globalchange  > 影响、适应和脆弱性
DOI: 10.1002/jgrd.50377
论文题名:
Assimilation of microwave brightness temperature in a land data assimilation system with multi-observation operators
作者: Jia B.; Tian X.; Xie Z.; Liu J.; Shi C.
刊名: Journal of Geophysical Research Atmospheres
ISSN: 21698996
出版年: 2013
卷: 118, 期:10
起始页码: 3972
结束页码: 3985
语种: 英语
英文关键词: Bayesian model averaging ; land data assimilation ; microwave brightness temperature ; observation operator ; soil moisture
Scopus关键词: Arid regions ; Bayesian networks ; Data acquisition ; Mean square error ; Microwaves ; Radiative transfer ; Soil moisture ; Temperature ; Advanced Microwave Scanning Radiometer for the Earth Observing System ; Arid and semi-arid regions ; Bayesian model averaging ; Land data assimilation ; Land data assimilation systems ; Microwave brightness temperature ; Middle and lower reaches of the yangtze rivers ; Observation operator ; Atmospheric humidity ; AMSR-E ; brightness temperature ; data assimilation ; emissivity ; moisture content ; radiative transfer ; satellite data ; simulation ; soil moisture ; soil water potential ; China ; Yangtze River
英文摘要: A radiative transfer model (RTM) that provides a link between model states and satellite observations (e.g., brightness temperature) can act as an observation operator in land data assimilation to directly assimilate brightness temperatures. In this study, a microwave Land Data Assimilation System (LDAS) was developed with three RTMs (The radiative transfer model for bare field (QH), land emissivity model (LandEM), and Community Microwave Emission Model (CMEM)) as its multi-observation operators (LDAS-MO). Assimilation experiments using the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) satellite brightness temperature data from July 2005 to December 2008 were then conducted to investigate the impact of the RTMs on the assimilated results over China. It was found that the assimilated volumetric soil-water content using each of the three observation operators improved the estimation of soil moisture content in the top soil layer (0-10 cm), with reduced root mean square errors (RMSEs), and increased correlation coefficients with field observations (OBS) as compared to a control run with no assimilation for the absence of frozen or snow-covered conditions. The assimilated soil moisture for the QH model, which was more sensitive to dry soil than the other models, produced closer correlations with OBS in arid and semi-arid regions while smaller RMSEs were observed for LandEM. CMEM agreed most closely with OBS over the middle and lower reaches of the Yangtze River due to its better simulation of the brightness temperature over densely vegetated areas. To improve assimilation accuracy, a Bayesian model averaging (BMA) scheme for the LDAS-MO was developed. The BMA scheme was found to significantly enhance assimilation capability producing the soil moisture analysis, showing the lowest RMSEs and highest correlations with OBS over all areas. It was demonstrated that the BMA scheme with LDAS-MO has the potential to estimate soil moisture with high accuracy. Key PointsWe developed a land data assimilation system with multi-observation operatorsThe impact of observation operators on assimilated results was investigatedA Bayesian model averaging scheme was used to enhance the assimilation skill ©2013. American Geophysical Union. All Rights Reserved.
资助项目: 41075062 ; 91125016
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/63755
Appears in Collections:影响、适应和脆弱性
气候减缓与适应

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作者单位: State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, P.O. Box 9804, Beijing 100029, China; National Meteorological Information Center, China Meteorological Administration, Beijing, China

Recommended Citation:
Jia B.,Tian X.,Xie Z.,et al. Assimilation of microwave brightness temperature in a land data assimilation system with multi-observation operators[J]. Journal of Geophysical Research Atmospheres,2013-01-01,118(10)
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