Environmental controls on storm intensity and charge structure in multiple regions of the continental United States

Abstract

A database consisting of approximately 4000 storm observations has been objectively analyzed to determine environmental characteristics that produce high radar reflectivities above the freezing level, large total lightning flash rates on the order of 10 flashes per minute, and anomalous vertical charge structures (most notably, dominant midlevel positive charge). The storm database is drawn from four regions of the United States featuring distinct environments, each with coinciding Lightning Mapping Array (LMA) network data. LMAs are able to infer total lightning flash rates using flash clustering algorithms, such as the one implemented in this study. Results show that anomalous charge structures inferred from LMA data, significant lightning flash rates, and increased radar reflectivities above the freezing level tend to be associated with environments that have high cloud base heights (approximately 3 km above ground level) and large atmospheric instability, quantified by normalized convective available potential energy (NCAPE) near 0.2 m s −2 . Additionally, we infer that aerosols may affect storm intensity. Maximum flash rates were observed in storms with attributed aerosol concentrations near 1000 cm −3 , while total flash rates decrease when aerosol concentrations exceed 1500 cm −3 , consistent with previous studies. However, this effect is more pronounced in regions where the NCAPE and cloud base height are low. The dearth of storms with estimated aerosol concentrations less than 700 cm −3 (approximately 1% of total sample) does not provide a complete depiction of aerosol invigoration.

The Intracloud Lightning Fraction in the Contiguous United States

“…When there is a large positive charge center at low altitude above ground level, this can also inhibit the production of negative CG flashes (Nag and Rakov 2009;Mansell et al 2010). Storms that exhibit inverted and/or complex charge structures frequently have high IC flash rates, discourage the production of negative CG flashes, and encourage the production of positive CG flashes within the convective region of the storm Rutledge 1998, 2003;Wiens et al 2005;Carey and Buffalo 2007;Qie et al 2009;Bruning et al 2014;Fuchs et al 2015). Sections 7.20 and 7.21 in MacGorman and Rust (1998) provide additional details.…”

“…This region also exhibits the largest percentage of positive polarity CG flashes. Additionally, storm severity is frequently associated with strong updrafts producing large mass flux through the mixed phase region, which is conducive to smaller flash sizes, higher IC flash rates, and sometimes associated with a reduction or elimination of CG flashes (MacGorman et al 1989;Williams et al 1999;Wiens et al 2005;MacGorman et al 2011;Makowski et al 2013;Bruning and MacGorman 2013;Fuchs et al 2015). These findings suggest that as a storm ''becomes severe,'' it becomes more likely to exhibit a high flash rate [e.g., ''lightning jump''-see Gatlin and Goodman (2010)] with most flashes being IC, also resulting in large temporal modulation of the relative occurrence of IC and CG flashes throughout the storm life cycle.…”

“…been raised about underestimation of satellite-derived flash density in northern Colorado where within-storm flash rates can be extremely high (Fuchs et al 2016), and this issue could also apply to the central and upper Great Plains. This factor would only serve to increase the already significant positive anomaly in the IC:CG ratio and IC fraction in that area.…”

Anvil microphysical signatures associated with lightning-produced NO<sub><i>x</i></sub>

“…By appropriately clustering VHF sources associated with a single flash and attributing detected flashes to individual storms, an estimated flash rate can be determined. In this study, LMA-detected VHF sources were sorted into flashes using an automated density-based clustering algorithm developed by E. Bruning and B. Fuchs (Bruning, 2013) and discussed in more detail by Basarab et al (2015) and Fuchs et al (2015). The algorithm was shown by both studies to produce flash rates in close agreement to a previouslydeveloped LMA flash counting algorithm.…”

“…Flash rates are displayed only for the cell of interest, which was tracked by an automated cell tracking algorithm, developed by Lang and Rutledge (2011) and Fuchs et al (2015). Because the sensitivities of the Colorado and Oklahoma networks are different, these contours should be interpreted only as a qualitative indicator of the regions of most intense electrical activity.…”