Identification, Representation, and Analysis of Convective Storms

Abstract

Large amount of time series of spatial snapshot data have been collected or generated for the monitoring and modeling of environmental systems. Those data provide an opportunity to study the movement and dynamics of natural phenomena. While the snapshot organization is conceptually simple and straightforward, it does not directly capture or represent the dynamic characteristics of the phenomena. This study presents computational methods to identify dynamic events from time series of spatial snapshots. Events are represented as directed spatiotemporal graphs to characterize their initiation, development, movement, and cessation. Graph-based algorithms are then used to analyze the dynamics of the events. The method is demonstrated using the time series radar reflectivity images during one of the deadliest storm outbreaks that impacted 15 states of southeastern U.S. between April 23 and 29, 2011. As shown in this case study, convective storm events identified using our methods are consistent with previous studies and our analysis indicates that the left split/merger occurs more than right split/merger in those convective storm events, which confirms theory, numerical simulations, and other observed case studies. This study also examines the spatial and temporal characteristics of thunderstorm life cycles in central United States mainly covering Kansas, Oklahoma, and northern Texas during the warm seasons from 2010 to 2014. Radar reflectivity and cloud-to-ground lightning data were used to identify thunderstorms. The thunderstorms were stored in a GIS database with a number of additional thunderstorm attributes. The spatial and temporal characteristics of thunderstorm occurrence, duration, initiation time, termination time, movement speed, and direction were analyzed. Results revealed that thunderstorms were most frequent in the eastern part of the study area, especially at the borders among Kansas, Missouri, Oklahoma, and Arkansas. We also linked thunderstorm features to land cover types and compared thunderstorm characteristics between urban and surrounding rural areas. Our results indicated that thunderstorms favor forests and urban areas. This research demonstrates that advanced GIS representations and analyses for spatiotemporal events provide insights in thunderstorm climatology study.