorm occurrence as a stochastic phenomenon and represents the substorm occurrence rate with a machine learning model. We then analyse the response of substorm activity to solar wind conditions by feeding synthetic solar wind data into the model.

The relationships between auroral activity and the solar-wind conditions are modeled with a machine-learning technique. The impact of various solar-wind parameters on the auroral activity is then evaluated by putting artificial inputs into the trained machine-learning model. One of the notable findings is that the solar-wind density effect on the auroral activity is emphasized under high solar-wind speed and weak solar-wind magnetic field.

We used up-to-date substorms, HILDCAAs and geomagnetic storms of varying intensity along with all available geomagnetic indices during the space exploration era to explore the seasonal features of the geomagnetic activity and their drivers. As substorms, HILDCAAs and magnetic storms of varying intensity have varying solar/interplanetary drivers, such a study is important for acomplete understanding of the seasonal features of the geomagnetic response to the solar/interplanetary events.

Comprehensive analysis of a moderate substorm was performed using optical observations inside the auroral oval and in the polar cap, combined with data from satellites, radars, and ground magnetometers. The onset took place near the poleward boundary of the auroral oval that is not typical for classical substorms. The data fit to the near-tail current disruption scenario of the substorm onset. The role of the 15 min oscillations in the IMF Bz component in the substorm initiation is discussed.

We study the impact of the geomagnetic storm of 7–9 September 2017 on the low- to mid-latitude ionosphere. The study is based on the analysis of data from the Global Positioning System (GPS) stations and magnetic observatories located at different longitudinal sectors corresponding to the Pacific, Asia, Africa and the Americas during the period 4–14 September 2017. The GPS data are used to derive the global, regional and vertical total electron content (vTEC) in the four selected regions.