Design and Implementation of Meteorological Disaster Risk Management System

Li Ying; Wang Guofu

doi:10.11898/1001-7313.20220510

Vol.33, NO.5, 2022

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Article Navigation > Journal of Applied Meteorological Science > 2022 > 33(5): 628-640

Li Ying, Wang Guofu. Design and implementation of Meteorological Disaster Risk Management System. J Appl Meteor Sci, 2022, 33(5): 628-640. DOI: 10.11898/1001-7313.20220510.

Citation:

Li Ying, Wang Guofu. Design and implementation of Meteorological Disaster Risk Management System. J Appl Meteor Sci, 2022, 33(5): 628-640. DOI: 10.11898/1001-7313.20220510.

Li Ying, Wang Guofu. Design and implementation of Meteorological Disaster Risk Management System. J Appl Meteor Sci, 2022, 33(5): 628-640. DOI: 10.11898/1001-7313.20220510.

Citation:

Li Ying, Wang Guofu. Design and implementation of Meteorological Disaster Risk Management System. J Appl Meteor Sci, 2022, 33(5): 628-640. DOI: 10.11898/1001-7313.20220510.

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Design and Implementation of Meteorological Disaster Risk Management System

DOI: 10.11898/1001-7313.20220510

Li Ying,
Wang Guofu^,

National Climate Center, Beijing 100081

Received Date: 2022-03-01
Rev Recd Date: 2022-06-16
Publish Date: 2022-09-15

Abstract

Abstract

China is one of the countries with the most serious meteorological disasters in the world. Reducing disaster losses and mitigating disaster risks is important for improving social governance and enhancing people's welfare, and it is also the fundamental goal of meteorological services. As a non-engineering measure for disaster prevention and mitigation, Meteorological Disaster Risk Management System (MDRMS) provides users with reference for decision making and is one of the most effective tools for mitigating meteorological disaster risks. In order to effectively reduce the risk of meteorological disasters and meet the urgent needs of service, China National Climate Center has designed and built MDRMS. MDRMS provides decision-makers and other stakeholders with professional services with four sub-systems:Big data application sub-system, model algorithm sub-system, online analysis sub-system, and comprehensive operation sub-system.From the view of application and function, MDRMS realizes the functions of disaster monitoring and identification, disaster impact assessment, risk assessment, risk prediction, risk zoning, and disaster information services for major meteorological disasters such as rainstorm, typhoon, drought, high temperature and low temperature. A series of products are also established.From the view of design and construction, the key technologies adopted are generic and the operation is intuitive and friendly. The system is built with key technologies such as big data fusion based on spatial-temporal matching, Web-GIS, distributed spatial data storage, micro service, and multi-tenant. The application of new technologies significantly improves the access efficiency and application capability of the system to multi-source, heterogeneous and massive data related to meteorology disaster risk management and enhances the user experience.From the view of deployment and openness, MDRMS has better integration, openness and scalability. It is deployed in China Meteorological Administration, ensuring access, personalized configuration, and service customization for internal users at four levels:National, provincial, municipal, and county levels, as well as access for external users at some product levels.The implementation of MDRMS shows that it has good operational capability and development prospect, which promotes the objective development of meteorological disaster risk management operation and enhances disaster prevention and mitigation decision-making service capability. In the future, the system will be improved following the principle of intensification, integration, and objectification. It will be integrated with the meteorological big data cloud platform in terms of data environment, product interfaces and algorithm functions, and provincial versions and mobile versions will be vigorously developed, focusing on creating an application ecosystem for MDRMS, providing richer and more practical meteorological disaster risk management products for users at all levels, and further playing an important role of information technology in meteorological disaster risk management.
- meteorological disaster;
- risk management;
- informatization;
- key technologies;
- implementation

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References(40)

Figures and Tables

Fig. 1 Framework of Meteorological Disaster Risk Management System

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Fig. 2 Layout of the online analysis and production sub-system

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Fig. 3 Direct economic losses from low temperature disaster events in Jan 2021

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Fig. 4 Total precipitation in rainstorm event from 27 Jun to 12 Jul in 2020

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Fig. 5 Daily variation of meteorological drought area from 24 Mar to 19 Apr in 2021(a) and distribution of meteorological drought on 4 Apr 2021(b)

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Fig. 6 Maximum number of meteorological stations affected by single regional high temperature event over China from 1961 to 2021

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Fig. 7 Risk prediction of Typhoon Maysak(2009) in 24 h forecasted on 2 Sep 2020

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Fig. 8 Risk zoning of waterlogging risk of county-level cities in the eastern China

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Table 1 Data types

数据类别	主要数据和产品集
致灾因子	站点观测、数值模式、台风路径和登陆信息等
承灾体	人口、社会经济、兴趣点等
孕灾环境	地形、土地利用、植被类型、河流分布、城市排水管网密度、城市绿地率等
灾害影响	灾情直报、省级灾情、县级灾情、重大灾害事件等
灾害事件	暴雨事件、干旱事件、低温事件、高温事件、台风等
风险评估	暴雨风险评估、干旱风险评估、低温风险评估、高温风险评估、台风风险评估、城市内涝风险评估等
风险预估	暴雨风险预估、干旱风险预估、低温风险预估、高温风险预估、台风风险预估等
风险区划	暴雨风险区划、干旱风险区划、低温风险区划、高温风险区划、台风风险区划等
风险普查	隐患点、预警点、致灾阈值、脆弱性曲线、中小河流洪水风险区划、山洪风险区划、城市内涝风险区划、风暴潮风险区划等
行业评估	农业、交通、环境、水资源等
评估指标	灾体量指数、气候指数、台风能量指数等
行业信息	农业、环境、水文水利等
基础信息	行政区划、气象站、水文站、水库、水资源分区、中小河流流域分区、山洪沟分区等

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References

[1]	China Meteorological Administration. China Meteorological Disaster Yearbook 2020. Beijing: China Meteorological Press, 2021.
[2]	China Meteorological Administration. Assessment Report on Extreme Weather and Disaster Risk Management in China. Beijing: Science Press, 2015.
[3]	IPCC. Climate Change 2014: Impacts, Adaptation, and Vulnerability. Cambridge and New York: Cambridge University Press, 2014.
[4]	IPCC. Managing The Risks of Extreme Events and Disasters to Advance Climate Change Adaptation: A Special Report of Working Groups Ⅰ and Ⅱ of the Intergovernmental Panel on Climate Change. Cambridge and New York: Cambridge University Press, 2012.
[5]	Li Y, Gao G, Song L C. Understanding of disaster risk and the management associated with climate change in IPCC AR5. Climate Change Research, 2014, 10(4): 260-267. doi: 10.3969/j.issn.1673-1719.2014.04.006
[6]	Shi P J. Sendai framework: World disaster reduction guidance document for the next 15 years. China Disaster Reduction, 2015(4): 30-33. doi: 10.3969/j.issn.1002-4549.2015.04.009
[7]	Kan F M. 30 years of international disaster reduction led by the United Nations: From disaster management to disaster risk management(1990-2019). China Disaster Reduction, 2020(5): 54-59. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGJI202005015.htm
[8]	Svoboda M D, Fuchs B A, Poulsen C C, et al. The drought risk atlas: Enhancing decision support for drought risk management in the United States. J Hydrol, 2015, 526: 274-286. doi: 10.1016/j.jhydrol.2015.01.006
[9]	Shi P J, Kasperson R. World Atlas of Natural Disaster Risk. Berlin: Springer, 2015.
[10]	National Climate Center. Atlas of Extreme Weather and Climate in China(1961-2015). Beijing: China Meteorological Press, 2018.
[11]	Zheng Z B. The trend of meteorological disaster risk management promoted by informatization. Hubei Agri Sci, 2018, 57(14): 118-121. https://www.cnki.com.cn/Article/CJFDTOTAL-HBNY201814030.htm
[12]	Jiao M Y, Song L C, Jiang T, et al. China's implementation of impact and risk-based early warning. Boletín-Organización Meteorológica Mundial, 2015, 64: 11-14.
[13]	Chen Z L. Capacity building and practice of meteorological disaster prevention and mitigation. Yuejiang Academic Journal, 2013(3): 21-25. https://www.cnki.com.cn/Article/CJFDTOTAL-YJXK201303007.htm
[14]	Zhuang Y, Bao R J, Zhang R Y, et al. Refined rsik assessment of tropical cyclone disaster in Fujian. J Appl Meteor Sci, 2022, 33(3): 319-328. doi: 10.11898/1001-7313.20220306
[15]	Zhang G C. Study and thinking on the construction of meteorological cloud. Meteor Environ Sci, 2015, 38(4): 1-11. https://www.cnki.com.cn/Article/CJFDTOTAL-HNQX201504001.htm
[16]	Shen W H. Significance of cloud computing in the process of meteorological informatization. Informatization Research, 2015(3): 80-88. https://www.cnki.com.cn/Article/CJFDTOTAL-IGXN201503030.htm
[17]	Shen W H. Reanalysis of meteorological big data and its application. Informatization Research, 2016(1): 85-96. https://www.cnki.com.cn/Article/CJFDTOTAL-IGXN201601032.htm
[18]	Wu H P, Zhang Y Q, Sun J M, et al. Designing and implementation of climate interactive plotting and analysis system. J Appl Meteor Sci, 2013, 24(5): 631-640. doi: 10.3969/j.issn.1001-7313.2013.05.013
[19]	Zhao F, Xiong A Y, Zhang X Y, et al. Technical characteristics of the architecture design of China integrated meteorological information sharing system. J Appl Meteor Sci, 2017, 28(6): 750-758. doi: 10.11898/1001-7313.20170610
[20]	Xiao H D, Sun J, Sun C Y, et al. Design of CMA S2S data archive center and key technology. J Appl Meteor Sci, 2017, 28(5): 632-640. doi: 10.11898/1001-7313.20170511
[21]	Wang X, Wang G F, Huang X M. The application of scientific big data in the research of global climate change. Climate Change Research, 2016, 12(4): 313-321. https://www.cnki.com.cn/Article/CJFDTOTAL-QHBH201604007.htm
[22]	Shi Y, Zhan M, Yin L, et al. Research on associated organization and analysis of target-oriented multi-source heterogeneous data. Bulletin of Surveying and Mapping, 2015(1): 102-104. https://www.cnki.com.cn/Article/CJFDTOTAL-CHTB201501025.htm
[23]	Jeong S, Cheong T. Web GIS based typhoon committee disaster information system for typhoon disaster risk management. Tropical Cyclone Research and Review, 2012, 1(2): 207-212.
[24]	Hu Z G, Zheng W J, Gao S, et al. Research and implementation of key technology in MeteoGIS web platform. J Appl Meteor Sci, 2014, 25(3): 365-374. http://qikan.camscma.cn/article/id/20140314
[25]	Lü Z L, Bai X P, Xue F. WebGIS-based meteorological service system and its key technology. J Appl Meteor Sci, 2018, 29(1): 120-128. doi: 10.11898/1001-7313.20180111
[26]	Wu H P, Luo B, Wang W G, et al. Application of geographic information system to decision-making meteorological service system. J Appl Meteor Sci, 2008, 19(3): 380-384. http://qikan.camscma.cn/article/id/20080362
[27]	Liu P G, Jiang N, Tan P, et al. Design and implementation of a geographic information system for meteorological applications. J Appl Meteor Sci, 2005, 16(4): 547-553. http://qikan.camscma.cn/article/id/20050469
[28]	Zeng Z M, Yun W Y, Lu H, et al. Research and practice on key technologies of big data GIS. Geomatics & Spatial Information Technology, 2017, 40(Suppl Ⅰ): 1-8. https://www.cnki.com.cn/Article/CJFDTOTAL-XJJS201605002.htm
[29]	Yun W Y, Gou Y, Wang J, et al. Research and implementation of the modules processing of real time data stream based on spark streaming. Geomatics & Spatial Information Technology, 2017, 40(Suppl Ⅰ): 48-55. https://www.cnki.com.cn/Article/CJFDTOTAL-JDQW201603013.htm
[30]	Su L L, Xiong L H. Research of GIS load balance based on container technology. Geomatics & Spatial Information Technology, 2017, 40(Suppl Ⅰ): 67-69.
[31]	Ye D X, Wang Z Y, Gao R, et al. Objective identification and climatic characters of the regional rainstorm event in China from 1961 to 2016. Climate Change Research, 2019, 15(6): 575-583. https://www.cnki.com.cn/Article/CJFDTOTAL-QHBH201906001.htm
[32]	Wang G F, Ye D X, Zhang Y X, et al. Characteristics and abnormal atmospheric circulation of regional high temperature process in 2017 over China. Climate Change Research, 2018, 14(4): 341-349. https://www.cnki.com.cn/Article/CJFDTOTAL-QHBH201804002.htm
[33]	Wu H Y, Zou Y, Liu W. Quantitative assessment of regional heavy rainfall process in Guangdong and its climatological characteristics. J Appl Meteor Sci, 2019, 30(2): 233-244. doi: 10.11898/1001-7313.20190210
[34]	He L F, Chen S, Guo Y Q. Observation characteristics and synoptic mechanisms of Typhoon Lekima extreme rainfall in 2019. J Appl Meteor Sci, 2020, 31(5): 513-526. doi: 10.11898/1001-7313.20200501
[35]	Wang Y J, Yin Y Z, Song L C. Risk assessment of typhoon disaster chains in the Guangdong-Hong Kong-Macau Greater Bay Area, China. Front Earth Sci, 2022. DOI: 10.3389/feart.2022.839733.
[36]	Sun S, Zhai J Q, Li Y, et al. Urban waterlogging risk assessment in well-developed region of Eastern China. Phys Chem Earth, 2020, 115: 102824.
[37]	Liu Y Y, He W C, Wang Y, et al. Design and implementation of meteorological big data cloud platform archive storage system. Meteor Sci Tech, 2021, 49(5): 697-706. https://www.cnki.com.cn/Article/CJFDTOTAL-QXKJ202105006.htm
[38]	Yang J Y, Huo Z G, Wang P J, et al. Evaluation index construction and hazard risk assessment on apple drought in northern China. J Appl Meteor Sci, 2021, 32(1): 25-37. doi: 10.11898/1001-7313.20210103
[39]	Zheng Y J, Yang Z Q, Wang L, et al. Refined risk zoning of high temperature and heat damage to greenhouse tomato in Southern China. J Appl Meteor Sci, 2021, 32(4): 432-442. doi: 10.11898/1001-7313.20210405
[40]	Huang X Y, Li X H. Future projection of rainstorm and flood disaster risk in Southwest China based on CMIP6 models. J Appl Meteor Sci, 2022, 33(2): 231-243. doi: 10.11898/1001-7313.20220209