Why Flash Type Matters: A Statistical Analysis

Mecikalski, John R.; Bitzer, Phillip M.; Carey, Lawrence D.

doi:10.1002/2017gl075003

Cited by 9 publications

(11 citation statements)

References 41 publications

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“…Further, only flashes that initiated within 100 km from the center of NALMA were used in the analysis, while all VHF sources related to each flash were included, even if the VHF source was located outside of the 100‐km range, leading to a 200 × 200‐km domain for our analysis (refer to Figure S1 in the supporting information for a map of the area of interest). The flash sizes were obtained by using the square root of the convex hull (or polygon) area surrounding the VHF sources in the horizontal, as in Bruning and MacGorman (), Mecikalski and Carey (), Mecikalski et al (), and Mecikalski et al ().…”

Section: Data Storm Type Classification and Methodologymentioning

confidence: 99%

Radar Reflectivity and Altitude Distributions of Lightning Flashes as a Function of Three Main Storm Types

Mecikalski

Carey

2018

JGR Atmospheres

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In an effort to improve our knowledge on the horizontal and vertical distribution of lightning initiation and propagation, ~500 multicells (producing a total of 72,619 flashes), 27 mesoscale convective systems (producing 214,417 flashes) and 23 supercells (producing 169,861 flashes) that occurred over northern Alabama and southern Tennessee were analyzed using data from the North Alabama Lightning Mapping Array and the Multi‐Radar Multi‐Sensor suite. From this analysis, two‐dimensional (2‐D) histograms of where flashes initiated and propagated relative to radar reflectivity and altitude were created for each storm type. The peak of the distributions occurred between 8.0 and 10.0 km (−24.0 to −38.5 °C) and between 30 and 35 dBZ for flashes that initiated within multicellular storms. For flashes that initiated within mesoscale convective systems, these peaks were 8.0–9.0 km (−27.1 to −34.6 °C) and 30–35 dBZ, respectively, and for supercells, they were 10.0–12.0 km (−42.6 to −58.1 °C) and 35–40 dBZ, respectively. The 2‐D histograms for the flash propagations were slightly different than for the flash initiations and showed that flashes propagated in lower reflectivities as compared to where they initiated. The 2‐D histograms were also compared to test cases; the root‐mean‐square errors and the Pearson product moment correlation coefficient (R) were calculated with several of the comparisons having R values >0.7 while the root‐mean‐square errors were always ≤0.017 (≤10%), irrespective of storm type. Finally, the mean flash sizes for the multicell, mesoscale convective system, and supercell flashes were 8.3, 9.9, and 7.4 km, respectively.

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Section: Data Storm Type Classification and Methodologymentioning

confidence: 99%

Radar Reflectivity and Altitude Distributions of Lightning Flashes as a Function of Three Main Storm Types

Mecikalski

Carey

2018

JGR Atmospheres

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“…However, early assessment studies have shown that the GLMs have a higher detection efficiency for CG flashes than IC flashes (Bitzer, 2019; Yoshida, 2019). The so‐called hybrid flashes with extensive IC components as well as connections to ground complicate this interpretation and may sometimes be as common as CG flashes that exclusively have channels in the lower part of the cloud (Mecikalski et al, 2017; Mecikalski & Carey, 2018).…”

Section: Introductionmentioning

confidence: 99%

Time Evolution of Satellite‐Based Optical Properties in Lightning Flashes, and its Impact on GLM Flash Detection

Zhang

Cummins

2020

JGR Atmospheres

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The GOES‐16 Geostationary Lightning Mapper (GLM) detection efficiency (DE) is studied over a full year (2018/19) in central Florida using the Kennedy Space Center Lightning Mapping Array (KSC LMA). Mean daily flash DE was 73.8%, and detection was highest during nighttime hours. GLM reported 86.5% of the LMA flashes that had coincident cloud‐to‐ground return strokes reported by the U.S. National Lightning Detection Network. Results also reveal that flash size and duration are critical parameters influencing GLM detection, regardless of the storm type, with 20–40% detection for small and short‐duration flashes and greater than 95% detection for very large and long‐duration flashes. These findings can be explained by examining the time‐evolution of cloud‐top optical emissions observed by the Lightning Imaging Sensor (LIS). Statistical simulations based on long‐term LIS group area observations indicate that about half of the above‐threshold light sources are smaller than a LIS pixel (~ 4 × 4 km) and are smallest during and just after an initial breakdown in IC flashes. This work also demonstrates that for sources smaller than a GLM pixel, the cloud‐top energy detection threshold for GLM is double that for LIS despite GLM's lower energy density threshold. Overall, these findings provide a framework for interpreting GLM performance under varying meteorological conditions, and help explain reports of low flash detection efficiency for storms associated with severe weather, as they typically exhibit high flash rates and resulting small and short‐duration flashes.

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“…Recent work by Carey et al () suggests that total flash rate may be well correlated to storm intensity, while flash extent (FE) might not be at times. Several studies have investigated changes in FCL profiles, but sensitivity of final LNO x concentration to initial placement remains unclear (e.g., Fehr et al, ; Fuchs & Rutledge, ; Hansen et al, ; Labrador et al, ; Mecikalski et al, ; Ott et al, , ; Pickering et al, ). For example, Hansen et al () found the vertical structure of flashes to vary, with more intense thunderstorms having a bimodal FE distribution compared to the predominant unimodal distribution seen in less intense thunderstorms, suggesting a correlation in vertical charge structure to storm intensity.…”

Section: Introductionmentioning

confidence: 99%

Lightning Location, NOx Production, and Transport by Anomalous and Normal Polarity Thunderstorms

2019

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Production and transport of NO x by convection is critical as it serves as a precursor to tropospheric ozone, an important greenhouse gas. Lightning serves as the largest source of nitrogen oxides (NO x = NO + NO 2 ) to the upper troposphere (UT) and is one of the largest natural sources of NO x . Interest is placed on the vertical advection of NO x because its lifetime increases to several days in the UT compared to roughly 3 hr in the lower troposphere and boundary layer. Thus, lightning can play an important role in ozone production within the UT. However, the amount of NO x produced per flash and NO x advection in storms remain uncertain. This study investigates lightning NO x (LNO x ) production and transport processes in anomalous (midlevel positive charge) and normal polarity (midlevel negative charge) thunderstorms by advecting parcels containing LNO x from the flash channels of over 5,600 lightning flashes observed during the Deep Convective Clouds and Chemistry (DC3) field campaign. Results reveal that most flash channels occur near 6-8 km in the normal polarity thunderstorms and 5-6 km within anomalous polarity thunderstorms. Larger flash rates and stronger updrafts in anomalous storms result in considerably larger LNO x mixing ratios (peaks of 0.75-1.75 ppb) in the UT compared to normal polarity storms (peaks < 0.5 ppb). A slightly lower mean flash LNO x production was also found among all five storms in this study (storm mean values of 72-158 moles per flash) compared to previous estimates, which generally parameterize LNO x by flash rate rather than flash count.

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Why Flash Type Matters: A Statistical Analysis

Cited by 9 publications

References 41 publications

Radar Reflectivity and Altitude Distributions of Lightning Flashes as a Function of Three Main Storm Types

Radar Reflectivity and Altitude Distributions of Lightning Flashes as a Function of Three Main Storm Types

Time Evolution of Satellite‐Based Optical Properties in Lightning Flashes, and its Impact on GLM Flash Detection

Lightning Location, NOx Production, and Transport by Anomalous and Normal Polarity Thunderstorms

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