Physics-Based Approach to Thermospheric Density Estimation Using CubeSat GPS Data

In Low Earth Orbit (LEO), atmospheric drag is the largest contributor to trajectory prediction error. The current thermospheric density model used in operations, the High Accuracy Satellite Drag Model (HASDM), applies corrections to an empirical density model every 3 hr using observations of 75+ calibration satellites. This work aims to improve global thermospheric density estimation by utilizing a physics-based space environment model and precise GPS-based orbit estimates of LEO CubeSats. The data assimilation approach presented here estimates drivers of the Thermosphere-Ionosphere-Electrodynamics General Circulation Model (TIE-GCM) every 1.5 hr using CubeSat GPS information. In this work, Spire Global CubeSat data are used to demonstrate the method using only 10 satellites; the true strength of the method is its potential to exploit data already collected on large LEO constellations (hundreds of CubeSats). Precise Orbit Determination (POD) information from 10 CubeSats over 12 days is used to sense a global density field when Kp historical data show a minor and moderate geomagnetic storm in succession. This paper provides a direct comparison of estimated density, derived by our new method, to HASDM and Swarm mission derived density. A propagation analysis is also executed by comparing the CubeSat POD data to orbits propagated using our estimated density versus HASDM density. The analyses show that the estimated density is within 35% of HASDM during storm-time conditions, and that the propagation using the estimated density yields an improvement of 26% over NRLMSISE-00 compared to HASDM, while outperforming HASDM during the second storm peak.