A Bayesian Approach to Solar Flare Prediction
A Bayesian Approach to Solar Flare Prediction
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DOI：
10.1086/421261
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发表时间：
2004-03
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影响因子：
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通讯作者：
M. Wheatland
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文献类型：
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作者：
M. Wheatland
一些耀斑预测方法依赖于对活动区域的物理特征进行分类，特别是太阳黑子的光学分类，以及给定分类的历史耀斑速率。然而，这些方法在很大程度上忽略了活动区已经产生的耀斑数量，特别是小事件的数量。过去发生火炬（各种规模）的历史是未来火炬生产的重要指标。我们提出了一个贝叶斯方法来预测耀斑，它使用的耀斑记录的活动区域与耀斑统计的现象学规则，以完善一个大耀斑的发生在随后的一段时间内的初始预测。最初的预测被认为是来自耀斑预测的现存方法之一。概述了该方法的理论，并进行了模拟，以显示该方法的细化步骤在实践中是如何工作的。
A number of methods of flare prediction rely on classification of physical characteristics of an active region, in particular optical classification of sunspots, and historical rates of flaring for a given classification. However, these methods largely ignore the number of flares the active region has already produced, in particular the number of small events. The past history of occurrence of flares (of all sizes) is an important indicator of future flare production. We present a Bayesian approach to flare prediction, which uses the flaring record of an active region together with phenomenological rules of flare statistics to refine an initial prediction for the occurrence of a big flare during a subsequent period of time. The initial prediction is assumed to come from one of the extant methods of flare prediction. The theory of the method is outlined, and simulations are presented to show how the refinement step of the method works in practice.