A High-Fidelity Parametric Model for Tropical Cyclone Boundary Layer Wind Field by Considering Effects of Land Cover and Terrain
The proper simulation of the tropical cyclone (TC) boundary layer wind field is essential to predict the maximum wind speed, the imposed wind loading on civil structures, and the related potential disasters. A high-fidelity parametric model is developed to produce the wind field and to study the wind characteristics of TCs in the boundary layer. This model adopts a widely used boundary layer turbulence closure scheme and is built on a terrain-following coordinate system, which can consider various land cover and terrain effects. To improve the computational efficiency, a nested grid system is adopted. The developed model is implemented to reproduce the wind fields of Hurricane Isabel (2003), Hurricane Katrina (2005), Hurricane Irene (2011), Typhoon Hagupit (2008), Typhoon Hato (2017), and Typhoon Mangkhut (2018). Different types of observation datasets are used to validate the developed model, including the Hurricane Research Division's H*Wind snapshots, the National Data Buoy Center's wind records, and several meteorological sounding stations' observations. The spatial wind fields and the wind speed time series are presented. The influence of land cover and terrain on the behavior of TC is investigated. The simulation results demonstrate the high-fidelity and computational efficiency of the developed model, implying its potential applications for TC hazard modeling.
J. Yang et al., "A High-Fidelity Parametric Model for Tropical Cyclone Boundary Layer Wind Field by Considering Effects of Land Cover and Terrain," Atmospheric Research, Elsevier, Jan 2021.
The definitive version is available at https://doi.org/10.1016/j.atmosres.2021.105701
Civil, Architectural and Environmental Engineering
Center for High Performance Computing Research
Keywords and Phrases
Boundary layer; Hazard modeling; Land cover; Terrain; Tropical cyclone; Wind field
International Standard Serial Number (ISSN)
Article - Journal
© 2021 Elsevier, All rights reserved.
01 Jan 2021