摘要
A statistical downscaling approach based on multiple-linear-regression(MLR) for the prediction of summer precipitation anomaly in southeastern China was established,which was based on the outputs of seven operational dynamical models of Development of a European Multi-model Ensemble System for Seasonal to Interannual Prediction(DEMETER) and observed data.It was found that the anomaly correlation coefficients(ACCs) spatial pattern of June-July-August(JJA) precipitation over southeastern China between the seven models and the observation were increased significantly;especially in the central and the northeastern areas,the ACCs were all larger than 0.42(above 95% level) and 0.53(above 99% level).Meanwhile,the root-mean-square errors(RMSE) were reduced in each model along with the multi-model ensemble(MME) for some of the stations in the northeastern area;additionally,the value of RMSE difference between before and after downscaling at some stations were larger than 1 mm d-1.Regionally averaged JJA rainfall anomaly temporal series of the downscaling scheme can capture the main characteristics of observation,while the correlation coefficients(CCs) between the temporal variations of the observation and downscaling results varied from 0.52 to 0.69 with corresponding variations from-0.27 to 0.22 for CCs between the observation and outputs of the models.
A statistical downscaling approach based on multiple-linear-regression （MLR） for the prediction of summer precipitation anomaly in southeastern China was established, which was based on the outputs of seven op- erational dynamical models of Development of a Euro- pean Multi-model Ensemble System for Seasonal to In- terannual Prediction （DEMETER） and observed data. It was found that the anomaly correlation coefficients （ACCs） spatial pattern of June-July-August （JJA） pre- cipitation over southeastern China between the seven models and the observation were increased significantly; especially in the central and the northeastern areas, the ACCs were all larger than 0.42 （above 95% level） and 0.53 （above 99% level）. Meanwhile, the root-mean-square errors （RMSE） were reduced in each model along with the multi-model ensemble （MME） for some of the stations in the northeastern area; additionally, the value of RMSE difference between before and after downscaling at some stations were larger than 1 mm d-1. Regionally averaged JJA rainfall anomaly temporal series of the downscaling scheme can capture the main characteristics of observa- tion, while the correlation coefficients （CCs） between the temporal variations of the observation and downscaling results varied from 0.52 to 0.69 with corresponding varia- tions from -0.27 to 0.22 for CCs between the observation and outputs of the models.
基金
supported by the special Fund for Public Welfare Industry (Meteorology) (Grant No. GYHY200906018)
the National Basic Research Program of China (Grant Nos. 2010CB950304 and 2009CB421406)
the Knowl-edge Innovation Program of the Chinese Academy of Sciences (Grant No. KZCX2-YW-QN202)
