动力延伸预报产品在广西月降水预报中的应用
Application of Dynamic Extended Forecast Products to Monthly Precipitation Forecast in Guangxi
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摘要: 利用1958—2005年NCEP/NCAR再分析资料和2003—2005年国家气候中心的动力延伸预报产品, 运用自然正交函数展开 (EOF) 求取预报关键区内的空间特征向量及其时间系数, 结合相似离度方法查找与预报月份相似的个例, 进而作出广西月降水量预报。独立样本试验证明, 利用动力延伸预报产品制作的区域月降水预报比利用前期实况高度距平场相关区域制作的预报效果更好。Abstract: Based on the monthly mean 500 hPa geopotential height fields from NCEP/NCAR reanalysis data during 1958 to 2005 and the products of dynamic extended forecast from China National Climate Center during 2003 to 2005, monthly precipitation in Guangxi is predicted.The eigenvectors with typical spatial distribution patterns for the predicting key areas and time coefficients reflecting their variation trends can be developed to by way of making empirical orthogonal function (EOF) with the 500 hPa geopotential height anomalies over every predicting key areas. The monthly precipitation in Guangxi is predicted by using analog deviation to find out historical samples similar to predictors in the prediction year. Prediction models are tested by independent samples and results show that the models with predictors from products of dynamic extended forecast are superior in prediction ability to those with predictors from higher correlation areas of former 500 hPa geopotential height.
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表 1 广西1958—2004年7月降水量的前5个特征值及各自的方差贡献
Table 1 The first 5 eigevalues and their variance contribution of monthly precipitation in July from 1958 to 2004 of Guangxi
表 2 y1关键区前8个特征向量的方差贡献及对应时间系数与y1的相关系数
Table 2 The first 8 eigenvectors variance contribution to key areas of y1 and the correlation coefficients between their time coefficients and y1
表 3 以同期实况为预报因子的2003—2005年广西4—9月各月降水趋势PS评分 (单位:%)
Table 3 The PS evaluation of precipitation forecast in Guangxi from April to September during 2003—2005 using simultaneous observation data as predictors (unit:%)
表 4 分别以动力延伸预报产品和前期实况为预报因子的2003—2005年广西4—9月各月降水趋势的PS评分 (单位:%)
Table 4 The PS evaluation of precipitation forecast in Guangxi from April to September during 2003—2005 using products of dynamic extended forecast and former observation data as predictors respectively (unit:%)
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