Attention-Based Machine Vision Models and Techniques for Solar Wind Speed Forecasting Using Solar EUV Images

Edward J.E.Brown; Filip Svoboda; Nigel P.MeredithNicholas LaneRichard B.Horne

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Attention-Based Machine Vision Models and Techniques for Solar Wind Speed Forecasting Using Solar EUV Images

机译：Attention-Based Machine Vision Models and Techniques for Solar Wind Speed Forecasting Using Solar EUV Images

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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.

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《Space weather》 |2022年第3期|1-19|共19页
作者
Edward J.E.Brown; Filip Svoboda; Nigel P.MeredithNicholas LaneRichard B.Horne;
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Department of Computer Science and Technology, University of Cambridge;

Space Weather and Atmosphere Team, British Antarctic Survey, NERC;

BAS AI Lab, British Antarctic Survey, NERC;

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Attention-Based Machine Vision Models and Techniques for Solar Wind Speed Forecasting Using Solar EUV Images

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