Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1002/2015WR017548 |
Soil moisture background error covariance and data assimilation in a coupled land-atmosphere model | |
Lin, Liao-Fan1; Ebtehaj, Ardeshir M.1,2; Wang, Jingfeng1; Bras, Rafael L.1 | |
2017-02-01 | |
发表期刊 | WATER RESOURCES RESEARCH
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ISSN | 0043-1397 |
EISSN | 1944-7973 |
出版年 | 2017 |
卷号 | 53期号:2 |
文章类型 | Article |
语种 | 英语 |
国家 | USA |
英文摘要 | This study characterizes the space-time structure of soil moisture background error covariance and paves the way for the development of a soil moisture variational data assimilation system for the Noah land surface model coupled to the Weather Research and Forecasting (WRF) model. The soil moisture background error covariance over the contiguous United States exhibits strong seasonal and regional variability with the largest values occurring in the uppermost soil layer during the summer. Large background error biases were identified, particularly over the southeastern United States, caused mainly by the discrepancy between the WRF-Noah simulations and the initial conditions derived from the used operational global analysis data set. The assimilation of the Soil Moisture and Ocean Salinity (SMOS) soil moisture data notably reduces the error of soil moisture simulations. On average, data assimilation with space-time varying background error covariance results in 33% and 35% reduction in the root-mean-square error and the mean absolute error, respectively, in the simulation of hourly top 10 cm soil moisture, mainly due to implicit reductions in soil moisture biases. In terms of correlation, the improvement in soil moisture simulations is also observed but less notable, indicating the limitation of coarse-scale soil moisture data assimilation in capturing fine-scale soil moisture variation. In addition, soil moisture data assimilation improves the simulations of latent heat fluxes but shows a marginal impact on the simulations of sensible latent heat fluxes and precipitation. |
领域 | 资源环境 |
收录类别 | SCI-E |
WOS记录号 | WOS:000398568800017 |
WOS关键词 | ENSEMBLE KALMAN FILTER ; SCALE DATA ASSIMILATION ; BULK PARAMETERIZATION ; EXPLICIT FORECASTS ; WEATHER RESEARCH ; SURFACE MODEL ; PART II ; PRECIPITATION ; CONVECTION ; MESOSCALE |
WOS类目 | Environmental Sciences ; Limnology ; Water Resources |
WOS研究方向 | Environmental Sciences & Ecology ; Marine & Freshwater Biology ; Water Resources |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/21361 |
专题 | 资源环境科学 |
作者单位 | 1.Georgia Inst Technol, Sch Civil & Environm Engn, Atlanta, GA 30332 USA; 2.Univ Minnesota, Dept Civil Environm & Geoengn, St Anthony Falls Lab, Minneapolis, MN USA |
推荐引用方式 GB/T 7714 | Lin, Liao-Fan,Ebtehaj, Ardeshir M.,Wang, Jingfeng,et al. Soil moisture background error covariance and data assimilation in a coupled land-atmosphere model[J]. WATER RESOURCES RESEARCH,2017,53(2). |
APA | Lin, Liao-Fan,Ebtehaj, Ardeshir M.,Wang, Jingfeng,&Bras, Rafael L..(2017).Soil moisture background error covariance and data assimilation in a coupled land-atmosphere model.WATER RESOURCES RESEARCH,53(2). |
MLA | Lin, Liao-Fan,et al."Soil moisture background error covariance and data assimilation in a coupled land-atmosphere model".WATER RESOURCES RESEARCH 53.2(2017). |
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