%0 Journal Article
%T 中国大陆地区温度集合预报的最优权重模型设计及其区域应用
%A 苏琪骅
%A 周任君
%A 柯宗建
%A 刘长征
%A 杜良敏
%A 颜妍
%J 中国科学技术大学学报
%D 2018
%R 10.3969/j.issn.0253-2778.2018.03.004
%X 本文采用耦合模式比较计划第五阶段#br#(CMIP5)中的多模式，针对中国大陆地区的温度集合预报，提出了一种区域最优权重模型（Op-SE），并将Op-SE与等权集合方法（EE）和传统的超级集合方法（SE）作对比.研究结果发现：①就模式预报与实测的距平相关系数(ACC)来说，在大部分区域Op-SE表现最优，EE最差，尤其在中国东部Op-SE优势明显，均通过了α=0.1的显著性检验，其中华东地区最高；②对于均方根误差(RMSE)而言，EE效果也相对最差，Op-SE在中国东部要优于SE，而在四川盆地等少数地区则比SE差；③综合ACC和RMSE评估，Op-SE在东北地区、华北地区、华东地区、西南地区和西北地区表现最优，而在四川盆地和甘肃南部等地区较SE没有明显改进.Op-SE给集合预报提供了新的成员择优方法，得到模式在集合预报中的区域最优权重，在一定程度上可以进一步改善区域气候预报的效果.</br>Abstract：Based on Coupled Model Intercomparison Project,Phase5(CMIP5) muti-models, an optimal weighted model for ensemble forecast (Op-SE) of the surface air temperature in mainland China was presented. In order to assess the capability of Op-SE, it was compared with equally-weighted ensemble (EE) and superensemble (SE), and anomaly correlation coefficient (ACC) and root-mean-square-error (RMSE) were chosen to evaluate their forecasting skills. As shown from the results, ACC between Op-SE and observation is optimal in most of China, especially eastern China, which indicates that ACC has passed the significance test at 0.5 level. However EE has poor performance in ACC. As for RMSE, EE is also relatively weak. And Op-SE is better than SE in eastern China, while SE is better in a few other areas like Sichuan Basin. The comprehensive assessments of ACC and RMSE show that the forecast skill of Op-SE is best in northeast, north, eastern, southwest and northwest of China, but is not good enough in Sichuan Basin and southern Gansu. In conclusion, Op-SE provides a new method on selecting outstanding models into ensemble climate forecast, which can improve the forecast skill in regions to some extent.
%K 全球气候模式
%K CMIP5
%K 多模式集合
%K 温度
%K 区域应用&lt
%K /br&gt
%K Key words： global climate model CMIP5 multi-model ensemble temperature regional application
%U http://just.ustc.edu.cn/CN/abstract/abstract218.shtml