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Abstract A large sample of active-region-targeted time-series images from the Solar Dynamics Observatory/Atmospheric Imaging Assembly (AIA), the AIA Active Region Patch database (Paper I) is used to investigate whether parameters describing the coronal, transition region, and chromospheric emission can differentiate a region that will imminently produce a solar flare from one that will not. Parameterizations based on moment analysis of direct and running-difference images provide for physically interpretable results from nonparametric discriminant analysis. Across four event definitions including both 24 hr and 6 hr validity periods, 160 image-based parameters capture the general state of the atmosphere, rapid brightness changes, and longer-term intensity evolution. We find top Brier Skill Scores in the 0.07–0.33 range, True Skill Statistics in the 0.68–0.82 range (both depending on event definition), and Receiver Operating Characteristic Skill Scores above 0.8. Total emission can perform notably, as can steeply increasing or decreasing brightness, although mean brightness measures do not, demonstrating the well-known active-region size/flare productivity relation. Once a region is flare productive, the active-region coronal plasma appears to stay hot. The 94 Å filter data provide the most parameters with discriminating power, with indications that it benefits from sampling multiple physical regimes. In particular, classification success using higher-order moments of running-difference images indicate a propensity for flare-imminent regions to display short-lived small-scale brightening events. Parameters describing the evolution of the corona can provide flare-imminent indicators, but at no preference over “static” parameters. Finally, all parameters and NPDA-derived probabilities are available to the community for additional research.
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The NWRA Classification Infrastructure: description and extension to the Discriminant Analysis Flare Forecasting System (DAFFS)
A classification infrastructure built upon Discriminant Analysis (DA) has been developed at NorthWest Research Associates for examining the statistical differences between samples of two known populations. Originating to examine the physical differences between flare-quiet and flare-imminent solar active regions, we describe herein some details of the infrastructure including: parametrization of large datasets, schemes for handling “null” and “bad” data in multi-parameter analysis, application of non-parametric multi-dimensional DA, an extension through Bayes’ theorem to probabilistic classification, and methods invoked for evaluating classifier success. The classifier infrastructure is applicable to a wide range of scientific questions in solar physics. We demonstrate its application to the question of distinguishing flare-imminent from flare-quiet solar active regions, updating results from the original publications that were based on different data and much smaller sample sizes. Finally, as a demonstration of “Research to Operations” efforts in the space-weather forecasting context, we present the Discriminant Analysis Flare Forecasting System (DAFFS), a near-real-time operationally-running solar flare forecasting tool that was developed from the research-directed infrastructure.
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- Award ID(s):
- 1630454
- PAR ID:
- 10404867
- Date Published:
- Journal Name:
- Journal of Space Weather and Space Climate
- Volume:
- 8
- ISSN:
- 2115-7251
- Page Range / eLocation ID:
- A25
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1051/swsc/2018004
