Attention-based machine vision models and techniques for solar wind speed forecasting using solar EUV images

Brown, Edward E.J. ORCID: https://orcid.org/0000-0002-4719-9518; Svoboda, Filip; Meredith, Nigel P. ORCID: https://orcid.org/0000-0001-5032-3463; Lane, Nicholas; Horne, Richard B. ORCID: https://orcid.org/0000-0002-0412-6407. 2022 Attention-based machine vision models and techniques for solar wind speed forecasting using solar EUV images. Space Weather, 20 (3), e2021SW002976. 19, pp. 10.1029/2021SW002976

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Official URL: https://agupubs.onlinelibrary.wiley.com/doi/10.102...

Abstract/Summary

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 four 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 minute 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.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	10.1029/2021SW002976
Additional Keywords:	Machine Learning, Solar Wind Speed, Solar Images, Computer Vision, Vision Transformer, Coronal Holes
Date made live:	03 Feb 2022 10:57 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/531871

Item Type:

Publication - Article

Digital Object Identifier (DOI):

10.1029/2021SW002976

Additional Keywords:

Machine Learning, Solar Wind Speed, Solar Images, Computer Vision, Vision Transformer, Coronal Holes

Date made live:

03 Feb 2022 10:57 +0 (UTC)

URI:

https://nora.nerc.ac.uk/id/eprint/531871