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1. Extreme hydroxyl amounts generated by thunderstorm-induced corona on grounded metal objects

   https://doi.org/10.1073/pnas.2201213119  

   Brune, William H.; Jenkins, Jena M.; Olson, Gabrielle A.; McFarland, Patrick J.; Miller, David O.; Mao, Jingqiu; Ren, Xinrong (September 2022, Proceedings of the National Academy of Sciences)

   Atmospheric electrical discharges are now known to generate unexpectedly large amounts of the atmosphere’s primary oxidant, hydroxyl (OH), in thunderstorm anvils, where electrical discharges are caused by atmospheric charge separation. The question is “Do other electrical discharges also generate large amounts of oxidants?” In this paper, we demonstrate that corona formed on grounded metal objects under thunderstorms produce extreme amounts of OH, hydroperoxyl (HO 2 ), and ozone (O 3 ). Hundreds of parts per trillion to parts per billion of OH and HO 2 were measured during seven thunderstorms that passed over the rooftop site during an air quality study in Houston, TX in summer 2006. A combination of analysis of these field results and laboratory experiments shows that these extreme oxidant amounts were generated by corona on the inlet of the OH-measuring instrument and that corona are easier to generate on lightning rods than on the inlet. In the laboratory, increasing the electric field increased OH, HO 2 , and O 3 , with 14 times more O 3 generated than OH and HO 2 , which were equal. Calculations show that corona on lightning rods can annually generate OH that is 10–100 times ambient amounts within centimeters of the lightning rod and on high-voltage electrical power lines can generate OH that is 500 times ambient a meter away from the corona. Contrary to current thinking, previously unrecognized corona-generated OH, not corona-generated UV radiation, mostly likely initiates premature degradation of high-voltage polymer insulators. 

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2. The Impact of Urban Particulate Matter on Lightning Frequency in Thunderstorms: A Case Study of the Bangkok Metropolitan Region

   https://doi.org/10.1007/s41748-024-00474-1  

   Sae-Jung, Jojinda; Bentley, Mace; Gerken, Tobias; Duan, Zhuojun (September 2024, Earth Systems and Environment)

   Abstract Bangkok, Thailand is a tropical Asian megacity with high aerosol concentrations and frequent thunderstorm activity. This investigation examines the covariation between thermodynamics, aerosols, and thunderstorms, using lightning stroke counts as a measure of intensity, for a five-year period (2016–2020). The investigation incorporates data from the aerosol robotic network (AERONET), ERA-5 reanalysis, ground-based air quality stations, and total lighting data from Vaisala Inc.’s GLD360 network to examine the aerosol-thermodynamic interrelationships within thunderstorm initiation environments. Results indicate that aerosol impacts on thunderstorms are robust and, when examined in concert with instability, can augment lightning. Thermodynamic instability is also positively correlated with stroke counts in thunderstorms. Particulate matter greater than 10 µg m^-3(PM10) concentration is significantly higher in thunderstorms containing more than 100 strokes, supporting the potential role of aerosols in promoting the non-inductive charge process. The emergence of a “boomerang” or threshold effect is also evident as aerosol optical depth (AOD) increases. Evidence suggests increasing AOD initially promotes, then limits, instability and thunderstorm intensity. Finally, there exists a positive relationship between aerosol concentration and particle size in thunderstorm initiation environments. 

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3. Developing an urban thunderstorm climatology for the Bangkok Metropolitan Region

   https://doi.org/10.1111/sjtg.12552  

   Sae‐Jung, Jojinda; Bentley, Mace L; Duan, Zhuojun; Szakal, Endre (September 2024, Singapore Journal of Tropical Geography)

   This investigation builds upon and extends prior lightning research in the Bangkok Metropolitan Region (BMR) through the reconstruction of thunderstorm distribution, utilizing a novel lightning tracking algorithm. Five years (2016–2020) of lightning stroke data from the Global Lightning Dataset (GLD360) were used to identify 52 608 thunderstorms. Optimized hotspot analyses, track densities, and analyses of thunderstorms with respect to winds, landcover, and seasons were performed. Our findings suggest that significant modification of thunderstorm distribution within the region was due to urban landcover impacts on the local environment. Thunderstorm intensity, as measured by stroke counts and track length, also appeared to be sensitive to the urban environment. The thunderstorm distribution also highlighted areas prone to hazards such as flash flooding. By visualizing thunderstorms grouped by winds, thunderstorm initiation hotspots and track density corridors were identified. These corridors of augmented thunderstorm production tended to occur during specific months given the seasonal monsoon wind regime occurring across the BMR. As urbanization within the BMR continues, geospatial assessment of thunderstorms is important to inform forecast meteorologists, urban planners, government officials, and others who play a critical role in developing strategies, policies and insfrastructure that could mitigate thunderstorm impacts. 

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4. Diagnosing Supercell Environments: A Machine Learning Approach

   https://doi.org/10.1175/WAF-D-21-0098.1  

   Shield, Stephen A.; Houston, Adam L. (May 2022, Weather and Forecasting)

   Abstract The importance of discriminating between environments supportive of supercell thunderstorms and those that are not supportive is widely recognized due to significant hazards associated with supercell storms. Previous research has led to forecast indices such as the energy helicity index and the supercell composite parameter to aid supercell forecasts. In this study three machine learning models are developed to identify environments supportive of supercells: a support vector machine, an artificial neural network, and an ensemble of gradient boosted trees. These models are trained and tested using a sample of over 1000 Rapid Update Cycle version 2 (RUC-2) model soundings from near-storm environments of both supercell and nonsupercell storms. Results show that all three machine learning models outperform classifications using either the energy helicity index or supercell composite parameter by a statistically significant margin. Using several model interpretability methods, it is concluded that generally speaking the relationships learned by the machine learning models are physically reasonable. These findings further illustrate the potential utility of machine learning–based forecast tools for severe storm forecasting. Significance Statement Supercell thunderstorms are a type of thunderstorm that are important to forecast because they produce more tornadoes, hail, and wind gusts compared to other types of thunderstorms. This study uses machine learning to create models that predict if a supercell thunderstorm or nonsupercell thunderstorm is favored for a given environment. These models outperform current methods of assessing if a storm that forms will be a supercell. Using these models as guidance forecasters can better understand and predict if atmospheric conditions are favorable for the development of supercell thunderstorms. Improving forecasts of supercell thunderstorms using machine learning methods like those used in this study has the potential to limit the economic and societal impacts of these storms. 

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5. The influence of climate and management on transpiration of residential trees during a bark beetle infestation

   https://doi.org/10.1002/ecs2.4881  

   Litvak, Elizaveta; Pataki, Diane E (May 2024, Ecosphere)

   Abstract Trees in residential environments are affected by a unique combination of environmental and anthropogenic factors, including occasional insect outbreaks that are increasing in frequency and severity due to climate change. We studied loblolly pine trees infested by bark beetles in a residential backyard in a southeastern US city. We investigated the responses of tree and stand‐level transpiration to environmental factors (solar radiation, atmospheric vapor pressure deficit, and soil moisture), severe weather events (strong winds and heavy storms), bark beetle infestation, and human actions (insecticide treatments and tree removals). We used constant heat dissipation probes to make continuous sap flux measurements (J₀) in tree boles. Over 22 months of the study,J₀of trees with confirmed infestation decreased from ~90 to ~60 g cm⁻² day⁻¹andJ₀of the rest of the trees increased from ~60 to ~80 g cm⁻² day⁻¹. One infested tree died, as itsJ₀steadily declined from 110 g cm⁻² day⁻¹to zero over the course of 2 months, followed by a loss of foliage and visible signs of severe infestation 6 months later.J₀was sensitive to variations in incoming solar radiation and atmospheric vapor pressure deficit. In most trees,J₀linearly responded to soil water content during drought periods. Yet despite complex dynamics ofJ₀variations, plot‐level transpiration at the end of the study was the same as at the beginning due to compensatory increases in tree transpiration rates. This study highlights the intrinsic interplay of environmental, biotic, and anthropogenic factors in residential environments where human actions may directly mediate ecosystem responses to climate. 

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