Equatorial plasma irregularities in the ionospheric F-region proliferate after sunset, causing the most apparent radio scintillation “hot-spot” in geospace. These irregularities are caused by plasma instabilities, and appear mostly in the form of under-densities that rise up from the F-region’s bottomside. After an irregularity production peak at sunset, the amplitude of the resulting turbulence decays with time. Analyzing a large database of plasma irregularity spectra observed by one of the European Space Agency’s Swarm satellites, we have applied a novel but conceptually simple statistical analysis to the data, finding in the process that post-sunset turbulence in the F-region tends to decay with a uniform, scale-independent rate at night, thereby confirming and extending the results from earlier case studies. Our results should be of utility for large-scale space weather modelling efforts that are unable to resolve turbulent effects.

Atmospheric neutral density is a crucial component to accurately predicting and tracking the motion of satellites. During periods of elevated solar and geomagnetic activity atmospheric neutral density becomes highly variable and dynamic. This variability and enhanced dynamics make it difficult to accurately model neutral density leading to increased errors which propagate from neutral density models through to orbit propagation models. In this paper we investigate the dynamics of neutral density during geomagnetic storms. We use a combination of solar and geomagnetic variables to develop three Random Forest machine learning models of neutral density. These models are based on (1) slow solar indices, (2) high cadence solar irradiance, and (3) combined high-cadence solar irradiance and geomagnetic indices. During quiet-times all three models perform well; however, during geomagnetic storms the combined high cadence solar iradiance/geomagnetic model performs significantly better than the models based solely on solar activity. Overall, this work demonstrates the importance of including geomagnetic activity in the modeling of atmospheric density and serves as a proof of concept for using machine learning algorithms to model, and in the future forecast atmospheric density for operational use.