Lightning forecasting in Bangladesh based on the lightning potential index and the electric potential

Rabbani, Khan Md. Golam; Islam, Md. Jafrul; Fierro, Alexandre O.; Mansell, Edward R.; Paul, Pappu

doi:10.1016/j.atmosres.2021.105973

Cited by 18 publications

(10 citation statements)

References 61 publications

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“…Consistent with the results of this study, the results of several other studies indicate that LPI is a good predictor of lightning on a regional scale (e.g., Brisson et al., 2021; Gharaylou et al., 2019; Rabbani et al., 2022; Uhlířová et al., 2022). For example, Gharaylou et al.…”

Section: Discussionsupporting

confidence: 92%

“…Consistent with the results of this study, the results of several other studies indicate that LPI is a good predictor of lightning on a regional scale (e.g., Brisson et al, 2021;Gharaylou et al, 2019;Rabbani et al, 2022;Uhlířová et al, 2022). LPI against CAPE × P in a regional climate model and found that LPI is more effective in replicating observed lightning climatology.…”

Section: Discussionsupporting

confidence: 91%

“…(2019) and Rabbani et al. (2022) found that LPI‐based predictions perform well compared to electric potential‐based predictions. Brisson et al.…”

Section: Discussionmentioning

confidence: 97%

“…(2010) introduced the lightning potential index (LPI) and revealed a strong positive correlation between this index and the observed heavy rainfall and lightning density. The performance of LPI in lightning prediction has also been evaluated in different regions across the globe (e.g., Brisson et al., 2021; Dementyeva et al., 2015; Fiori et al., 2016; Gharaylou et al., 2020; Lagasio et al., 2017; Rabbani et al., 2022; Uhlířová et al., 2022), and the results indicate that, overall, LPI performs well for lightning prediction. Frisbie et al.…”

Section: Introductionmentioning

confidence: 99%

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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

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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.

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Section: Discussionsupporting

confidence: 92%

Section: Discussionsupporting

confidence: 91%

“…(2019) and Rabbani et al. (2022) found that LPI‐based predictions perform well compared to electric potential‐based predictions. Brisson et al.…”

Section: Discussionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

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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

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“…Numerical models can simulate the lightning formation explicitly with parameterized microphysics processes 9,10 . However, current numerical models fail to balance high lightning detectability and low false alarm, thus has limited applicability to forecast lightning [11][12][13][14][15] . Moreover, the high computation cost of lightning simulation in the numerical models impedes the e ciency of lightning nowcast, where timeliness is of high priority in potential practices such as aviation and manufacture.…”

Section: Introductionmentioning

confidence: 99%

Lightning nowcasting with aerosol-informed machine learning and satellite-enriched dataset

Song

Xing

2023

Preprint

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Fast and accurate prediction of lightning occurrence is important for protecting human health and global environment. The machine learning (ML)-based model has advantages in computation efficiency; however it suffers low accuracy due to insufficient depiction of complex lightning mechanisms and poor training dataset. To address such limitations, here we propose a novel ML-based lightning nowcast model (LightGBM) by incorporating aerosol feature to better capture the lightning mechanisms, together with the aid of enriched satellite observations from Geostationary Lightning Mapper (GLM). The well-trained LightGBM model has successfully predicted spatially continuous (0.25° by 0.25°) and hourly level lightning occurrence over summertime Contiguous United Stated (CONUS) with improved performance (accuracy = 94.3%, POD = 75.0%, FAR = 38.1%, PRC-AUC = 0.727) from competitive baselines (POD < 60%, FAR > 40%). Besides enriched dataset, such improved performance is also associated with the inclusion of aerosol feature (with > 10% enhancement on correct rejection rate when demand for POD exceeds 75%) which is proven to be essential for predicting lightning but overlooked in previous studies. Furthermore, the model reveals the contribution of aerosol composition and loading to the formation of lightning, suggesting high loading of aerosols compositing of sulfate and organic compounds tend to enhance the lightning while black carbon inhibits it. These findings are consistent with the current scientific knowledge and demonstrate the great potentials to explore the complex mechanisms of lightning associated with aerosols.

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Sensitivity of PBL parameterization schemes in simulating lightning and thunderstorm using WRF-ELEC model

Wasson,

Panda

2024

Clim Dyn

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Lightning forecasting in Bangladesh based on the lightning potential index and the electric potential

Cited by 18 publications

References 61 publications

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

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

Lightning nowcasting with aerosol-informed machine learning and satellite-enriched dataset

Sensitivity of PBL parameterization schemes in simulating lightning and thunderstorm using WRF-ELEC model

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