Can A Lightning Location Network Play A Vital Role In Meteorology?  

Lightning has a direct and indirect effect on living bodies and human beings, and losses are very high in both cases (Srivastava et al., 2015). The importance of atmospheric electricity and lightning physics has been discussed by Qie et al. (2015) and suggested to more focused research on thunderstorm electricity. Development of Lightning location network (LLN) and mapping can help in all aspects in terms of lightning physics and meteorology.

Recently, many small, regional and long-range network have been developed depends on low frequency (VLF) / low frequency (LF).  However, the main goals of every network are different and most of them can only locate cloud to ground (CG) flashed although few can locate total lightning included CG and intra-cloud (IC). Beijing Lightning NETwork (BLNET) operated in Beijing-Tianjin-Hebei urban cluster area is one of them that specially designed to focus the lightning physics and thunderstorm that can locate total lightning.

Generally, all the LLN have to find the performance in term of effective detection efficiency (DE) and good location accuracy (LA). Researchers have done this using various methodologies and all have some limitations. Like, Idone et al. (1998a, 1998b) discussed the performance of national lightning detection network (NLDN) the in USA using ground truth. In a similar context, World Wide Lightning Location Network (WWLLN) performance have been obtain in relative terms.

Normally long-range network have limited ground truth and are verified from regional or local networks (Abarca et al., 2010; Abreu et al., 2010). Recently, a researcher had shown the relative performance of WWLLN that varies regionally and is approximately 12.4% (16.8%) in the context of a fast antenna (BLNET) and relative location accuracy has southeast compared with radar echo after finding the BLNET performance using self-reference method (Srivastava et al., 2017).

Wang et al. (2016) introduced the performance of BLNET as 250 meters from a ground truth that indicates a good LA and highly helpful for lightning forecasting and scientific studies. They also estimated LA using Monte Carlo simulation and suggested that the DE of BLNET is an important key question in future studies. So, not only the LA but DE is also an important factor for the performance. However, a small number of ground truth is not sufficient to say the proper performance and an alternate method need to be used.

Srivastava et al. (2017) discussed in detailed about the performance of BLNET,  and based on an updated algorithm and recent ground truth the LA was 52 meters. Using self-referencing method the total lightning DE of BLNET is around 93.2% which varies day to day thunderstorm and sometimes depends on instruments performance and local noise. The study also shows flashes which are far from the network may not be located from the LLN because a sufficient number of sensors can’t detected the signal. There are some possibilities as differences in the signal strength between IC and CG. IC signal can’t trigger some sensors in any network if they and sufficiently far from the network. High DE of a dense network with good LA is extremely used for the lightning location cell tracking and a severe weather warning.

These findings are described in the article entitled Performance assessment of Beijing Lightning Network (BLNET) and comparison with other lightning location networks across Beijing, recently published in the journal Atmospheric Research. This work was conducted by Abhay Srivastava, Ye TianXiushu QieDongfang WangZhuling SunShanfeng Yuan, Zhixiong Chen, Hongbo Zhang, and Rubin Jiang from the Chinese Academy of Sciences, Yu Wang from the State Grid Electric Power Research Institute, and Wenjing Xu and Debin Su from the China Meteorological Administration.

References:

  1. Abarca, S.F., Corbosiero, K.L., Galarneau, T.J., 2010. An evaluation of the Worldwide Lightning Location Network (WWLLN) using the National Lightning Detection Network (NLDN) as ground truth. J. Geophys. Res. Atmos. 115 (D18), 1–11. https://onlinelibrary.wiley.com/action/cookieAbsent.
  2. Abreu, D., Chandan, D., Holzworth, R.H., Strong, K., 2010. A performance assessment of the World Wide Lightning Location Network (WWLLN) via comparison with the Canadian Lightning Detection Network (CLDN). Atmos. Meas. Tech. 3, 1143–1153. https://www.atmos-meas-tech.net/3/1143/2010/.
  3.  Idone, V.P., Davis, D.A., Moore, P.K., Wang, Y., Henderson, R.W., Ries, M., Jamason, P.F., 1998a. Performance evaluation of the U.S. National Lightning Detection Network in eastern New York 1. Detection efficiency. J. Geophys. Res. Atmos. 103 (D8), 9045–9055. https://onlinelibrary.wiley.com/action/cookieAbsent.
  4. Idone, V.P., Davis, D.A., Moore, P.K., Wang, Y., Henderson, R.W., Ries, M., Jamason, P.F., 1998b. Performance evaluation of the U.S. National Lightning Detection Network in eastern New York 2. Location accuracy. J. Geophys. Res. Atmos. 103 (D8), 9057–9069. https://onlinelibrary.wiley.com/action/cookieAbsent.
  5. Srivastava, A., Mishra, M., Kumar, M., 2015. Lightning alarm system using stochastic modelling. Natural Hazards, 75(1), 1-11.
  6. Srivastava, A., et al. 2017: Performance assessment of Beijing Lightning Network (BLNET) and comparison with other lightning location networks across Beijing. Atmospheric Research, 197, 76-83.
  7. Qie, X., Zhang, Y., Yuan, T., Zhang, Q., Zhang, T., Zhu, B., Lu, W., Ma, M., Yang, J., Zhou, Y., Feng, G., 2015. A review of atmospheric electricity research in China. Adv. Atmos. Sci. 32 (2), 169–191. https://link.springer.com/article/10.1007%2Fs00376-014-0003-z.
  8. Wang, Y., X.Qie, D. Wang, , Liu, M., Su, D., Wang, Z., Liu, D., Wu, Z., Sun, Z., Tian, Y., 2016. Beijing Lightning Network (BLNET) and the observation on preliminary breakdown processes. Atmos. Res. 171, 121–132.

About The Author

Abhay Srivastava

Abhay Srivastava is from the Institute of Atmospheric Physics, Key Laboratory of Middle Atmosphere and Global Environment Observation (LAGEO).

Xiushu Qie

Xiushu Qie is professor at the Key Laboratory of Middle Atmosphere and Global Environment Observation (LAGEO). Her areas of expertise include electricity, meteorology, atmosphere, mineralogy, spectra, rainfall, radar, electromagnetic fields, antenna, and lightning.

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