DRC: DTM-Anchored Residual Correction for Long-Horizon Thermospheric Density Forecasting

Accurate thermospheric density forecasting is essential for a growing range of low Earth orbit (LEO) applications, including satellite formation maintenance, space-debris collision avoidance, and reentry prediction. However, learning density dynamics directly from data remains challenging, particularly under strong and rapidly varying solar and geomagnetic activity. Rather than replacing established empirical models with neural predictors, we ask a different question: can a neural network learn to correct an empirical model while retaining it as a reliable prior? We propose DRC, a DTM-anchored Residual Correction framework, which uses the Drag Temperature Model (DTM) as a persistent prior and learns a bounded residual correction from historical observations and independently encoded space-weather context. A gated mechanism adaptively controls the contribution of the learned correction, while the bounded residual limits deviations from the empirical prior. We evaluate DRC on a curated dataset covering five satellites and periods of elevated solar and geomagnetic activity. DRC consistently reduces forecasting errors compared with representative deep learning baselines, demonstrating the potential of residual learning anchored in empirical models for long-horizon scientific forecasting.