🍩 Database of Original & Non-Theoretical Uses of Topology

(found 6 matches in 0.001491s)

  1. SuPerPoV: Score and Evolution of the Stratospheric Polar Vortex via Persistent Homology (2026)

    Jake Cordes, Barbara Giunti, Zheng Wu
    Abstract Classifying the stratospheric polar vortex provides predictability for surface weather on extended-range timescales definitions of these events proposed in over 60 years of study depend on empirically chosen parameters and yield different results when one of them changes. Moreover, as previous definitions are based on static thresholds, it is not straightforward to use them to study the spatiotemporal evolution of the vortexe introduce SuPerPoV, a score system that computes displacement and split ratiossing tools from applied topology. The computation is entirely threshold-free, open source, and does not require familiarity with applied topology. 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.

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  2. Topological Data Analysis and Machine Learning for Recognizing Atmospheric River Patterns in Large Climate Datasets (2019)

    Grzegorz Muszynski, Karthik Kashinath, Vitaliy Kurlin, Michael Wehner, Prabhat
    Abstract Identifying weather patterns that frequently lead to extreme weather events is a crucial first step in understanding how they may vary under different climate change scenarios. Here, we propose an automated method for recognizing atmospheric rivers (ARs) in climate data using topological data analysis and machine learning. The method provides useful information about topological features (shape characteristics) and statistics of ARs. We illustrate this method by applying it to outputs of version 5.1 of the Community Atmosphere Model version 5.1 (CAM5.1) and the reanalysis product of the second Modern-Era Retrospective Analysis for Research and Applications (MERRA-2). An advantage of the proposed method is that it is threshold-free – there is no need to determine any threshold criteria for the detection method – when the spatial resolution of the climate model changes. Hence, this method may be useful in evaluating model biases in calculating AR statistics. Further, the method can be applied to different climate scenarios without tuning since it does not rely on threshold conditions. We show that the method is suitable for rapidly analyzing large amounts of climate model and reanalysis output data.

  3. Using Persistent Homology to Quantify a Diurnal Cycle in Hurricanes (2020)

    Sarah Tymochko, Elizabeth Munch, Jason Dunion, Kristen Corbosiero, Ryan Torn
    Abstract The diurnal cycle of tropical cyclones (TCs) is a daily cycle in clouds that appears in satellite images and may have implications for TC structure and intensity. The diurnal pattern can be seen in infrared (IR) satellite imagery as cyclical pulses in the cloud field that propagate radially outward from the center of nearly all Atlantic-basin TCs. These diurnal pulses, a distinguishing characteristic of this diurnal cycle, begin forming in the storm’s inner core near sunset each day, appearing as a region of cooling cloud-top temperatures. The area of cooling takes on a ring-like appearance as cloud-top warming occurs on its inside edge and the cooling moves away from the storm overnight, reaching several hundred kilometers from the circulation center by the following afternoon. The state-of-the-art TC diurnal cycle measurement in IR satellite imagery has a limited ability to analyze the behavior beyond qualitative observations. We present a method for quantifying the TC diurnal cycle using one-dimensional persistent homology, a tool from Topological Data Analysis, by tracking maximum persistence and quantifying the cycle using the discrete Fourier transform. Using Geostationary Operational Environmental Satellite IR imagery from Hurricanes Felix and Ivan, our method is able to detect an approximate daily cycle.

  4. A Topological Perspective on Regimes in Dynamical Systems (2021)

    Kristian Strommen, Matthew Chantry, Joshua Dorrington, Nina Otter
    Abstract The existence and behaviour of so-called `regimes' has been extensively studied in dynamical systems ranging from simple toy models to the atmosphere itself, due to their potential of drastically simplifying complex and chaotic dynamics. Nevertheless, no agreed-upon and clear-cut definition of a `regime' or a `regime system' exists in the literature. We argue here for a definition which equates the existence of regimes in a system with the existence of non-trivial topological structure. We show, using persistent homology, a tool in topological data analysis, that this definition is both computationally tractable, practically informative, and accounts for a variety of different examples. We further show that alternative, more strict definitions based on clustering and/or temporal persistence criteria fail to account for one or more examples of dynamical systems typically thought of as having regimes. We finally discuss how our methodology can shed light on regime behaviour in the atmosphere, and discuss future prospects.