云贵川三省汛期月降水的模式释用集成预测研究

基于国家气候中心（NCC_CSM11）、美国国家环境预报中心（NCEP_CFS2）和欧洲中期天气预报中心（ECMWF_SYSTEM4）三种模式产品资料，以及美国国家环境预报中心（NCEP）的大气环流再分析资料和云南、贵州及四川三省359个站点月降水资料，搜寻模式“有预测技巧的高影响关键区”，使用逐步回归方法建立预测释用模型，比较采用原始资料和年际增量两种资料序列的释用结果与模式结果的预测性能。结果表明:原始资料释用结果月均PC评分与模式结果相近，PS和ACC评分则有所提高；增量资料释用结果对月降水预测性能的改进优于原始资料释用结果；进一步采用超级集合平均方法对各月三种模式增量资料释用结果进行集成，预测评分在绝大多数情形下超过参与集成的各成员评分最高值，集成结果对ACC提升最明显，其次为PC评分，对PS评分改进不明显。 

Research of Model Interpretation and Integrated Prediction of Monthly Precipitation in Flood Season in Yunnan,Guizhou and Sichuan Provinces

Based on three climate model products from NCC_CSM11, NCEP_CFS2 and ECMWF_SYSTEM4, atmospheric circulation reanalysis data from NCEP and monthly precipitation data of 359 stations in Yunnan, Guizhou and Sichuan provinces, seeking "key area with high prediction skills" of models and implementing stepwise regression scheme, the performances of monthly precipitation prediction of model interpretation results using interannual increment data and source data are discussed compared to model direct output results. The conclusions show that the PC score of model interpretation results using source data is close to that of model direct outputs, while the PS and ACC scores are slightly improved. The performances of model interpretation results using interannual increment data are superior to those of using source data. Furthermore, the integrated prediction results are obtained based on superensemble mean integration method with three model interpretation results using interannual increment data each month. In most cases, the prediction scores of the integrated result are higher than the maximum score of the model interpretation results involved in ensemble process. The improvement of ACC of integrated result is the most obvious, followed by PC, while the improvement of PS is not clear.