陆面数据同化系统的输入和输出数据以其格式多样性、海量性为主要特征。以GIS二次开发组件ArcGIS Engine,ArcSDE空间数据库引擎和SQL Server 2005数据库管理工具,利用C#、IDL编程语言,构建土壤湿度同化数据空间数据库,并将气象数据具有...陆面数据同化系统的输入和输出数据以其格式多样性、海量性为主要特征。以GIS二次开发组件ArcGIS Engine,ArcSDE空间数据库引擎和SQL Server 2005数据库管理工具,利用C#、IDL编程语言,构建土壤湿度同化数据空间数据库,并将气象数据具有时间域、空间域和属性域等多维属性与GIS数据模型相结合,研制开发综合分析处理系统,实现土壤湿度同化输入参数与输出数据的空间分析与管理。系统能够满足陆面同化系统对数据的处理与分析需求,为土壤湿度同化产品的业务应用提供强大的支撑。展开更多
以内蒙古地区为研究区域,对中国气象局陆面数据同化系统(CMA Land Data Assimilation System,CLDAS)的土壤湿度和降水数据进行了评估,使用土壤相对湿度法和连续无降水日数法监测2014年夏季干旱,并选择干旱年(2014年)和湿润年(2013年)与...以内蒙古地区为研究区域,对中国气象局陆面数据同化系统(CMA Land Data Assimilation System,CLDAS)的土壤湿度和降水数据进行了评估,使用土壤相对湿度法和连续无降水日数法监测2014年夏季干旱,并选择干旱年(2014年)和湿润年(2013年)与标准化降水指数和降水百分位指数法进行验证分析。结果表明:CLDAS资料能够很好地再现日土壤相对湿度动态变化情况和降水落区与量级,能够满足干旱监测的需求;基于CLDAS数据的土壤相对湿度法可以方便、快捷地监测干旱日变化和区域性变化,连续无有效降水日数法对评估长时间、持续性干旱较为有效;CLDAS同化数据在时效性、分辨率、代表性上能够满足气象服务的需求,可作为观测资料的重要补充广泛应用于业务和科研,特别是对于地广人稀且气象站点相对较少的内蒙古地区气象服务潜力巨大。展开更多
A land surface reanalysis dataset covering the most recent decades is able to provide temporally consistent initial conditions for weather and climate models,and thus is crucial to verifying/improving numerical weathe...A land surface reanalysis dataset covering the most recent decades is able to provide temporally consistent initial conditions for weather and climate models,and thus is crucial to verifying/improving numerical weather/climate forecasts/predictions.In this paper,we report the development of a 10-yr China Meteorological Administration(CMA)global Land surface ReAnalysis Interim dataset(CRA-Interim/Land;2007–2016,6-h intervals,approximately 34-km horizontal resolution).The dataset was produced and evaluated by using the Global Land Data Assimilation System(GLDAS)and NCEP Climate Forecast System Reanalysis(CFSR)global land surface reanalysis datasets,as well as in situ observations in China.The results show that the global spatial patterns and monthly variations of the CRA-Interim/Land,GLDAS,and CFSR climatology are highly consistent,while the soil moisture and temperature values of the CRA-Interim/Land dataset are in between those of the GLDAS and CFSR datasets.Compared with ground observations in China,CRA-Interim/Land soil moisture is comparable to or better than that of GLDAS and CFSR datasets for the 0–10-cm soil layer and has higher correlations and slightly lower root mean square errors(RMSE)for the 10–40-cm soil layer.However,CRA-Interim/Land shows negative biases in 10–40-cm soil moisture in Northeast China and north of central China.For ground temperature and the soil temperature in different layers,CRA-Interim/Land behaves better than the CFSR,especially in East and central China.CRA-Interim/Land has added value over the land components of CRA-Interim due to the introduction of global precipitation observations and improved soil/vegetation parameters.Therefore,this dataset is potentially a critical supplement to the CRA-Interim.Further evaluation of the CRA-Interim/Land,assimilation of near-surface atmospheric forcing variables,and extension of the current dataset to 40 yr(1979–2018)are in progress.展开更多
积雪因为其特定的属性在气候变化和水文循环中扮演着重要角色,在大气和陆面之间起到了调节能量和水交换的显著作用,而陆面驱动数据的质量直接决定着模式对积雪的模拟效果。本文采用CLDAS(CMA Land Data Assimilation System)和改进后的...积雪因为其特定的属性在气候变化和水文循环中扮演着重要角色,在大气和陆面之间起到了调节能量和水交换的显著作用,而陆面驱动数据的质量直接决定着模式对积雪的模拟效果。本文采用CLDAS(CMA Land Data Assimilation System)和改进后的降水驱动(CLDAS-Prcp)分别驱动Noah3.6陆面模式对积雪变量进行模拟,并对中国主要的积雪区东北区域、新疆区域、青藏高原区域的积雪覆盖率、雪深、雪水当量的模拟效果进行了评估。结果表明,CLDAS-Prcp改善了原有驱动在冬季由于低估降水所造成的模拟积雪量偏少的情况;东北区域模拟结果与观测的时间变率最为一致,积雪覆盖率、雪深、雪水当量的相关系数分别为0.42,0.78,0.93;而雪水当量的改进效果最明显,均方根误差和偏差分别减小了54.8%和83.1%,相关系数提高了0.47;同时,CLDAS-Prcp不仅能反映积雪变量的年际变率,而且能够较准确地反映出强度较大的突发降雪事件。展开更多
The accuracy of land surface hydrological simulations using an offline land surface model(LSM)depends largely on the quality of the atmospheric forcing data.In this study,Global Land Data Assimilation System(GLDAS)for...The accuracy of land surface hydrological simulations using an offline land surface model(LSM)depends largely on the quality of the atmospheric forcing data.In this study,Global Land Data Assimilation System(GLDAS)forcing data and the newly developed China Meteorological Administration Land Data Assimilation System(CLDAS)forcing data are used to drive the Noah LSM with multiple parameterizations(Noah-MP)and to explore how the newly developed CLDAS forcing data improve land surface hydrological simulations over China's Mainland.The monthly soil moisture(SM)and evapotranspiration(ET)simulations are then compared and evaluated against observations.The results show that the Noah-MP driven by the CLDAS forcing data(referred to as CLDASNoah-MP)significantly improves the simulations in most cases over China's Mainland and its eight river basins.CLDASNoahMP increases the correlation coefficient(R)values from 0.451 to 0.534 for the SM simulations at a depth range of 0–10 cm in China's Mainland,especially in the eastern monsoon area such as the Huang–Huai–Hai Plain,the southern Yangtze River basin,and the Zhujiang River basin.Moreover,the root-mean-square error is reduced from 0.078 to0.068 m3 m-3 for the SM simulations,and from 12.9 to 11.4 mm month-1 for the ET simulations over China's Mainland,especially in the southern Yangtze River basin and Zhujiang River basin.This study demonstrates that,by merging more in situ and remote sensing observations in regional atmospheric forcing data,offline LSM simulations can better simulate regional-scale land surface hydrological processes.展开更多
文摘陆面数据同化系统的输入和输出数据以其格式多样性、海量性为主要特征。以GIS二次开发组件ArcGIS Engine,ArcSDE空间数据库引擎和SQL Server 2005数据库管理工具,利用C#、IDL编程语言,构建土壤湿度同化数据空间数据库,并将气象数据具有时间域、空间域和属性域等多维属性与GIS数据模型相结合,研制开发综合分析处理系统,实现土壤湿度同化输入参数与输出数据的空间分析与管理。系统能够满足陆面同化系统对数据的处理与分析需求,为土壤湿度同化产品的业务应用提供强大的支撑。
文摘以内蒙古地区为研究区域,对中国气象局陆面数据同化系统(CMA Land Data Assimilation System,CLDAS)的土壤湿度和降水数据进行了评估,使用土壤相对湿度法和连续无降水日数法监测2014年夏季干旱,并选择干旱年(2014年)和湿润年(2013年)与标准化降水指数和降水百分位指数法进行验证分析。结果表明:CLDAS资料能够很好地再现日土壤相对湿度动态变化情况和降水落区与量级,能够满足干旱监测的需求;基于CLDAS数据的土壤相对湿度法可以方便、快捷地监测干旱日变化和区域性变化,连续无有效降水日数法对评估长时间、持续性干旱较为有效;CLDAS同化数据在时效性、分辨率、代表性上能够满足气象服务的需求,可作为观测资料的重要补充广泛应用于业务和科研,特别是对于地广人稀且气象站点相对较少的内蒙古地区气象服务潜力巨大。
基金Supported by the China Meteorological Administration Special Public Welfare Research Fund(GYHY201506002)National Key Research and Development Program of China(2018YFC1506601)+1 种基金National Natural Science Foundation of China(91437220)National Innovation Project for Meteorological Science and Technology(CMAGGTD003-5).
文摘A land surface reanalysis dataset covering the most recent decades is able to provide temporally consistent initial conditions for weather and climate models,and thus is crucial to verifying/improving numerical weather/climate forecasts/predictions.In this paper,we report the development of a 10-yr China Meteorological Administration(CMA)global Land surface ReAnalysis Interim dataset(CRA-Interim/Land;2007–2016,6-h intervals,approximately 34-km horizontal resolution).The dataset was produced and evaluated by using the Global Land Data Assimilation System(GLDAS)and NCEP Climate Forecast System Reanalysis(CFSR)global land surface reanalysis datasets,as well as in situ observations in China.The results show that the global spatial patterns and monthly variations of the CRA-Interim/Land,GLDAS,and CFSR climatology are highly consistent,while the soil moisture and temperature values of the CRA-Interim/Land dataset are in between those of the GLDAS and CFSR datasets.Compared with ground observations in China,CRA-Interim/Land soil moisture is comparable to or better than that of GLDAS and CFSR datasets for the 0–10-cm soil layer and has higher correlations and slightly lower root mean square errors(RMSE)for the 10–40-cm soil layer.However,CRA-Interim/Land shows negative biases in 10–40-cm soil moisture in Northeast China and north of central China.For ground temperature and the soil temperature in different layers,CRA-Interim/Land behaves better than the CFSR,especially in East and central China.CRA-Interim/Land has added value over the land components of CRA-Interim due to the introduction of global precipitation observations and improved soil/vegetation parameters.Therefore,this dataset is potentially a critical supplement to the CRA-Interim.Further evaluation of the CRA-Interim/Land,assimilation of near-surface atmospheric forcing variables,and extension of the current dataset to 40 yr(1979–2018)are in progress.
文摘积雪因为其特定的属性在气候变化和水文循环中扮演着重要角色,在大气和陆面之间起到了调节能量和水交换的显著作用,而陆面驱动数据的质量直接决定着模式对积雪的模拟效果。本文采用CLDAS(CMA Land Data Assimilation System)和改进后的降水驱动(CLDAS-Prcp)分别驱动Noah3.6陆面模式对积雪变量进行模拟,并对中国主要的积雪区东北区域、新疆区域、青藏高原区域的积雪覆盖率、雪深、雪水当量的模拟效果进行了评估。结果表明,CLDAS-Prcp改善了原有驱动在冬季由于低估降水所造成的模拟积雪量偏少的情况;东北区域模拟结果与观测的时间变率最为一致,积雪覆盖率、雪深、雪水当量的相关系数分别为0.42,0.78,0.93;而雪水当量的改进效果最明显,均方根误差和偏差分别减小了54.8%和83.1%,相关系数提高了0.47;同时,CLDAS-Prcp不仅能反映积雪变量的年际变率,而且能够较准确地反映出强度较大的突发降雪事件。
基金Supported by the National Natural Science Foundation of China(91437220 and 41405083)Project Fund from the Education Department of Hunan Province(14C0897)Huaihua University Double First-Class Initiative in Applied Characteristic Discipline of Control Science and Engineering.
文摘The accuracy of land surface hydrological simulations using an offline land surface model(LSM)depends largely on the quality of the atmospheric forcing data.In this study,Global Land Data Assimilation System(GLDAS)forcing data and the newly developed China Meteorological Administration Land Data Assimilation System(CLDAS)forcing data are used to drive the Noah LSM with multiple parameterizations(Noah-MP)and to explore how the newly developed CLDAS forcing data improve land surface hydrological simulations over China's Mainland.The monthly soil moisture(SM)and evapotranspiration(ET)simulations are then compared and evaluated against observations.The results show that the Noah-MP driven by the CLDAS forcing data(referred to as CLDASNoah-MP)significantly improves the simulations in most cases over China's Mainland and its eight river basins.CLDASNoahMP increases the correlation coefficient(R)values from 0.451 to 0.534 for the SM simulations at a depth range of 0–10 cm in China's Mainland,especially in the eastern monsoon area such as the Huang–Huai–Hai Plain,the southern Yangtze River basin,and the Zhujiang River basin.Moreover,the root-mean-square error is reduced from 0.078 to0.068 m3 m-3 for the SM simulations,and from 12.9 to 11.4 mm month-1 for the ET simulations over China's Mainland,especially in the southern Yangtze River basin and Zhujiang River basin.This study demonstrates that,by merging more in situ and remote sensing observations in regional atmospheric forcing data,offline LSM simulations can better simulate regional-scale land surface hydrological processes.
