Title: Data-driven framework for real-time thermospheric density estimation,
Abstract: In this paper, we demonstrate a new data-driven framework for real-time
neutral density estimation via model-data fusion in quasi-physical
ionosphere-thermosphere models. The framework has two main components: (i) the
development of a quasi-physical dynamic reduced order model (ROM) that uses a
linear approximation of the underlying dynamics and effect of the drivers, and
(ii) dynamic calibration of the ROM through estimation of the ROM coefficients
that represent the model parameters. We have previously demonstrated the
development of a quasi-physical ROM using simulation output from a physical
model and assimilation of non-operational density estimates derived from
accelerometer measurements along a single orbit. In this paper, we demonstrate
the potential of the framework for use with operational measurements. We use
simulated GPS-derived orbit ephemerides with 5 minute resolution as
measurements. The framework is a first of its kind, simple yet robust and
accurate method with high potential for providing real-time operational updates
to the state of the upper atmosphere using quasi-physical models with inherent
forecasting/predictive capabilities.