Journal cover Journal topic
Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
https://doi.org/10.5194/hess-2017-188
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
Research article
18 Apr 2017
Review status
This discussion paper is under review for the journal Hydrology and Earth System Sciences (HESS).
SMOS brightness temperature assimilation into the Community Land Model
Dominik Rains1, Xujun Han2, Hans Lievens1,3, Carsten Montzka2, and Niko E. C. Verhoest1 1Ghent University, Ghent, Belgium
2Forschungszentrum Jülich, Jülich, Germany
3NASA Goddard Space Flight Center, USA
Abstract. SMOS (Soil Moisture and Ocean Salinity mission) brightness temperatures at a single incident angle are assimilated into the Community Land Model (CLM), improving soil moisture simulations over the Australian continent. Therefore the data assimilation system DasPy is coupled to the Local Ensemble Transform Kalman Filter (LETKF) as well as to the Community Microwave Emission Model (CMEM). Brightness temperature climatologies are precomputed to enable the assimilation of brightness temperature anomalies, making use of 6 years of SMOS data (2010–2015). Mean correlation R increases moderately from 0.61 to 0.68 when the root-zone is included in the updates. A slightly reduced improvement is achieved when restricting the assimilation to the upper soil layers. Furthermore, the long-term assimilation impact is analysed by looking at a set of quantiles computed at each grid cell. Within hydrological monitoring systems, extreme dry or wet conditions are often defined via their relative occurrence, adding great importance to assimilation induced quantile changes. Although now still limited, longer L-band radiometer time series will become available and make model output improved by assimilating such data more usable for extreme event statistics.

Citation: Rains, D., Han, X., Lievens, H., Montzka, C., and Verhoest, N. E. C.: SMOS brightness temperature assimilation into the Community Land Model, Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2017-188, in review, 2017.
Dominik Rains et al.
Dominik Rains et al.
Dominik Rains et al.

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Short summary
We have assimilated 6 years of satellite observed passive microwave data into a state-of-the-art land surface model to improve both surface soil moisture as well as root-zone soil moisture simulations. Long term assimilation effects/biases are identified and they are especially dependent on model perturbations, applied to simulate model uncertainty. The implications are put into context of using such assimilation improved data for classifying extremes within hydrological monitoring systems.
We have assimilated 6 years of satellite observed passive microwave data into a state-of-the-art...
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