Error Features in Predicting Typhoon Winds

A Case Study Comparing Simulated and Measured Data

Journal Article (2022)
Author(s)

Shaoyuan Peng (China Construction Science and Industry Co., Shenzhen)

Yichao Liu (TU Delft - Team Riccardo Ferrari)

Renge Li (China Construction Science and Industry Co., Shenzhen)

Ying Wei (China Construction Science and Industry Co., Shenzhen)

PW Chan (Hong Kong Observatory)

Sunwei Li (Tsinghua University)

Research Group
Team Riccardo Ferrari
Copyright
© 2022 Shaoyuan Peng, Y. Liu, Renge Li, Ying Wei, Pak Wai Chan, Sunwei Li
DOI related publication
https://doi.org/10.3390/atmos13020158
More Info
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Publication Year
2022
Language
English
Copyright
© 2022 Shaoyuan Peng, Y. Liu, Renge Li, Ying Wei, Pak Wai Chan, Sunwei Li
Research Group
Team Riccardo Ferrari
Issue number
2
Volume number
13
Reuse Rights

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Abstract

Simulating a typhoon’s wind field via mesoscale models is important in terms of providing not only the guidelines for urban planning and onshore/offshore constructions, but also the provision of insight into the dynamics and thermodynamics of tropical cyclone systems. Therefore, the errors that are contained in simulation results were investigated in the present study, in association with large-scale meteorological patterns and localized wind conditions in the typhoon boundary layer. In detail, the full-set three-dimensional simulations of three typhoon cases were carried out in order to provide the typhoon wind fields that were required to compare with the observations that were obtained through land weather stations and offshore buoys. Although the reliability of typhoon simulations has been thoroughly investigated, the previous works mostly concentrated on the configurations and dynamic core of the model. The present study reveals, however, the influences of the characteristics of the specific weather system on the simulation’s results, which provides the foundation for the proposition of empirical corrections to improve the mesoscale simulation results of typhoon wind fields without updating the model’s algorithm.