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Data Details

Dataset of Risk Assessment of Tropical Cyclone on the Northwest Pacific (1980-2022)


TONG Junyue1,2WU Qitao*1QIAN Qinglan2
1 Guangzhou Institute of Geography，Guangzhou Academy of Sciences，Guangzhou 510070，China2 School of Geography and Remote Sensing，Guangzhou University，Guangzhou 510006，China

DOI:10.3974/geodb.2024.01.04.V1

Published:Jan. 2024

Visitors：4424       Data Files Downloaded：55      
Data Downloaded：12.08 MB      Citations：

Key Words：

tropical cyclone,risk assessment,risk level,kernel density estimation,number of tropical cyclone

Abstract：

The dataset of risk assessment of tropical cyclone on the Northwest Pacific (1980-2022) was developed based on the Northwest Pacific Best Tropical Cyclone Track Dataset and the kernel density approach to examine the impact degree and risk level of tropical cyclones in the Northwest Pacific Ocean between 99°E and 160°E, 2°N and 52°N. The total number of tropical cyclones that occurred in China's land areas from 1980 to 2022 was counted. The data shows that: (1) the South China Sea and the Philippine Sea have the highest risk level and are most severely impacted by tropical storms in the northwest Pacific Ocean; (2) the law of gradually decreasing tropical cyclone cumulative infestation from coastal to interior locations applies to China's land area; Hainan Province experienced the highest number of tropical cyclones, followed by Taiwan Province and Leizhou Peninsula in Guangdong Province. The dataset includes: (1) the risk level of tropical cyclone disasters in the northwest Pacific Ocean; (2) the cumulative number of tropical cyclones in China land area; (3) the tropical cyclone statistics for the Northwest Pacific. The dataset is archived in .tif and .xls formats, consisting of 3 data files with data size of 1.15 MB (Compressed into one file with 224 KB).Browse

Foundation Item：

The National Natural Science Foundation of China (42071165)

Data Citation：

TONG Junyue, WU Qitao*, QIAN Qinglan. Dataset of Risk Assessment of Tropical Cyclone on the Northwest Pacific (1980-2022)[J/DB/OL]. Digital Journal of Global Change Data Repository, 2024. https://doi.org/10.3974/geodb.2024.01.04.V1.

TONG Junyue, WU Qitao, QIAN Qinglan. Dataset of risk assessment of tropical cyclone on the Northwest Pacific Basin (1980-2022) [J]. Journal of Global Change Data & Discovery, 2023, 7(4): 343-353.

References：


     [1] The State Council of the PRC. Notice of the State Council on issuing the 14th Five-Year Plan for the national emergency response system [J]. Gazette of the State Council of the People's Republic of China, 2022(6): 30-48.
     [2] Zhu, H. M., Lin, G. F., Zhang, M. F., et al. Construction of typhoon disaster chain knowledge graph based on disaster risk survey knowledge base [J/OL]. Journal of Catastrophology, 2023. http://kns.cnki.net/kcms/detail/61.1097.p.20230802.1805.008.html.
     [3] Wang, L., Lu, Y., Zhao, H. K. Analysis of temporal and spatial characteristics of typhoon disasters and construction of evaluation model [J]. Journal of Catastrophology, 2023, 38(4): 187-194.
     [4] Sun, X. X. Risk assessment and analysis of the gravity net cage based on machine learning [D]. Dalian: Dalian university of technology, 2020.
     [5] Zhu, J.，Lu, Y.，Li, G. P., et al．Risk assessment of typhoon disasters in Fujian Province of each county [J]. Journal of Catastrophology, 2017, 32(3): 204-209.
     [6] Wei, Z. J.，Ma, H. L., Tang, D. L. Trend assessment of typhoon disasters based on the improved entropy method [J]. Journal of Catastrophology, 2017, 32(3): 7-11.
     [7] Lin, P. Y., Lin, P. H., Wang, J., et al. Typhoon disaster risk assessment and dynamic risk forecasts in Zhejiang province based on machine learning methods [J]. Journal of Natural Disasters, 2023, 32(4): 13-24.
     [8] Bao, W. X., Gong, A. D., Xu, C., et al. Typhoon disaster risk assessment in Guangdong province by regional disaster system theory: case of “Mangkhut” [J]. Journal of Beijing Normal University (Natural Science), 2023, 59(1): 94-103.
     [9] Ming, Y., Wei, Z., Hui, Y., et al. An overview of the China Meteorological Administration tropical cyclone database [J]. Journal of Atmospheric and Oceanic Technology, 2014, 31(2): 287-301.
     [10] Lu, X. Q., Yu, H., Ying, M., et al. Western North Pacific tropical cyclone database created by the China Meteorological Administration [J]. Advances in Atmospheric Sciences, 2021, 38(4): 690-699.
     [11] Chen, G. M., Yang, M. Q., Zhang, X. P., et al. Verification on forecasts of typhoons over Western North Pacific and South China Sea in 2020 [J]. Meteorological Monthly, 2022, 48(4): 516-525.
     [12] Wang, X. X. Comparative analysis on typhoon center location methods based on multi-source satellite data [D]. Beijing: National Marine Environmental Forecasting Center, 2020.
     

Data Product:

ID Data Name Data Size Operation
0Datapaper_RiskTropCycloneNWPacific.pdf1671.00kbDownLoad
1 RiskTropCycloneNWPacific.rar 224.98KB
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