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Stochastic model of tropical cyclones along China coast including the effects of spatial heterogeneity and ocean feedback

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  • Shen, Zhonghui
  • Wei, Kai

Abstract

Historical tropical cyclones (TCs) are usually insufficient in determining extreme hazards due to limited data. This study developed a full-track stochastic model to generate synthetic TCs based on historical TCs and climatology data in Northwest Pacific. A variable geographical sampling approach was proposed to capture the spatial heterogeneity as a function of the density of historical TC data. The track model is derived using the Cartesian coordinate system and considers the statistical characteristics of TC lysis to simulate the TCs better regardless of their heading direction. The effect of ocean feedback was included in the TC intensity model by the one-dimensional ocean model. The track and intensity models were then validated with the statistical characteristics of historical TCs. The TC wind hazards along the China coast were assessed. The main findings include: (1) the developed TC track and intensity model agree well with the historical data; (2) the accuracy of stochastic modeling of TCs is enhanced by including the spatial heterogeneity; (3) considering ocean feedback by one-dimensional ocean model arrives in better modeling of TC intensity. The study provides an efficient tool to generate synthetic TCs for reliability and safety assessment of coastal infrastructures under typhoon-induced multiple hazards.

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  • Shen, Zhonghui & Wei, Kai, 2021. "Stochastic model of tropical cyclones along China coast including the effects of spatial heterogeneity and ocean feedback," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    • Handle: RePEc:eee:reensy:v:216:y:2021:i:c:s095183202100510x
    DOI: 10.1016/j.ress.2021.108000
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    1. Hong, Xu & Wan, Zhiqiang & Chen, Jianbing, 2023. "Parallel assessment of the tropical cyclone wind hazard at multiple locations using the probability density evolution method integrated with the change of probability measure," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    2. Hu, Xiaonong & Fang, Genshen & Yang, Jiayu & Zhao, Lin & Ge, Yaojun, 2023. "Simplified models for uncertainty quantification of extreme events using Monte Carlo technique," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    3. Rui Tang & Yongyi Wang & Weili Zhang & Yuyong Jiao, 2022. "Load-Bearing Performance and Safety Assessment of Grid Pile Foundation," Sustainability, MDPI, vol. 14(15), pages 1-15, August.
    4. Hughes, William & Zhang, Wei & Cerrai, Diego & Bagtzoglou, Amvrossios & Wanik, David & Anagnostou, Emmanouil, 2022. "A Hybrid Physics-Based and Data-Driven Model for Power Distribution System Infrastructure Hardening and Outage Simulation," Reliability Engineering and System Safety, Elsevier, vol. 225(C).

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