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Wind and Structures Volume 31, Number 3, September 2020, pages 269-285 DOI: http://dx.doi.org/10.12989/was.2020.31.3.269 |
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Comparative analysis of the wind characteristics of three landfall typhoons based on stationary and nonstationary wind models |
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Yong Quan, Guo Qiang Fu, Zi Feng Huang and Ming Gu
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| Abstract | ||
| The statistical characteristics of typhoon wind speed records tend to have a considerable time-varying trend; thus, the stationary wind model may not be appropriate to estimate the wind characteristics of typhoon events. Several nonstationary wind speed models have been proposed by pioneers to characterize wind characteristics more accurately, but comparative studies on the applicability of the different wind models are still lacking. In this study, three landfall typhoons, Ampil, Jongdari, and Rumbia, recorded by ultrasonic anemometers atop the Shanghai World Financial Center (SWFC), are used for the comparative analysis of stationary and nonstationary wind characteristics. The time-varying mean is extracted with the discrete wavelet transform (DWT) method, and the time-varying standard deviation is calculated by the autoregressive moving average generalized autoregressive conditional heteroscedasticity (ARMA-GARCH) model. After extracting the time-varying trend, the longitudinal wind characteristics, e.g., the probability distribution, power spectral density (PSD), turbulence integral scale, turbulence intensity, gust factor, and peak factor, are comparatively analyzed based on the stationary wind speed model, time-varying mean wind speed model and time-varying standard deviation wind speed model. The comparative analysis of the different wind models emphasizes the significance of the nonstationary considerations in typhoon events. The time-varying standard deviation model can better identify the similarities among the different typhoons and appropriately describe the nonstationary wind characteristics of the typhoons. | ||
| Key Words | ||
| Field measurement; Landfall typhoons; Nonstationary wind characteristics; Time-varying mean; Time-varying standard deviation | ||
| Address | ||
| State Key Laboratory of Disaster Reduction in Civil Engineering, Tongji University, Shanghai 200092, China | ||