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000155400 0247_ $$2doi$$a10.1002/2013WR014586
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000155400 0247_ $$2ISSN$$a1944-7973
000155400 0247_ $$2ISSN$$a0043-1397
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000155400 037__ $$aFZJ-2014-04567
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000155400 1001_ $$0P:(DE-Juel1)144738$$aHan, Xujun$$b0$$eCorresponding Author$$ufzj
000155400 245__ $$aSoil moisture and soil properties estimation in the Community Land Model with synthetic brightness temperature observations
000155400 260__ $$aWashington, DC$$bAGU$$c2014
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000155400 520__ $$aThe Community Land Model (CLM) includes a large variety of parameterizations, also for flow in the unsaturated zone and soil properties. Soil properties introduce uncertainties into land surface model predictions. In this paper, soil moisture and soil properties are updated for the coupled CLM and Community Microwave Emission Model (CMEM) by the Local Ensemble Transform Kalman Filter (LETKF) and the state augmentation method. Soil properties are estimated through the update of soil textural properties and soil organic matter density. These variables are used in CLM for predicting the soil moisture retention characteristic and the unsaturated hydraulic conductivity, and the soil texture is used in CMEM to calculate the soil dielectric constant. The following scenarios were evaluated for the joint state and parameter estimation with help of synthetic L-band brightness temperature data assimilation: (i) the impact of joint state and parameter estimation; (ii) updating of soil properties in CLM alone, CMEM alone or both CLM and CMEM; (iii) updating of soil properties without soil moisture update; (iv) the observation localization of LETKF. The results show that the characterization of soil properties through the update of textural properties and soil organic matter density can strongly improve with assimilation of brightness temperature data. The optimized soil properties also improve the characterization of soil moisture, soil temperature, actual evapotranspiration, sensible heat flux, and soil heat flux. The best results are obtained if the soil properties are updated only. The coupled CLM and CMEM model is helpful for the parameter estimation. If soil properties are biased, assimilation of soil moisture data with only state updates increases the root mean square error for evapotranspiration, sensible heat flux, and soil heat flux.
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000155400 536__ $$0G:(DE-HGF)POF3-255$$a255 - Terrestrial Systems: From Observation to Prediction (POF3-255)$$cPOF3-255$$fPOF III$$x1
000155400 588__ $$aDataset connected to CrossRef, juser.fz-juelich.de
000155400 7001_ $$0P:(DE-HGF)0$$aFranssen, Harrie-Jan Hendricks$$b1
000155400 7001_ $$0P:(DE-Juel1)129506$$aMontzka, Carsten$$b2$$ufzj
000155400 7001_ $$0P:(DE-Juel1)129549$$aVereecken, Harry$$b3$$ufzj
000155400 773__ $$0PERI:(DE-600)2029553-4$$a10.1002/2013WR014586$$gVol. 50, no. 7, p. 6081 - 6105$$n7$$p6081 - 6105$$tWater resources research$$v50$$x0043-1397$$y2014
000155400 8564_ $$uhttps://juser.fz-juelich.de/record/155400/files/FZJ-2014-04567.pdf$$yOpenAccess
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000155400 9141_ $$y2014
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