At present the lightning flash density is a key input parameter for assessing the risk of occurrence of a lightning strike in a particular region of interest. Since it is known that flashes tend to have more than one ground termination point on average, the use of ground strike point densities as opposed to flash densities is more appropriate. Lightning location systems (LLSs) do not directly provide ground strike point densities. However, ingesting their observations into an algorithm that groups strokes into respective ground strike points results in the sought-after density value. The aim of this study is to assess the ability of three distinct ground strike point algorithms to correctly determine the observed ground-truth strike points. The output of the algorithms is tested against a large set of ground-truth observations taken from different regions around the world, including Austria, Brazil, France, Spain, South Africa and the United States of America. These observations are linked to the observations made by a local LLS in order to retrieve the necessary parameters of each lightning discharge, which serve as input for the algorithms. Median values of the separation distance between the first stroke in the flash and subsequent ground strike points are found to vary between 1.3 and 2.75 km. It follows that all three of the algorithms perform well, with success rates of up to about 90 % to retrieve the correct type of the strokes in the flash, i.e., whether the stroke creates a new termination point or follows a pre-existing channel. The most important factor that influences the algorithms' performance is the accuracy by which the strokes are located by the LLS. Additionally, it is shown that the strokes' peak current plays an important role, whereby strokes with a larger absolute peak current have a higher probability of being correctly classified compared to the weaker strokes.

中文翻译：

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负向下闪电中的全球接地点特征——第 2 部分：算法验证

目前，闪电密度是评估特定感兴趣区域发生雷击风险的关键输入参数。由于已知闪电平均往往有一个以上的地面终止点，因此使用地面冲击点密度而不是闪电密度更合适。闪电定位系统 (LLS) 不直接提供地面打击点密度。然而，将他们的观察结果输入到算法中，该算法将笔画分组到各自的地面打击点中，从而得到广受欢迎的密度值。本研究的目的是评估三种不同的地面打击点算法正确确定观察到的地面实况打击点的能力。算法的输出针对来自世界各地不同地区的大量地面实况观察进行了测试，这些地区包括奥地利、巴西、法国、西班牙、南非和美利坚合众国。这些观察结果与本地 LLS 的观察结果相关联，以检索每次闪电放电的必要参数，这些参数用作算法的输入。发现闪光中的第一次撞击和随后的地面撞击点之间的间隔距离的中值在 1.3 和 2.75 公里之间变化。因此，所有三种算法都表现良好，在 flash 中检索正确类型的笔画的成功率高达约 90%，即，笔画是创建新的终止点还是遵循预先存在的通道。影响算法性能的最重要因素是 LLS 定位笔画的准确性。此外，还表明笔画的峰值电流起着重要作用，因此与较弱的笔画相比，具有较大绝对峰值电流的笔画被正确分类的概率更高。