胡 鹏, 谷湘潜, 康红文. 2005. 泛相似预报法在汛期降水预报中的应用[J]. 气象学报, 63(2):250-256, doi:10.11676/qxxb2005.025
泛相似预报法在汛期降水预报中的应用
PAN-SIMILARITY METHOD AND ITS FORECAST EXPERIMENTS ON SUMMER PRECIPITATION IN CHINA
投稿时间:2003-07-09  修订日期:2003-10-12
DOI:10.11676/qxxb2005.025
中文关键词:  最佳相似,泛相似,汛期降水预报,距平相关系数
英文关键词:Best similarity, Pan similarity, Summer precipitation forecast,Anomaly cor relation coefficient.
基金项目:国家自然科学基金(40175027)和科技部公益专项“突发性强灾害天气预警系统 的研制”
作者单位
胡 鹏 南京信息工程大学南京210044 
谷湘潜 中国气象科学研究院北京100081 
康红文 中国气象科学研究院北京100081 
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中文摘要:
      文中分析了中国汛期降水的最佳相似情况,将传统的相似方法推广为包含有相似和反相似两个方面,得出近23 a的最佳相似的距平相关系数(ACC)可以达到0.382。在此基础上提出泛相 似预报的方法。利用近23 a降水资料试验,表明泛相似的汛期降水预报评分的ACC可达0.130。
英文摘要:
      The similarity prediction method has been adopted extensively in short-rang climate prediction for many years, the best similarities of Chinese summer preci pitation have been analyzed and discovered that the anomaly correlation coeffici ent (ACC) of the best similarity of recent 23 years can achieve at 0.382, which is far higher than other methods, and can be regarded as the upper limit of simi larity prediction. Its prediction accuracy is about 70%, lower than the theoret ically limitation of seasonal precipitation forecast.The traditional similarity method has been extended to the extensive similar ity one, or called pan similarity method. The extensive one contains the both of plus and negative similarity. When the anomaly correlation coefficients of the former fields such as temperature, height of 500 hPa are negative, the predictio n should also be negative. The various results have been got through the various fields, and the last one is the mean of them.The experiments of recent 23 years indicate that the forecasting score (ACC) reaches 0.130,which is higher than the multi year mean one. But the good effect cannot be got in every year, maybe only one similar criterion used. Soother criterions such as distance function can be used as another one to improve the pr ediction level. It is obvious that the method has good application value in active operatio n. As present forecast level is not high, the statistic forecast research should not be abandoned while developing the numerical model in the short-rang climate prediction.
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