Search

Satellite drag modeling remains the largest source of uncertainty affecting space operations in low Earth orbit. The uncertainty stems from inaccurate models for mass density and drag coefficient. Drag coefficient modeling also impacts scientific knowledge on the physics and dynamics of the upper atmosphere through the estimation of high-fidelity mass density from measurements of acceleration on-board satellites. Efforts over the last decade have pushed drag coefficient modeling in the right direction, however, have resulted in multiple methods and tools. We provide a comprehensive review of the drag coefficient modeling methods and tools. Current scale differences between thermospheric data sets mostly originate from errors in the aerodynamic modeling, specifically in the modeling of the satellite outer surface geometry and the gas-surface interactions. Enhancing these models' accuracy is intrinsically connected to the satellite drag fidelity for science and operations. A team of invested scientists recently met under the community-driven International Space Weather Action Teams (ISWAT) initiative to discuss and consolidate the efforts towards a drag coefficient modeling baseline for consistency in science and operations. In this paper, we compare the available methods for drag coefficient modeling, their impact on the derived density estimates, and make recommendations for adoption of baseline methods for science and operations. Results show that the differences in derived densities estimates can reach tens of percent at altitudes above 4̃50 km during solar minimum conditions resulting mainly from differences in the modeling of gas-surface interactions. As a result, we conclude and recommend that robust uncertainty quantification be an integral part of any modeling efforts that employ the high-fidelity accelerometer derived density estimates. We also conclude and recommend that gas-surface interaction models that account for impact of altitude and solar variations be employed moving forward. Finally, we recommend future science missions to improve our understanding of gas-surface interactions and eventually the upper thermosphere variability. [ABSTRACT FROM AUTHOR]

Abstract: Thermospheric mass density is a major driver of satellite drag, the largest source of uncertainty in accurately predicting the orbit of satellites in low Earth orbit (LEO) pertinent to space situational awareness. Most existing models for thermosphere are either physics based or empirical. Physics‐based models offer the potential for good predictive/forecast capabilities but require dedicated parallel resources for real‐time evaluation and data assimilative capabilities that are still under development. Empirical models are fast to evaluate but offer very limited forecasting abilities. This paper presents methodology for developing a reduced order dynamic model from high‐dimensional physics‐based models by capturing the underlying dynamical behavior. The quasi‐physical reduced order model for thermospheric mass density is developed using a large dataset of Thermosphere‐Ionosphere‐Electrodynamics General Circulation Model (TIE‐GCM) simulations spanning 12 years and covering a complete solar cycle. Toward this end, a new reduced order modeling approach, based on dynamic mode decomposition with control that uses the Hermitian space of the problem to derive the dynamic and input matrices in a tractable manner is developed. Results show that the reduced order model performs well in serving as a reduced order surrogate for TIE‐GCM while almost always maintaining the forecast error to within 5% of the simulated densities after 24 hrs. [ABSTRACT FROM AUTHOR]

For objects in the low Earth orbit region, uncertainty in atmospheric density estimation is an important source of orbit prediction error, which is critical for space traffic management activities such as the satellite conjunction analysis. This paper investigates the evolution of orbit error distribution in the presence of atmospheric density uncertainties, which are modeled using probabilistic machine learning techniques. The recently proposed "HASDM-ML," "CHAMP-ML," and "MSIS-UQ" machine learning models for density estimation (Licata and Mehta, 2022b; Licata et al., 2022b) are used in this work. The investigation is convoluted because of the spatial and temporal correlation of the atmospheric density values. We develop several Monte Carlo methods, each capturing a different spatiotemporal density correlation, to study the effects of density uncertainty on orbit uncertainty propagation. However, Monte Carlo analysis is computationally expensive, so a faster method based on the Kalman filtering technique for orbit uncertainty propagation is also explored. It is difficult to translate the uncertainty in atmospheric density to the uncertainty in orbital states under a standard extended Kalman filter or unscented Kalman filter framework. This work uses the so-called "consider covariance sigma point (CCSP)" filter that can account for the density uncertainties during orbit propagation. As a test-bed for validation purposes, a comparison between CCSP and Monte Carlo methods of orbit uncertainty propagation is carried out. Finally, using the HASDM-ML, CHAMP-ML, and MSIS-UQ density models, we propose an ensemble approach for orbit uncertainty quantification for four different space weather conditions. [ABSTRACT FROM AUTHOR]