Documenting the Progressions of Secondary Eyewall Formations

Author:

Cheung Alex Alvin1ORCID,Slocum Christopher J.2,Knaff John A.2,Razin Muhammad Naufal3

Affiliation:

1. a Department of Atmospheric and Oceanic Science, University of Maryland, College Park, College Park, Maryland

2. b NOAA/Center for Satellite Research and Applications, Fort Collins, Colorado

3. c Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, Colorado

Abstract

Abstract Intense tropical cyclones can form secondary eyewalls (SEs) that contract toward the storm center and eventually replace the inner eyewall, a process known as an eyewall replacement cycle (ERC). However, SE formation does not guarantee an eventual ERC, and often, SEs follow differing evolutionary pathways. This study documents SE evolution and progressions observed in numerous tropical cyclones, and results in two new datasets using passive microwave imagery: a global subjectively labeled dataset of SEs and eyes and their uncertainties from 72 storms between 2016 and 2019, and a dataset of 87 SE progressions that highlights the broad convective organization preceding and following an SE formation. The results show that two primary SE pathways exist: “No Replacement,” known as “Path 1,” and “Replacement,” known as the “Classic Path.” Most interestingly, 53% of the most certain SE formations result in an eyewall replacement. The Classic Path is associated with stronger column average meridional wind, a faster poleward component of storm motion, more intense storms, weaker vertical wind shear, greater relative humidity, a larger storm wind field, and stronger cold-air advection. This study highlights that a greater number of potential SE pathways exist than previously thought. The results of this study detail several observational features of SE evolution that raise questions about the physical processes that drive SE formations. Most important, environmental conditions and storm metrics identified here provide guidance for predictors in artificial intelligence applications for future tropical cyclone SE detection algorithms.

Funder

National Oceanic and Atmospheric Administration

National Environmental Satellite, Data, and Information Service

Office of Naval Research

Publisher

American Meteorological Society

Subject

Atmospheric Science

Reference40 articles.

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