摘要：The largest obstacle to managing satellites in low Earth orbit (LEO) is accurately forecasting the neutral mass densities that appreciably impact atmospheric drag. Empirical thermospheric models are often used to estimate neutral densities but they struggle to forecast neutral densities during geomagnetic storms when they are highly variable. Physics-based models are thus increasingly turned to for their ability to describe the dynamical evolution of neutral densities. However, these models require observations to constrain dynamical state variables to be able to forecast mass densities with adequate fidelity. The LEO environment has scarce neutral state observations. Here, we demonstrate, in simulated experiments, a reduction in orbit errors and neutral densities using a physics-based, data assimilation approach with ionospheric observations. Using a coupled thermosphere-ionosphere model, the Thermosphere Ionosphere Electrodynamics General Circulation Model, we assimilate Constellation Observing System for Meterology, Ionosphere, and Climate electron density profiles (EDPs) derived from radio occultation (RO) observations. We use the EDPs to directly update neutral states, improving errors for neutral temperature by 70% and neutral winds by 20%. Updated neutral temperature and neutral winds additionally improve helium composition errors by 60% and 40%, respectively. Improved neutral density estimates correspond to a reduction in orbit errors of 1.2 km over 2 days, a 70% reduction over a no-assimilation control, and a 29 km improvement over 9 days. This study builds on the results of our earlier work to further develop and demonstrate the potential of using a vast and growing RO data source, with a physics-based model, to overcome our limited number of neutral observations.
