%0 Journal Article
%T A Dynamic-Analogue Error Correction Model for ENSO Prediction and Its Initial Hindcast Verification<br>一个ENSO动力-相似误差订正模式及其后报初检验
%A SUN Cheng-Hu
%A LI Wei-Jing
%A REN Hong-Li
%A ZHANG Pei-Qun
%A WANG Dong-Yan
%A <br>孙丞虎
%A 李维京
%A 任宏利
%A 张培群
%A 王冬艳
%J 大气科学
%D 2006
%I 
%X To further reduce the impact of model error on the short term climate prediction,on the basis of an analogue correction method of errors,which utilizes the analog information from the historical datasets to estimate the evolution of model errors,a dynamical-analogue error correction model for ENSO prediction based on NCCo intermediate ocean-atmosphere coupled model has been developed.The difference between this model and the NCCo model is only that an error correction sub-model is added in the ocean and atmosphere part respectively.The impact of some basic model parameters as mentioned follows on prediction results are investigated to get the optimal parameters choices: firstly,the effect of analogue degree including the part analogue and comprehensive analogue is compared,the results exhibit that in a coupled system the comprehensive analogue is much better than the part analogue for the model in this paper,because the former can really depict the analogue degree between the current initial value and its historical partners,thus leading to a well estimation of model error.Secondly,the investigation on the effect of the re-estimate period of error(RPE) denotes that RPE is also a crucial parameter to this model.Usually,there is an optimal combination between the RPE of atmosphere and ocean model under different analogue degrees to make a good prediction.Furthermore,the results in this paper also display that there are finite analogue samples in the datasets that the authors hold,and the hindcasting skill has a linear response to the analogue sample sizes due mainly to the fact that more analogue samples can supply more error information to the model thus leading to better estimation of model error and more improvement of prediction skill.Based on the above parameter choices,the initial verification in this paper shows that the hindcast skill of this model is better than that of NCCo model for the SST prediction of the tropical Pacific Ocean,which may imply its potential application to real-time prediction.
%K combination of statistical and dynamical method
%K ENSO prediction
%K analogue error correction method<br>动力统计相结合
%K ENSO预测
%K 相似误差订正法
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=E62459D214FD64A3C8082E4ED1ABABED5711027BBBDDD35B&cid=28A2F569B2458C17&jid=46874A5A102033D774D00D819E91CD68&aid=AF687FD2CB71B1AD&yid=37904DC365DD7266&vid=340AC2BF8E7AB4FD&iid=94C357A881DFC066&sid=78AF84DBB4041008&eid=816AB2919A4FEDD7&journal_id=1006-9895&journal_name=大气科学&referenced_num=3&reference_num=36