The atmospheric models in use today are incapable of properly modeling all of the density variations in the Earth's upper atmosphere. This research utilized precision orbit ephemerides (POE) in an orbit determination process to generate improved atmospheric density estimates. Based on their correlation to the accelerometer density, the resulting POE density estimates were demonstrated to be an improvement over existing atmospheric models regardless of solar and geomagnetic activity levels. Also, the POE density estimates were somewhat better in terms of their correlation with the accelerometer density than the improved density estimates obtained by the High Accuracy Satellite Drag Model (HASDM). The results showed that the POE density estimates were obtained with the desired accuracy for a +/-10% variation in the nominal ballistic coefficient used to initialize the orbit determination process. Also, the length of the fit span showed little influence on the accuracy of the POE density estimates. Overlap regions of POE density estimates demonstrated a method of determining the consistency of the solutions. Finally, Gravity Recovery and Climate Experiment (GRACE) POE density estimates showed consistent results with the Challenging Mini-Satellite Payload (CHAMP) POE density estimates.;Modeling the atmospheric density has always been and continues to be one of the greatest uncertainties related to the dynamics of satellites in low Earth orbit. The unmodeled density variations directly influence a satellite's motion thereby causing difficulty in determining the satellite's orbit resulting in possibly large errors in orbit prediction. Many factors influence the variations observed in the Earth's atmospheric density with many of the processes responsible for these variations not modeled at all or not modeled completely. The Earth's atmospheric density is affected to the greatest extent by direct heating from the Sun and through the influence of geomagnetic storms. Deficiencies in existing atmospheric models require corrections be made to improve satellite orbit determination and prediction.;This research used precision orbit ephemerides in an orbit determination process to generate density corrections to existing atmospheric models, including Jacchia 1971, Jacchia-Roberts, CIRA-1972, MSISE-1990, and NRLMSISE-2000. This work examined dates consisting of days from every year ranging from 2001 to 2007 covering the complete range of solar and geomagnetic activity. The density and ballistic coefficient correlated half-lives were considered and are a user controlled parameter in the orbit determination process affecting the way the unmodeled or inaccurately modeled drag forces influence a satellite's motion. The values primarily used in this work for both the density and ballistic coefficient correlated half-lives were 1.8, 18, and 180 minutes. The POE density estimates were evaluated by examining the position and velocity consistency test graphs, residuals, and most importantly the cross correlation coefficients from comparison with accelerometer density.;The POE density estimates were demonstrated to have significant improvements over existing atmospheric models. Also, the POE density estimates were found to have comparable and often superior results compared with the HASDM density. For the overall summary, the best choice was the CIRA-1972 baseline model with a density and ballistic coefficient correlated half-life of 18 and 1.8 minutes, respectively. The best choice refers to the baseline atmospheric model and half-life combination used to obtain the POE density estimate having the best correlation with the accelerometer density.;During periods of low solar activity, the best choice was the CIRA-1972 baseline model with a density and ballistic coefficient correlated half-life of 180 and 18 minutes, respectively. When considering days with moderate solar activity, using a density correlated half-life of 180 minutes and a ballistic coefficient correlated half-life of 1.8 minutes for the Jacchia 1971 baseline model was the best combination or choice. During periods of elevated and high solar activity, the best combination was the CIRA-1972 baseline atmospheric model with a density and ballistic coefficient correlated half-life of 18 and 1.8 minutes, respectively. When considering times of quiet geomagnetic activity, the best choice was the Jacchia 1971 baseline model with a density and ballistic coefficient correlated half-life of 1.8 and 18 minutes, respectively. During times of moderate and active geomagnetic activity, the CIRA-1972 baseline atmospheric model with a density correlated half-life of 18 minutes and a ballistic coefficient correlated half-life of 1.8 minutes was the best combination. The conclusions found for the overall and binned results did not hold true for every solution. However, using CIRA-1972 as the baseline atmospheric model with a density and ballistic coefficient correlated half-life of 18 and 1.8 minutes, respectively, is the recommended combination for generating the most accurate POE density estimates.;Variations of +/-10% in the nominal ballistic coefficient used to initialize the orbit determination process provided sufficiently accurate POE density estimates as compared with the accelerometer density. The extent of this sensitivity remains unclear and requires additional study. The dependence of the POE density estimate on the solution fit span length was shown to be very low. Six hour fit span lengths considered to be the worst case scenario were shown to provide good agreement with the accelerometer density and POE density estimates with longer fit span lengths. Also, regions of overlap between successive solutions demonstrated good agreement between the individual POE density estimates indicating consistent solutions were obtained from the orbit determination process. The GRACE-A POE density estimates demonstrated consistent results compared with the CHAMP POE density estimates. Additional research is required that utilizes GRACE-A POE data to generate POE density estimates to confirm the CHAMP POE density estimate results.
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