Attention‐Based Machine Vision Models and Techniques for Solar Wind Speed Forecasting Using Solar EUV Images. Issue 3 (17th March 2022)

• Record Type: Journal Article
• Authors: Brown, Edward J. E.; Svoboda, Filip; Meredith, Nigel P.; Lane, Nicholas; Horne, Richard B.
• Is Part Of: Space weather. Volume 20:Issue 3(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-03-17
• Subjects: machine learning -- solar wind speed -- solar images -- computer vision -- vision transformer -- coronal holes; Space environment -- Periodicals; 551.509992
• DOI: 10.1029/2021SW002976
• Languages: English
• ISSNs: 1542-7390

Abstract

Abstract: Extreme ultraviolet images taken by the Atmospheric Imaging Assembly on board the Solar Dynamics Observatory make it possible to use deep vision techniques to forecast solar wind speed—a difficult, high‐impact, and unsolved problem. At a 4 day time horizon, this study uses attention‐based models and a set of methodological improvements to deliver an 11.1% lower RMSE and a 17.4% higher prediction correlation compared to the previous work testing on the period from 2010 to 2018. Our analysis shows that attention‐based models combined with our pipeline consistently outperform convolutional alternatives. Our study shows a large performance improvement by using a 30 min as opposed to a daily sampling frequency. Our model has learned relationships between coronal holes' characteristics and the speed of their associated high‐speed streams, agreeing with empirical results. Our study finds a strong dependence of our best model on the phase of the solar cycle, with the best performance occurring in the declining phase. Plain Language Summary: Solar images contain rich information that can be used to forecast conditions at Earth. This study develops a robust methodology for processing solar images and trains machine learning models that can use them to predict the solar wind speed. Combined, these deliver a very significant 17.4% improvement in the correlation between the prediction and the ground truth over previous works. The models perform better during the quieter, declining phase of the solar cycle when the solar activity is driven by coronal holes. Finally, the trained models learn properties of coronal holes that agree with prior empirical studies. Key Points: Attention‐based machine vision models and methodological enhancements are developed to improve solar wind speed forecasts from solar images Attention‐based architectures outperform convolutional models, motivating their use in future studies and production systems The models perform best when solar activity is driven by coronal holes, such as in the declining phase of the solar cycle