Advanced Radiation Pressure Modelling for Improving the Satellite Thermosphere Density and Wind Observations

Abstract

Knowledge of thermosphere mass density and wind is essential for awide range of applications, including the development of thermosphere models and advancing the understanding of thermosphere–ionosphere coupling and solar–terrestrial physics. It is also widely used in space operations, such as mission planning, fuel budget estimation, reentry prediction, and collision risk assessment. Thermosphere mass density and wind can be obtained in situ from accelerometer measurements onboard Low Earth Orbit (LEO) satellites combined with precise GNSS positioning. Since the beginning of the 21st century, numerous LEO satellites equipped with accelerometers have been launched, providing several invaluable mass density and wind datasets. This dissertation focuses on two accelerometer-carrying LEO missions: the Gravity Field and Steady-StateOcean Circulation Explorer (GOCE), which was part of ESA’s Living Planet Program, and the Gravity Recovery and Climate Experiment Follow-On (GRACE-FO), a joint initiative between NASA and DLR.

The accuracy of the accelerometer-derived thermosphere mass density and wind datasets is coupled with uncertainties in the aerodynamic and radiation pressure modelling, where the latter plays a major role, especially during the periods of low solar activity. This dissertation aims to advance the radiation pressure models for GRACE-FO and GOCE. This is achieved by using satellites’ high-fidelity geometries supplemented by the thermo-optical properties of the surface materials. These thermo-optical properties are first redefined and fine-tuned using numerical optimisation, satellite photos and synergy with other missions. Finally, the augmented satellite models are analysed using the ray-tracing technique, which additionally accounts for self-shadowing and multiple reflections, to derive the force coefficients.

For Earth-orbiting satellites, the thermal radiation pressure accounts for one-fifth of the total cross-track radiation pressure acceleration. This research utilises the thermal model based on the concept of thermal inertia, in which the satellite heats up by absorbing incoming radiation and cools down by emitting radiation. This process was implemented using thermal model control parameters such as the internal heat generation from batteries and onboard electronics, heat capacity of the panels, conductance towards the satellite’s inner parts, and efficiency of the solar panels. Moreover, this research leverages in-situ measurements from onboard thermistors, which provide additional insights for selecting realistic thermal model control parameters.

The goal of this dissertation was to improve the accelerometer-derived thermosphere mass density and wind datasets of the GRACE-FO and GOCE satellites by advancing the modelling of radiation pressure and satellite thermal emission. The newly produced datasets were then compared with the previously available products and models. Additionally, the impact of introducing various modelling approaches was assessed and quantified.

Current accelerometer-derived thermosphere mass density and wind data are provided without comprehensive uncertainty information. This information is particularly important for data assimilation and for comparing thermosphere products obtained by different measurement techniques. This dissertation builds on the recently developed thermosphere density error propagation method and extends it to propagate errors in wind data. In this research, a sensitivity analysis was performed to assess the impact of uncertainties arising from measurement noise, radiation pressure, relative velocity, and aerodynamics on the GRACE-B satellite thermospheremass density and wind data. The objective of this study was to explore the potential of the propagation tool to augment the existing density and crosswind datasets with uncertainty information.