J. Space Weather Space Clim.
Volume 8, 2018
Developing New Space Weather Tools: Transitioning fundamental science to operational prediction systems
|Number of page(s)||23|
|Published online||17 April 2018|
The NWRA Classification Infrastructure: description and extension to the Discriminant Analysis Flare Forecasting System (DAFFS)
NorthWest Research Associates, Boulder Office,
3380 Mitchell Ln.,
* Corresponding author: firstname.lastname@example.org
Accepted: 16 January 2018
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.
Key words: Sun / statistical analysis / flares / prediction / Discriminant Analysis / Bayes
© K.D. Leka et al., Published by EDP Sciences 2018
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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