Abstract
We introduce and make openly accessible a comprehensive, multivariate time series (MVTS) dataset extracted from solar photospheric vector magnetograms in Spaceweather HMI Active Region Patch (SHARP) series. Our dataset also includes a cross-checked NOAA solar flare catalog that immediately facilitates solar flare prediction efforts. We discuss methods used for data collection, cleaning and pre-processing of the solar active region and flare data, and we further describe a novel data integration and sampling methodology. Our dataset covers 4,098 MVTS data collections from active regions occurring between May 2010 and December 2018, includes 51 flare-predictive parameters, and integrates over 10,000 flare reports. Potential directions toward expansion of the time series, either “horizontally” – by adding more prediction-specific parameters, or “vertically” – by generalizing flare into integrated solar eruption prediction, are also explained. The immediate tasks enabled by the disseminated dataset include: optimization of solar flare prediction and detailed investigation for elusive flare predictors or precursors, with both operational (research-to-operations), and basic research (operations-to-research) benefits potentially following in the future.
Highlights
Background & SummarySolar flares and coronal mass ejections (CMEs)[1,2,3] are events occurring in the solar corona and heliosphere that can have a major negative impact on our technology-dependent society[4]
We introduce and make openly accessible a comprehensive, multivariate time series (MVTS) dataset extracted from solar photospheric vector magnetograms in Spaceweather Helioseismic Magnetic Imager (HMI) Active Region Patch (SHARP) series
We discuss methods used for data collection, cleaning and preprocessing of the solar active region and flare data, and we further describe a novel data integration and sampling methodology
Summary
Solar flares and coronal mass ejections (CMEs)[1,2,3] are events occurring in the solar corona and heliosphere that can have a major negative impact on our technology-dependent society[4]. Successful flare predictions via machine learning models trained and tested on this dataset intend to (1) tackle a central problem in space weather forecasting and (2) help identify physical mechanisms pertaining, or even giving rise, to solar flares. This dataset is a reliable resource for providing an unbiased comparison between results from various solar flare prediction algorithms. Our benchmark dataset mainly relies on Spaceweather HMI Active Region Patches (SHARPs)[10] available from the Joint Science Operations Center (JSOC) This product stems from solar vector magnetograms obtained by the Helioseismic Magnetic Imager (HMI)[11] onboard the Solar Dynamics Observatory (SDO)[12]. The methods utilized in the process of cleaning, verifying, and combining the individual flare source data are described
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