Solar proton events have been regarded to be very important in that they may cause the damage of spacecrafts and human activities. In this study, we have examined the longitudinal dependence of solar proton events and their relationships with x-ray fl...
Solar proton events have been regarded to be very important in that they may cause the damage of spacecrafts and human activities. In this study, we have examined the longitudinal dependence of solar proton events and their relationships with x-ray flares. For this we used NOAA solar energetic particle (SEP) events whose fluxes of > 10 MeV protons are greater than or equal to 10 particles cm^(-2) sec^(-1) ster^(-1) from 1976 to 2006, and their associated X-ray flare data. As a result, we found 166 proton events associated with major flares; 85 events associated with X-class flares and 81 events associated with M-class flares. Then we examined the fraction of proton events relative to total major X-ray flares and its longitudinal dependence. We found that about only 3.5% (1.9% for M-class and 21.3% for X-class) of the flares are associated with the proton events. We found that this fraction strongly depends on longitude; for example, the fraction for 30°W < L ≤ 90°W is about three times larger than that for 30°E < L ≤ 90°E. We also note that the occurrence probability of solar proton events for flares with long duration (≥ 0.3 hours) is about 2 (X-class flare) to 7 (M-class flare) times larger than that for flares with short duration (< 0.3 hours). In addition, the relationship between X-ray flares peak fluxes and protons fluxes as well as its correlation coefficient are strongly dependent on longitude, for example the correlation (r=0.68) for 30°E < L ≤ 90°E is much larger than that (r=0.39) for 30°W < L ≤ 90°W. Finally, we suggest a new proton event forecast method with two-steps : (1) solar proton occurrence probability prediction according to the contingency tables depending on its associated flare strength, longitude, and impulsive time, (2) solar proton peak flux prediction using the result of a multiple linear regression method for three different longitudinal regions.