The China Meteorological Administration(CMA)recently produced a CMA Global Atmospheric Interim Reanalysis(CRAI)dataset for the years 2007–2016.A comprehensive evaluation of the ability of CRAI to capture the spatiote...The China Meteorological Administration(CMA)recently produced a CMA Global Atmospheric Interim Reanalysis(CRAI)dataset for the years 2007–2016.A comprehensive evaluation of the ability of CRAI to capture the spatiotemporal variability of observed precipitation,in terms of both mean states and extreme indicators over China,is performed.Comparisons are made with other current reanalysis datasets,namely,the ECMWF interim reanalysis(ERAI),Japanese 55-yr reanalysis(JRA55),NCEP Climate Forecast System Reanalysis(CFSR),and NASA Modern-Era Retrospective analysis for Research and Applications version 2(MERRA2),as well as NCEP Climate Prediction Center(CPC)observations.The results show that,for daily variations of rainfall during warm seasons in eastern China,CRAI and CFSR overestimate the precipitation of the main rain belt,while the overestimation is confined to the area south of 25°N in JRA55 but north of 24°N in MERRA2;whereas ERAI tends to underestimate the precipitation in most regions of eastern China.Two extreme metrics,the total amount of precipitation on days where daily precipitation exceeds the 95 th percentile(R95 pTOT)and the number of consecutive dry days(CDDs)in one month,are examined to assess the performance of reanalysis datasets.In terms of extreme events,CRAI,ERAI,and JRA55 tend to underestimate the R95 pTOT in most of eastern China,whereas more frequent extreme rainfall can be found in most regions of China in both CFSR and MERRA2;and all of the reanalyses underestimate the CDDs.Among the reanalysis products,CRAI and JRA55 show better agreement with the observed R95 pTOT than the other datasets,with fewer biases,higher correlation coefficients,and much more similar linear trend patterns,while ERAI stands out in better capturing the amount and temporal variations of the observed CDDs.展开更多
The global energy cycle is a diagnostic metric widely used to gauge the quality of datasets. In this paper, the "Mixed Space-Time Domain" method for diagnosis of energy cycle is evaluated by using newly deve...The global energy cycle is a diagnostic metric widely used to gauge the quality of datasets. In this paper, the "Mixed Space-Time Domain" method for diagnosis of energy cycle is evaluated by using newly developed datasets-the Chinese Reanalysis Interim (CRAI) and ECMWF Reanalysis version 5 (ERA5), over a 7-yr period (2010-16) on seasonal and monthly timescales. The results show that the energy components calculated from the two reanalysis datasets are highly consistent;however, some components in the global energy integral from CRAI are slightly larger than those from ERA5. The main discrepancy in the energy components stems from the conversion of baroclinic process, whereas the dominant difference originates from the conversion from stationary eddy available potential energy to stationary eddy kinetic energy (CES), which is caused by systematic differences in the temperature and vertical velocity in low-mid latitudes of the Northern Hemisphere and near the Antarctic, where there exist complex terrains. Furthermore, the monthly analysis reveals that the general discrepancy in the temporal variation between the two datasets also lie mainly in the CES as well as corresponding generation and dissipation rates.展开更多
基金Supported by the China Meteorological Administration Special Public Welfare Research Fund(GYHY201506002)National Natural Science Foundation of China(41790475,41675094,and 41605066).
文摘The China Meteorological Administration(CMA)recently produced a CMA Global Atmospheric Interim Reanalysis(CRAI)dataset for the years 2007–2016.A comprehensive evaluation of the ability of CRAI to capture the spatiotemporal variability of observed precipitation,in terms of both mean states and extreme indicators over China,is performed.Comparisons are made with other current reanalysis datasets,namely,the ECMWF interim reanalysis(ERAI),Japanese 55-yr reanalysis(JRA55),NCEP Climate Forecast System Reanalysis(CFSR),and NASA Modern-Era Retrospective analysis for Research and Applications version 2(MERRA2),as well as NCEP Climate Prediction Center(CPC)observations.The results show that,for daily variations of rainfall during warm seasons in eastern China,CRAI and CFSR overestimate the precipitation of the main rain belt,while the overestimation is confined to the area south of 25°N in JRA55 but north of 24°N in MERRA2;whereas ERAI tends to underestimate the precipitation in most regions of eastern China.Two extreme metrics,the total amount of precipitation on days where daily precipitation exceeds the 95 th percentile(R95 pTOT)and the number of consecutive dry days(CDDs)in one month,are examined to assess the performance of reanalysis datasets.In terms of extreme events,CRAI,ERAI,and JRA55 tend to underestimate the R95 pTOT in most of eastern China,whereas more frequent extreme rainfall can be found in most regions of China in both CFSR and MERRA2;and all of the reanalyses underestimate the CDDs.Among the reanalysis products,CRAI and JRA55 show better agreement with the observed R95 pTOT than the other datasets,with fewer biases,higher correlation coefficients,and much more similar linear trend patterns,while ERAI stands out in better capturing the amount and temporal variations of the observed CDDs.
基金Supported by the China Meteorological Administration(CMA)Special Public Welfare Research Fund(GYHY201506002)National Key Research and Development Program of China(2017YFA0604500)+1 种基金CMA Special Project for Developing Key Techniques for Operational Meteorological Forecast(YBGJXM201706)National Natural Science Foundation of China(41305091)
文摘The global energy cycle is a diagnostic metric widely used to gauge the quality of datasets. In this paper, the "Mixed Space-Time Domain" method for diagnosis of energy cycle is evaluated by using newly developed datasets-the Chinese Reanalysis Interim (CRAI) and ECMWF Reanalysis version 5 (ERA5), over a 7-yr period (2010-16) on seasonal and monthly timescales. The results show that the energy components calculated from the two reanalysis datasets are highly consistent;however, some components in the global energy integral from CRAI are slightly larger than those from ERA5. The main discrepancy in the energy components stems from the conversion of baroclinic process, whereas the dominant difference originates from the conversion from stationary eddy available potential energy to stationary eddy kinetic energy (CES), which is caused by systematic differences in the temperature and vertical velocity in low-mid latitudes of the Northern Hemisphere and near the Antarctic, where there exist complex terrains. Furthermore, the monthly analysis reveals that the general discrepancy in the temporal variation between the two datasets also lie mainly in the CES as well as corresponding generation and dissipation rates.
