Applicability Evaluation of the Global Synthetic Tropical Cyclone Hazard Dataset in Coastal China

被引:0
|
作者
Li, Xiaomin [1 ]
Hou, Qi [2 ]
Zhang, Jie [1 ,2 ]
Zhang, Suming [2 ]
Du, Xuexue [2 ,3 ]
Zhao, Tangqi [2 ]
机构
[1] Minist Nat Resources China, Inst Oceanog 1, Qingdao 266061, Peoples R China
[2] China Univ Petr East China, Coll Oceanog & Space Informat, Qingdao 266580, Peoples R China
[3] Guizhou Power Grid Co Ltd, Duyun Power Supply Bur, Duyun 558000, Peoples R China
基金
国家重点研发计划;
关键词
Global Synthetic Tropical Cyclone Hazard (GSTCH) dataset; Tropical Cyclone Best Track (TCBT) dataset; tropical cyclone; applicability evaluation; coastal China; HURRICANE RISK; SIMULATION; SURGE; MODEL;
D O I
10.3390/jmse12010073
中图分类号
U6 [水路运输]; P75 [海洋工程];
学科分类号
0814 ; 081505 ; 0824 ; 082401 ;
摘要
A tropical cyclone dataset is an important data source for tropical cyclone disaster research, and the evaluation of its applicability is a necessary prerequisite. The Global Synthetic Tropical Cyclone Hazard (GSTCH) dataset is a dataset of global tropical cyclone activity for 10,000 years from 2018, and has become accepted as a major data source for the study of global tropical cyclone hazards. On the basis of the authoritative Tropical Cyclone Best Track (TCBT) dataset proposed by the China Meteorological Administration, this study evaluated the applicability of the GSTCH dataset in relation to two regions: the Northwest Pacific and China ' s coastal provinces. For the Northwest Pacific, the results show no significant differences in the means and standard deviations of landfall wind speed, landfall pressure, and annual occurrence number between the two datasets at the 95% confidence level. They also show the cumulative distributions of central minimum pressure and central maximum wind speed along the track passed the Kolmogorov-Smirnov (K-S) test at the 95% confidence level, thereby verifying that the GSTCH dataset is consistent with the TCBT dataset at sea-area scale. For China's coastal provinces, the results show that the means or standard deviations of tropical cyclone characteristics between the two datasets were not significantly different in provinces other than Guangdong and Hainan, and further analysis revealed that the cumulative distributions of the tropical cyclone characteristics in Guangdong and Hainan provinces passed the K-S test at the 95% confidence level, thereby verifying that the GSTCH dataset is consistent with the TCBT dataset at province scale. The applicability evaluation revealed that no significant differences exist between most of the tropical cyclone characteristics in the TCBT and GSTCH datasets, and that the GSTCH dataset is an available and reliable data source for tropical cyclone hazard studies in China's coastal areas.
引用
收藏
页数:19
相关论文
共 50 条
  • [1] Generation of a global synthetic tropical cyclone hazard dataset using STORM
    Bloemendaal, Nadia
    Haigh, Ivan D.
    de Moel, Hans
    Muis, Sanne
    Haarsma, Reindert J.
    Aerts, Jeroen C. J. H.
    [J]. SCIENTIFIC DATA, 2020, 7 (01)
  • [2] Generation of a global synthetic tropical cyclone hazard dataset using STORM
    Nadia Bloemendaal
    Ivan D. Haigh
    Hans de Moel
    Sanne Muis
    Reindert J. Haarsma
    Jeroen C. J. H. Aerts
    [J]. Scientific Data, 7
  • [3] Coastal Inundation Hazard Assessment in Australian Tropical Cyclone Prone Regions
    Nguyen, Jane
    Kuleshov, Yuriy
    [J]. HYDROLOGY, 2023, 10 (12)
  • [4] Evaluations of storm tide hazard along the coast of China using synthetic dynamic tropical cyclone events
    Yang, Jian
    Chen, Yu
    Tang, Yanan
    Duan, Zhongdong
    Yan, Guirong
    Ou, Jinping
    Gong, Ting
    Yang, Zhe
    Yin, Jianming
    [J]. COASTAL ENGINEERING, 2024, 194
  • [5] A North Atlantic synthetic tropical cyclone track, intensity, and rainfall dataset
    Wenwei Xu
    Karthik Balaguru
    David R. Judi
    Julian Rice
    L. Ruby Leung
    Serena Lipari
    [J]. Scientific Data, 11
  • [6] A North Atlantic synthetic tropical cyclone track, intensity, and rainfall dataset
    Xu, Wenwei
    Balaguru, Karthik
    Judi, David R.
    Rice, Julian
    Leung, L. Ruby
    Lipari, Serena
    [J]. SCIENTIFIC DATA, 2024, 11 (01)
  • [7] A Global Multi-Source Tropical Cyclone Precipitation (MSTCP) Dataset
    Morin, Gabriel
    Boudreault, Mathieu
    Garcia-Franco, Jorge L.
    [J]. SCIENTIFIC DATA, 2024, 11 (01)
  • [8] TOWARD A HOMOGENOUS GLOBAL TROPICAL CYCLONE BEST-TRACK DATASET
    Levinson, David H.
    Diamond, Howard J.
    Knapp, Kenneth R.
    Kruk, Michael C.
    Gibney, Ethan J.
    [J]. BULLETIN OF THE AMERICAN METEOROLOGICAL SOCIETY, 2010, 91 (03) : 377 - 380
  • [9] Estimation of tropical cyclone wind hazards in coastal regions of China
    Fang, Genshen
    Zhao, Lin
    Cao, Shuyang
    Zhu, Ledong
    Ge, Yaojun
    [J]. NATURAL HAZARDS AND EARTH SYSTEM SCIENCES, 2020, 20 (06) : 1617 - 1637
  • [10] TROPICAL CYCLONE DAMAGES IN CHINA UNDER GLOBAL WARMING
    Zhang Jiao-yan
    Wu Li-guang
    Zhang Qiang
    [J]. JOURNAL OF TROPICAL METEOROLOGY, 2013, 19 (02) : 120 - 129