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@ARTICLE{Wu:857751,
      author       = {Wu, Xueran and Elbern, Hendrik and Jacob, Birgit},
      title        = {{T}he degree of freedom for signal assessment of
                      measurement networks for joint chemical state and emission
                      analysis},
      journal      = {Geoscientific model development discussions},
      volume       = {220},
      issn         = {1991-962X},
      address      = {Katlenburg-Lindau},
      publisher    = {Copernicus},
      reportid     = {FZJ-2018-06719},
      pages        = {1 - 29},
      year         = {2018},
      abstract     = {The Degree of Freedom for Signal (DFS) is generalized and
                      applied to estimate the potential observability of
                      observation networks for augmented model state and parameter
                      estimations. The control of predictive geophysical model
                      systems by measurements is dependent on a sufficient
                      observational basis. Control parameters may include
                      prognostic state variables, mostly the initial values, and
                      insufficiently known model parameters, to which the
                      simulation is sensitive. As for chemistry-transport models,
                      emission rates are at least as important as initial values
                      for model evolution control. Extending the optimisation
                      parameter set must be met by observation networks, which
                      allows for controlling the entire optimisation task. In this
                      paper, we introduce a DFS based approach with respect to
                      address both, emission rates and initial value
                      observability. By applying a Kalman smoother, a quantitative
                      assessment method on the efficiency of observation
                      configurations is developed based on the singular value
                      decomposition. For practical reasons an ensemble based
                      version is derived for covariance modelling. The
                      observability analysis tool can be generalized to additional
                      model parameters.},
      cin          = {IEK-8},
      ddc          = {910},
      cid          = {I:(DE-Juel1)IEK-8-20101013},
      pnm          = {243 - Tropospheric trace substances and their
                      transformation processes (POF3-243)},
      pid          = {G:(DE-HGF)POF3-243},
      typ          = {PUB:(DE-HGF)16},
      doi          = {10.5194/gmd-2017-220},
      url          = {https://juser.fz-juelich.de/record/857751},
}