Improving orbital decay estimation of LEO objects under space weather variability using an adaptive drag normalization coefficient

Kurzfassung

Atmospheric drag remains a major source of uncertainty in orbit prediction for Low Earth Orbit (LEO) spacecraft, particularly during periods of enhanced solar and geomagnetic activity. In previous work, we used a single global drag normalization coefficient (DNC) in numerical propagation scheme to successfully reproduce the long-term orbital decay of selected LEO spacecrafts. This study advances the model toward predictive capability through the development and implementation of an adaptive drag normalization coefficient (aDNC) for improved robustness. The adaptive formulation dynamically recalibrate the coefficient constrained by real orbital data, allowing the drag model to respond to temporal variability in the thermosphere. The framework directly incorporates forecast solar-geophysical indices into the atmospheric density and drag estimation process, enabling a transition from historical reconstruction to forward drag prediction with defined lead times. The model performance is assessed using selected, well-parameterised LEO spacecrafts, with emphasis on orbital decay metrics under both quiet and geomagnetically active conditions. Our results show that the aDNC approach improves predictive stability and reduces bias relative to a fixed normalization scheme. This strategy effectively supports the development of forecast-driven drag estimation tools for space weather monitoring and space situational awareness applications.