An effective statistical downscaling scheme was developed on the basis of singular value decomposition to predict boreal winter(December-January-February)precipitation over China.The variable geopotential height at 50...An effective statistical downscaling scheme was developed on the basis of singular value decomposition to predict boreal winter(December-January-February)precipitation over China.The variable geopotential height at 500 hPa(GH5)over East Asia,which was obtained from National Centers for Environmental Prediction’s Coupled Forecast System(NCEP CFS),was used as one predictor for the scheme.The preceding sea ice concentration(SIC)signal obtained from observed data over high latitudes of the Northern Hemisphere was chosen as an additional predictor.This downscaling scheme showed significantly improvement in predictability over the original CFS general circulation model(GCM)output in cross validation.The multi-year average spatial anomaly correlation coefficient increased from–0.03 to 0.31,and the downscaling temporal root-mean-square-error(RMSE)decreased significantly over that of the original CFS GCM for most China stations.Furthermore,large precipitation anomaly centers were reproduced with greater accuracy in the downscaling scheme than those in the original CFS GCM,and the anomaly correlation coefficient between the observation and downscaling results reached～0.6 in the winter of 2008.展开更多
Understanding the characteristics of the dynamic relationship between the onshore Ren- minbi （CNY） and the offshore Renminbi （CNH） exchange rates considering the impact of some extreme events is very important and...Understanding the characteristics of the dynamic relationship between the onshore Ren- minbi （CNY） and the offshore Renminbi （CNH） exchange rates considering the impact of some extreme events is very important and it has wide implications in several areas such as hedging. For better esti- mating the dynamic relationship between CNY and CNH, the Granger-causality test and Bry-Boschan Business Cycle Dating Algorithm were employed in this paper. Due to the intrinsic complexity of the lead-lag relationships between CNY and CNH, the empirical mode decomposition （EMD） algorithm is used to decompose those time series data into several intrinsic mode function （IMF） components and a residual sequence, from high to low frequency. Based on the frequencies, the IMFs and a residual sequence are combined into three components, identified as short-term composition caused by some market activities, medium-term composition caused by some extreme events and the long-term trend.The empirical results indicate that when it only matters the short-term market activities, CNH always leads CNY; while the medium-term impact caused by those extreme events may alternate the lead-lag relationships between CNY and CNH.展开更多
基金supported by the China Meteorological Special Project(GYHY201206016)the National Basic Research Program of China(2010CB950304)the Innovation Key Program of the Chinese Academy of Sciences(KZCX2-YW-QN202)
文摘An effective statistical downscaling scheme was developed on the basis of singular value decomposition to predict boreal winter(December-January-February)precipitation over China.The variable geopotential height at 500 hPa(GH5)over East Asia,which was obtained from National Centers for Environmental Prediction’s Coupled Forecast System(NCEP CFS),was used as one predictor for the scheme.The preceding sea ice concentration(SIC)signal obtained from observed data over high latitudes of the Northern Hemisphere was chosen as an additional predictor.This downscaling scheme showed significantly improvement in predictability over the original CFS general circulation model(GCM)output in cross validation.The multi-year average spatial anomaly correlation coefficient increased from–0.03 to 0.31,and the downscaling temporal root-mean-square-error(RMSE)decreased significantly over that of the original CFS GCM for most China stations.Furthermore,large precipitation anomaly centers were reproduced with greater accuracy in the downscaling scheme than those in the original CFS GCM,and the anomaly correlation coefficient between the observation and downscaling results reached～0.6 in the winter of 2008.
基金partially supported by the National Natural Science Foundation of China under Grant Nos.71390330,71390331,71390335the National Nature Science Foundation of China for financial support to this study+1 种基金supported by the Postdoctorate Programme of Centre University of Economics and Financethe Postodctorate Programme of China Great Wall Asset Management Corporation
文摘Understanding the characteristics of the dynamic relationship between the onshore Ren- minbi （CNY） and the offshore Renminbi （CNH） exchange rates considering the impact of some extreme events is very important and it has wide implications in several areas such as hedging. For better esti- mating the dynamic relationship between CNY and CNH, the Granger-causality test and Bry-Boschan Business Cycle Dating Algorithm were employed in this paper. Due to the intrinsic complexity of the lead-lag relationships between CNY and CNH, the empirical mode decomposition （EMD） algorithm is used to decompose those time series data into several intrinsic mode function （IMF） components and a residual sequence, from high to low frequency. Based on the frequencies, the IMFs and a residual sequence are combined into three components, identified as short-term composition caused by some market activities, medium-term composition caused by some extreme events and the long-term trend.The empirical results indicate that when it only matters the short-term market activities, CNH always leads CNY; while the medium-term impact caused by those extreme events may alternate the lead-lag relationships between CNY and CNH.
