Characterizing Stratospheric Polar Vortex Variability With Computer Vision Techniques. Issue 3 (5th February 2018)

• Record Type: Journal Article
• Authors: Lawrence, Zachary D.; Manney, Gloria L.
• Is Part Of: Journal of geophysical research. Volume 123:Issue 3(2018)
• Page Start: 1510
• Page End: 1535
• Publication Date: 2018-02-05
• Subjects: computer vision -- stratospheric polar vortex -- sudden stratospheric warmings -- vortex splits -- analysis techniques; Atmospheric physics -- Periodicals; Geophysics -- Periodicals; 551.5
• DOI: 10.1002/2017JD027556
• Languages: English
• ISSNs: 2169-897X

Abstract

Abstract: Computer vision techniques are used to characterize the Arctic stratospheric polar vortex in 38 years of reanalysis data. Such techniques are typically applied to analyses of digital images, but they represent powerful tools that are more widely applicable: basic techniques and considerations for geophysical applications are outlined herein. Segmentation, descriptive, and tracking algorithms are combined in the Characterization and Analysis of Vortex Evolution using Algorithms for Region Tracking (CAVE‐ART) package, which was developed to comprehensively describe dynamical and geometrical evolution of polar vortices. CAVE‐ART can characterize and track multiple vortex regions through time, providing an extensive suite of region, moments, and edge diagnostics for each. CAVE‐ART is valuable for identifying vortex‐splitting events including, but not limited to, previously cataloged vortex‐split sudden stratospheric warmings. An algorithm for identifying such events detects 52 potential events between 1980 and 2017; of these, 38 are subjectively classified as distinct "split‐like" events. The algorithm based on CAVE‐ART is also compared with moment‐based methods previously used to detect split events. Furthermore, vortex edge‐averaged wind speeds from CAVE‐ART are used to define extreme weak and strong polar vortex events over multiple vertical levels; this allows characterization of their occurrence frequencies and extents in time and altitude. Weak and strong events show distinct signatures in CAVE‐ART diagnostics: in contrast to weak events, strong vortices are more cylindrical and pole centered, and less filamented, than the climatological state. These results from CAVE‐ART exemplify the value of computer vision techniques for analysis of geophysical phenomena. Plain Language Summary: A large‐scale cyclone called the stratospheric polar vortex forms in the middle atmosphere over the pole every fall in each hemisphere and lasts until spring. The Arctic and Antarctic vortices share many characteristics, including that they consist of strong eastward winds, and they extend from roughly 14 km above the surface to beyond 50 km. Understanding the behavior of these vortices is important because they affect stratospheric ozone depletion and influence weather and climate. In general, the Arctic stratospheric vortex exhibits anomalous behavior more often than its Antarctic counterpart; understanding such behavior can be aided by examining vortex geometry–their size, shape, location, etc. Here we use computer vision techniques similar to those used to analyze digital images to analyze the geometry of the Arctic polar vortex over years from 1980 to 2017. With these techniques, we find a large number of split‐like events in which the Arctic polar vortex breaks apart into two separate vortices. We also find important differences in vortex geometry when the Arctic vortex is strong versus when it is weak. Our results draw attention to details of stratospheric vortex variability that motivate further investigation of the physics that can help scientists better understand middle atmosphere weather and climate. Key Points: Methods and considerations for using computer vision techniques in geophysical applications are described Thirty‐eight split‐like vortex events are found by applying computer vision techniques to the Arctic stratospheric polar vortex A new vortex strength index based on vortex edge wind speeds is used to characterize strong and weak vortex features