Observations of tropical cyclone inner-core fine-scale structure, and its link to intensity variations

Type Article
Date 2021-11
Language English
Author(s) Vinour Leo1, Jullien SwenORCID1, Mouche AlexisORCID1, Combot Clement3, Mangeas Morgan2
Affiliation(s) 1 : Ifremer, Univ. Brest, CNRS, IRD, Laboratoire d’Océanographie Physique et Spatiale (LOPS), IUEM, Plouzané, France.
2 : IRD / UMR ENTROPIE, BP A5, 98848 Nouméa cedex, New Caledonia
3 : Ifremer, Univ. Brest, CNRS, IRD, Laboratoire d’Océanographie Physique et Spatiale (LOPS), IUEM, Plouzané, France.
Source Journal Of The Atmospheric Sciences (0022-4928) (American Meteorological Society), 2021-11 , Vol. 78 , N. 11 , P. 3651-3671
DOI 10.1175/JAS-D-20-0245.1
WOS© Times Cited 2
Keyword(s) Tropical cyclones, Wind, Statistics, Radars/radar observations, Satellite observations, Classification

Tropical Cyclone (TC) internal dynamics have emerged over recent decades as a key to understand their intensity variations, but are difficult to observe, as they are sporadic, multi-scale, and occur in areas of very strong wind gradients. The present work aims at describing the internal structure of TCs, as observed with newly available satellite synthetic aperture radars (SARs) wind products, and at evaluating relations between this structure and the TC life cycle. It is based on a unique dataset of 188 SAR high-resolution (1 km) images, containing 15 to 47 by intensity category. An extraction method is designed to retrieve and characterize, the TC radial profile, its azimuthal degree of asymmetry, and the energy distribution in the eyewall and maximum wind areas. Vortex contraction and sharpening of the eyewall wind radial gradient with increasing TC intensity are observed, as well as a symmetrization of energy distribution around the vortex. Eyewall high wave number structures show a dependence on the life cycle phase, supporting previous findings discussing the vortex rapid evolution with onset and propagation of eyewall mesovortices and associated vortex Rossby wave generation. A machine learning approach finally highlights that the eye shape and eyewall radial wind gradient fine-scale dynamics have the potential to improve the statistical prediction of TC intensity variations, compared to the sole use of vortex averaged parameters and synoptic information. The high-resolution radial and azimuthal coverage provided by SARs make these acquisitions a very valuable tool for TC research and operational application.

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