Observations of single-stroke flashes from five isolated small thunderstorms in East China

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. •The multiplicity characteristics of negative CGs are examined in five isolated small thunderstorms.•Single-stroke percentage shows a clear relationship with the maximum flash multiplicity and thunderstorm stage.•Neural network method is applied to lightning waveform classification to speed up data processing.