Type: Presentation
Venue: Space Control Conference
Citation:
Dynamic Density Modeling and Related Space Weather Impacts on Prediction Errors for the De-Orbiting Timeline of Resident Space Objects; James M. Griffin, Richard A. Quinn, and Robert A. Morris; Space Control Conference,
MIT-Lincoln Laboratory, Lexington MA, 1-2 May 2012.
Resource File: AER_Griffin_James_Satellite_Drag_Case_Studies_2012_04_24_Griffs_U.pptx_.pdf
We investigated the effects of several environmental models on space object orbit determination and reentry prediction. The orbital integrator, which used the JPL Artificial Satellite Analysis Program (ASAP) as the driver, was expanded to make use of most recent models representing dynamic thermosphere neutral density which are being driven by current and new classes of solar and geophysical environment indicators (e.g., Jacchia-Bass 1970, NRL MSISE-2000, and Jacchia-Bowman 2008). We also employed and investigated different versions of high-resolution gravity potential fields, supplemental models for atmospheric variability, and additional sources of external forces affecting the space object motion. The means to allow for user-defined temporal variability of the ballistic coefficient as part of the drag specification for the space object was also added to the code.
For assessment of the performance of the orbit integrator, recent cases of significant-sized objects dropping out of Earth orbit were analyzed: UARS (decayed from orbit on 2011 Sep 24), ROSAT (decayed from orbit 2011 Oct 23), and several other cases of high interest. Differences were found in the prediction of de-orbit timelines as a function of density models and epochs of the NORAD two-line element sets. Effects from the inclusion of additional force terms and atmospheric variability such as the thermosphere neutral winds were studied, as was the sensitivity to the use of different gravity potential fields. The results provide the basis for more effective uses of these drag and drag-related algorithms in optimizing orbital determination and improved reentry prediction.