Flare forecasting using the evolution of McIntosh sunspot classifications
Flare forecasting using the evolution of McIntosh sunspot classifications
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DOI：
10.1051/swsc/2018022
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发表时间：
2018-05
影响因子：
3.3
通讯作者：
A. McCloskey;Peter T. Gallagher;D. S. Bloomfield
中科院分区：
文献类型：
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作者：
A. McCloskey;Peter T. Gallagher;D. S. Bloomfield
大多数太阳耀斑起源于太阳黑子群，那里的磁场变化导致能量积累和释放。然而，很少有耀斑预测方法使用太阳黑子群演化的信息，而不是专注于静态的时间点观测。本文提出了一种新的基于McIntosh黑子群分类24小时演化的预测方法。演化相关的≥C1.0和≥M1.0的耀斑速率是在1988年12月至1996年6月期间（太阳活动周期22; SC 22）从NOAA编号的太阳黑子群中发现的，然后转换为假设泊松统计的概率。这些耀斑的概率是用来产生业务预测太阳黑子群在1996年7月至2008年12月（SC 23），与验证指标的性能研究。主要调查结果如下：（i）考虑≥C1.0耀斑的Brier技能得分（BSS），（BSS evolution = 0.09）的性能优于静态McIntosh-Poisson方法（BSS static = − 0.09）;（ii）低BSS值部分是由于两种方法都根据SC 22速率过度预测了SC 23耀斑，SC 23中症状性≥C1.0的发生率平均为SC 22中的80%（≥M1.0为≥ 50%）;（iii）应用偏差校正因子来降低用于预测SC23耀斑的SC22率，这两种方法相对于气候学的技能略有改善（和）和改进的预测可靠性图。
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 ( and ) and improved forecast reliability diagrams.