loading page

Toward Accurate Physics-Based Specifications of Neutral Density using GNSS-Enabled Small Satellites
  • +4
  • Eric K. Sutton,
  • Jeffrey P. Thayer,
  • Marcin D. Pilinski,
  • Shaylah M. Mutschler,
  • Thomas E. Berger,
  • Vu Nguyen,
  • Dallas Masters
Eric K. Sutton
University of Colorado at Boulder, University of Colorado at Boulder

Corresponding Author:[email protected]

Author Profile
Jeffrey P. Thayer
University of Colorado Boulder, University of Colorado Boulder
Author Profile
Marcin D. Pilinski
Laboratory for Atmospheric and Space Physics, Laboratory for Atmospheric and Space Physics
Author Profile
Shaylah M. Mutschler
University of Colorado at Boulder, University of Colorado at Boulder
Author Profile
Thomas E. Berger
University of Colorado at Boulder, University of Colorado at Boulder
Author Profile
Vu Nguyen
Spire Global, Inc, Spire Global
Author Profile
Dallas Masters
Spire Global, Inc, Spire Global, Inc
Author Profile

Abstract

Satellite-atmosphere interactions cause large uncertainties in low-Earth orbit determination and prediction. Thus, knowledge of and the ability to predict the space environment, most notably thermospheric mass density, are essential for operating satellites in this domain. Recent progress has been made toward supplanting the existing empirical, operational methods with physics-based data-assimilative models by accounting for the complex relationship between external drivers such as solar irradiance, Joule, and particle heating, and their response in the upper atmosphere. Simultaneously, a new era of CubeSat constellations is set to provide data with which to calibrate our upper-atmosphere models at higher spatial resolution and temporal cadence. With this in mind, we provide an initial method for converting precision orbit determination (POD) solutions from global navigation satellite system (GNSS) enabled CubeSats into timeseries of thermospheric mass density. This information is then fused with a physics-based, data-assimilative technique to provide calibrated model densities.