Solar Full-disk Flare Forecasting Model Based on 10.7 cm Solar Radio Flux
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摘要: 太阳耀斑是一种重要的太阳爆发活动现象,表现为近乎全波段的电磁辐射增强。统计表明,太阳活动水平越高,太阳爆发越频繁,耀斑爆发的概率越大。利用1975-2007年10.7 cm流量与耀斑爆发的统计关系,建立了一种可行的全日面爆发耀斑概率的预报方法,能够实现C,M,X三种级别的耀斑在全日面爆发的概率预报。通过2008-2016年的观测数据,对模型进行了预报性能的评估,得到模型对C,M,X级耀斑发生概率的预报误差均较小,Brier评分误差分别为0.113,0.087,0.012;模型的预报性能均比平均模型有提高,对C,M,X级耀斑发生概率预报的Brier技巧评分分别为0.250,0.106,0.012。在2008-2016年未来1天耀斑预报的模型实测中,模型的预报效果与中国科学院空间环境预报中心的预报效果相当,这说明该模型在实际的空间环境预报中切实可行。Abstract: Solar flare is an important solar active phenomenon, which is manifested as electromagnetic radiant enhancement in almost all wave bands. The statistics indicate that the solar flare is positively associated with the solar active levels. In this paper, a method for predicting the probability of solar flare is established based on the statistical relationship between 10.7 cm flux and solar flare during 1975 to 2007. The forecasting model can be used to predict the probability of C, M and X class flares. During 2008 to 2016, the predicted errors of the model for C, M and X class flares are 0.113, 0.087 and 0.012, respectively, and the predicted skill scores are 0.250, 0.106 and 0.012, respectively. It means that our method has less predicted errors and more skill scores than the average model for predicting C, M and X class flares. During the period from 2008 to 2016, the predicted results of the model are similar to that of Space Environment Prediction Center in National Space Science Center. It indicates that the model is feasible in the actual space environment prediction.
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图 3 2014年10月全日面耀斑预报模型的预报结果与实测结果的比较 (红色散点为预报值,蓝色散点为耀斑实际发生情况)
Figure 3. Comparison of the output of the solar full-disk flare prediction model with the observation results in October 2014 (The red scatter points are the predicted values, and the blue scatter points are the actual occurrence of the flares)
表 1 F10.7分别与C,M,X级耀斑频率进行Boltzmann函数拟合的拟合系数
Table 1. Boltzmann function coefficients for fitting F10.7 to the production frequencies of C, M, and X-class flare, respectively
C M X A1 –0.62756 –1.17626 –6.77724×10–5 A2 0.98561 4.71786 0.35925 Z0 77.30752 435.65705 254.95498 W 19.23882 262.55073 42.62485 表 2 模型基于预报中心F10.7进行的耀斑预报与预报中心耀斑预报的评估对比
Table 2. Comparison of results between the model flare forecasts based on SPEC-predicted F10.7 values as inputs and the flare forecasts provided by the SEPC
耀斑级别 Bs Bss 模型预报 M 0.0895 0.1590 X 0.0119 0.0358 预报中心的预报 M 0.0860 0.1914 X 0.0120 0.0290 -
[1] SHARP L E, HARRIS D E. Enhanced interplanetary scintillations associated with solar flares[J]. Nature, 1967, 213(5074): 377-378 doi: 10.1038/213377a0 [2] YASHIRO S, AKIYAMA S, GOPALSWAMY N, et al. Different power-law indices in the frequency distributions of flares with and without coronal mass ejections[J]. The Astrophysical Journal, 2006, 650(2): L143-L146 doi: 10.1086/508876 [3] NITTA N V, MULLIGAN T, KILPUA E K J, et al. Understanding the origins of problem geomagnetic storms associated with “stealth” coronal mass ejections[J]. Space Science Reviews, 2021, 217(8): 82 doi: 10.1007/s11214-021-00857-0 [4] HUANG X, WANG H N, XU L, et al. Deep learning based solar flare forecasting model. I. results for line-of-sight magnetograms[J]. The Astrophysical Journal, 2018, 856(1): 7 doi: 10.3847/1538-4357/aaae00 [5] LEKA K D, PARK S H, KUSANO K, et al. A comparison of flare forecasting methods. II. benchmarks, metrics, and performance results for operational solar flare forecasting systems[J]. The Astrophysical Journal Supplement Series, 2019, 243(2): 36 doi: 10.3847/1538-4365/ab2e12 [6] PARK E, MOON Y J, SHIN S, et al. Application of the deep convolutional neural network to the forecast of solar flare occurrence using full-disk solar magnetograms[J]. The Astrophysical Journal, 2018, 869(2): 91 doi: 10.3847/1538-4357/aaed40 [7] TAPPING K F, DETRACEY B. The origin of the 10.7 cm flux[J]. Solar Physics, 1990, 127(2): 321-332 doi: 10.1007/BF00152171 [8] TAPPING K F. The 10.7 cm solar radio flux (F10.7)[J]. Space Weather, 2013, 11(7): 394-406 doi: 10.1002/swe.20064 -