| 基于交叉验证技术的KNN方法在降水预报中的试验 |
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| 引用本文： | 曾晓青,邵明轩,王式功,刘还珠.基于交叉验证技术的KNN方法在降水预报中的试验[J].应用气象学报,2008,19(4):471-478. |
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| 作者姓名： | 曾晓青 邵明轩 王式功 刘还珠 |
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| 作者单位： | 1.兰州大学大气科学学院, 兰州 730000 |
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| 基金项目： | 国家自然科学基金，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 |
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| 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。
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| 关 键 词： | K邻近域 正负样本 交叉验证 降水预报 |
| 收稿时间： | 2007-10-18 |
Forecasting Precipitation Experiment with KNN Based on Crossing Verification Technology |
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| Zeng Xiaoqing,Shao Mingxuan,Wang Shigong and Liu Huanzhu.Forecasting Precipitation Experiment with KNN Based on Crossing Verification Technology[J].Quarterly Journal of Applied Meteorology,2008,19(4):471-478. |
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| Authors: | Zeng Xiaoqing Shao Mingxuan Wang Shigong and Liu Huanzhu |
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| Institution: | 1.Atmospheric Science School, Lanzhou University, Lanzhou 7300002.National Meteorological Center, Beijing 100081 |
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| Abstract: | 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 frommany 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 pat terns 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 rates of forecasting, which are corresponded with optimal K+ and K-. After K+ and K- are chosen, historical patterns are revised. The forecasting and distinguishing value of some stations is computed by comparing the results. To a certain extent, the rate of false forecasting decreases. Based on the forecasting experimentation from April 1st to September 30th in 2006 to forecast 24 hour and 48 hour qualitative prediction of 0 mm and 10 mm precipitation in different area stations, the improved KNN approach obtains a much higher technical score than KNN approach used before. The forecasting results of the improved KNN method are compared with the results of direct model output (DMO) and the result of MOS precipitation prediction. KNN approach gets more technical score than that of DMO and MOS, especially the rate of false forecasting of KNN approach sharply decreases, which is superior to DMO and MOS precipitation forecast, and better than KNN approach used before. It is a useful model for the actual operational forecasting of precipitation. |
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| Keywords: | KNN positive and negative pattern cross validation precipitation forecast |
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