Lightning Over Central Canada: Skill Assessment for Various Land‐Atmosphere Model Configurations and Lightning Indices Over a Boreal Study Area

Mortelmans, Jonas; Bechtold, Michel; Brisson, Erwan; Lynn, Barry; Kumar, Sujay V.; Lannoy, Gabriëlle De

doi:10.1029/2022jd037236

Cited by 3 publications

(6 citation statements)

References 95 publications

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“…For all cases, LPI, LTI, and CAPE × P show high POD but a rather low SR, in agreement with the results of Mortelmans et al. (2023) for the skill assessment of LPI. The high bias of LPI, LTI, and CAPE × P can explain both the higher POD and the lower SR, which based on Equations and , it can be concluded that the model is simulating both more false alarms and more correctly predicted events.…”

Section: Resultssupporting

confidence: 91%

“…The high bias (>1) of LPI for Cases 1, 5, 6, and 7, LTI for Cases 1, 2, 5, 6, and 7, and CAPE × P for all cases indicate that more lightning flashes are predicted than the observation. For all cases, LPI, LTI, and CAPE × P show high POD but a rather low SR, in agreement with the results of Mortelmans et al (2023) for the skill assessment of LPI. The high bias of LPI, LTI, and CAPE × P can explain both the higher POD and the lower SR, which based on Equations 6, 8 and 9, it can be concluded that the model is simulating both more false alarms and more correctly predicted events.…”

Section: Predictive Performance Analysissupporting

confidence: 86%

“…The high bias of LPI, LTI, and CAPE × P can explain both the higher POD and the lower SR, which based on Equations and , it can be concluded that the model is simulating both more false alarms and more correctly predicted events. The higher POD means that more lightning is predicted, while the lower SR means that there are more false alarms (Mortelmans et al., 2023). Based on the evaluation of POD, SR, bias, and CSI, Figure 8d indicates that LPI has a superior predictive capability compared to LTI and CAPE × P.…”

Section: Resultsmentioning

confidence: 99%

“…However, this approach requires both the observed and simulated gridded lightning flash data. As suggested by Brisson et al (2021) and Mortelmans et al (2023), we rescaled LPI, LTI, and CAPE × P using a single linear model. This involves fitting a linear regression model to the data by selecting a dependent variable (the observed WWLLN data) and an independent variable (the LPI, LTI, or CAPE × P values).…”

Section: Correlation Analysismentioning

confidence: 99%

See 3 more Smart Citations

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: Resultssupporting

confidence: 91%

Section: Predictive Performance Analysissupporting

confidence: 86%

Section: Resultsmentioning

confidence: 99%

Section: Correlation Analysismentioning

confidence: 99%

See 2 more Smart Citations

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

show abstract

“…Furthermore, the lightning potential index (LPI) and the index of potential electrical energy index (EP) have been introduced into convection-permitting regional climate models, which integrate the dynamics and microphysics factors to predict lightning activity associated with thunderstorms [8,42,43]. However, Mortelmans et al [44] pointed out that there is no single best method to predict lightning, but a finer model resolution improves the accuracy of the models. Different numerical models on a global scale can predict a wide range and long periods of lightning activity and its change.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Lightning Activity of Squall Lines by Different Lightning Parameterization Schemes in the Weather Research and Forecasting Model

Liu,

Yu,

Sun

2023

Remote Sensing

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Based on three-dimensional lightning data and an S-band Doppler radar, a strong relationship was identified between lightning activity and the radar volume of squall lines. A detailed analysis of the squall line investigates the relationship following an exponential relationship. According to the correlation between lightning and the radar volume, three radar-volume-based lightning parameterization schemes, named the V30dBZ, V35dBZ, and V40dBZ lightning schemes, have been established and introduced into the weather research and forecasting (WRF) model. The performance of lightning precondition by different lightning parameterization schemes was evaluated, including the radar-volume-based schemes (V30dBZ, V35dBZ, and V40dBZ), as well as existing lightning schemes (PR92_1, PR92_2, and the Lightning Potential Index (LPI)). The evaluation shows that the simulated spatial lightning density and temporal lightning frequency by the radar-volume-based lightning schemes are more consistent with the observations. While the two PR_92 lightning schemes significantly underestimated the magnitude of lightning density. The radar-volume-based lightning parameterization schemes are proven to be more reliable in estimating lightning activity than other lightning schemes.

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A physics-based ensemble machine-learning approach to identifying a relationship between lightning indices and binary lightning hazard

Thomas,

Noble

2024

Front. Earth Sci.

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To convert lightning indices generated by numerical weather prediction experiments into binary lightning hazard, a machine-learning tool was developed. This tool, consisting of parallel multilayer perceptron classifiers, was trained on an ensemble of planetary boundary layer schemes and microphysics parameterizations that generated four different lightning indices over 1 week. In a subsequent week, the multi-physics ensemble was applied and the machine-learning tool was used to evaluate the accuracy. Unintuitively, the machine-learning tool performed better on the testing dataset than the training dataset. Much of the error may be attributed to mischaracterizing the convection. The combination of the machine learning model and simulations could not differentiate between cloud-to-cloud lightning and cloud-to-ground lightning, despite being trained on cloud-to-ground lightning. It was found that the simulation most representative of the local operational model was the most accurate simulation tested.

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Lightning Over Central Canada: Skill Assessment for Various Land‐Atmosphere Model Configurations and Lightning Indices Over a Boreal Study Area

Cited by 3 publications

References 95 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

Estimation of Lightning Activity of Squall Lines by Different Lightning Parameterization Schemes in the Weather Research and Forecasting Model

A physics-based ensemble machine-learning approach to identifying a relationship between lightning indices and binary lightning hazard

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