Extreme Typhoon Wind Speed Mapping for Coastal Region of China: Geographically Weighted Regression–Based Circular Subregion Algorithm
Publication: Journal of Structural Engineering
Volume 147, Issue 10
Abstract
The coastal region of China is exposed to severe typhoon threats and suffers significant casualties as well as economic losses due to seven to eight landed storms every year. This study developed a geographically weighted regression (GWR) circular subregion algorithm that considers the spatial variation characteristics of typhoon storms to map extreme-typhoon wind speeds. The continuously varying coefficient maps for typhoon-tracking, intensity, and wind field parameter recursive models are determined in the simulation domain using the best track data set from the Japan Meteorological Agency and geographically weighted regression. By introducing a genesis parameter model, each model module is validated independently or together by comparing the records in the best track data set in various circular subregions. The effects of the size of the circular subregion on the convergence of the typhoon wind hazard curve are investigated at two sites before an appropriate size is recommended. Extreme typhoon wind speeds of 10 coastal cities are modeled using the present model coupled with a well-developed analytical wind field model. Several factors that potentially contribute to the remarkable differences between this research and code suggestions as well as other studies are discussed. The estimated return period values of the annual maximum typhoon wind speed at 1,079 grid points in the coastal region of China are interpolated to achieve wind hazard contour maps. Several new findings regarding the spatial characteristics of extreme typhoon winds speed along the coastal region of China are examined.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
The authors gratefully acknowledge the support of the National Key Research and Development Program of China (2018YFC0809600, 2018YFC0809604), the National Natural Science Foundation of China (51678451, 51778495), the Shanghai Pujiang Program (20PJ1413600), and the China Scholarship Council, as well as the technical support of Palmetto Cluster at Clemson University.
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Published In
Journal of Structural Engineering
Volume 147 • Issue 10 • October 2021
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© 2021 American Society of Civil Engineers.
History
Received: May 21, 2020
Accepted: Apr 30, 2021
Published online: Jul 26, 2021
Published in print: Oct 1, 2021
Discussion open until: Dec 26, 2021
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