Modeling the Flash Rate of Thunderstorms. Part I: Framework

Dahl, Johannes M. L.; Höller, H.; Schumann, U.

doi:10.1175/mwr-d-10-05031.1

Cited by 24 publications

(9 citation statements)

References 44 publications

(68 reference statements)

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“…The reported LINET "strokes" are grouped into "flashes" before the comparison with simulated flashes. For this purpose all events recorded by LINET that occur within 1 s and in an area with a radius of 10 km are binned into a single flash (Dahl et al, 2011b). The sensitivity of the results for a different choice of the binning parameters (5 km and 0.5 s) is shown in Sect.…”

Section: Lightning Datamentioning

confidence: 99%

Simulating lightning into the RAMS model: implementation and preliminary results

Federico¹,

Avolio²,

Petracca³

et al. 2014

Preprint

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Abstract. This paper shows the results of a tailored version of a previously published methodology, designed to simulate lightning activity, implemented into the Regional Atmospheric Modeling System (RAMS). The method gives the flash density at the resolution of the RAMS grid-scale allowing for a detailed analysis of the evolution of simulated lightning activity. The system is applied in detail to two case studies occurred over the Lazio Region, in Central Italy. Simulations are compared with the lightning activity detected by the LINET network. The cases refer to two thunderstorms of different intensity. Results show that the model predicts reasonably well both cases and that the lightning activity is well reproduced especially for the most intense case. However, there are errors in timing and positioning of the convection, whose magnitude depends on the case study, which mirrors in timing and positioning errors of the lightning distribution. To assess objectively the performance of the methodology, standard scores are presented for four additional case studies. Scores show the ability of the methodology to simulate the daily lightning activity for different spatial scales and for two different minimum thresholds of flash number density. The performance decreases at finer spatial scales and for higher thresholds. The comparison of simulated and observed lighting activity is an immediate and powerful tool to assess the model ability to reproduce the intensity and the evolution of the convection. This shows the importance of the use of computationally efficient lightning schemes, such as the one described in this paper, in forecast models.

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Section: Lightning Datamentioning

confidence: 99%

Simulating lightning into the RAMS model: implementation and preliminary results

Federico¹,

Avolio²,

Petracca³

et al. 2014

Preprint

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“…Those parameterization functions have been applied to parameterize flash rates in models, where convection cannot be resolved. As it is discussed in detail in Dahl et al (2011), all these "single-parameter" approaches, which derive flash rates from only one other storm parameter, simply cannot be capable of reproducing the electrical development of an individual storm correctly. Since there is strong evidence that close correlations exist, they still seem to be feasible methods to obtain general guesses for flash rates.…”

Section: K Meyer Et Al: 3-d Lightning Parameters In Thunderstormmentioning

confidence: 99%

The temporal evolution of three-dimensional lightning parameters and their suitability for thunderstorm tracking and nowcasting

2013

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Abstract. Total lightning (TL) data have been found to provide valuable information about the internal dynamics of a thunderstorm allowing conclusions about its further development as well as indicating potential of thunderstorm-related severe weather at the ground. This paper investigates electrical discharge correlations of strokes and flashes with respect to the temporal evolution of thunderstorms in case studies as well as by statistical means. The recently developed algorithm li-TRAM (tracking and monitoring of lightning cells, Meyer et al., 2013) has been employed to track and monitor thunderstorms based on three-dimensionally resolved TL data provided as stroke events by the European lightning location network LINET. From statistical investigation of 863 suited thunderstorm life cycles, the cell area turned out to correlate well with (a) the total discharge rate, (b) the in-cloud (IC) discharge rate, and (c) the mean IC discharge height per lightning cell as identified by li-TRAM. All three parameter correlations consistently show an abrupt change in discharge characteristics around a cell area of 170 km2. Statistical investigations supported by the comparison of three case studies – selected to represent a single storm, a multi-cell and a supercell – strongly suggest that the correlation functions include the temporal evolution as well as the storm type. With the help of volumetric radar data, it can also be suggested that the well-defined break observed at 170 km2 marks the region where the transition occurs from short-lived and rather simple structured single storm cells to better organized, more persistent, and more complex structured thunderstorm forms, e.g. multi-cells and supercells. All three storm types experience similar discharge characteristics during their growing and dissipating phases. However, while the poorly organized and short-lived cells preferentially remain small during a short mature phase, mainly the more persistent thunderstorm types develop to sizes above 170 km2 during a pronounced mature stage. At that stage they exhibit on average higher discharge rates at higher altitudes as compared with matured single cells. With the maximum stroke distance set to 10 km and a flash duration set to 1 s, the parameterization functions found for the stroke rate as a function of the cell area have been transformed to a flash rate. The presented study suggests that, with respect to the storm type, stroke and flash correlations can be parameterized. There is also strong evidence that parameterization functions include the time parameter, so that altogether TL stroke information has good potential to pre-estimate the further evolution (nowcast) of a currently observed storm in an object-oriented thunderstorm nowcasting approach.

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“…There has been growing public concern in recent years in the forecasting and assessment of the intensity of convective storms around the world based on lightning (e.g., MacGorman and Burgess 1994;Lang and Rutledge 2002;Carey et al 2003). Previous researchers have developed several empirical relationships between lightning and convective parameters, such as cloud-top height (Price and Rind 1992;Boccippio et al 2002;Barthe et al 2010;Dahl et al 2011;Basarab et al 2015), upward ice mass flux (Allen and Pickering 2002;Deierling et al 2008;Finney et al 2014), convective precipitation rate (Meijer et al 2001), and updraft characteristics (Wiens 2005;Deierling and Petersen 2008). For example, Deierling and Petersen (2008) found that the total flash rate is highly related to the updraft volume with vertical velocity greater than 5 m s 21 as well as greater than 10 m s 21 .…”

Section: Introductionmentioning

confidence: 99%

What Are the Favorable Large-Scale Environments for the Highest-Flash-Rate Thunderstorms on Earth?

Liu

Chen

et al. 2020

Journal of the Atmospheric Sciences

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A 16-yr Tropical Rainfall Measuring Mission (TRMM) convective feature (CF) dataset and ERA-Interim data are used to understand the favorable thermodynamic and kinematic environments for high-flash-rate thunderstorms globally as well as regionally. We find that intense thunderstorms, defined as having more than 50 lightning flashes within a CF during the ~90-s TRMM overpassing time share a few common thermodynamic features over various regions. These include large convective available potential energy (>1000 J kg−1), small to moderate convection inhibition (CIN), and abundant moisture convergence associated with low-level warm advection. However, each region has its own specific features. Generally, thunderstorms with high lightning flash rates have greater CAPE and wind shear than those with low flash rates, but the differences are much smaller in tropical regions than in subtropical regions. The magnitude of the low- to midtropospheric wind shear is greater over the subtropical regions, including the south-central United States, Argentina, and southwest of the Himalayas, than tropical regions, including central Africa, Colombia, and northwest Mexico, with the exception of Sahel region. Relatively, favorable environments of high-flash-rate thunderstorms in the tropical regions are characterized by higher CAPE, lower CIN, and weaker wind shear compared to the high-flash-rate thunderstorms in the subtropical regions, which have a moderate CAPE and CIN, and stronger low to midtropospheric wind shear.

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Modeling the Flash Rate of Thunderstorms. Part I: Framework

Cited by 24 publications

References 44 publications

Simulating lightning into the RAMS model: implementation and preliminary results

Simulating lightning into the RAMS model: implementation and preliminary results

The temporal evolution of three-dimensional lightning parameters and their suitability for thunderstorm tracking and nowcasting

What Are the Favorable Large-Scale Environments for the Highest-Flash-Rate Thunderstorms on Earth?

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