CNN-Based Deep Learning in Solar Wind Forecasting

TL;DR

This paper presents a CNN-based deep learning model that predicts solar-wind speed using AIA images and ACE data, achieving improved accuracy and threat score over benchmark models.

Contribution

The study introduces a novel CNN architecture trained on four years of data for solar-wind forecasting, integrating coronal imaging and ballistic tracing.

Findings

RMSE of 76.3 km/s in predictions

Correlation coefficient of 0.57 for 2018 data

Threat score of 0.46 for HSE detection

Abstract

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.