ALBERT

Description: Air pollution has the potential to modify the lifespan and intensity of thunderstorms and properties of lightning. Using data mining and geovisualization, we investigate how weather conditions and urban air pollution impacts thunderstorms as measured by the intensity, distribution and frequency of cloud-to-ground lightning. A spatiotemporal flash tracking algorithm was developed in order to identify thunderstorms using high-resolution lightning detection network data. Over seven million flashes were used to identify more than 196,000 thunderstorm events that occurred between 2006 - 2020 in the Washington, DC Metropolitan Region. After constructing the thunderstorm event database, hourly wind direction, wind speed, and atmospheric thermodynamic data were joined to the initiation and dissipation times for the identified thunderstorms. Hourly aerosol and air quality data for the closest thunderstorm initiation times and locations were also incorporated into the dataset. The database was then analyzed for commonalities among the thunderstorm initiation environments. Additionally, visualizations were produced that illustrate the initiation, development, and movement of thunderstorms under differing thermodynamic and aerosol environments. Constructing thunderstorm climatologies employing a lightning flash tracking algorithm and observational data were found to be useful for visualizing the spatial and temporal distribution of urban augmented thunderstorms in the region. Integrating wind, thermodynamic, and pollution data allowed for further investigation of the ambient environments present during the initiation of these events.