  The geospace environment is volatile and highly driven. Space weather has
effects on Earth's magnetosphere that cause a dynamic and enigmatic response in
the thermosphere, particularly on the evolution of neutral mass density. Many
models exist that use space weather drivers to produce a density response, but
these models are typically computationally expensive or inaccurate for certain
space weather conditions. In response, this work aims to employ a probabilistic
machine learning (ML) method to create an efficient surrogate for the
Thermosphere Ionosphere Electrodynamics General Circulation Model (TIE-GCM), a
physics-based thermosphere model. Our method leverages principal component
analysis to reduce the dimensionality of TIE-GCM and recurrent neural networks
to model the dynamic behavior of the thermosphere much quicker than the
numerical model. The newly developed reduced order probabilistic emulator
(ROPE) uses Long-Short Term Memory neural networks to perform time-series
forecasting in the reduced state and provide distributions for future density.
We show that across the available data, TIE-GCM ROPE has similar error to
previous linear approaches while improving storm-time modeling. We also conduct
a satellite propagation study for the significant November 2003 storm which
shows that TIE-GCM ROPE can capture the position resulting from TIE-GCM density
with < 5 km bias. Simultaneously, linear approaches provide point estimates
that can result in biases of 7 - 18 km.
