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@ARTICLE{Montzka:127042,
      author       = {Montzka, Carsten and Pauwels, Valentijn and Franssen,
                      Harrie-Jan and Han, Xujun and Vereecken, Harry},
      title        = {{M}ultivariate and {M}ultiscale {D}ata {A}ssimilation in
                      {T}errestrial {S}ystems: {A} {R}eview},
      journal      = {Sensors},
      volume       = {12},
      number       = {12},
      address      = {Basel},
      publisher    = {MDPI},
      reportid     = {FZJ-2012-00109},
      pages        = {16291 - 16333},
      year         = {2012},
      abstract     = {More and more terrestrial observational networks are being
                      established to monitor climatic, hydrological and land-use
                      changes in different regions of the World. In these
                      networks, time series of states and fluxes are recorded in
                      an automated manner, often with a high temporal resolution.
                      These data are important for the understanding of water,
                      energy, and/or matter fluxes, as well as their biological
                      and physical drivers and interactions with and within the
                      terrestrial system. Similarly, the number and accuracy of
                      variables, which can be observed by spaceborne sensors, are
                      increasing. Data assimilation (DA) methods utilize these
                      observations in terrestrial models in order to increase
                      process knowledge as well as to improve forecasts for the
                      system being studied. The widely implemented automation in
                      observing environmental states and fluxes makes an
                      operational computation more and more feasible, and it opens
                      the perspective of short-time forecasts of the state of
                      terrestrial systems. In this paper, we review the state of
                      the art with respect to DA focusing on the joint
                      assimilation of observational data precedents from different
                      spatial scales and different data types. An introduction is
                      given to different DA methods, such as the Ensemble Kalman
                      Filter (EnKF), Particle Filter (PF) and variational methods
                      (3/4D-VAR). In this review, we distinguish between four
                      major DA approaches: (1) univariate single-scale DA (UVSS),
                      which is the approach used in the majority of published DA
                      applications, (2) univariate multiscale DA (UVMS) referring
                      to a methodology which acknowledges that at least some of
                      the assimilated data are measured at a different scale than
                      the computational grid scale, (3) multivariate single-scale
                      DA (MVSS) dealing with the assimilation of at least two
                      different data types, and (4) combined multivariate
                      multiscale DA (MVMS). Finally, we conclude with a discussion
                      on the advantages and disadvantages of the assimilation of
                      multiple data types in a simulation model. Existing
                      approaches can be used to simultaneously update several
                      model states and model parameters if applicable. In other
                      words, the basic principles for multivariate data
                      assimilation are already available. We argue that a better
                      understanding of the measurement errors for different
                      observation types, improved estimates of observation bias
                      and improved multiscale assimilation methods for data which
                      scale nonlinearly is important to properly weight them in
                      multiscale multivariate data assimilation. In this context,
                      improved cross-validation of different data types, and
                      increased ground truth verification of remote sensing
                      products are required.},
      cin          = {IBG-3},
      ddc          = {620},
      cid          = {I:(DE-Juel1)IBG-3-20101118},
      pnm          = {246 - Modelling and Monitoring Terrestrial Systems: Methods
                      and Technologies (POF2-246)},
      pid          = {G:(DE-HGF)POF2-246},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000312607500018},
      doi          = {10.3390/s121216291},
      url          = {https://juser.fz-juelich.de/record/127042},
}