降尺度方法在中国不同区域夏季降水预测中的应用

Development of a downscaling method in China regional summer precipitation prediction

中文关键词:  海-气耦合模式， 降尺度方法， 区域降水， 预测

英文关键词:Coupled Ocean-Atmosphere General Climate Model (CGCM), Downscaling method, Regional precipitation, Prediction

中文摘要:

      在中国降水气候分区的基础上，利用降尺度方法进行区域夏季降水预测（RSPP），预测模型建立的基础是寻找影响区域气候的关键因子。降尺度预测模型中使用的资料有国家气候中心海 气耦合模式（CGCM/NCC）回报资料、NCEP/NCAR再分析资料和台站观测资料。为了避免年代际变化特征对季节尺度降水预测的影响，首先对CGCM/NCC模式输出资料、NCEP/NCAR再分析资料、区域平均降水资料去除年代际线性变化趋势，即去除所有预报因子场和预报对象场的长期变化趋势。然后分别计算预报对象和模式资料的预报因子场以及再分析资料的预报因子场的相关系数，把相关系数值同时达到 0.05 显著性检验水平的区域平均环流特征作为预测因子，保证挑选出的预测因子既能反映实际大气中预测因子与预报对象的关系，同时又是海 气耦合模式预测的高技巧信息。利用最优子集回归作为转换函数的降尺度方法建立区域夏季降水预测模型。交叉检验和独立样本检验结果表明，文中设计的区域夏季降水预测模型对中国大部分地区的夏季降水趋势预测的准确率较高且比较稳定，其预测效果远高于CGCM/NCC直接输出降水结果。进一步对具有较高预测技巧的代表性区域的可预报性来源分析发现，物理意义明确且独立性强的预测因子有助于提高预测准确率。

英文摘要:

      A downscaling method taking into account of precipitation regionalization is developed and used in the regional summer precipitation prediction (RSPP) in China. The downscaling is realized by utilizing the optimal subset regression based on the hindcast data of the Coupled Ocean Atmosphere General Climate Model of National Climate Center (CGCM/NCC), the historical reanalysis data, and the observations. The data are detrended in order to remove the influence of the interannual variations on the selection of predictors for the RSPP. Optimal predictors are selected through calculation of anomaly correlation coefficients (ACC) twice to ensure that the high skill areas of the CGCM/NCC are also those of observations, with the ACC value reaching the 0.05 significant level. One-year out cross-validation and independent sample tests indicate that the downscaling method is applicable in the prediction of summer precipitation anomaly across most of China with high and stable accuracy, and is much better than the direct CGCM/NCC prediction. The predictors used in the downscaling method for the RSPP are independent and have strong physical meanings, thus leading to the improvements in the prediction of regional precipitation anomalies.