High-Cadence Thermospheric Density Estimation enabled by Machine
  Learning on Solar Imagery
Shreshth A. Malik James Walsh Giacomo Acciarini Thomas E. Berger Atılım Güneş Baydin
Accurate estimation of thermospheric density is critical for precise modeling
of satellite drag forces in low Earth orbit (LEO). Improving this estimation is
crucial to tasks such as state estimation, collision avoidance, and re-entry
calculations. The largest source of uncertainty in determining thermospheric
density is modeling the effects of space weather driven by solar and
geomagnetic activity. Current operational models rely on ground-based proxy
indices which imperfectly correlate with the complexity of solar outputs and
geomagnetic responses. In this work, we directly incorporate NASA's Solar
Dynamics Observatory (SDO) extreme ultraviolet (EUV) spectral images into a
neural thermospheric density model to determine whether the predictive
performance of the model is increased by using space-based EUV imagery data
instead of, or in addition to, the ground-based proxy indices. We demonstrate
that EUV imagery can enable predictions with much higher temporal resolution
and replace ground-based proxies while significantly increasing performance
relative to current operational models. Our method paves the way for
assimilating EUV image data into operational thermospheric density forecasting
models for use in LEO satellite navigation processes.