We develop forecast models of daily probabilities of major flares (M- and X-class) based on empirical relationships between photospheric magnetic parameters and daily flaring rates from May 2010 to April 2018. In this study, we consider ten magnetic parameters characterizing size, distribution, and non-potentiality of vector magnetic fields from Solar Dynamics Observatory (SDO)/Helioseismic and Magnetic Imager (HMI) and Geostationary Operational Environmental Satellites (GOES) X-ray flare data. The magnetic parameters are classified into three types: the total unsigned parameters, the total signed parameters, and the mean parameters. We divide the data into two sets chronologically: 70% for training and 30% for testing. The empirical relationships between the parameters and flaring rates are used to predict flare occurrence probabilities for a given magnetic parameter value. Major results of this study are as follows. First, major flare occurrence rates are well correlated with ten parameters having correlation coefficients above 0.85. Second, logarithmic values of flaring rates are well approximated by linear equations. Third, using total unsigned and signed parameters achieved better performance for predicting flares than the mean parameters in terms of verification measures of probabilistic and converted binary forecasts. We conclude that the total quantity of non-potentiality of magnetic fields is crucial for flare forecasting among the magnetic parameters considered in this study. When this model is applied for operational use, it can be used using the data of 21:00 TAI with a slight underestimation of 2–6.3%.

Abstract
1. INTRODUCTION
2. DATA AND ANALYSIS
    2.1. Geostationary Operational EnvironmentalSatellites (GOES) X-ray flares
    2.2. SDO/HMI and Magnetic Parameters
3. EMPIRICAL RELATIONSHIPS BETWEENMAGNETIC PARAMETERS AND SOLAR MAJORFLARE OCCURRENCE RATES
    3.1. Flare Identification
    3.2. Data Set
    3.3. Major Flare Occurrence Rates as a Function ofMagnetic Parameters
4. FORECAST MODELS AND THEIR VERIFICATION
    4.1. Forecast Models Based on Empirical Relationshipsbetween Parameters and Flaring Rates
    4.2. Verification Measures
5. SUMMARY AND DISCUSSION
REFERENCES

• Daye Lim(School of Space Research, Kyung Hee University)
• Yong-Jae Moon(School of Space Research, Kyung Hee University) Corresponding author
• Jongyeob Park(Korea Astronomy and Space Science Institute)
• Eunsu Park(School of Space Research, Kyung Hee University)
• Kangjin Lee(School of Space Research, Kyung Hee University/Electronics and Telecommunications Research Institute)
• Jin-Yi Lee(Department of Astronomy & Space Science, Kyung Hee University)
• Soojeong Jang(Korea Astronomy and Space Science Institute)