Predicting >10 MeV SEP Events from Solar Flare and Radio Burst Data
Predicting >10 MeV SEP Events from Solar Flare and Radio Burst Data
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DOI：
10.3390/universe6100161
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发表时间：
2020-09
期刊：
影响因子：
2.9
通讯作者：
Marlon Núñez;Daniel Paul-Pena
中科院分区：
文献类型：
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作者：
Marlon Núñez;Daniel Paul-Pena
太阳高能粒子（SEP）事件或太阳辐射风暴的预测是空间天气领域最重要的问题之一。这些事件可能对太空技术基础设施和人类产生不利影响；它们还可能辐射在极地纬度飞行的商用飞机上的乘客和机组人员。本文探讨了使用 ≥ M2 太阳耀斑和射电爆发观测作为预测地球上 >10 MeV SEP 事件的代理。这些观测结果是与 SEP 事件相关的太阳母事件的表现。由于在导致辐射风暴的物理过程开始时处理数据，该模型可能会提供高预期的预测。本方法的主要优点是该模型分析每 30 分钟更新一次的太阳数据，因此可以操作；然而，其缺点是无法预测那些与强关联耀斑相关的 SEP 事件。从1997年11月到2014年2月，我们的检测概率为70.2%，误报率为40.2%，平均预测时间为9小时52分钟。在本研究中，预测模型是使用决策树（一种可解释的机器学习技术）构建的。这种方法产生的输出和结果与太阳质子事件实时警报 (ESPERTA) 模型的经验模型得出的结果相当。获得的决策树表明，区分 pre-SEP 场景和非 pre-SEP 场景的最佳标准是软 X 射线耀斑和射电 III 型爆发的峰值和积分通量。
The prediction of solar energetic particle (SEP) events or solar radiation storms is one of the most important problems in the space weather field. These events may have adverse effects on technology infrastructures and humans in space; they may also irradiate passengers and flight crews in commercial aircraft flying at polar latitudes. This paper explores the use of ≥ M2 solar flares and radio burst observations as proxies for predicting >10 MeV SEP events on Earth. These observations are manifestations of the parent event at the sun associated with the SEP event. As a consequence of processing data at the beginning of the physical process that leads to the radiation storm, the model may provide its predictions with large anticipation. The main advantage of the present approach is that the model analyzes solar data that are updated every 30 min and, as such, it may be operational; however, a disadvantage is that those SEP events associated with strong well-connected flares cannot be predicted. For the period from November 1997 to February 2014, we obtained a probability of detection of 70.2%, a false alarm ratio of 40.2%, and an average anticipation time of 9 h 52 min. In this study, the prediction model was built using decision trees, an interpretable machine learning technique. This approach leads to outputs and results comparable to those derived by the Empirical model for Solar Proton Event Real Time Alert (ESPERTA) model. The obtained decision tree shows that the best criteria to differentiate pre-SEP scenarios and non-pre-SEP scenarios are the peak and integrated flux for soft X-ray flares and the radio type III bursts.