This article implements a Convolutional Neural Network (CNN)-based deep learning model for solar-wind prediction. Images from the Atmospheric Imaging Assembly (AIA) at 193\.A wavelength are used for training. Solar-wind speed is taken from the Advanced Composition Explorer (ACE) located at the Lagrangian L1 point. The proposed CNN architecture is designed from scratch for training with four years' data. The solar-wind has been ballistically traced back to the Sun assuming a constant speed during propagation, to obtain the corresponding coronal intensity data from AIA images. This forecasting scheme can predict the solar-wind speed well with a RMSE of 76.3 km\s and an overall correlation coefficient of 0.57 for the year 2018, while significantly outperforming benchmark models. The threat score for the model is around 0.46 in identifying the HSEs with zero false alarms.
@article{arxiv.2108.09114,
title = {CNN-Based Deep Learning in Solar Wind Forecasting},
author = {Hemapriya Raju and Saurabh Das},
journal= {arXiv preprint arXiv:2108.09114},
year = {2021}
}
Comments
21 pages,13 figures. Accepted for publication in Solar Physics. After published, it will be available at https://www.springer.com/journal/11207