用深度学习预测太阳辐射指数，提升空间天气预报精度
Daily Predictions of F10.7 and F30 Solar Indices with Deep Learning
- 构建SINet模型，基于历史数据做1-60天的中长期预测
- 对F10.7预测优于5种已有方法，首次实现F30指数的深度学习预测
- 适合空间天气、航天轨道预报等领域的研究人员使用
F10.7和F30是分别在10.7厘米和30厘米波长测得的太阳射电通量，是太阳活动的关键指标。F10.7有助于解释太阳紫外辐射对地球高层大气的影响，而F30更敏感，可提升热层密度对太阳扰动的响应精度。本文提出一种名为SINet的深度学习模型，用于预测每日的F10.7和F30指数，支持1至60天的中长期预测。训练数据来自美国国家海洋与大气管理局（NOAA）以及日本户谷和野边山观测站。实验表明，SINet在F10.7预测上优于五种相关统计与深度学习方法；同时，这是首次将深度学习应用于F30指数预测。
原文摘要 · Abstract (English)
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.
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