Technical Papers
Oct 23, 2018

Characterizing Nonstationary Wind Speed Using the ARMA-GARCH Model

Publication: Journal of Structural Engineering
Volume 145, Issue 1

Abstract

This paper aims to accurately calculate the time-varying standard deviation of nonstationary wind speed by modeling wind speed as a time-varying mean wind speed plus a uniformly modulated nonstationary process and applying the autoregressive moving average–generalized autoregressive conditional heteroscedasticity (ARMA-GARCH) model to analyze the time-varying standard deviation of the wind speed. This paper also proposes a convenient and practical first-order difference GARCH method for calculating the time-varying standard deviation of uniformly modulated nonstationary processes that can decrease the computation time. The applicability of the ARMA–GARCH model, first-order difference GARCH method, and existing common methods for calculating the time-varying standard deviation of nonstationary wind speed are verified and analyzed using numerical simulations. The results show that the ARMA-GARCH model and first-order difference GARCH method are superior to the existing common methods. Finally, with the combination of the ARMA-GARCH model and first-order difference GARCH method, the nonstationary wind characteristics (considering the nonstationarities of mean wind speed and standard deviation) of Typhoon Chan-hom are investigated and compared with the results only taking into account time-varying mean wind speed.

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Acknowledgments

The authors gratefully acknowledge the support from the National Natural Science Foundation of China (90715040, 91215302) and State Key Laboratory of Disaster Reduction in Civil Engineering (SLDRCE15-A-04).

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Information & Authors

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Published In

Go to Journal of Structural Engineering
Journal of Structural Engineering
Volume 145Issue 1January 2019

History

Received: Aug 28, 2017
Accepted: Jun 5, 2018
Published online: Oct 23, 2018
Published in print: Jan 1, 2019
Discussion open until: Mar 23, 2019

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Authors

Affiliations

Zifeng Huang [email protected]
Ph.D. Candidate, State Key Laboratory of Disaster Reduction in Civil Engineering, Tongji Univ., Shanghai 200092, China. Email: [email protected]
Professor, State Key Laboratory of Disaster Reduction in Civil Engineering, Tongji Univ., Shanghai 200092, China (corresponding author). Email: [email protected]

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