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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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    1. Long-Gang Pang & Kai Zhou & Nan Su & Hannah Petersen & Horst Stöcker & Xin-Nian Wang, 2018. "An equation-of-state-meter of quantum chromodynamics transition from deep learning," Nature Communications, Nature, vol. 9(1), pages 1-6, December.
    2. Lalmuanawma, Samuel & Hussain, Jamal & Chhakchhuak, Lalrinfela, 2020. "Applications of machine learning and artificial intelligence for Covid-19 (SARS-CoV-2) pandemic: A review," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    3. Pessa, Arthur A.B. & Zola, Rafael S. & Perc, Matjaž & Ribeiro, Haroldo V., 2022. "Determining liquid crystal properties with ordinal networks and machine learning," Chaos, Solitons & Fractals, Elsevier, vol. 154(C).
    4. B. M. Hare & O. Scholten & J. Dwyer & T. N. G. Trinh & S. Buitink & S. Veen & A. Bonardi & A. Corstanje & H. Falcke & J. R. Hörandel & T. Huege & P. Mitra & K. Mulrey & A. Nelles & J. P. Rachen & L. R, 2019. "Needle-like structures discovered on positively charged lightning branches," Nature, Nature, vol. 568(7752), pages 360-363, April.
    5. William Rison & Paul R. Krehbiel & Michael G. Stock & Harald E. Edens & Xuan-Min Shao & Ronald J. Thomas & Mark A. Stanley & Yang Zhang, 2016. "Observations of narrow bipolar events reveal how lightning is initiated in thunderstorms," Nature Communications, Nature, vol. 7(1), pages 1-12, April.
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