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POTENTIAL PREDICTABILITY OF MONTHLY PRECIPITATION OVER CHINA
作者姓名:柳艳菊  马开玉  林振山
作者单位:National Climate Center.Beijing 100081,Department of Atmospheric Sciences.Nanjing University.Nanjing 210093,Department of Atmospheric Sciences.Nanjing University.Nanjing 210093
基金项目:This paper is supported by Studies on Short Term Climate Predictions in China(96-908-01-01-2).
摘    要:In this paper,based on the data at 70 stations selected evenly over China for 31 years from1961—1991.three methods to estimate climatic noise have been discussed and then the climaticnoise and potential predictability of monthly precipitation(January.July.April and October)havebeen examined.The estimating of climatic noise is based on the method of Madden and improvedmethods of Trenberth and Yamamoto et al.(1985).The potential predictability is approximatedby the ratio of the estimated interannual variation to the natural variation.Generally.the climaticnoise of monthly precipitation over China has obvious seasonal variation and it is greater in summerthan in winter,a bit greater in autumn than in spring.In most areas,the climatic noise isprominently decreasing from south to north and from coast to inland.The potential predictabilityof monthly precipitation also has obvious seasonal and regional difference,but the potentialpredictability is greater in winter than in summer in most parts of China.Whereas the comparisonof spring and autumn is not obvious.Comparing with the method of Madden,the estimated valuesof climatic noise based on the improved methods of Trenberth and Yamamoto et al.are relativelylower.


POTENTIAL PREDICTABILITY OF MONTHLY PRECIPITATION OVER CHINA
LIU Yanju National Climate Center.POTENTIAL PREDICTABILITY OF MONTHLY PRECIPITATION OVER CHINA[J].Acta Meteorologica Sinica,2000(3).
Authors:LIU Yanju National Climate Center
Affiliation:LIU Yanju National Climate Center.Beijing 100081 MA Kaiyu LIN Zhenshan Department of Atmospheric Sciences.Nanjing University.Nanjing 210093
Abstract:
Keywords:monthly precipitation  climatic noise  potential predictability
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