Forecasting Extreme Ultraviolet Thermospheric Drivers from Solar Imaging
using Deep Learning
Abstract
The thermosphere is heated by incident extreme ultraviolet (EUV)
radiation from the Sun. Accurately forecasting this driver of
thermospheric density is of paramount importance to spacecraft
operations, such as collision avoidance, in an increasingly crowded low
Earth orbit environment. This study uses deep learning techniques to
forecast solar irradiance from three solar EUV/UV image channels (9.4nm,
33.5nm, and 160.0nm) taken by the Solar Dynamics Observatory up to four
days in advance. The proposed model is able to forecast 23 wavelength
bands from 0.05nm to 121nm (produced from the FISM2 empirical irradiance
model (Chamberlin et al., 2020)) with a mean absolute percentage error
of 6.0% and an average improvement in the mean absolute percentage
error of 36.45% over the baseline persistence model at a 4 day time
horizon. The study further validates the model and derives insights
about its internal working by testing and investigating the importance
of its core components.