Forecasting Lightning Threat Using Cloud-Resolving Model Simulations

McCaul, Eugene W.; Goodman, Steven J.; Lacasse, Katherine; Cecil, Daniel J.

doi:10.1175/2008waf2222152.1

Cited by 179 publications

(214 citation statements)

References 25 publications

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“…These three lightning threat indexes are the graupel flux on the −15 • C isothermal plane, the integration of ice particles in the vertical direction and the weight sum of the formal two indexes (the weights are 0.95 and 0.05), respectively [32]. The lightning threat indexes are respectively named LTI01, LTI02 and LTI03.…”

Section: Some Diagnosed Methods Of Lightning and Analysismentioning

confidence: 99%

“…At the same time, a radar data assimilation experiment was also conducted as a contrastive experiment. Moreover, the diagnosis basing on reflectivity and temperature and three kinds of lightning threat scores [32] in the National Severe Storms Laboratory (NSSL) are utilized to provide an equitable evaluation of the subsequent lightning forecast.…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of Lightning Forecasting Based on One Lightning Parameterization Scheme and Two Diagnostic Methods

2018

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Abstract:Lightning forecasting is a vital item in server convective system short-time forecasting. However, lightning parameterization in mesoscale numerical prediction models is still in its early stages of development. Several lightning parameterization schemes are implemented in the Weather Research and Forecasting (WRF) model. Data assimilation can provide a more accurate initial field, which could be useful for subsequent lightning forecasting. To evaluate its effect on lightning forecasting, a severe convective case that influenced Jiangsu and Anhui Province on 5 June 2009 is utilized and a series of experiments are conducted including assimilating radar reflectivity and lightning location network data via the three-dimensional variational (3DVar) method. Results show that data assimilation can effectively improve reflectivity forecasting and subsequent lightning forecasting. Lightning forecasting based on the PR92 lightning parameterization scheme, which is based on the convective cloud top height, offers a weaker magnitude forecast. The diagnostic method based on reflectivity and temperature has some spatial displacement. The potential forecast provided by lightning threat indexes produced an improvement in Anhui Province, while in other regions, it is located further east than the observation.

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Section: Some Diagnosed Methods Of Lightning and Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Evaluation of Lightning Forecasting Based on One Lightning Parameterization Scheme and Two Diagnostic Methods

2018

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“…The GLM proxy dataset accomplishes this by using flash statistics collected from the space-borne Lightning Imager Sensor (LIS) onboard the Tropical Rainfall Measuring Mission (TRMM; Kummerow et al 1998) and the NALMA (Bateman et al 2008). Like the GLM, the LIS records optical events that are grouped into flashes (Mach et al 2007), whereas the LMA detects VHF electromagnetic radiation sources that are combined into flashes using a separate clustering algorithm (McCaul et al 2009). An example of a visual comparison for a flash between the LIS and LMA is shown in Fig.…”

Section: Methodsmentioning

confidence: 99%

Automated storm tracking and the lightning jump algorithm using GOES-R Geostationary Lightning Mapper (GLM) proxy data

Schultz¹,

Schultz²,

Carey³

et al. 2016

J. Operational Meteor.

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This study develops a fully automated lightning jump system encompassing objective storm tracking, Geostationary Lightning Mapper proxy data, and the lightning jump algorithm (LJA)-which are important elements in the transition of the LJA concept from a research to an operational-based algorithm. Storm cluster tracking is based on a product created from the combination of a radar parameter (vertically integrated liquid) and lightning information (flash rate density). Evaluations show that the spatial scale of tracked features or storm clusters has a large impact on the lightning jump system performance, where increasing spatial scale results in a decreased dynamic range of the system's performance. This framework also serves as a means to refine the LJA itself to enhance its operational applicability. Parameters within the system are isolated and the system's performance is evaluated with adjustments to parameter sensitivity. The system's performance is evaluated using the probability of detection and false alarm ratio statistics. Of the algorithm parameters tested, the sigma-level (i.e., a metric of lightning jump strength) and flash rate threshold influence the system's performance the most. Finally, minor changes in verification methods can dramatically impact the evaluation of the lightning jump system.

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“…1) and these locations are termed VHF source points. VHF source points are then combined using spatial criteria of 0.5 radians and a time criteria of 0.3s to identify VHF source points associated with a single flash (McCaul et al 2009). All VHF source points that meet these criteria are grouped into a single flash and a minimum of 10 VHF source points must be associated with a lightning flash for this analysis.…”

Section: B the North Alabama Lightning Mapping Arraymentioning

confidence: 99%

Lightning Decision Support Using VHF Total Lightning Mapping and NLDN Cloud-to-Ground Data in North Alabama

Schultz¹,

Stano²,

Meyer³

et al. 2017

J. Operational Meteor.

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Forecasting Lightning Threat Using Cloud-Resolving Model Simulations

Cited by 179 publications

References 25 publications

Evaluation of Lightning Forecasting Based on One Lightning Parameterization Scheme and Two Diagnostic Methods

Evaluation of Lightning Forecasting Based on One Lightning Parameterization Scheme and Two Diagnostic Methods

Automated storm tracking and the lightning jump algorithm using GOES-R Geostationary Lightning Mapper (GLM) proxy data

Lightning Decision Support Using VHF Total Lightning Mapping and NLDN Cloud-to-Ground Data in North Alabama

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