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@ARTICLE{Rains:841759,
      author       = {Rains, Dominik and Han, Xujun and Lievens, Hans and
                      Montzka, Carsten and Verhoest, Niko E. C.},
      title        = {{SMOS} brightness temperature assimilation into the
                      {C}ommunity {L}and {M}odel},
      journal      = {Hydrology and earth system sciences},
      volume       = {21},
      number       = {11},
      issn         = {1607-7938},
      address      = {Katlenburg-Lindau},
      publisher    = {EGU},
      reportid     = {FZJ-2018-00063},
      pages        = {5929 - 5951},
      year         = {2017},
      abstract     = {SMOS (Soil Moisture and Ocean Salinity mission) brightness
                      temperatures at a single incident angle are assimilated into
                      the Community Land Model (CLM) across Australia to improve
                      soil moisture simulations. 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 with in situ measurements
                      increases moderately from 0.61 to 0.68 $(11 \%)$ for upper
                      soil layers if the root zone is included in the updates. A
                      reduced improvement of $5 \%$ is achieved if the
                      assimilation is restricted to the upper soil layers.
                      Root-zone simulations improve by $7 \%$ when updating both
                      the top layers and root zone, and by $4 \%$ when only
                      updating the top layers. Mean increments and increment
                      standard deviations are compared for the experiments. The
                      long-term assimilation impact is analysed by looking at a
                      set of quantiles computed for soil moisture 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 still being limited now, longer
                      L-band radiometer time series will become available and make
                      model output improved by assimilating such data that are
                      more usable for extreme event statistics.},
      cin          = {IBG-3},
      ddc          = {550},
      cid          = {I:(DE-Juel1)IBG-3-20101118},
      pnm          = {255 - Terrestrial Systems: From Observation to Prediction
                      (POF3-255)},
      pid          = {G:(DE-HGF)POF3-255},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000416334700002},
      doi          = {10.5194/hess-21-5929-2017},
      url          = {https://juser.fz-juelich.de/record/841759},
}