A Process-Oriented Approach to Identify Evolutions of Sea Surface Temperature Anomalies with a Time-Series of a Raster Dataset

It is important to consider where, when, and how the evolution of sea surface temperature anomalies (SSTA) plays significant roles in regional or global climate changes. In comparison where there a great challenge clearly describing SSTA evolves space time. light from generation, through development, dissipation SSTA, this paper proposes novel approach identifying an time time-series raster dataset. This method, called PoAIES, includes three key steps. Firstly, cluster-based method enhanced explore spatiotemporal clusters each cluster at snapshot taken as object SSTA. Secondly, topologies objects successive snapshots are used link into which process object. Here, linking threshold automatically determined according overlapped areas objects, only those that meet specified linked together Thirdly, we use graph-based model represent A node represents edge between two objects. Using number child nodes edge’s parent node, type (an relationship) identified, shows its splitting, merging, splitting/merging. Finally, experiment on simulated dataset demonstrate effectiveness advantages real satellite-SSTA verify rationality PoAIES with help ENSO’s relevant knowledge, may provide new references for change research.