ECC方法在中国夏季气温预测研究中的应用

Application of an Ensemble Canonical Correlation Method in Summer Temperature Prediction over China

以中国夏季气温为预测对象,选取东亚地区冬季500 h Pa高度场、海平面气压场、地表温度场和850 h Pa温度场为预测因子,采用1951～2009年去趋势处理后的资料,通过变形的典型相关分析（Barnett-Preisendorfer Canonical Correlation Analysis,BP-CCA）方法分别建立单因子预测模型,再利用集合典型相关分析（Ensemble Canonical Correlation,ECC）方法建立集合预测模型,对中国夏季气温进行基于交叉检验方法的预测试验,然后利用2010～2014年的资料对中国夏季气温进行独立样本检验。通过分析BP-CCA模态可知,一对BP-CCA模态的空间型在一定程度上可以反映预报因子场和对象场的遥相关特征。通过基于交叉检验方法的预测试验表明环流场和热力场均能为气温提供预测信息。ECC预测模型综合了各个预报因子的在不同地区的预报技巧,比单因子BP-CCA预测模型有更高、更稳定的预报技巧。独立样本检验表明ECC模型与单因子BP-CCA预测模型相比,对中国夏季气温有更高、更稳定的实际预测能力,对气温季节预测具有参考价值。

Using geopotential height at 500 h Pa, sea level pressure, surface temperature, and temperature at 850 h Pa in winter over East Asia as predictors, predictability in summer temperature over China is analyzed. Based on the detrended datasets during the period 1951–2009, individual forecasting models produced separately by Barnett-Preisendorfer canonical correlation analysis（BP-CCA） are established, and the ensemble canonical correlation（ECC） prediction based on one-year-out cross validation is used to predict the summer temperature over China during the same period. Independent sample tests are then performed based on these datasets over the period 2010–2014. Analyzing the BP-CCA mode shows that the spatial patterns of BP-CCA can in general reflect the remote correlation characteristics between predictor and predictand. By the prediction test based on one-year-out cross validation, it is confirmed that circulation and thermal fields can provide effective information for temperature prediction. Since ECC prediction collected the skill of each predictor in different areas, its skill is higher and more stable than any individual BP-CCA prediction. Compared with these individual BP-CCA models, the ECC model in the independent sample test shows a better and more stable performance in predicting summer temperature, which is effective for seasonal temperature prediction.