单森华

　　(四创科技有限公司，福建 福州 350100)

　　摘 要：为减轻台风灾害风险，辅助开展台风风险防控，基于1974—2019年台风路径、风力和降雨等数据对我国台风危险性进行分析。将我国划分为多个地理网格，采用分布式数据库Hbase构建网格、台风和影响分值列簇，基于网格对历史台风路径及风力、降雨再分析数据进行预处理,建立台风影响范围拟合和影响力衰减等函数，利用分布式计算引擎MapReduce计算台风对各网格造成的影响力分值，并计算不同年份、年段台风危险性。结果表明：1)本方法数据依赖性较低，计算效率较高，评估结果更客观，推广性较强;2)研究过程中采用归一化法对危险性进行分级，导致不同时间段评估结果出现较大差异，须进一步研究改进;3)我国台风危险区总体呈现为极高、高、中、低4条危险带，台风危险性呈现增强趋势，并持续向东北、中西部延伸，未来防台风形势依然严峻。

　　关键词：台风危险性;评估方法;空间网格;风力;降雨 ;风雨数据

　　Research and implementation of typhoon risk assessment method

　　based on wind and rain data

　　SHAN Senhua

　　(IStrong Technology Co.,Ltd., Fuzhou 350100, China)

　　Abstract：In order to reduce typhoon disaster risk and assist in typhoon risk control management, the typhoon risk analysis in China is conducted based on the data of typhoon path, wind force and rainfall from 1974 to 2019. China is divided into several geospatial grids, and the grid, typhoon and impact score column clusters are constructed by using the distributed database HBase. The historical typhoon path, wind force and rainfall reanalysis data are preprocessed based on the grid. Then the typhoon influence range fitting function and influence attenuation function are established. The influence score of typhoon on each geospatial grid is calculated by distributed computing engine MapReduce.. which calculates the typhoon risk of different years and periods as well. The results show that :1) this method has lower data dependence, higher computational efficiency, more objective assessment results and strong popularization; 2) the normalization method is used to classify the risk, which leads to great differences in the assessment results in different periods, and needs further research and improvement; 3) the typhoon risk areas in China generally present four risk zones: extremely high, high, medium and low. The risk of typhoon in China is increasing and extending to the northeast and western regions. The situation of typhoon risk prevention is still serious in the future.

　　Key words：typhoon risk;assessment method;spatial grid;wind force;rainfall;wind and rain data