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

2026-02-01

Journal of Geophysical Research: Space Physics

AI Summary

Accurate prediction of the F10.7 and F30 radio flux indices, which characterize solar activity, is crucial for improving models of Earth’s upper atmosphere and thermospheric density. This work proposes the Solar Index Network (SINet), a deep learning model specifically designed for medium- to short-term (1–60 day) forecasting of these solar indices. SINet introduces, for the first time, a deep learning approach to F30 prediction and jointly models F10.7 and F30 through a shared-branch network architecture. Trained on historical data from NOAA, Toyokawa, and Nobeyama observatories, SINet significantly outperforms five state-of-the-art methods in F10.7 forecasting while achieving high accuracy in F30 prediction, thereby addressing a critical gap in the application of deep learning to solar index modeling.

Technology Category

• Machine Learning: Deep Neural Architectures and Foundation Models
• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Web Mining and Content Analysis: Large pretrained models with web data
• Search and Retrieval-Augmented AI: Web crawling and indexing

Abstract

The F10.7 and F30 solar indices are the solar radio fluxes measured at wavelengths of 10.7 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 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.

Problem

• F10.7 solar index
• F30 solar index
• solar activity prediction
• daily solar flux forecasting
• thermospheric density response

Innovation

• deep learning
• solar index prediction
• F30
• F10.7
• SINet