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The High Accuracy Satellite Drag Model (HASDM) is the operational thermospheric density model used by the US Space Force Combined Space Operations Center. By using real‐time data assimilation, HASDM can provide density estimates with increased accuracy over other empirical models. With historical HASDM density data being released publicly for the first time, we can analyze the data to compare dominant modes of variability in the upper atmosphere as modeled by HASDM and the Jacchia‐Bowman 2008 Empirical Thermospheric Density Model (JB2008), a Jacchia family model upon which density corrections are made as a part of the HASDM framework. This model comparison is conducted through the use of principal component analysis (PCA) which shows the increased variability of the HASDM dataset. We highlight HASDM's ability to capture the movement of lighter species during solar minimum conditions, unlike many empirical models. We then compare density from both models to the CHAllenging Minisatellite Payload (CHAMP) and Gravity Recovery and Climate Experiment (GRACE) accelerometer‐derived density estimates. This comparison shows that HASDM more closely matches the accelerometer‐derived densities with mean absolute differences of 30.93% compared to CHAMP and GRACE‐A, respectively. The comparison also reveals improved representation of cooling mechanisms due to NO and CO2 by the HASDM database. Plain Language Summary: Many density models for the upper atmosphere have been developed using data from various sources to fit parametric equations. These are classified as empirical models which make up a large portion of those used in operations. While they are generally reliable, there are conditions where their accuracy falls short, such as during geomagnetic storms or during periods of low solar activity. The High Accuracy Satellite Drag Model (HASDM) uses observations from calibration satellites to make real‐time corrections (a process known as Dynamic Calibration of the Atmosphere) to density outputs from an empirical model, making it far more robust. In this paper, we analyze a 20 year dataset of HASDM outputs and compare it the baseline JB2008 model, upon which data is assimilated. By leveraging a mathematical tool called principal component analysis, we identify features in the HASDM dataset that are not modeled by JB2008, such as the movement of lighter species during solar minimum conditions. We also perform a case study where we compare both model outputs to high‐fidelity density observations from different satellites during two unique periods. Through this comparison, we find evidence that the HASDM dataset captures the effects of NO and CO2 cooling mechanisms. Key Points: We conduct principal component analysis of High Accuracy Satellite Drag Model (HASDM) and JB2008 database covering almost two solar cyclesHASDM models the movement of lighter species during solar minimum conditionsHASDM exhibits the ability to capture/model NO and CO2 cooling mechanisms [ABSTRACT FROM AUTHOR]

Well-known tools developed for satellite and debris re-entry perform break-up and trajectory simulations in a deterministic sense and do not perform any uncertainty treatment. The treatment of uncertainties associated with the re-entry of a space object requires a probabilistic approach. A Monte Carlo campaign is the intuitive approach to performing a probabilistic analysis, however, it is computationally very expensive. In this work, we use a recently developed approach based on a new derivation of the high dimensional model representation method for implementing a computationally efficient probabilistic analysis approach for re-entry. Both aleatoric and epistemic uncertainties that affect aerodynamic trajectory and ground impact location are considered. The method is applicable to both controlled and un-controlled re-entry scenarios. The resulting ground impact distributions are far from the typically used Gaussian or ellipsoid distributions. [ABSTRACT FROM AUTHOR]

Satellite drag coefficients are a major source of uncertainty in predicting the drag force on satellites in low Earth orbit. Among other things, accurately predicting the orbit requires detailed knowledge of the satellite drag coefficient. Computational methods are an important tool in computing the drag coefficient but are too intensive for real-time and predictive applications. Therefore, analytic or empirical models that can accurately predict drag coefficients are desired. This work uses response surfaces to model drag coefficients. The response surface methodology is validated by developing a response surface model for the drag coefficient of a sphere where the closed-form solution is known. The response surface model performs well in predicting the drag coefficient of a sphere with a root mean square percentage error less than 0.3% over the entire parameter space. For more complex geometries, such as the GRACE satellite, the Hubble Space Telescope, and the International Space Station, the model errors are only slightly larger at about 0.9%, 0.6%, and 1.0%, respectively. [ABSTRACT FROM AUTHOR]