摘要:现阶段的动力气候模式尚不能满足东亚区域气候预测的实际需求，这就需要动力和统计相结合的方法，将动力模式中具有较高预测技巧的大尺度环流信息应用到降水等气象要素的统计预测模型当中，以改善后者预测效果。本文中所介绍的组合统计降尺度模型，可将动力气候模式预测的大尺度环流变量和前期观测的外强迫信号作为预测因子来预测中国夏季降水异常。交叉检验结果显示，组合统计降尺度预测模型的距平相关系数较原始模式结果有较大提高。在实时夏季降水预测中，2013～2018年平均的预测技巧相对较高，趋势异常综合检验（PS）评分平均为71.5分，特别是2015～2018年平均的PS评分预测技巧达到72.7分，总体上高于业务模式原始预测和业务发布预测的技巧。该组合统计降尺度模型预测性能稳定，为我国季节预测业务提供了一种有效参考。
关键词:组合统计降尺度模型/
季节预测/
夏季降水
Abstract:Nowadays, dynamical climate models are inefficient in meeting the real needs of climate prediction. An effective method is the combination of dynamical and statistical models. This combination integrates large-scale circulation information from the dynamical models into the statistical model to improve the prediction skill. On the basis of the higher prediction skill for the large-scale summer circulation variable of climate models and the significant relationship between the preceding ENSO signal and summer precipitation in China, a hybrid statistical downscaling prediction method for summer precipitation anomaly prediction in China was proposed in this paper. Cross validation of seasonal prediction for summer precipitation in China was performed, and results showed that the downscaling method improved the multi-year average of anomaly correlation coefficient significantly. In real application, the average PS score reached 71.5/72.7 during 2013–2018/2015–2018, which is higher than that of the original model and the operational predictions issued by the Beijing Climate Center. This statistical downscaling model, which has stable predictive skill, is one of the effective references for operational seasonal prediction in China.
Key words:Hybrid statistical downscaling model/
Seasonal prediction/
Summer precipitation
PDF全文下载地址:
http://www.iapjournals.ac.cn/qhhj/article/exportPdf?id=20200205
