Identifying lightning structures via machine learning

Lingxiao Wang, Brian M. Hare, Kai Zhou, Horst Stöcker and Olaf Scholten

Chaos, Solitons & Fractals, 2023, vol. 170, issue C

Abstract: Lightning is a fascinating yet insufficiently understood phenomenon. Very high frequency (VHF, 30–300 MHz) observations of lightning yield an ever-growing amount of data. In particular, LOFAR (LOw Frequency ARray) can reach meter and nanosecond precision with up to a million radio source locations per second. This lightning data is extremely complex, as a single lightning flash can contain hundreds of lightning channels and a myriad of different phenomena. However, so far this process has been mostly analyzed by-eye, which is very time-consuming. Thus, this increase in complexity of VHF lightning data calls for the application of machine learning algorithms. To identify structures from numerous spatio-temporal points in a high dimensional space, we designed an analysis pipeline combining a t-distributed stochastic neighbor embedding (t-SNE) algorithm and a clustering algorithm. We show that this combination allows for distinguishing correlated structures in an unsupervised approach. This novel method is a powerful tool to search vast multidimensional data sets for unique structures.

Keywords: Lightning; Machine learning; Correlation analysis (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:170:y:2023:i:c:s0960077923002473

DOI: 10.1016/j.chaos.2023.113346

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