Atmospheric Density Modeling for High-Precision Deorbit Prediction During Space Weather Events

With the number of satellites in low Earth orbit rapidly growing and a predicted solar cycle maximum in 2025, accurate atmospheric density estimation for a range of solar and geomagnetic conditions is essential in deorbit event prediction, satellite orbit prediction, and collision avoidance. This work extends the use of atmospheric density models for satellite deorbit prediction. Atmospheric density states and ballistic coefficient estimates are input into a high-precision orbit propagator to determine the predicted time of satellite reentry. A series of case studies, including the Starlink, Humanity Star, and Freedom Drag Sail CubeSat deorbit events, are predicted by both proper orthogonal decomposition and machine learning reduced-order atmospheric density models with data assimilation and compared to results for the JB2008 and NRLMSISE-00 models. Sensitivity to space weather inputs is assessed by a Monte Carlo sensitivity analysis for the initial and updated atmospheric states.