The Deep Convective Clouds and Chemistry (DC3) field experiment produced an exceptional dataset on thunderstorms, including their dynamical, physical, and electrical structures and their impact on the chemical composition of the troposphere. The field experiment gathered detailed information on the chemical composition of the inflow and outflow regions of midlatitude thunderstorms in northeast Colorado, west Texas to central Oklahoma, and northern Alabama. A unique aspect of the DC3 strategy was to locate and sample the convective outflow a day after active convection in order to measure the chemical transformations within the upper-tropospheric convective plume. These data are being analyzed to investigate transport and dynamics of the storms, scavenging of soluble trace gases and aerosols, production of nitrogen oxides by lightning, relationships between lightning flash rates and storm parameters, chemistry in the upper troposphere that is affected by the convection, and related source characterization of the three sampling regions. DC3 also documented biomass-burning plumes and the interactions of these plumes with deep convection.

One of the primary scientific objectives of the Maritime Continent Thunderstorm Experiment was to study cloud electrification processes in tropical island convection, in particular, the coupling between ice phase precipitation and lightning production. To accomplish this goal, a C-band polarimetric radar was deployed in the Tropics (11.6°S, 130.8°E) for the first time, accompanied by a suite of lightning measurements. Using observations of the propagation-corrected horizontal reflectivity and differential reflectivity, along with specific differential phase, rain and ice masses were estimated during the entire life cycle of an electrically active tropical convective complex (known locally as Hector) over the Tiwi Islands on 28 November 1995. Hector’s precipitation structure as inferred from these raw and derived radar fields was then compared in time and space to the measured surface electric field, cloud-to-ground (CG) and total lightning flash rates, and ground strike locations. During Hector’s... Abstract One of the primary scientific objectives of the Maritime Continent Thunderstorm Experiment was to study cloud electrification processes in tropical island convection, in particular, the coupling between ice phase precipitation and lightning production. To accomplish this goal, a C-band polarimetric radar was deployed in the Tropics (11.6°S, 130.8°E) for the first time, accompanied by a suite of lightning measurements. Using observations of the propagation-corrected horizontal reflectivity and differential reflectivity, along with specific differential phase, rain and ice masses were estimated during the entire life cycle of an electrically active tropical convective complex (known locally as Hector) over the Tiwi Islands on 28 November 1995. Hector’s precipitation structure as inferred from these raw and derived radar fields was then compared in time and space to the measured surface electric field, cloud-to-ground (CG) and total lightning flash rates, and ground strike locations. During Hector’s...

Many studies over the past several decades have attempted to correlate trends in lightning (e.g., rates, polarity) to severe weather occurrence. These studies mainly used cloud-to-ground (CG) lightning information due to the ease of data availability, high detection efficiency, and broad coverage across the United States, with somewhat inconclusive results. Conversely, it has been demonstrated that trends in total lightning are more robustly correlated to severe weather occurrence, with rapid increases in total lightning observed 10s of minutes prior to the onset of severe weather. Unfortunately, total lightning observations are not as numerous, or available over the same areal coverage domain, as provided by CG networks. Relatively few studies have examined concurrent trends in both total and CG lightning within the same severe thunderstorm, or even large sets of thunderstorms using an objective lightning jump algorithm. Multiple studies have shown that the total flash rate rapidly increases prior to the onset of severe weather. What is untested within the same framework is the use of CG information to perform the same task. Herein, total and CG lightning trends for 711 thunderstorms occurring in four regions of the country were examined to demonstrate the increased utility that total lightning provides over CG lightning, specifically within the framework of developing a useful lightning-based severe weather warning decision support tool. Results indicate that while both lightning datasets demonstrate the presence of increased lightning activity prior to the onset of severe weather, the use of total lightning trends was more effective than CG trends [probability of detection (POD), 79% versus 66%; false alarm rate (FAR), 36% versus 53%; critical success index (CSI), 55% versus 38%; Heidke skill score (HSS), 0.71 versus 0.55]. Moreover, 40% of false alarms associated with total lightning, and 16% of false alarms with CG lightning trends, occurred when a lightning jump associated with a severe weather “warning” was already in effect. If these false alarms are removed, the FAR drops from 36% to 22% for total lightning and from 53% to 44% for CG lightning. Importantly, average lead times prior to severe weather occurrence were higher using total lightning as compared with CG lightning (20.65 versus 13.54 min). The ultimate goal of this study was to demonstrate the increased utility of total lightning information that the Geostationary Lightning Mapper (GLM) will provide to operational meteorology in anticipation of severe convective weather on a hemispheric scale once Geostationary Operational Environmental Satellite-R (GOES-R) is deployed in the next decade.

Previous studies have demonstrated that rapid increases in total lightning activity (intracloud + cloud-to-ground) are often observed tens of minutes in advance of the occurrence of severe weather at the ground. These rapid increases in lightning activity have been termed “lightning jumps.” Herein, the authors document a positive correlation between lightning jumps and the manifestation of severe weather in thunderstorms occurring across the Tennessee Valley and Washington D.C. A total of 107 thunderstorms from the Tennessee Valley; Washington, D.C.; Dallas, Texas; and Houston, Texas, were examined in this study. Of the 107 thunderstorms, 69 thunderstorms fall into the category of nonsevere and 38 into the category of severe. From the dataset of 69 isolated nonsevere thunderstorms, an average, peak, 1-min flash rate of 10 flashes per minute was determined. A variety of severe thunderstorm types were examined for this study, including a mesoscale convective system, mesoscale convective vortex, tornadic outer rainbands of tropical remnants, supercells, and pulse severe thunderstorms. Of the 107 thunderstorms, 85 thunderstorms (47 nonsevere, 38 severe) were from the Tennessee Valley and Washington, D.C., and these 85 thunderstorms tested six lightning jump algorithm configurations (Gatlin, Gatlin 45, 2σ, 3σ, Threshold 10, and Threshold 8). Performance metrics for each algorithm were then calculated, yielding encouraging results from the limited sample of 85 thunderstorms. The 2σ lightning jump algorithm had a high probability of detection (POD; 87%), a modest false-alarm rate (FAR; 33%), and a solid Heidke skill score (0.75). These statistics exceed current NWS warning statistics with this dataset; however, this algorithm needs further testing because there is a large difference in sample sizes. A second and more simplistic lightning jump algorithm named the Threshold 8 lightning jump algorithm also shows promise, with a POD of 81% and a FAR of 41%. Average lead times to severe weather occurrence for these two algorithms were 23 min. The overall goal of this study is to advance the development of an operationally applicable jump algorithm that can be used with either total lightning observations made from the ground, or in the near future from space using the Geostationary Operational Environmental Satellite Series R (GOES-R) Geostationary Lightning Mapper.