EAGER-DynamicData: Reducing Orbital Position Uncertainty with Ensembles of Upper Atmospheric Models

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The United States has many tens of billions of dollars of assets in low-Earth orbit, including the International Space Station. In addition, more than 15,000 pieces of debris are orbiting the Earth, and all of these objects are potential projectiles that could easily destroy valuable assets. Further, if any of this debris collides, more debris will result, possibly causing a cascade of collisions, termed the Kessler Syndrome. The present goal of the United States, therefore, is to track as many objects in low-Earth orbit as possible and, when a collision is predicted, move the operational satellites to avoid collision. The problem is fuel is needed to move operational satellites, and those satellites cannot be refueled. Therefore, it is essential to have as accurate as possible predictions of the tracks of all of the objects in orbit. In reality, these tracks are uncertain, just as the future path of a hurricane is uncertain. In order to more accurately predict the tracks of all of these objects, it is important to take into account the atmospheric density and account for how that density changes as a function of energy inputs, such as when the northern lights intensify. This research will explore how to use models to predict energy inputs into the upper atmosphere in order to better predict the tracks of orbiting objects.The main technical goals of this proposal are to: (1) use probability distribution functions to determine the drivers of the thermosphere and ionosphere; (2) use a new technique to remove bias in different models of the upper atmosphere; (3) drive an ensemble of models of the upper atmosphere using those predictions; (4) use model predictions along with a catalog of current satellite locations to determine the orbital tracks of objects in low-Earth orbit; and (5) train a graduate student to conduct scientific investigations and use models of the upper atmosphere. The predictions will be made using a statistical analysis of historical data. The bias removal will be done using Retrospective Cost Adaptive Input and State Estimation, which allows missing physics within models to be accounted for by comparing the model output to data sources and adjusting the model. Various statistical and physics-based models of the upper atmosphere will be driven with ensembles of drivers to create ensembles of model output, allowing the uncertainty in the thermospheric density to be accounted for in orbital-track prediction. The graduate student will run the ensemble of models and track various satellites provided by the Air Force catalog. The student will write papers and attend conferences in order to provide updates on the research.