Identification of atmospheric blocking with morphological type by topological flow data analysis

This study proposes an algorithm detecting atmospheric blocking by extracting topological features of geo-potential height data at 500 hPa. The algorithm uses topological flow data analysis (TFDA) providing a unique symbolic representation, named the partially cyclically ordered rooted tree (COT) representation, and a discrete graph structure, called a Reeb graph, to each structurally stable Hamiltonian vector field based on the mathematical theory of topological classifications for streamline patterns. It recognizes blocking events more simply and effectively using fewer meteorological parameters than conventional algorithms. Furthermore, the algorithm can determine morphological types of blocking events, an Omega shape or a dipole pattern, whereas no effective algorithm has been available so far. The identified blocking events and their morphological types are consistent with synopticians’ subjective judgments.