Real‐Time Thermospheric Density Estimation via Two‐Line Element Data Assimilation

David J Gondelach,Richard Linares

doi:10.1029/2019sw002356

Abstract

AbstractInaccurate estimates of the thermospheric density are a major source of error in low Earth orbit prediction. Therefore, real‐time density estimation is required to improve orbit prediction. In this work, we develop a dynamic reduced‐order model for the thermospheric density that enables real‐time density estimation using two‐line element (TLE) data. For this, the global thermospheric density is represented by the main spatial modes of the atmosphere and a time‐varying low‐dimensional state and a linear model is derived for the dynamics. Three different models are developed based on density data from the TIE‐GCM, NRLMSISE‐00, and JB2008 thermosphere models and are valid from 100 to maximum 800 km altitude. Using the models and TLE data, the global density is estimated by simultaneously estimating the density and the orbits and ballistic coefficients of several objects using a Kalman filter. The sequential estimation provides both estimates of the density and corresponding uncertainty. Accurate density estimation using the TLEs of 17 objects is demonstrated and validated against CHAMP and GRACE accelerometer‐derived densities. The estimated densities are shown to be significantly more accurate and less biased than NRLMSISE‐00 and JB2008 modeled densities. The uncertainty in the density estimates is quantified and shown to be dependent on the geographical location, solar activity, and objects used for estimation. In addition, the data assimilation capability of the model is highlighted by assimilating CHAMP accelerometer‐derived density data together with TLE data to obtain more accurate global density estimates. Finally, the dynamic thermosphere model is used to forecast the density.

Highlights

Accurate knowledge of the thermospheric density is essential for orbit prediction in low Earth orbit and in particular for conjunction assessments
Accurate global density estimates were computed by assimilating two-line element (TLE) data in a dynamic reduced-order density model by simultaneously estimating the orbits, ballistic coefficients, and density
The TLE data of only 17 spatially spread objects were used and the estimated densities were compared with NRLMSISE-00and Jacchia-Bowman 2008 (JB2008)-modeled densities and CHAMP and GRACE-A accelerometer-derived densities

Summary

Introduction

Accurate knowledge of the thermospheric density is essential for orbit prediction in low Earth orbit and in particular for conjunction assessments. The models of the thermosphere with most potential for good forecast capabilities, in particular during storm conditions, are physics-based models (Sutton, 2018b), such as the Global Ionosphere-Thermosphere Model (GITM) (Ridley et al, 2006) and the Thermosphere-Ionosphere-Electrodynamics General Circulation Model (TIE-GCM) (Qian et al, 2014). These models solve the continuity, momentum, and energy equations for a number of neutral and charged components. Their modeling and prediction performance comes, at a high computational cost. To fully exploit the forecasting potential of physics-based models the schemes employed for data assimilation need to be improved (Sutton, 2018b)

Methods

Results

Discussion

Conclusion