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Forecasting Extreme Ultraviolet Thermospheric Drivers from Solar Imaging using Deep Learning
  • Edward J. E. Brown,
  • Filip Svoboda,
  • Nigel P. Meredith
Edward J. E. Brown
British Antarctic Survey

Corresponding Author:ejeb4@cam.ac.uk

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Filip Svoboda
University of Cambridge
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Nigel P. Meredith
British Antarctic Survey
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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.
18 May 2023Submitted to ESS Open Archive
25 May 2023Published in ESS Open Archive