Auroral precipitation refers to energetic particles that come down into the upper part of our atmosphere, the ionosphere. There, they collide with atoms and molecules and transfer some of their energy, causing aurora. The most rapid time-variation of this energy deposition and its consequences on the ionosphere are not fully understood. We show here that one can use a new model to study auroral precipitation on sub-second timescales and advance our understanding about small-scale dynamic aurora.

The E-region of the Earth’s ionosphere plays an important role in atmospheric energy balance and High Frequency radio propagation. In this paper, we compare predictions from two recently developed ionospheric models to observations by ionospheric sounders (ionosondes). Overall, the models show reasonable agreement with the observations. However, there are several areas for improvement in the models as well as questions about the accuracy of the automatically processed ionosonde dataset.

Sawtooth events are storm‑time surges of energetic particles with sharp rises and slow decays repeating every 2–4 h. From 2008–2016 they occur mostly in the solar cycle’s rising and declining phases and nearly always during geomagnetic storms. Geostationary data show near‑global, near‑simultaneous injections but strong magnetic changes only at midnight. This favors magnetotail reconnection and fast convection; sawteeth resemble a storm‑time substorm mode.

This study utilizes data from the FAST spacecraft orbiting over the auroral regions to investigate the temporal progression of energy inputs resulting from a Coronal Mass Ejection (CME) and how that energy increases the outflow of ionospheric plasma. We found that the various forms of energy are all enhanced during the initial phase of a storm, and the energy inputs are strongly correlated with the solar wind-magnetosphere coupling functions, especially in the dayside cusp region.

During the New Year’s Day storm of 2025, we observed rare auroral features: thin, short-lived green stripes and a “picket fence” near the poleward edge of the auroral oval. Using ground cameras and satellites, we found that the stripes sometimes appeared at widely separated longitudes at the same time and often tracked the motion of nearby red auroras. Some stripes were aligned with the magnetic field, while others were not, implying that multiple local processes contribute to their generation.

Knowledge of the charged environment in the upper atmosphere is essential for understanding space weather effects on satellites and radio communication. This environment is difficult to estimate at high latitudes, where aurora cause strong variability. We developed an artificial intelligence model to estimate this environment continuously. Our results show that the model provides reliable estimates even during auroral activity, improving monitoring of the polar upper atmosphere.