Understanding and Modeling the Dynamics of Storm-time Atmospheric
Neutral Density using Random Forests
Abstract
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.