J/ApJ/747/L41ivo://CDS.VizieR/J/ApJ/747/L41doi:10.26093/cds/vizier.17479041CDSBloomfield D.S.Higgins P.A.McAteer R.T.J.Gallagher P.T.2013-11-21T07:33:11Z2013-11-05T08:10:11ZCDS support team

CDS, Observatoire de Strasbourg, 11 rue de l'Universite, F-67000 Strasbourg, France

cds-question@unistra.frthe-sunSolar flares occur in complex sunspot groups, but it remains unclear how the probability of producing a flare of a given magnitude relates to the characteristics of the sunspot group. Here, we use Geostationary Operational Environmental Satellite X-ray flares and McIntosh group classifications from solar cycles 21 and 22 to calculate average flare rates for each McIntosh class and use these to determine Poisson probabilities for different flare magnitudes. Forecast verification measures are studied to find optimum thresholds to convert Poisson flare probabilities into yes/no predictions of cycle 23 flares. A case is presented to adopt the true skill statistic (TSS) as a standard for forecast comparison over the commonly used Heidke skill score (HSS). In predicting flares over 24 hr, the maximum values of TSS achieved are 0.44 (C-class), 0.53 (M-class), 0.74 (X-class), 0.54 (>=M1.0), and 0.46 (>=C1.0). The maximum values of HSS are 0.38 (C-class), 0.27 (M-class), 0.14 (X-class), 0.28 (>=M1.0), and 0.41 (>=C1.0). These show that Poisson probabilities perform comparably to some more complex prediction systems, but the overall inaccuracy highlights the problem with using average values to represent flaring rate distributions.2012ApJ...747L..41Bhttps://cdsarc.cds.unistra.fr/viz-bin/cat/J/ApJ/747/L41CatalogResearchIsServedByTAP VizieR generic servicerelated-toJ/A+A/304/563 : Cool X-ray flares of Sun with GOES (Phillips+, 1995)https://cds.unistra.fr/vizier-org/licences_vizier.htmlhttps://vizier.cds.unistra.fr/viz-bin/VizieR-2?-source=J/ApJ/747/L41https://vizier.iucaa.in/viz-bin/VizieR-2?-source=J/ApJ/747/L41http://vizieridia.saao.ac.za/viz-bin/VizieR-2?-source=J/ApJ/747/L41https://vizier.cds.unistra.fr/viz-bin/votable?-source=J/ApJ/747/L41https://vizier.iucaa.in/viz-bin/votable?-source=J/ApJ/747/L41http://vizieridia.saao.ac.za/viz-bin/votable?-source=J/ApJ/747/L41GETtext/xml+votablehttps://tapvizier.cds.unistra.fr/TAPVizieR/tapdefaultJ/ApJ/747/L41/table3Flare forecast contingency table and skill score dependence on threshold Poisson probabilityrecnoRecord number assigned by the VizieR team. Should Not be used for identification.meta.recordintTpr[0/100] Threshold probability (percent) (2)%stat.fit.goodnessintC-TP[0/3667] C-class true positivesmeta.numberintC-FN[0/3667] C-class false negativesmeta.numberintC-FP[0/18609] C-class false positivesmeta.numberintC-TN[0/18609] C-class true negativesmeta.numberintC-HSS[-0.002/0.384] C-class Heidke skill score (1)meta.numberfloatC-TSS[-0.007/0.443] C-class true skill statistic (1)meta.numberfloatC-FN/FP? C-class failure ratio FN/FP (3)stat.likelihoodfloatnullableM-TP[0/810] M-class true positivesmeta.numberintM-FN[0/810] M-class false negativesmeta.numberintM-FP[0/21466] M-class false positivesmeta.numberintM-TN[0/21466] M-class true negativesmeta.numberintM-HSS[0/0.273] M-class Heidke skill score (1)meta.numberfloatM-TSS[0/0.526] M-class true skill statistic (1)meta.numberfloatM-FN/FP? M-class failure ratio FN/FP (3)stat.likelihoodfloatnullableX-TP[0/92] X-class true positivesmeta.numberintX-FN[0/92] X-class false negativesmeta.numberintX-FP[0/22184] X-class false positivesmeta.numberintX-TN[0/22184] X-class true negativesmeta.numberintX-HSS[0/0.142] X-class Heidke skill score (1)meta.numberfloatX-TSS[0/0.74] X-class true skill statistic (1)meta.numberfloatX-FN/FP? X-class failure ratio FN/FP (3)stat.likelihoodfloatnullableMX-TP[0/858] Class >=M1.0 true positivesmeta.numberintMX-FN[0/858] Class >=M1.0 false negativesmeta.numberintMX-FP[0/21418] Class >=M1.0 false positivesmeta.numberintMX-TN[0/21418] Class >=M1.0 true negativesmeta.numberintMX-HSS[0/0.28] Class >=M1.0 Heidke skill score (1)meta.numberfloatMX-TSS[0/0.539] Class >=M1.0 true skill statistic (1)meta.numberfloatMX-FN/FP? Class >=M1.0 failure ratio FN/FP (3)stat.likelihoodfloatnullableCX-TP[0/3912] Class >=C1.0 true positivesmeta.numberintCX-FN[0/3912] Class >=C1.0 false negativesmeta.numberintCX-FP[0/18364] Class >=C1.0 false positivesmeta.numberintCX-TN[0/18364] Class >=C1.0 true negativesmeta.numberintCX-HSS[-0.002/0.407] Class >=C1.0 HSS (1)meta.numberfloatCX-TSS[-0.005/0.456] Class >=C1.0 TSS (1)meta.numberfloatCX-FN/FP? Class >=C1.0 failure ratio FN/FP (3)stat.likelihoodfloatnullable