Abstract This paper presents the ground truth datasets of negative cloud-to-ground (NCG) flashes with one single stroke in five isolated small thunderstorms. By developing a machine-learning method based on the convolutional neural network, we identify the return strokes during the whole life of thunderstorms with the wideband electric field waveform of lightning discharges detected by Jianghuai Area Sferic Array. The distribution of flash multiplicity in these thunderstorms exhibits an exponential decrease pattern. The single-stroke flashes (with multiplicity = 1) account for more than 30% of all NCG flashes on a thunderstorm basis, and the proportion of single-stroke flashes tended to be most abundant. When observed at a 20-min interval, the percentage of single-stroke flashes tends to change dramatically during thunderstorm developments, which seems to show an opposite trend with time to the maximum flash multiplicity. Single-stroke flashes tended to be associated with a weaker peak current of initial stroke compared to that of multiple-stroke flashes. It is inferred that the horizontal scale of negative charge regions in thunderclouds might play an important role in enhancing the flash multiplicity.

中文翻译：

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华东地区五次孤立小雷暴单冲闪观测

摘要 本文介绍了在五个孤立的小雷暴中一次击中的负云对地 (NCG) 闪光的地面实况数据集。通过开发基于卷积神经网络的机器学习方法，我们利用江淮地区超导阵列检测到的闪电放电宽带电场波形识别雷暴整个生命周期中的回击。这些雷暴中的闪电多样性分布呈指数下降模式。以雷暴为基础，单次闪光（多重性=1）占所有NCG闪光的30%以上，而且单次闪光的比例往往是最多的。当以 20 分钟的间隔观察时，在雷暴发展期间，单次闪电的百分比往往会发生显着变化，这似乎随​​着时间的推移显示出与最大闪光多重性相反的趋势。与多冲程闪电相比，单冲程闪电往往与较弱的初始冲程峰值电流相关。推测雷云中负电荷区域的水平尺度可能对增强闪光的多重性起重要作用。