J. Space Weather Space Clim.
Volume 8, 2018
Developing New Space Weather Tools: Transitioning fundamental science to operational prediction systems
|Number of page(s)||14|
|Published online||22 June 2018|
Flare forecasting using the evolution of McIntosh sunspot classifications
School of Physics, Trinity College Dublin, College Green,
Dublin 2, Ireland
2 Northumbria University, Newcastle upon Tyne, NE1 8ST, UK
* Corresponding author: email@example.com
Accepted: 2 May 2018
Most solar flares originate in sunspot groups, where magnetic field changes lead to energy build-up and release. However, few flare-forecasting methods use information of sunspot-group evolution, instead focusing on static point-in-time observations. Here, a new forecast method is presented based upon the 24-h evolution in McIntosh classification of sunspot groups. Evolution-dependent ≥C1.0 and ≥M1.0 flaring rates are found from NOAA-numbered sunspot groups over December 1988–June 1996 (Solar Cycle 22; SC22) before converting to probabilities assuming Poisson statistics. These flaring probabilities are used to generate operational forecasts for sunspot groups over July 1996–December 2008 (SC23), with performance studied by verification metrics. Major findings are: (i) considering Brier skill score (BSS) for ≥C1.0 flares, the evolution-dependent McIntosh-Poisson method (BSSevolution = 0.09) performs better than the static McIntosh-Poisson method (BSSstatic = − 0.09); (ii) low BSS values arise partly from both methods over-forecasting SC23 flares from the SC22 rates, symptomatic of ≥C1.0 rates in SC23 being on average ≈80% of those in SC22 (with ≥M1.0 being ≈50%); (iii) applying a bias-correction factor to reduce the SC22 rates used in forecasting SC23 flares yields modest improvement in skill relative to climatology for both methods (BSSstaticcorr = 0.09 and BSSevolutioncorr = 0.0.20) and improved forecast reliability diagrams.
Key words: operational forecasting / solar flares / sunspot groups
© A.E. McCloskey 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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