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投稿时间：2007-11-02 修订日期：2007-12-19
投稿时间：2007-11-02 修订日期：2007-12-19
中文摘要: 利用类似KALMAN滤波的自适应误差订正法对中国国家气象中心、日本气象厅、美国
国家环境预报中心、加拿大气象中心和澳大利亚－法国气象局的区域集合预报模式2m温度预
报做订正，并对订正后的结果采用算术平均和多元回归两种方法进行集成。结果表明：订正
后温度预报的各项检验指标都显示出不同程度的改善。36h内平均绝对误差在1.8℃以内；均
方根误差也有明显减小，且与离散度大小更接近；talagrand图的U型分布仍然存在，但个别
成员异常的现象得到改善；集合成员预报分簇的现象得到了很好的矫正；此外预报误差存
在日变化。两种集成方法的温度预报结果都优于单一模式预报，并且不存在明显的系统误差
，预报达到了一定精度。其中多元回归方法的集成效果胜于算术平均集成。
Abstract:Based on temperature forecasts of regional ensemble forecast models from six dif
ferent operational centers and scientific research institutions, a study of bias
correction and multi model consensus has been done to reduce the system error
and to improve the forecast precision. The objective and quantitative verificat
ion results show that the forecast precision could be improved observably with t
he bias correction method. Both of the two methods used to be build the co
nsensus forecast equations show a great improvement on forecast ability too. Cons
ensus forecasts are more precise than any single model. And between the two kind
s of consensus forecasts, the multiple regression analysis shows a better effect than the arithmetic average method.
keywords: consensus forecast temperature forecast bias correction multi model region
al ensemble forecast
文章编号： 中图分类号： 文献标志码：
基金项目:国家科技攻关计划奥运科技专项项目“北京奥运短时临近预报实时业务
系统开发”（2005BA904B05）资助
引用文本：
马清,龚建东,李莉,李应林,2008.超级集合预报的误差订正与集成研究[J].气象,34(3):42-48.
Ma Qing,Gong Jiandong,Li Li,Li Yinglin,2008.Study of Bias correction and Consensus in Regional Multi model Super ensemble Forecast[J].Meteor Mon,34(3):42-48.
马清,龚建东,李莉,李应林,2008.超级集合预报的误差订正与集成研究[J].气象,34(3):42-48.
Ma Qing,Gong Jiandong,Li Li,Li Yinglin,2008.Study of Bias correction and Consensus in Regional Multi model Super ensemble Forecast[J].Meteor Mon,34(3):42-48.