Analogue-Dynamical Prediction of Monsoon Precipitation in Northeast China Based on Dynamic and Optimal Configuration of Multiple Predictors  

作　　者：熊开国 封国林 黄建平 丑纪范 

机构地区：[1]Key Laboratory for Semi-Arid Climate Change of the Ministry of Education,College of Atmospheric Sciences,Lanzhou University [2]Laboratory for Climate Studies,National Climate Center,China Meteorological Administration [3]Key Laboratory of Regional Climate-Environment Research for Temperate East Asia(RCE-TEA),Institute of Atmospheric Physics,Chinese Academy of Sciences

出　　处：《Acta meteorologica Sinica》2011年第3期316-326,共11页

基　　金：Supported by the Special Public Welfare Research Fund for Meteorological Profession of China Meteorological Administration(GYHY200806005);National Natural Science Foundation of China(40875040 and 40930952);National Science and Technology Support Program of China(2007BAC29B01 and 2009BAC51B04)

摘　　要：Based on the National Climate Center （NCC） of China operational seasonal prediction model results for the period 1983-2009 and the US National Weather Service Climate Prediction Center merged analysis of precipitation in the same period, together with the 74 circulation indices of NCC Climate System Diagnostic Division and 40 climate indices of NOAA of US during 1951 2009, an analogue-dynamical technique for objective and quantitative prediction of monsoon precipitation in Northeast China is proposed and implemented. Useful information is extracted from the historical data to estimate the model forecast errors. Dominant predictors and the predictors that exhibit evolving analogues are identified through cross validating the anomaly correlation coefficients （ACC） among single predictors, meanwhile with reference of the results from the dynamic analogue bias correction using four analogue samples. Next, an optimal configuration of multiple predictors is set up and compared with historical optimal multi-predictor configurations and then dynamically adjusted. Finally, the model errors are evaluated and utilized to correct the NCC operational seasonal prediction model results, and the forecast of monsoon precipitation is obtained at last. The independent sample validation shows that this technique has effectively improved the monsoon precipitation prediction skill during 2005 -2009. This study demonstrates that the analogue-dynamical approach is feasible in operational prediction of monsoon precipitation.Based on the National Climate Center （NCC） of China operational seasonal prediction model results for the period 1983-2009 and the US National Weather Service Climate Prediction Center merged analysis of precipitation in the same period, together with the 74 circulation indices of NCC Climate System Diagnostic Division and 40 climate indices of NOAA of US during 1951 2009, an analogue-dynamical technique for objective and quantitative prediction of monsoon precipitation in Northeast China is proposed and implemented. Useful information is extracted from the historical data to estimate the model forecast errors. Dominant predictors and the predictors that exhibit evolving analogues are identified through cross validating the anomaly correlation coefficients （ACC） among single predictors, meanwhile with reference of the results from the dynamic analogue bias correction using four analogue samples. Next, an optimal configuration of multiple predictors is set up and compared with historical optimal multi-predictor configurations and then dynamically adjusted. Finally, the model errors are evaluated and utilized to correct the NCC operational seasonal prediction model results, and the forecast of monsoon precipitation is obtained at last. The independent sample validation shows that this technique has effectively improved the monsoon precipitation prediction skill during 2005 -2009. This study demonstrates that the analogue-dynamical approach is feasible in operational prediction of monsoon precipitation.

关 键 词：analogue-dynamical prediction monsoon precipitation correction of errors dynamic and optimal configuration 

分 类 号：P467[天文地球—大气科学及气象学]