Diagnostic Study of the Lightning Potential Index and Electric Field in Two Thunderstorm Cases over Bangladesh

Paul, Pappu; Imran, Ashik; Mallik, Muhammad Abul Kalam; Syed, Ishtiaque M.

doi:10.1134/s1024856022050177

Cited by 1 publication

(1 citation statement)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Paul et al. (2022) also showed that a low cloud top temperature is indicative of a high probability of the development of deep convective clouds and the occurrence of lightning. According to Table 1, Cases 1, 3, 4, and 7 had a high potential for deep convection based on their relatively low CIN values, while relatively low cloud top temperatures indicate a high potential for lightning in Cases 2, 4, 6, and 7.…”

Section: Case Studies and Methodologymentioning

confidence: 99%

Performance of Lightning Potential Index, Lightning Threat Index, and the Product of CAPE and Precipitation in the WRF Model

Saleh,

Gharaylou,

Farahani

et al. 2023

Earth and Space Science

View full text Add to dashboard Cite

Lightning is a naturally occurring phenomenon with significant socioeconomic and environmental impacts, underlining the need for the application of advanced methods to predict it. This study aims to evaluate the performance of the Weather Research and Forecasting (WRF) model in the prediction of the lightning potential index (LPI), the lightning threat index (LTI), and the product of convective available potential energy (CAPE) and precipitation (CAPE × P). Based on the intensity of convection and the number of lightning flashes, we simulated seven thundercloud events that occurred in Tehran between 2008 and 2013. The simulated LPI, LTI, and CAPE × P values are compared both qualitatively and quantitatively against ground‐based lighting data obtained from the world wide lightning location network (WWLLN). LPI, LTI, and CAPE × P predict the location of lightning with relatively good accuracy, although LPI outperforms LTI and CAPE × P. We also compared the simulated area‐averaged LPI, LTI, and CAPE × P against the number of lightning flashes from the WWLLN data, based on which LPI shows a better performance. Overall, LPI can be used as an effective index for the prediction of lightning. As LPI is based on cloud microphysics and LTI and CAPE × P are thermodynamic‐based methods, a better performance of LPI implies that a method based on the microphysical approach can better predict lightning. The performance of LPI, LTI, and CAPE × P is also sensitive to the horizontal resolution of model simulations, with a considerable improvement in simulations with higher horizontal resolutions.

show abstract

Section: Case Studies and Methodologymentioning

confidence: 99%