Misoscale Vortices in the Eyewall of the Tropical Cyclone Boundary Layer

Conventionally, tropical cyclone strength is classified by sustained wind speed; however, wind gusts in tropical cyclones are subject to tremendous variability and are likely to be responsible for much of the damage upon landfall. Wind gusts in tropical cyclones are difficult to measure, but chance observations of strong gusts associated with severe turbulence have been documented in the past. A research aircraft in Hurricane Hugo encountered several updraft–downdraft couplets associated with large negative pressure perturbations and increases in wind speed (Marks et al. 2008). Dropsondes have documented similar features in hurricanes Isabel and Felix (Aberson et al. 2006, 2017). A damage survey of Hurricane Andrew revealed small-scale swaths of F2–F3 equivalent damage within a zone of broader eyewall impacts (Wakimoto and Black 1992). More recently, Wurman and Kosiba (2018) utilized the Doppler on Wheels to detect misoscale vortices in the eyewall of Hurricane Harvey during landfall. Understanding these features has also been the topic of several recent modeling studies (Ito et al. 2017; Wu et al. 2018; Stern and Bryan 2018; Wu et al. 2019; Feng et al. 2021; Li and Pu 2023). These studies have largely focused on individual features and their vorticity, velocity, and wind speed characteristics.

In order to understand the spin-up and importance of these features, this study will investigate a sample of such features using a Cloud Model 1 (CM1) large eddy simulation (LES) (Stern and Bryan 2018). A census of misoscale coherent features in the boundary layer of a strong simulated tropical cyclone in an environment with no mean flow will be conducted. The horizontal grid spacing is 30 m, with a vertical grid spacing of 15 m, and the dataset spans the innermost 25 km of a tropical cyclone within the lowest 1 km. Through the 30-min simulation, there are different evolutions of these misoscale features. A preliminary analysis shows that some of these features are short lived and merge with other ones, while some do not and are sustained for longer periods. Analysis of several features of each type will be performed with the goal of documenting their vorticity and kinetic energy budgets, and to better understand their interaction with the mean vortex structure.