We analyzed the spatiotemporal variations in surface air temperature and key climate change indicators over the Tibetan Plateau during a common valid period from 1979 to 2018 to evaluate the performance of different d...We analyzed the spatiotemporal variations in surface air temperature and key climate change indicators over the Tibetan Plateau during a common valid period from 1979 to 2018 to evaluate the performance of different datasets on various timescales.We used observations from 22 in-situ observation sites,the CRA-40/Land(CRA)reanalysis dataset,the China Meteorological Forcing Dataset(CMFD),and the ERA-Interim(ERA)reanalysis dataset.The three datasets are spatially consistent with the in-situ observations,but slightly underestimate the annual mean surface air temperature.The daily mean surface air temperature estimated by the CRA,CMFD,and ERA datasets is closer to the in-situ observations after correction for elevation.The CMFD shows the best performance in simulating the annual mean surface air temperature over the Tibetan Plateau,followed by the CRA and ERA datasets with comparable performances.The CMFD is relatively accurate in simulating the daily mean surface air temperature over the Tibetan Plateau on an annual scale,whereas both the CRA and ERA datasets perform better in summer than in winter.The increasing trends in the annual mean surface air temperature over the Tibetan Plateau from 1979 to 2018 reflected by the CRA dataset and the CMFD are 0.5℃(10 yr)^(-1),similar to the in-situ observations,whereas the warming rate in the ERA dataset is only 0.3℃(10 yr)^(-1).The trends in the length of the growing season derived from the in-situ observations,the CRA,CMFD,and ERA datasets are 5.3,4.8,6.1,and 3.2 day(10 yr)^(-1),respectively.Our analyses suggest that both the CRA dataset and the CMFD perform better than the ERA dataset in modeling the changes in surface air temperature over the Tibetan Plateau.展开更多
Assimilation of atmospheric motion vectors(AMVs)is important in the initialization of the atmospheric state in numerical weather prediction models,especially over oceans and at high latitudes where conventional data a...Assimilation of atmospheric motion vectors(AMVs)is important in the initialization of the atmospheric state in numerical weather prediction models,especially over oceans and at high latitudes where conventional data are sparse.This paper presents a detailed description of the pre-processing,quality assurance,and use of global AMVs in China’s first generation of the 40-yr(1979-2018)CRA global atmospheric reanalysis product.A new AMV archive is integrated from near real-time operational Global Telecommunication System data and reprocessed AMV datasets released or produced mainly during 2014-2016 according to a priority principle.To avoid the misuse of data with systematic quality problems,the observations of all 18 types of AMVs from 54 satellites are pre-evaluated over the whole time series.The pre-evaluation system developed by the CRA team is based on the NCEP Gridpoint Statistical Interpolation(GSI)three-dimensional variational assimilation system and the ERA-Interim reanalysis product.The AMVs in the new AMV archive are denser than the AMVs prepared for the Climate Forecast System Reanalysis product,the bias and root-mean-square values are smaller,and the time series are steadier.The new AMV archive is assimilated in the CRA product based on the NCEP GSI assimilation procedure and quality control configuration with reference to the pre-evaluation results.This is the first time that the reprocessed AMVs from Fengyun-2 satellites from June 2005 to July 2017 are assimilated in a reanalysis product.The assimilation features inspire confidence in the accuracy and stability of these data.The mean root-mean-square values of the observation minus analysis infrared,water vapor,and visible AMV were 1.5-3.4,2.7-3.6,and 1.3-2.1 m s-1,respectively.This experience of integrating,pre-evaluating,and assimilating AMV observations is valuable for the next generation of reanalysis products.展开更多
基金Supported by the Second Tibetan Plateau Scientific Expedition and Research Program(2019QZKK1001)Science Funds from Beijing Meteorological Service(BMBKJ202003008)。
文摘We analyzed the spatiotemporal variations in surface air temperature and key climate change indicators over the Tibetan Plateau during a common valid period from 1979 to 2018 to evaluate the performance of different datasets on various timescales.We used observations from 22 in-situ observation sites,the CRA-40/Land(CRA)reanalysis dataset,the China Meteorological Forcing Dataset(CMFD),and the ERA-Interim(ERA)reanalysis dataset.The three datasets are spatially consistent with the in-situ observations,but slightly underestimate the annual mean surface air temperature.The daily mean surface air temperature estimated by the CRA,CMFD,and ERA datasets is closer to the in-situ observations after correction for elevation.The CMFD shows the best performance in simulating the annual mean surface air temperature over the Tibetan Plateau,followed by the CRA and ERA datasets with comparable performances.The CMFD is relatively accurate in simulating the daily mean surface air temperature over the Tibetan Plateau on an annual scale,whereas both the CRA and ERA datasets perform better in summer than in winter.The increasing trends in the annual mean surface air temperature over the Tibetan Plateau from 1979 to 2018 reflected by the CRA dataset and the CMFD are 0.5℃(10 yr)^(-1),similar to the in-situ observations,whereas the warming rate in the ERA dataset is only 0.3℃(10 yr)^(-1).The trends in the length of the growing season derived from the in-situ observations,the CRA,CMFD,and ERA datasets are 5.3,4.8,6.1,and 3.2 day(10 yr)^(-1),respectively.Our analyses suggest that both the CRA dataset and the CMFD perform better than the ERA dataset in modeling the changes in surface air temperature over the Tibetan Plateau.
基金Supported by the China Meteorological Administration Special Public Welfare Research Fund (GYHY201506002)National Natural Science Foundation of China (92037000)+1 种基金National Innovation Project for Meteorological Science and Technology (CMAGGTD003-5)Balance Fund of the National Meteorological Information Centre (NMICJY202106)。
文摘Assimilation of atmospheric motion vectors(AMVs)is important in the initialization of the atmospheric state in numerical weather prediction models,especially over oceans and at high latitudes where conventional data are sparse.This paper presents a detailed description of the pre-processing,quality assurance,and use of global AMVs in China’s first generation of the 40-yr(1979-2018)CRA global atmospheric reanalysis product.A new AMV archive is integrated from near real-time operational Global Telecommunication System data and reprocessed AMV datasets released or produced mainly during 2014-2016 according to a priority principle.To avoid the misuse of data with systematic quality problems,the observations of all 18 types of AMVs from 54 satellites are pre-evaluated over the whole time series.The pre-evaluation system developed by the CRA team is based on the NCEP Gridpoint Statistical Interpolation(GSI)three-dimensional variational assimilation system and the ERA-Interim reanalysis product.The AMVs in the new AMV archive are denser than the AMVs prepared for the Climate Forecast System Reanalysis product,the bias and root-mean-square values are smaller,and the time series are steadier.The new AMV archive is assimilated in the CRA product based on the NCEP GSI assimilation procedure and quality control configuration with reference to the pre-evaluation results.This is the first time that the reprocessed AMVs from Fengyun-2 satellites from June 2005 to July 2017 are assimilated in a reanalysis product.The assimilation features inspire confidence in the accuracy and stability of these data.The mean root-mean-square values of the observation minus analysis infrared,water vapor,and visible AMV were 1.5-3.4,2.7-3.6,and 1.3-2.1 m s-1,respectively.This experience of integrating,pre-evaluating,and assimilating AMV observations is valuable for the next generation of reanalysis products.
