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1. Machine Learning based investigation of the variables affecting summertime lightning frequency over the Southern Great Plains

   https://doi.org/10.5194/egusphere-2023-1020  

   Shan, Siyu; Allen, Dale; Li, Zhanqing; Pickering, Kenneth; Lapierre, Jeff (June 2023, EGU)

   Abstract. Lightning is affected by many factors, many of which are not routinely measured, well understood, or accounted for in physical models. Machine learning (ML) excels in exploring and revealing complex relationships between meteorological variables such as those measured at the South Great Plains (SGP) Atmospheric Radiation Measurement (ARM) site; a site that provides an unprecedented level of detail on atmospheric conditions and clouds. Several commonly used ML models have been applied to analyse the relationship between ARM data and lightning data from the Earth Networks Total Lightning Network (ENTLN) in order to identify important variables affecting lightning occurrence in the vicinity of the SGP site during the summers (June, July, August and September) of 2012 to 2020. Testing various ML models, we found that the Random Forest model is the best predictor among common classifiers. It predicted lightning occurrence with an accuracy of 76.9 % and an area under curve (AUC) of 0.850. Using this model, we further ranked the variables in terms of their effectiveness in predicting lightning and identified geometric cloud thickness, rain rate and convective available potential energy (CAPE) as the most effective predictors. The contrast in meteorological variables between no-lightning and frequent-lightning periods was examined on hours with CAPE values conducive to thunderstorm formation. Besides the variables considered for the ML models, surface variables such as equivalent potential temperature and mid-altitude variables such as minimum equivalent potential temperature have a large contrast between no-lightning and frequent-lightning hours. Finally, a notable positive relationship between intra-cloud (IC) flash fraction and the square root of CAPE was found suggesting that stronger updrafts increase the height of the electrification zone, resulting in fewer flashes reaching the surface and consequently a greater IC flash fraction. 

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2. Distinctive Signals in 1‐min Observations of Overshooting Tops and Lightning Activity in a Severe Supercell Thunderstorm

   https://doi.org/10.1029/2020JD032856  

   Borque, Paloma; Vidal, Luciano; Rugna, Martín; Lang, Timothy J.; Nicora, María Gabriela; Nesbitt, Stephen W. (October 2020, Journal of Geophysical Research: Atmospheres)

   Abstract This work examines a severe weather event that took place over central Argentina on 11 December 2018. The evolution of the storm from its initiation, rapid organization into a supercell, and eventual decay was analyzed with high‐temporal resolution observations. This work provides insight into the spatio‐temporal co‐evolution of storm kinematics (updraft area and lifespan), cloud‐top cooling rates, and lightning production that led to severe weather. The analyzed storm presented two convective periods with associated severe weather. An overall decrease in cloud‐top local minima IR brightness temperature (MinIR) and lightning jump (LJ) preceded both periods. LJs provided the highest lead time to the occurrence of severe weather, with the ground‐based lightning networks providing the maximum warning time of around 30 min. Lightning flash counts from the Geostationary Lightning Mapper (GLM) were underestimated when compared to detections from ground‐based lightning networks. Among the possible reasons for GLM's lower detection efficiency were an optically dense medium located above lightning sources and the occurrence of flashes smaller than GLM's footprint. The minimum MinIR provided the shorter warning time to severe weather occurrence. However, the secondary minima in MinIR that preceded the absolute minima improved this warning time by more than 10 min. Trends in MinIR for time scales shorter than 6 min revealed shorter cycles of fast cooling and warming, which provided information about the lifecycle of updrafts within the storm. The advantages of using observations with high‐temporal resolution to analyze the evolution and intensity of convective storms are discussed. 

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3. Severe Convective Storms in Limited Instability Organized by Pattern and Distribution

   https://doi.org/10.1175/WAF-D-23-0130.1  

   Campbell, Trevor A.; Lackmann, Gary M.; Molina, Maria J.; Parker, Matthew D. (January 2024, Weather and Forecasting)

   Abstract Severe convection occurring in high-shear, low-CAPE (HSLC) environments is a common cool-season threat in the southeastern United States. Previous studies of HSLC convection document the increased operational challenges that these environments present compared to their high-CAPE counterparts, corresponding to higher false-alarm ratios and lower probability of detection for severe watches and warnings. These environments can exhibit rapid destabilization in the hours prior to convection, sometimes associated with the release of potential instability. Here, we use self-organizing maps (SOMs) to objectively identify environmental patterns accompanying HSLC cool-season severe events and associate them with variations in severe weather frequency and distribution. Large-scale patterns exhibit modest variation within the HSLC subclass, featuring strong surface cyclones accompanied by vigorous upper-tropospheric troughs and northward-extending regions of instability, consistent with prior studies. In most patterns, severe weather occurs immediately ahead of a cold front. Other convective ingredients, such as lower-tropospheric vertical wind shear, near-surface equivalent potential temperature (θ_e) advection, and the release of potential instability, varied more significantly across patterns. No single variable used to train SOMs consistently demonstrated differences in the distribution of severe weather occurrence across patterns. Comparison of SOMs based on upper and lower quartiles of severe occurrence demonstrated that the release of potential instability was most consistently associated with higher-impact events in comparison to other convective ingredients. Overall, we find that previously developed HSLC composite parameters reasonably identify high-impact HSLC events. Significance StatementEven when atmospheric instability is not optimal for severe convective storms, in some situations they can still occur, presenting increased challenges to forecasters. These marginal environments may occur at night or during the cool season, when people are less attuned to severe weather threats. Here, we use a sorting algorithm to classify different weather patterns accompanying such storms, and we distinguish which specific patterns and weather system features are most strongly associated with severe storms. Our goals are to increase situational awareness for forecasters and to improve understanding of the processes leading to severe convection in marginal environments. 

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4. Toward untangling thunderstorm-aerosol relationships: An observational study of regions centered on Washington, DC and Kansas City, MO

   https://doi.org/10.1016/j.atmosres.2024.107402  

   Bentley, Mace; Gerken, Tobias; Duan, Zhuojun; Bonsal, Dudley; Way, Henry; Szakal, Endre; Pham, Mia; Donaldson, Hunter; Griffith, Lucie (July 2024, Atmospheric Research)

   A multi-variable investigation of thunderstorm environments in two distinct geographic regions is conducted to assess the aerosol and thermodynamic environments surrounding thunderstorm initiation. 12-years of cloud-to-ground (CG) lightning flash data are used to reconstruct thunderstorms occurring in a 225 km radius centered on the Washington, DC. and Kansas City Metropolitan Regions. A total of 196,836 and 310,209 thunderstorms were identified for Washington, D.C. and Kansas City, MO, respectively. Hourly meteorological and aerosol data were then merged with the thunderstorm event database. Evidence suggests, warm season thunderstorm environments in benign synoptic conditions are considerably different in thermodynamics, aerosol properties, and aerosol concentrations within the Washington, D.C. and Kansas City regions. However, thunderstorm intensity, as measured by flash counts, appears regulated by similar thermodynamic-aerosol relationships despite the differences in their ambient environments. When examining thunderstorm initiation environments, there exists statistically significant, positive relationships between convective available potential energy (CAPE) and flash counts. Aerosol concentration also appears to be a more important quantity than particle size for lightning augmentation. 

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5. Future Global Convective Environments in CMIP6 Models

   https://doi.org/10.1029/2021EF002277  

   Lepore, Chiara; Abernathey, Ryan; Henderson, Naomi; Allen, John T.; Tippett, Michael K. (December 2021, Earth's Future)

   Abstract The response of severe convective storms to a warming climate is poorly understood outside of a few well studied regions. Here, projections from seven global climate models from the CMIP6 archive, for both historical and future scenarios, are used to explore the global response in variables that describe favorability of conditions for the development of severe storms. The variables include convective available potential energy (CAPE), convection inhibition (CIN), 0–6 km vertical wind shear (S06), storm relative helicity (SRH), and covariate indices (i.e., severe weather proxies) that combine them. To better quantify uncertainty, understand variable sensitivity to increasing temperature, and present results independent from a specific scenario, we consider changes in convective variables as a function of global average temperature increase across each ensemble member. Increases to favorable convective environments show an overall frequency increases on the order of 5%–20% per °C of global temperature increase, but are not regionally uniform, with higher latitudes, particularly in the Northern Hemisphere, showing much larger relative changes. The driving mechanism of these changes is a strong increase in CAPE that is not offset by factors that either resist convection (CIN), or modify the likelihood of storm organization (S06, SRH). Severe weather proxies are not the same as severe weather events. Hence, their projected increases will not necessarily translate to severe weather occurrences, but they allow us to quantify how increases in global temperature will affect the occurrence of conditions favorable to severe weather. 

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