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.
The space weather research community relies heavily on thermospheric density data to understand long‐term thermospheric variability, construct assimilative, empirical, and semiempirical global atmospheric models and validate model performance. One of the challenges in resolving accurate thermospheric density data sets from satellite orbital drag measurements is modeling appropriate physical aerodynamic drag force coefficients. The drag coefficient may change throughout the thermosphere due to model dependencies on composition and altitude. As such, existing drag coefficient model errors and corresponding errors in orbit‐derived density data sets and models may be altitude and solar cycle dependent with greater errors at higher altitudes around 500 km near the oxygen‐to‐helium transition region. In this paper, inter‐satellite observed‐to‐modeled density comparisons at ∼500 km are evaluated to constrain drag coefficient modeling assumptions. Observed densities are derived from accelerometer data for the Gravity Recovery and Climate Experiment (GRACE) satellites and Two‐Line Element data for a set of compact satellites, while the NRLMSISE‐00 atmospheric model is used to obtain modeled densities and composition information. Density consistency results indicate that drag coefficient models with incomplete energy and momentum accommodation produce the most consistent densities, while the standard diffuse modeling approach may not be appropriate at these altitudes. Models with momentum accommodation between 0.5 and 0.9 and energy accommodation between 0.83 and 0.96 may be most appropriate at upper thermospheric altitudes. Modeling drag coefficients with diffuse gas‐surface interactions for the GRACE satellites could lead to errors in derived density of ∼25% and in‐track satellite orbit prediction uncertainty during solar maximum conditions on the order of kilometers.
more » « less- NSF-PAR ID:
- 10375598
- Publisher / Repository:
- DOI PREFIX: 10.1029
- Date Published:
- Journal Name:
- Space Weather
- Volume:
- 20
- Issue:
- 5
- ISSN:
- 1542-7390
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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