华北汛期降水分离时间尺度降尺度预测模型的改进

An Improvement in a Time-Scale Decomposition Statistical Downscaling Prediction Model for Summer Rainfall over North China

本文采用偏相关预报因子挑选法和条件降尺度法,对已有的华北汛期（7~8月）降水时间尺度分离（TSD）降尺度模型进行了改进。利用偏相关法,找到一个新的影响华北汛期降水年际分量的前期预报因子,即6月北大西洋—欧亚遥相关（AEAT）。该因子将扰动信号储存于北大西洋三极子结构,并在7~8月释放出来影响下游贝加尔湖低压系统的发展,从而影响华北汛期降水。利用6月Ni？o3指数和AEAT指数,本文建立了条件TSD统计降尺度模型,即按照预报因子的强度进行逐年分类,对于每个分类设计相应的预报模型,从而避免信息较弱因子的干扰。条件TSD降尺度方法显著改善了华北汛期降水的预测技巧,在独立检验阶段,预报降水与观测降水的相关系数由原模型的0.61提高到0.77,符号一致率从70%提高到87%。

This paper applies partial-correlation predictor selection and a conditional downscaling method to improve a Time-Scale Decomposition（TSD） statistical downscaling model of summer（July and August, JA） rainfall over North China. A new preceding predictor, the North Atlantic–Eurasia Teleconnection（AEAT） in June is found by using the partial-correlation predictor selection method. This predictor stores its signal in the tripole sea surface temperature pattern in the North Atlantic and impacts on the development of depressions over Baikal in the following July and August, which further influences the rainfall over North China. A conditional TSD statistical downscaling model is built with the predictors of Ni？o3 index and AEAT Index（AEATI）. Rather than fixed models for every year, indices are classified into several types according to the predictor strength, and corresponding models are built for each type. The conditional statistical model avoids the influence from weak predictors for a particular year. In independent validation, the conditional TSD downscaling model improves the performance of Summer Rainfall over North China（NCSR） prediction. The correlation coefficient between observed and predicted rainfall increases from 0.61 to 0.77 and the anomaly sign consistency rate increases from 70% to 87%.