Application of Kohonen self-organizing map for clustering negative cloud-to-ground lightning electric field waveforms



Lightning, lightning detection, LDWSS, Kohonen self-organizing map


Lightning electric field (E-field) waveforms are widely used in understanding the physical processes that take place during different lightning events and for improving engineering return-stroke models that help the design of better lightning protection systems. The Lightning Detection and Waveform Storage System (LDWSS) is a low-cost system that record relatively wideband E-fields of lightning. In this work, we use the Kohonen self-organizing map for clustering electric field waveforms of negative cloud-to-ground lightning (-CGs) recorded by the LDWSS. This approach allowed us to determine a standard/typical E-field waveform for a distinct range of distance. The data was split into two subsets, one for events recorded under daytime conditions and the other for events recorded at nighttime. The results showed that it is possible to identify twenty-five standard/typical E-field waveforms of -CGs, being 15 groups of waveforms that occurred during daytime and 10 groups that occurred at nighttime conditions


Download data is not yet available.


V. A. Rakov and M. A. Uman, “Lightning: Physics and Effects". NewYork: Cambridge Universty Press, 2003.

A. F. R. Leal, V. A. Rakov and B. R. P. da Rocha, “Upgrading the Low-Cost Lightning Detection and Waveform Storage System", IEEE Transactions on Electromagnetic Compatibility, vol. 61, no. 2, pp. 595- 598, Apr. 2019.

A. F. R. Leal, V. A. Rakov, J. Pissolato Filho, Rocha, B. R. P. da Rocha and M. D. Tran, “A Low-Cost System for Measuring Lightning Electric Field Waveforms, its Calibration and Application to Remote Measurements of Currents", IEEE Transactions on Electromagnetic Compatibility, vol. 60, no. 2, pp. 414–422. Abr. 2018.

R Thottappillil and V. A. Rakov, “On Different Approaches to Calculating Lightning Electric Fields", J. Geophys. Res., vol. 10, no. D13, pp. 14191–14205, July 2001.

H. Volland, “Longwave Sferics Propagation Within the Atmospheric Waveguide", in: Handbook of Atmospheric Electrodynamics, vol. 2, pp.65-93, CRC Press, 1995.

K. G. Budden, “The propagation of Radio Waves: The theory of radio waves of low power in the ionosphere and magnetosphere", UK: Cambridge University Press, 1988.

E. H. Lay and X.-M. Shao, “Multi-Station Probing of Thunderstorm-Generated D-layer Fluctuations by Using Time-Domain Lightning Waveforms", Geophysical Research Letters, vol. 38, no. 23, pp. 1-4, 2001.

A. F. R. Leal, V. A. Rakov and B. R. P. Rocha, “Estimation of Ionospheric Reflection Heights Using CG and IC Lightning Electric Field Waveforms", in in Proceedings of International Symposium on Lightning Protection, 2017, pp. 212-217.

Y. T. Lin, M. A. Uman, J. A. Tiller, R. D. Brantley, W. H. Beasley, E. P. Krider and C. D. Weidman, “Characterization of Lightning Return Stroke Electric and Magnetic Fields from Simultaneous Two-Station measurements", Journal of Geophysical Research, vol. 84, n. C10, pp. 6307-6314, 1979.

J. L. Bermudez, A. Piras, M. Rubinstein, “Artificial Neural Network in Lightning Location Systems", in in Proceedings of the International Symposium on Neuro-Fuzzy Systems, AT ’96, Conference Report, Lausanne, Switzerland, 1996, pp. 177-178.

A. Mostajabi, et al., “Single-Sensor Source Localization Using Electromagnetic Time Reversal and Deep Transfer Learning: Application to Lightning", Scientific reports, vol. 9, no. 1, pp. 1-4, 2019.

H. Karami, A. Mostajabi, M. Azadifar, M. Rubinstein, C. Zhuang and F. Rachidi, “Machine Learning-Based Lightning Localization Algorithm Using Lightning-Induced Voltages on Transmission Lines", IEEE Transactions on Electromagnetic Compatibility, pp. 1-8, Mar. 2020.

T. Kohonen, E. Oja, O. Simula, A. Visa and J. Kangas, “Engineering Applications of the Self-Organizing Map", Proceedings of the IEEE, vol. 84, no. 10, pp. 1358–1384, Oct. 1996.

T. Kohonen, “Self-organizing Maps". New York: Springer Berlin Heidelberg, 2001.

V.A. Rakov, S. Mallick, A. Nag and V.B. Somu, “Lightning Observatory in Gainesville (LOG), Florida: A review of recent results", Electric Power Systems Research, vol. 113, pp. 93-103, Aug. 2014.

S. Mallick et al., “Performance characteristics of the NLDN for return strokes and pulses superimposed on steady currents, based on rocket triggered lightning data acquired in Florida in 2004–2012", Journal of Geophysical Research, vol. 119, no. 7, pp. 3825–3856, 2014.

M. S. Aldenderfer and R. k. Blashfield, Cluster Analysis, Sage University paper series on Quantitative Applications Social Science in the Social Science, series nº07-044, Newbury Park, California: Sage Publications, 1984.

J. S. R. Jang, C. T. Sun and E. Mizutami, Neuro-Fuzzy and Softcomputing: a computacional approach to learning and machine intelligente. Prentice Hall, Inc., Simon & Schuster/A Viacom Company, Upper Saddle River, NJ 7458, 1997.

S. Haykin, Neural Network: A Comprehensive Foundation. NJ, USA: Prentice Hall PTRUpper Saddle River, 1994.

N. Kitagawa, M. Brook and E. J. Workman, “Continuing currents in cloud-to-ground lightning discharges", Journal of Geophysical Research, vol. 67, no. 2, pp. 637– 647, 1962.

N L. A. de Sá and R. A. Marshall, “Lightning Distance Estimation Using LF Lightning Radio Signals via Analytical and Machine-Learned Models", in Proceedings of the IEEE Transactions on Geoscience and Remote Sensing, vol. 58, no. 8, pp. 5892 - 5907, 2020.

A. F. R. Leal, V. A. Rakov, E. R. A. and M. N. G. Lopes, “Estimation of –CG lightning distances using single-station E-field measurements and machine learning techniques", in Proceedings of the IEEE International Symposium on Lightning Protection (XV SIPDA), São Paulo, Brazil, Feb, 2019, pp. 1-8.



How to Cite

Alves, E., & Leal, A. (2021). Application of Kohonen self-organizing map for clustering negative cloud-to-ground lightning electric field waveforms. IEEE Latin America Transactions, 19(11), 1959–1966. Retrieved from