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

Abstract

Atmospheric neutral density is a crucial component to accurately predict and track 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. Each model is validated using an out-of-sample dataset using analysis of residuals and typical metrics. During quiet-times, all three models perform well; however, during geomagnetic storms, the combined high cadence solar irradiance/geomagnetic model performs significantly better than the models based solely on solar activity. The combined model capturing an additional 10\% in the variability of density and having an error up to six times smaller during geomagnetic storms then the solar models. 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.