基于季节气候可预报分量的相似误差订正方法和数值实验

基于由历史相似信息对模式预报误差进行预报的思路，发展了一种针对季节气候可预报分量的相似误差订正新方法（FACEPC），目的是识别模式预报结果中对初值相对不够敏感的可预报分量，将其作为对象进行历史相似选取和误差订正.该方法被应用到国家气候中心业务季节预报模式实验中，对不同区域给出有针对性的相似选取指标和预报方案.25年的交叉检验结果表明，夏季降水和环流的预报技巧评分相对于系统误差订正有明显提高，在发生中等及以上强度ENSO事件年和可预报分量贡献较大地区的预报技巧提高更为显著.特别是中国区域降水和关键区环流的 关键词： 短期气候预测 相似误差订正 可预报分量

Analogue correction of errors based on seasonal climatic predictable components and numerical experiments

Based on the idea of using historical-analogue information to predict the prediction errors of model, a new method named analogue correction of errors by predictable component （FACEPC） was developed. This method is adopted to identify the predicable components for which the prediction result is relatively not quite sensitive to the initial values. And then for predicable components, an associated scheme is chosen for historical-analogue selection and error correction. This method was further applied to experiments on operational seasonal prediction model of National Climate Center. By selecting suitable analogues and prediction schemes for different regions, the results from cross-validation indicate that the predictive skill scores of summer precipitation and circulation have got significant improvement relative to systematic error correction, which looks more obvious in ENSO episodes and over regions with more predictable components. Especially, the skill scores over China area have also been clearly improved, exhibiting its potential application perspective to operational seasonal prediction. Besides, preliminary sensitive experiments show that the FACEPC-based predictions are also obviously influenced by the analogue-selected factors and the length of historical data.