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Identifying lightning structures via machine learning

Author

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

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.

Suggested Citation

  • Wang, Lingxiao & Hare, Brian M. & Zhou, Kai & Stöcker, Horst & Scholten, Olaf, 2023. "Identifying lightning structures via machine learning," Chaos, Solitons & Fractals, Elsevier, vol. 170(C).
    • Handle: RePEc:eee:chsofr:v:170:y:2023:i:c:s0960077923002473
    DOI: 10.1016/j.chaos.2023.113346
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    References listed on IDEAS

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