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An Evaluation of Hurricane Superintensity in Axisymmetric Numerical Models

Author(s)
Rousseau-Rizzi, Raphael; Emanuel, Kerry Andrew
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Abstract
Potential intensity (PI) is an analytical bound on steady, inviscid, axisymmetric hurricane wind speed. Studies have shown that simulated hurricane azimuthal wind speed can greatly exceed a PI bound on the maximum gradient wind. This disparity is called superintensity (SI) and has been attributed to the contribution of the unbalanced flow to the azimuthal wind. The goals of this study are 1) to introduce a new surface wind PI (PIs), based on a differential Carnot cycle and bounding the magnitude of the surface winds; 2) to evaluate SI in numerical simulations with respect to diagnostic PI bounds on gradient wind (PIg), azimuthal wind (PIa), and surface wind (PIs); and 3) to evaluate the validity of each PI bound based on the SI computations. Here, we define superintensity as the normalized amount by which each version of PI is exceeded by the quantity it bounds. Axisymmetric tropical cyclone simulations are performed while varying the parameterized turbulent mixing as a way of estimating SI in the inviscid limit. As the mixing length decreases, all three bounded wind speeds increase similarly from a sub-PI state to a marginally superintense state. This shows that all three forms of PI evaluated here are good approximations to their respective metrics in numerical simulations. Keywords: Frontogenesis/frontolysis; Tropical cyclones; Mesoscale models
Date issued
2019-06
URI
https://hdl.handle.net/1721.1/124717
Department
Massachusetts Institute of Technology. Department of Earth, Atmospheric, and Planetary Sciences
Journal
Journal of the Atmospheric Sciences
Publisher
American Meteorological Society
Citation
Zawadowicz, Maria A., et al. “Model-Measurement Consistency and Limits of Bioaerosol Abundance over the Continental United States.” Atmospheric Chemistry and Physics 19, 22 (November 2019): 1697–708. © 2019 American Meteorological Society
Version: Final published version
ISSN
1680-7324
1680-7316

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