Attention-Based Machine Vision Models and Techniques for Solar Wind Speed Forecasting Using Solar EUV Images
Publication Date
2022Journal Title
Space Weather
ISSN
1542-7390
Publisher
American Geophysical Union (AGU)
Volume
20
Issue
3
Language
en
Type
Article
This Version
AO
VoR
Metadata
Show full item recordCitation
Brown, E., Svoboda, F., Meredith, N., Lane, N., & Horne, R. (2022). Attention-Based Machine Vision Models and Techniques for Solar Wind Speed Forecasting Using Solar EUV Images. Space Weather, 20 (3) https://doi.org/10.1029/2021SW002976
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.
Keywords
Heliophysics and Space Weather Studies from the Sun‐Earth Lagrange Points, COMPUTATIONAL GEOPHYSICS, Neural networks, fuzzy logic, machine learning, INFORMATICS, Machine learning, INTERPLANETARY PHYSICS, Solar wind sources, Coronal mass ejections, MAGNETOSPHERIC PHYSICS, Magnetic storms and substorms, NATURAL HAZARDS, Space weather, SOLAR PHYSICS, ASTROPHYSICS, AND ASTRONOMY, SPACE WEATHER, Models, Solar effects, Magnetic storms, Research Article, machine learning, solar wind speed, solar images, computer vision, vision transformer, coronal holes
Sponsorship
Natural Environment Research Council (NERC) (NE/V00249X/1, NE/R016038/1)
UKRI | Engineering and Physical Sciences Research Council (EPSRC) (EP/M50659X/1, EP/S001530/1)
Identifiers
swe21271, 2021sw002976
External DOI: https://doi.org/10.1029/2021SW002976
This record's URL: https://www.repository.cam.ac.uk/handle/1810/335135
Rights
Licence:
http://creativecommons.org/licenses/by/4.0/
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