Determining real-time patterns of lightning strikes from sensor observations

Abstract

Transient spatiotemporal events occur within a short interval of time, in a particular location. If such events occur unexpectedly with varying durations, frequencies, and intensities, they pose a challenge for near-real-time monitoring. Lightning strikes are examples of such events and they can have severe negative consequences, such as fires, or they precede sudden flash storms, which can result in damage to infrastructure, loss of Internet connectivity, interruption of electrical power supply, and loss of life or property. Furthermore, they are unexpected, momentary in occurrence, sometimes with high frequency and then again with long intervals between them, their intensity varies considerably, and they are difficult to trace once they have occurred. Despite their unpredictable and irregular nature, timely analysis of lightning events is crucial for understanding their patterns and behaviour so that any adverse effects can be mitigated. However, near-real-time monitoring of unexpected and irregular transient events presents technical challenges for their analysis and visualisation. This paper demonstrates an approach for overcoming some of the challenges by clustering and visualising data streams with information about lightning events during thunderstorms, in real time. The contribution is twofold. Firstly, we detect clusters in dynamic spatiotemporal lightning events based on space, time, and attributes, using graph theory, that is adaptive and does not prescribe number and size of clusters beforehand, and allows for use of multiple clustering criteria and thresholds, and formation of different cluster shapes. Secondly, we demonstrate how the space time cube can be used to visualise unexpected and irregular transient events. Along with the visualisation, we identify the interactive elements required to counter challenges related to visualising unexpected and irregular transient events through space time cubes.