DOI: 10.5194/hess-20-4895-2016
Scopus记录号: 2-s2.0-85006412287
论文题名: Assimilation of SMOS brightness temperatures or soil moisture retrievals into a land surface model
作者: De Lannoy G ; J ; M ; , Reichle R ; H
刊名: Hydrology and Earth System Sciences
ISSN: 10275606
出版年: 2016
卷: 20, 期: 12 起始页码: 4895
结束页码: 4911
语种: 英语
Scopus关键词: Atmospheric humidity
; Errors
; Luminance
; Moisture
; Soil moisture
; Soils
; Temperature
; Brightness temperatures
; Cumulative density functions
; Goddard earth observing systems
; Root mean square differences
; Root zone soil moistures
; Soil moisture active passive (SMAP)
; Soil moisture ocean salinities
; Soil moisture retrievals
; Soil surveys
; brightness temperature
; data assimilation
; EOS
; experimental study
; Kalman filter
; land surface
; numerical model
; optimization
; rhizosphere
; satellite data
; satellite mission
; SMOS
; soil moisture
英文摘要: Three different data products from the Soil Moisture Ocean Salinity (SMOS) mission are assimilated separately into the Goddard Earth Observing System Model, version 5 (GEOS-5) to improve estimates of surface and root-zone soil moisture. The first product consists of multi-angle, dual-polarization brightness temperature (Tb) observations at the bottom of the atmosphere extracted from Level 1 data. The second product is a derived SMOS Tb product that mimics the data at a 40° incidence angle from the Soil Moisture Active Passive (SMAP) mission. The third product is the operational SMOS Level 2 surface soil moisture (SM) retrieval product. The assimilation system uses a spatially distributed ensemble Kalman filter (EnKF) with seasonally varying climatological bias mitigation for Tb assimilation, whereas a time-invariant cumulative density function matching is used for SM retrieval assimilation. All assimilation experiments improve the soil moisture estimates compared to model-only simulations in terms of unbiased root-mean-square differences and anomaly correlations during the period from 1 July 2010 to 1 May 2015 and for 187 sites across the US. Especially in areas where the satellite data are most sensitive to surface soil moisture, large skill improvements (e.g., an increase in the anomaly correlation by 0.1) are found in the surface soil moisture. The domain-average surface and root-zone skill metrics are similar among the various assimilation experiments, but large differences in skill are found locally. The observation-minus-forecast residuals and analysis increments reveal large differences in how the observations add value in the Tb and SM retrieval assimilation systems. The distinct patterns of these diagnostics in the two systems reflect observation and model errors patterns that are not well captured in the assigned EnKF error parameters. Consequently, a localized optimization of the EnKF error parameters is needed to further improve Tb or SM retrieval assimilation. © Author(s) 2016. CC Attribution 3.0 License. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/78666
Appears in Collections: 气候变化事实与影响
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作者单位: KU Leuven, Department of Earth and Environmental Sciences, Heverlee, Belgium; NASA Goddard Space Flight Center, Global Modeling and Assimilation Office, Greenbelt, MD, United States
Recommended Citation:
De Lannoy G,J,M,et al. Assimilation of SMOS brightness temperatures or soil moisture retrievals into a land surface model[J]. Hydrology and Earth System Sciences,2016-01-01,20(12)