Presentation #117.07 in the session Solar Flare Prediction — Poster Session.
Solar flare forecasting has historically focused on characterizing an active region’s complexity and propensity to flare through the analysis of white-light images, photospheric magnetic field maps (and extrapolations that use the maps as boundary conditions), and chromospheric morphology. We report on the results of a large-sample study of “AIA Active-Region Patches” (AARP) regions that invokes NonParametric Discriminant Analysis through the NWRA Classification Infrastructure (NCI) to ask the question of what distinguishes flare-imminent vs. flare-quiet active regions (or epochs during a region’s evolution). By invoking area-totals and high-order moment analysis on both direct and running-difference images in multiple AIA wavelength bands, we find positive-skill classification results (Brier Skill Scores for C1.0+/24h of order 0.3) for enhanced heating and kinematics in AARP samples that are flare-imminent. In this manner, we present coronal- and chromospheric- targeted parameters, much like the commonly-used photosphere-magnetic field parameters, that are possible candidates for improved physics-based understanding of the flare-ready atmosphere, and hence improved flare forecasting. This work was supported by NASA/GI grant 80NSSC19K0285.