A graph-based spatiotemporal data framework for 4D natural phenomena representation and quantification-an example of dust events

Natural phenomena are intrinsically spatiotemporal and often highly dynamic. The increasing availability of simulation and observation datasets has provided us a great opportunity to better capture and understand the complexity and dynamics of natural phenomena. Challenges are posed by the formalization of the representation of such phenomena in terms of their non-rigid boundaries and the quantification of event dynamics over space and time. The objectives of this research are to (1) conceptually represent the natural phenomenon as an event, and (2) quantify the dynamic movements and evolutions of events using a graph-based approach. This proposed data framework is applied to a dust simulation dataset to represent the 4D dynamic dust events. Dust events are identified, and movements are tracked to reconstruct dust events in the Northern Africa region from December 2013 to November 2014. Quantified dynamics of different dust events are demonstrated and verified to be in alignment with observations.