Forecasting Thermospheric Density with Transformers for Multi-Satellite Orbit Management
This work addresses the need for accurate and efficient density predictions to support satellite operations in Low Earth Orbits, representing an incremental improvement over existing methods.
The paper tackles the problem of forecasting thermospheric density for satellite orbit management by developing a transformer-based model that predicts densities up to three days ahead, improving key prediction metrics compared to empirical baselines.
Accurate thermospheric density prediction is crucial for reliable satellite operations in Low Earth Orbits, especially at high solar and geomagnetic activity. Physics-based models such as TIE-GCM offer high fidelity but are computationally expensive, while empirical models like NRLMSIS are efficient yet lack predictive power. This work presents a transformer-based model that forecasts densities up to three days ahead and is intended as a drop-in replacement for an empirical baseline. Unlike recent approaches, it avoids spatial reduction and complex input pipelines, operating directly on a compact input set. Validated on real-world data, the model improves key prediction metrics and shows potential to support mission planning.