Flare forecasting using the evolution of McIntosh sunspot classifications

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.

[1]  George E. Hale,et al.  The Magnetic Polarity of Sun-Spots , 1919 .

[2]  Haimin Wang,et al.  Active-Region Monitoring and Flare Forecasting – I. Data Processing and First Results , 2002 .

[3]  M. S. Wheatland.  A statistical solar flare forecast method , 2005 .

[4]  P. Bornmann,et al.  Flare rates and the McIntosh active-region classifications , 1994 .

[5]  P. McIntosh.  The classification of sunspot groups , 1990 .

[6]  D. S. Bloomfield,et al.  Flaring Rates and the Evolution of Sunspot Group McIntosh Classifications , 2016, 1607.00903.

[7]  Yong-Jae Moon,et al.  Solar Flare Occurrence Rate and Probability in Terms of the Sunspot Classification Supplemented with Sunspot Area and Its Changes , 2012 .

[8]  Michael S. Wheatland,et al.  Rates of Flaring in Individual Active Regions , 2001 .

[9]  E. Priest,et al.  The magnetic nature of solar flares , 2002 .

[10]  D. Jackson,et al.  Flare forecasting at the Met Office Space Weather Operations Centre , 2017, 1703.06754.

[11]  Monica G. Bobra,et al.  SOLAR FLARE PREDICTION USING SDO/HMI VECTOR MAGNETIC FIELD DATA WITH A MACHINE-LEARNING ALGORITHM , 2014, 1411.1405.

[12]  Rami Qahwaji,et al.  Automated Solar Activity Prediction: A hybrid computer platform using machine learning and solar imaging for automated prediction of solar flares , 2009 .

[13]  Rami Qahwaji,et al.  Solar Flare Prediction Using Advanced Feature Extraction, Machine Learning, and Feature Selection , 2011, Solar Physics.

[14]  A. Cortie.  On the Types of Sun-Spot Disturbances , 1901 .

[15]  A. Mitra,et al.  Ionospheric effects of solar flares , 1974 .

[16]  David H. Hathaway,et al.  A synthesis of solar cycle prediction techniques , 1999 .

[17]  Andy Devos,et al.  Verification of space weather forecasting at the Regional Warning Center in Belgium , 2014 .

[18]  D. S. Bloomfield,et al.  A COMPARISON OF FLARE FORECASTING METHODS. I. RESULTS FROM THE “ALL-CLEAR” WORKSHOP , 2016, 1608.06319.

[19]  B. Joshi,et al.  Distribution of Hα flares during solar cycle 23 , 2005 .

[20]  Y. Moon,et al.  Evolutionary aspects and north-south asymmetry of soft X-ray flare index during solar cycles 21, 22, and 23 , 2015, 1705.02107.

[21]  M. Crown,et al.  Validation of the NOAA Space Weather Prediction Center's solar flare forecasting look‐up table and forecaster‐issued probabilities , 2012 .

[22]  A. H. Murphy.  A New Vector Partition of the Probability Score , 1973 .

[23]  D. S. Bloomfield,et al.  TOWARD RELIABLE BENCHMARKING OF SOLAR FLARE FORECASTING METHODS , 2012, 1202.5995.