Using Satellite Drag to Understand the Thermosphere

John Noto, Chief Scientist and Jeff Steward, Director of Scientific Research 

The thermosphere, a region of Earth’s upper atmosphere, is constantly in motion, driven by complex processes such as solar forcing, tides, plasma convection and Joule heating. Neutral winds in the thermosphere, which are crucial for understanding atmospheric dynamics, have historically been measured by ground-based instruments like Fabry-Perot interferometers. These tools, in use since 1966, rely on airglow and auroral emissions to capture wind patterns at thermospheric altitudes. While valuable, these instruments are limited to local nighttime observations and are often affected by weather conditions, creating a need for alternative methods to expand our understanding. 

A promising solution comes from an unexpected source: satellites already in orbit. As they travel through the thermosphere, satellites experience small but measurable drag caused by collisions with neutral particles. This drag offers an opportunistic way to observe the thermosphere’s neutral winds and density, providing a new perspective on this dynamic region of the atmosphere. 

HOW SATELLITE DRAG BECOMES A SCIENTIFIC TOOL

Satellite drag can be predicted using the drag equation, which incorporates factors such as the drag coefficient, cross-sectional area of the satellite, thermospheric density, and the squared velocity of the satellite relative to the atmosphere. With accurate thermospheric density and wind models and proper orbital dynamics, the location of satellites becomes highly predictable. 

However, when discrepancies arise between predicted and actual satellite positions, data assimilation techniques can be used to correct these differences. By integrating observed satellite positions with physical models, scientists can refine their estimates of both neutral density and neutral winds in the thermosphere. 

IMPROVING NEUTRAL WIND OBSERVATIONS 

Previously, data assimilation methods focused on improving estimates of neutral density by assuming fixed neutral winds based on empirical models. The current research presented by shows that satellite positions can also provide a valuable observation of neutral winds. 

While the position of a single satellite cannot uniquely separate neutral winds from neutral density, combining data from multiple satellites in diverse orbits significantly improves accuracy. This approach enhances background estimates from established models such as Mass Spectrometer and Incoherent Scatter Radar (MSIS) and the Thermosphere-Ionosphere- Electrodynamics General Circulation Model (TIE-GCM), providing a clearer picture of the thermosphere. 

LOOKING AHEAD 

Future advancements in satellite tracking could make these observations even more precise. High-frequency updates to satellite position data—often called ephemerides—would allow for greater sensitivity to smaller-scale neutral wind features. This improvement could be incorporated into upcoming satellite missions with minimal additional costs or enabled through the development of dedicated, low-cost satellite missions designed to focus exclusively on drag. 

By unlocking the potential of satellite drag as a tool for studying the thermosphere, this research paves the way for more comprehensive and cost-effective understanding of neutral winds and density. These findings could significantly enhance our ability to model the thermosphere, with implications for satellite operations, space weather forecasting and Earth-atmosphere interactions. 

This work was originally presented at AGU 2024 by John Noto.

ABOUT THE AUTHORS 

John Noto, Ph.D. is the chief scientist at Orion Space Solutions, an Arcfield company. As an experienced optical physicist and aeronomer he leads the development of optical sensors for ground and space-based sensing for space domain awareness, earth imaging and atmospheric sensing. 

Jeff Steward, Ph.D. is director for scientific analysis and research at Orion Space Solutions. His specialty is in data assimilation using high performance computing to leverage satellite and remotely sensed observations to improve forecast skill.