Daily Predictions of F10.7 and F30 Solar Indices with Deep Learning

TL;DR

This paper introduces SINet, a deep learning model for medium-term prediction of solar indices F10.7 and F30, outperforming existing methods and marking the first deep learning application for F30 prediction.

Contribution

The study develops and validates a novel deep learning model, SINet, for predicting F10.7 and F30 solar indices, with superior performance and first-time application to F30.

Findings

SINet outperforms five related statistical and deep learning methods for F10.7 prediction.

First application of deep learning to predict the F30 solar index.

SINet achieves better accuracy for medium-term solar index forecasts.

Abstract

The F10.7 and F30 solar indices are the solar radio fluxes measured at wavelengths of 10.7 cm and 30 cm, respectively, which are key indicators of solar activity. F10.7 is valuable for explaining the impact of solar ultraviolet (UV) radiation on the upper atmosphere of Earth, while F30 is more sensitive and could improve the reaction of thermospheric density to solar stimulation. In this study, we present a new deep learning model, named the Solar Index Network, or SINet for short, to predict daily values of the F10.7 and F30 solar indices. The SINet model is designed to make medium-term predictions of the index values (1-60 days in advance). The observed data used for SINet training were taken from the National Oceanic and Atmospheric Administration (NOAA) as well as Toyokawa and Nobeyama facilities. Our experimental results show that SINet performs better than five closely related statistical and deep learning methods for the prediction of F10.7. Furthermore, to our knowledge, this is the first time deep learning has been used to predict the F30 solar index.