  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 have yet to be developed. Empirical models are
fast to evaluate, but offer very limited forecasting abilities. This paper
presents a methodology of developing a reduced-order dynamic model from
high-dimensional physics-based models by capturing the underlying dynamical
behavior. This work develops a quasi-physical reduced order model (ROM) for
thermospheric mass density using simulated output from NCAR's
Thermosphere-Ionosphere-Electrodynamics General Circular Model (TIE-GCM). The
ROM is derived using a dynamic system formulation from a large dataset of
TIE-GCM simulations spanning 12 years and covering a complete solar cycle.
Towards this end, a new reduced order modeling approach, based on Dynamic Mode
Decomposition with control (DMDc), that uses the Hermitian space of the problem
to derive the dynamics and input matrices in a tractable manner is developed.
Results show that the ROM 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 hours.
