Satellite Drag Physical Model Module for a Near Real Time Operation Test Bed

ABSTRACT: Satellite drag is the leading cause of orbit prediction error in low earth orbit. Modeling of this drag force and its variability is critical to accurate orbit determination and prediction. One of the leading contributing factors to error in drag prediction is thermospheric neutral density variability. Operational models up until the present time have been empirical, with inputs including solar flux and geophysical indices. These empirical models fail to reproduce density variations, especially during periods of large solar flux and geomagnetic storms. Therefore, in order to provide better orbit determination, we propose to develop a physics-based model, which will predict thermospheric density variations and the associated satellite drag in real time. Work to be done in Phase-I includes demonstration of realtime operation, development of appropriate validation metrics, validation exercises, and comparison of TIME-GCM and CTIPe in order to determine strengths and weaknesses of each. We will also validate the use of the AMIE procedure in order to better specify high-latitude inputs. Finally, we will relate the model density errors to expected drag errors. Phase-I buys down risk in preparation for a modeling framework for real time prediction of thermospheric density variability. BENEFIT: Phase-I will buy down risk for the development of a physical model for satellite drag applications, with an accuracy and capability equivalent to the current empirical model, JB-08. The proposed work will lead to the development of innovative algorithms based on near real-time space weather data and geophysical indices. These algorithms will improve upon the current thermospheric density specification models, and will eventually lead to improved 3-day satellite drag forecast capabilities. The anticipated impact of the proposed work is to buy down the risk of developing a physics-based model that can be run in a real time assimilative mode to aid in accurate satellite orbit prediction. The resulting tools will be used by the Air Force for risk-mitigation exercises, including the reduction of the prediction errors of satellite positions.