摘要
利用2003—2005年4—9月国家气象中心T213的数值预报产品,通过动力诊断,从大量数值预报因子中提取不同层次、不同时效与降水关系较好的多个因子,使用K最邻近域(KNN)方法,制作不同代表站点的晴雨预报和大于或等于10 mm的降水预报试验。在搜索K邻近域的过程中,考虑天气事件出现的概率不同,而分别求取有天气事件的正样本K+值和无天气事件的负样本K-值,使该方法选择的最邻近域中的K值取得更为合理。利用交叉验证的方法,对历史资料依次选取部分样本作为预报测试集,通过预测结果的检验评分,选取获得最大准确率和最大概括率的K+和K-作为最佳邻近域的组合。确定了最优K值后,反算历史样本,通过比较,得到某站出现降水天气事件的预报判别值,在一定程度上减少了预报的空报率。经过对2006年4—9月的预报试验,改进后的KNN方法使24,48 h的晴雨预报和大于或等于10 mm降水预报的TS评分大多数高于未改进前的,也高于T213模式本身的降水预报和MOS方法动力统计释用的降水预报,特别是克服了模式降水预报和MOS方法预报中空报率较高的现象,达到了较好的预报效果。
In order to improve objective precipitation forecasting level, non-parameter estimate technology is used in research in application and interpretation of numerical prediction products. T213 numerical prediction products from national meteorological center are used as primary data from April to September during 2003 to 2005. By diagnostic analysis and Stepwise Regression, 10--20 factors are selected from many factors of different levels and various times. The factors from numerical prediction products are well relevant to the rain observation precipitation data. An improved K-nearest neighbor approach （KNN） is used to forecast precipitation and that more than 10 mm at dissimilar area stations from April to September in 2006. In searching K-nearest neighbor process, different types of weather events such as rain-free days, drizzle days and moderate rain days, have diverse probability. Then, the different K （K^＋ and K ） values are computed to match the different weather events. The number of exiting weather event is represented by the value of K^＋ . The number of no weather event is represented by the value of K^- . It is reasonable for different weather event to use KNN method. Forecasting and test patterns are selected in turn from history patterns by crossing verification method. Forecasting and test patterns are replaced by other ones in historical patterns. Until all historical patterns are gone through thoroughly as forecasting and test patterns before an accuracy rate and a summary rate of forecasting are computed. To reduce the rate of miss forecast and to put the main emphasis on accuracy rate and summary rate of forecasting, the values of K^＋ and K are continually adjusted. Different accuracy rate and summary rate of forecasting can be computed for different K^＋ and K^- value. The result of tentative forecasting is compared. When both the accuracy rate and summary rate of forecasting are comparatively better, one optimal K is selected from a number of the accuracy rates and the summary rate
出处
《应用气象学报》
CSCD
北大核心
2008年第4期471-478,共8页
Journal of Applied Meteorological Science
基金
国家自然科学基金项目(40675077)
中国气象局“精细化客观天气预报开发”课题
国家科技支撑计划项目(2007BAC29B03)共同资助
关键词
K邻近域
正负样本
交叉验证
降水预报
KNN
positive and negative pattern
cross validation
precipitation forecast
