Lightning Potential Index and its spatial and temporal characteristics in COSMO NWP model

Uhlířová, Iva Babuňková; Popová, Jana; Sokol, Zbyněk

doi:10.1016/j.atmosres.2022.106025

Cited by 3 publications

(8 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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%

“…As demonstrated by Uhlířová et al (2022), simulations with higher horizontal resolutions generally produce better results. To examine that, we compare the simulated LPI, LTI, and CAPE × P from three nested domains with different resolutions against the NoL flashes from the WWLLN data during lightning hours for one of the cases (Case 4).…”

Section: Impact Of Increasing Horizontal Resolutionmentioning

confidence: 86%

“…(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%

“…Based on cloud microphysics, Lynn and Yair (2008) and Yair et al (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 (2009) introduced the lightning threat index (LTI), which uses atmospheric variables such as relative humidity, temperature, lapse rate of equilibrium potential temperature, CAPE, LI, and precipitable water to diagnose areas that experience lightning.…”

mentioning

confidence: 99%

See 4 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

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: Discussionsupporting

confidence: 92%

Section: Discussionsupporting

confidence: 91%

Section: Impact Of Increasing Horizontal Resolutionmentioning

confidence: 86%

Section: Introductionmentioning

confidence: 99%

mentioning

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

View full text Add to dashboard Cite

show abstract

A Year-Long Total Lightning Forecast over Italy with a Dynamic Lightning Scheme and WRF

et al. 2022

View full text Add to dashboard Cite

Lightning is an important threat to life and properties and its forecast is important for several applications. In this paper, we show the performance of the “dynamic lightning scheme” for next-day total strokes forecast. The predictions were compared against strokes recorded by a ground observational network for a forecast period spanning one year. Specifically, a total of 162 case studies were selected between 1 March 2020 and 28 February 2021, characterized by at least 3000 observed strokes over Italy. The events span a broad range of lightning intensity from about 3000 to 600,000 strokes in one day: 69 cases occurred in summer, 46 in fall, 18 in winter, and 29 in spring. The meteorological driver was the Weather Research and Forecasting (WRF) model (version 4.1) and we focused on the next-day forecast. Strokes were simulated by adding three extra variables to WRF, namely, the potential energies for positive and negative cloud to ground flashes and intracloud strokes. Each potential energy is advected by WRF, it is built by the electrification processes occurring into the cloud, and it is dissipated by lightning. Observed strokes were remapped onto the WRF model grid with a 3 km horizontal resolution for comparison with the strokes forecast. Results are discussed for the whole year and for different seasons. Moreover, statistics are presented for the land and the sea. In general, the results of this study show that lightning forecast with the dynamic lightning scheme and WRF model was successful for Italy; nevertheless, a careful inspection of forecast performance is necessary for tuning the scheme. This tuning is dependent on the season. A numerical experiment changing the microphysics scheme used in WRF shows the sensitivity of the results according to the choice of the microphysics scheme.

show abstract

Forecasting large hail and lightning using additive logistic regression models and the ECMWF reforecasts

Battaglioli,

Groenemeijer,

Tsonevsky

et al. 2023

Nat. Hazards Earth Syst. Sci.

View full text Add to dashboard Cite

Abstract. Additive logistic regression models for lightning (ARlig) and large hail (ARhail) were developed using convective parameters from the ERA5 reanalysis, hail reports from the European Severe Weather Database (ESWD), and lightning observations from the Met Office Arrival Time Difference network (ATDnet). The models yield the probability of lightning and large hail in a given timeframe over a particular grid point. To explore the value of this approach to medium-range forecasting, the models were applied to the European Centre for Medium Range Weather Forecasts (ECMWF) reforecasts to reconstruct probabilistic lightning and large hail forecasts for 11 ensemble members, from 2008 to 2019 and for lead times up to 228 h. The lightning and large hail models were based on different predictor parameters: most unstable convective available potential energy (CAPE), 925–500 hPa bulk shear, mixed layer mixing ratio, wet bulb zero height (for large hail), most unstable lifted index, mean relative humidity between 850 and 500 hPa, 1 hourly accumulated convective precipitation and specific humidity at 925 hPa (for lightning). First, we compared the lightning and hail ensemble forecasts for different lead times with observed lightning and hail focusing on a recent hail outbreak. Second, we evaluated the predictive skill of the model as a function of forecast lead time using the area under the ROC curve (AUC) as a validation score. This analysis showed that ARhail has a very high predictive skill (AUC > 0.95) for a lead time up to 60 h. ARhail retains a high predictive skill even for extended forecasts (AUC = 0.86 at 180 h lead time). Although ARlig exhibits a lower predictive skill than ARhail, lightning forecasts are also skilful both in the short term (AUC = 0.92 at 60 h) and in the medium range (AUC = 0.82 at 180 h). Finally, we compared the performance of the 4-dimensional hail model with that of composite parameters such as the significant hail parameter (SHP) or the product of CAPE and the 925–500 hPa bulk shear (CAPESHEAR). Results show that ARhail outperforms CAPESHEAR at all lead times and SHP at short-to-medium lead times. These findings suggests that the combination of additive logistic regression models and ECMWF ensemble forecasts can create highly skilful medium-range hail and lightning forecasts for Europe.

show abstract

Lightning Potential Index and its spatial and temporal characteristics in COSMO NWP model

Cited by 3 publications

References 17 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

A Year-Long Total Lightning Forecast over Italy with a Dynamic Lightning Scheme and WRF

Forecasting large hail and lightning using additive logistic regression models and the ECMWF reforecasts

Contact Info

Product

Resources

About