Transformer-based Atmospheric Density Forecasting
Julia Briden Peng Mun Siew Victor Rodriguez-Fernandez Richard Linares
As the peak of the solar cycle approaches in 2025 and the ability of a single
geomagnetic storm to significantly alter the orbit of Resident Space Objects
(RSOs), techniques for atmospheric density forecasting are vital for space
situational awareness. While linear data-driven methods, such as dynamic mode
decomposition with control (DMDc), have been used previously for forecasting
atmospheric density, deep learning-based forecasting has the ability to capture
nonlinearities in data. By learning multiple layer weights from historical
atmospheric density data, long-term dependencies in the dataset are captured in
the mapping between the current atmospheric density state and control input to
the atmospheric density state at the next timestep. This work improves upon
previous linear propagation methods for atmospheric density forecasting, by
developing a nonlinear transformer-based architecture for atmospheric density
forecasting. Empirical NRLMSISE-00 and JB2008, as well as physics-based TIEGCM
atmospheric density models are compared for forecasting with DMDc and with the
transformer-based propagator.