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Explore a threshold-free method for classifying the stratospheric polar vortex into displaced, split, or normal states using persistent homology in this 24-minute conference talk. Learn how topological data analysis techniques can be applied to atmospheric science to better understand and categorize polar vortex behavior without relying on arbitrary threshold values. Discover the SuPerPoV (Score and evolution of the stratospheric polar vortex) approach that leverages persistent homology to provide a more robust and mathematically rigorous framework for polar vortex classification. Gain insights into how algebraic topology methods can advance meteorological research and improve our understanding of stratospheric dynamics through this innovative application of topological data analysis to atmospheric phenomena.
