  Obtaining accurate estimates of satellite drag coefficients in low Earth
orbit is a crucial component in positioning and collision avoidance. Simulators
can produce accurate estimates, but their computational expense is much too
large for real-time application. A pilot study showed that Gaussian process
(GP) surrogate models could accurately emulate simulations. However, cubic
runtime for training GPs means that they could only be applied to a narrow
range of input configurations to achieve the desired level of accuracy. In this
paper we show how extensions to the local approximate Gaussian Process (laGP)
method allow accurate full-scale emulation. The new methodological
contributions, which involve a multi-level global/local modeling approach, and
a set-wise approach to local subset selection, are shown to perform well in
benchmark and synthetic data settings. We conclude by demonstrating that our
method achieves the desired level of accuracy, besting simpler viable (i.e.,
computationally tractable) global and local modeling approaches, when trained
on seventy thousand core hours of drag simulations for two real-world
satellites: the Hubble space telescope (HST) and the gravity recovery and
climate experiment (GRACE).
