官雨洁,王伟,刘寿东.基于CART算法的夏季高温预测模型构建与应用.气象科学,2018,(4):539-544 GUAN Yujie,WANG Wei,LIU Shoudong.Building and application of summer high temperature prediction model based on CART algorithm.Journal of the Meteorological Sciences,2018,(4):539-544
基于CART算法的夏季高温预测模型构建与应用
Building and application of summer high temperature prediction model based on CART algorithm
投稿时间:2017-06-24  修订日期:2017-09-05
DOI:10.3969/2017jms.0070
中文关键词:  CART  高温有效积温  夏季高温  预测模型
英文关键词:CART  High temperature effective accumulated temperature  Summer high temperature  Prediction model
基金项目:教育部长江学者和创新团队发展计划项目(PCSIRT);江苏高校优势学科建设工程项目(PAPD)
作者单位E-mail
官雨洁 南京信息工程大学 气象灾害教育部重点实验室/气候与环境变化国际合作联合实验室/气象灾害预报预警与评估协同创新中心/大气环境中心, 南京 210044  
王伟 南京信息工程大学 大气科学学院, 南京 210044  
刘寿东 南京信息工程大学 气象灾害教育部重点实验室/气候与环境变化国际合作联合实验室/气象灾害预报预警与评估协同创新中心/大气环境中心, 南京 210044 605854811@qq.com 
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中文摘要:
      以夏季高温有效积温的多年平均值作为判断夏季高温炎热程度的标准,借助CART算法探究东亚夏季风指数,夏季印缅槽,夏季北大西洋涛动(NAO),赤道太平洋海温等多项气候因子与高温的关系,得到高温预测规则集,建立高温的预测模型。研究中选取1955—2012年福建漳州夏季的日最高气温等站点气温资料,通过计算58 a的夏季高温有效积温数值来判定夏季的炎热程度。将同一时期的多项气候因子数据作为输入变量输入,算法会随机选出其中46 a的数据得到10条分类规则集,建立的预测模型准确率达到91.49%。用剩下的12 a数据进行检验,准确率达到91.67%。研究结果较好地验证了高温预测模型的可行性和有效性,为灾害性天气模型的研究提供了新思路。
英文摘要:
      The average value of effective accumulated high temperature in summer for many years was considered as a standard to judge the extent of a hot summer. The CART algorithm was employed to explore the relationship between the high temperature and the climatic factors such as East Asian summer monsoon index, summer India-Myanmar trough, summer North Atlantic Oscillation (NAO) and equatorial Pacific sea temperature, and the high-temperature prediction rule set was obtained to build a high-temperature prediction model. The study selected the daily maximum temperature data in summer among 1955—2012 in Zhangzhou of Fujian Province. The extent of a hot summer was determined through the effective accumulated temperature of high temperature in summer for 58 years. A number of climatic factors in the same period were input as the input variables, and 46a data were randomly selected to get 10 classification rule sets. The accuracy of the built predication model reached 91.49%. The remaining 12a data were used for test, with an accuracy up to 91.67%. Generally speaking, the results of this paper have verified the feasibility and validity of the high temperature prediction model, which provides a new idea for the research of the catastrophic weather model.
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