Storm event representation and analysis based on a directed spatiotemporal graph model

Abstract

Large amounts of time series of spatial snapshot data have been collected or generated for the monitoring and modeling of environmental systems. Those time series of data also provide the opportunity to study the movements and dynamics of many different 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 used to analyze the dynamics of the events. The method is applied to time series of high-resolution radar reflectivity images during one of the deadliest storm outbreaks that impacted 15 states of southeastern United States between 23 and 29 April 2011. As shown in this case study, convective storm events identified using our methods are consistent with previous studies, and our analysis confirms 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. While this study does not differentiate between storm modes, the method shows potential for capturing a more detailed climatology of precipitation characteristics.