Uncertainty Response of Physics-Based Atmospheric Models Due to Internal Heating Parameters and Geomagnetic Storms

Recent events in space, including the collision of Russia’s Cosmos 2251 satellite with Iridium 33 and China’s Feng Yun 1C anti-satellite demonstration, have stressed the capabilities of the Space Surveillance Network and its ability to provide accurate and actionable impact probability estimates. In particular low-Earth orbiting satellites are heavily influenced by upper atmospheric density, due to drag, which is very difficult to model accurately. This work focuses on the generalized Polynomial Chaos (gPC) technique for Uncertainty Quantification (UQ) in physics-based atmospheric models. The advantage of the gPC approach is that it can efficiently model non-Gaussian probability distribution functions (pdfs). The gPC approach is used to perform UQ on future atmospheric conditions. A number of physics-based models are used as test cases, including GITM and TIE-GCM, and the gPC is shown to have good performance in modeling non-Gaussian pdfs.

Los Alamos National Laboratory (LANL) has established a research effort, called IMPACT (Integrated Modeling of Perturbations in Atmospheres for Conjunction Tracking), to improve impact assessment via improved physics-based modeling. A number of atmospheric models exist which can be classified as either empirical or physics-based. Physics-based models can be used to provide a forward prediction which is required for accurate collision assessments. As part of this effort, accurate and consistent UQ is required for the atmospheric models used. One of the primary sources of uncertainty is input parameter uncertainty. These input parameters, which include F10.7, AP, and solar wind parameters, are measured constantly. In turn, these measurements are used to provide a prediction for future parameter values. Therefore, the uncertainty of the atmospheric model forecast, due to potential error in the input parameters, must be correctly characterized to estimate orbital uncertainty. Internal model parameters that model how the atmosphere is heated by the sun are not know exactly and therefore this work will look at the effects of these parameters on the uncertainty of the atmospheric model. Finally, geomagnetic storms have been shown to increase the uncertainty and this work will investigate the uncertainty response to geomagnetic storms.