Open AccessAttention‐Based Machine Vision Models and Techniques for Solar Wind Speed Forecasting Using Solar EUV Images
Author(s) -
Brown Edward J. E.,
Svoboda Filip,
Meredith Nigel P.,
Lane Nicholas,
Horne Richard B.
Publication year - 2022
Publication title -
space weather
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.254
H-Index - 56
ISSN - 1542-7390
DOI - 10.1029/2021sw002976
Subject(s) - wind speed , meteorology , computer science , extreme ultraviolet lithography , environmental science , empirical modelling , convolutional neural network , solar cycle 24 , extreme ultraviolet , observatory , pipeline (software) , coronal mass ejection , artificial intelligence , simulation , solar wind , physics , optics , astronomy , laser , quantum mechanics , magnetic field , programming language
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.