MODES月预测产品在贵州的释用

Application of Multi-Model Downscaling Ensemble Prediction System(MODES)in Guizhou

基于符号一致率评分(Pc)法,对多模式解释应用集成预测系统(MODES)的6种模式产品开展贵州85站2要素月预测释用,根据评估结果进一步采用等权平均方案和最优方案对6种模式产品展开2012—2015年48个月的回算统计。分析表明:不同的模式产品的预报性能各不相同,其中ECMWF的模式产品的预测性能较NCC和NCEP来说相对较高;模式产品释用时,最优方案的预报评分均高于其它6个模式产品的Pc评分平均值,且最优方案的预报评分高于等权平均方法的预报评分;回算结果,无论是气温还是降水,最优方案的Pc评分均都高于省级发布的月预测产品评分,这说明利用最优方案可以有效地提高MODES对贵州月气温和降水预测能力。

Based on the six outputs of models estimated by the multi-model downscaling ensemble prediction system（MODES）,temperature and precipitation were evaluated in the prediction performance and analyzed by using the symbol consistency method（Pc）.According to the results of the evaluation,the equal-weight averaging scheme and the optimal scheme are used to statistically integrate the six model products for local application from 2012 to 2015.The results indicate that the prediction performance of the model products is different,and the prediction performance of ECMWF is higher than that of NCC and NCEP.By comparing the two schemes,the scores of the optimal scheme are higher than the average score of six outputs and the equal-weight averaging scheme no matter temperature or precipitation.Meanwhile,the scores of the optimal scheme are higher than those of the released prediction products.Therefore,using the optimal scheme can effectively improve the effect of MODES on predicted performance.