Virtual EVE: a Deep Learning Model for Solar Irradiance Prediction

TL;DR

This paper introduces a deep learning model that accurately predicts solar irradiance using data from the Solar Dynamic Observatory, effectively virtualizing a malfunctioned instrument and advancing space weather prediction capabilities.

Contribution

The paper presents a novel deep learning architecture combining linear and CNN components, utilizing AIA and HMI data to accurately predict solar irradiance and virtualize a damaged instrument.

Findings

AIA data alone suffices for accurate irradiance prediction.

The model outperforms existing state-of-the-art methods.

Deep learning proves effective for instrument virtualization.

Abstract

Understanding space weather is vital for the protection of our terrestrial and space infrastructure. In order to predict space weather accurately, large amounts of data are required, particularly in the extreme ultraviolet (EUV) spectrum. An exquisite source of information for such data is provided by the Solar Dynamic Observatory (SDO), which has been gathering solar measurements for the past 13 years. However, after a malfunction in 2014 affecting the onboard Multiple EUV Grating Spectrograph A (MEGS-A) instrument, the scientific output in terms of EUV measurements has been significantly degraded. Building upon existing research, we propose to utilize deep learning for the virtualization of the defective instrument. Our architecture features a linear component and a convolutional neural network (CNN) -- with EfficientNet as a backbone. The architecture utilizes as input grayscale images of the Sun at multiple frequencies -- provided by the Atmospheric Imaging Assembly (AIA) -- as well as solar magnetograms produced by the Helioseismic and Magnetic Imager (HMI). Our findings highlight how AIA data are all that is needed for accurate predictions of solar irradiance. Additionally, our model constitutes an improvement with respect to the state-of-the-art in the field, further promoting the idea of deep learning as a viable option for the virtualization of scientific instruments.