SuPerPoV: Score and evolution of the stratospheric polar vortex via persistent homology

TL;DR

SuPerPoV introduces a threshold-free, topological data analysis-based scoring system to classify and study the spatiotemporal evolution of the polar vortex, improving predictability and understanding of related extreme events.

Contribution

It presents a novel, scientifically sound classification method for the polar vortex using persistent homology, overcoming limitations of previous threshold-dependent approaches.

Findings

Recovers previous vortex definitions

Provides continuous evolution scores over days

Enhances understanding of vortex-related extreme events

Abstract

Classifying the stratospheric polar vortex provides predictability for surface weather on extended-range timescales. However, providing a scientifically sound classification is challenging: all the definitions proposed in over 60 years of study depend on empirically chosen parameters and yield different results when one of them changes. Moreover, as they are based on static thresholds, it is not straightforward to use them to study the spatiotemporal evolution of the vortex. Here, we introduce SuPerPoV, a score system that computes displacement and split ratios of the polar vortex using tools from topological data analysis, thus providing a sound classification of the polar vortex. The scores are computed by adapting superlevel set persistence and comparing prominent features. Our definition is entirely threshold-free and implemented open source. The scores generally recovers previous definitions and are output for a user-defined number of days, thus showing the evolution of the event. SuPerPoV offers a paradigm shift in the study of the polar vortex, hopefully bringing a deeper understanding of the polar vortex and related extreme events, such as sudden stratospheric warmings.