A Global Thermospheric Density Prediction Framework Based on a Deep Evidential Method - Yiran Wang; Xiaoli Bai (Space Weather)
Abstract Thermospheric density influences the atmospheric drag and is crucial for space missions. This paper introduces a global thermospheric density prediction framework based on a deep evidential method. The proposed framework predicts thermospheric density at the required time and geographic position with given geomagnetic and solar indices. It is called global to differentiate it from existing research that only predicts density along a satellite orbit. Through the deep evidential method, we assimilate data from various sources including solar and geomagnetic conditions, accelerometer‐derived density data, and empirical models including the Jacchia‐Bowman model (JB‐2008) and the Naval Research Laboratory Mass Spectrometer and Incoherent Scatter Radar Extended (NRLMSISE‐00) model. The framework is investigated on five test cases along various satellites from 2003 to 2015 involving geomagnetic storms with Disturbance Storm Time (Dst) values smaller than −50 nT. Results show that the proposed framework can generate density with higher accuracy than the two empirical models. It can also obtain reliable uncertainty estimations. Global density estimations at altitudes from 200 to 550 km are also presented and compared with empirical models on both quiet and storm conditions.