Transformer-based Atmospheric Density Forecasting
This work addresses the need for accurate atmospheric density predictions to manage satellite orbits, but it appears incremental as it builds on existing deep learning approaches for forecasting.
This paper tackles the problem of atmospheric density forecasting for space situational awareness by developing a transformer-based architecture that captures nonlinearities, improving upon previous linear methods like DMDc, with comparisons using empirical and physics-based models.
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.