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@ARTICLE{Wu:902298,
      author       = {Wu, Xueran and Elbern, Hendrik and Jacob, Birgit},
      title        = {{T}he assessment of potential observability for joint
                      chemical states and emissions in atmospheric modelings},
      journal      = {Stochastic environmental research and risk assessment},
      volume       = {36},
      issn         = {1436-3259},
      address      = {New York, NY},
      publisher    = {Springer},
      reportid     = {FZJ-2021-04161},
      pages        = {1743–1760},
      year         = {2022},
      abstract     = {In predictive geophysical model systems, uncertain initial
                      values and model parameters jointly influence the temporal
                      evolution of the system. This renders initial-value-only
                      optimization by traditional data assimilation methods as
                      insufficient. However, blindly extending the optimization
                      parameter set jeopardizes the validity of the resulting
                      analysis because of the increase of the ill-posedness of the
                      inversion task. Hence, it becomes important to assess the
                      potential observability of measurement networks for model
                      state and parameters in atmospheric modelings in advance of
                      the optimization. In this paper, we novelly establish the
                      dynamic model of emission rates and extend the
                      transport-diffusion model extended by emission rates.
                      Considering the Kalman smoother as underlying assimilation
                      technique, we develop a quantitative assessment method to
                      evaluate the potential observability and the sensitivity of
                      observation networks to initial values and emission rates
                      jointly. This benefits us to determine the optimizable
                      parameters to observation configurations before the data
                      assimilation procedure and make the optimization more
                      efficiently. For high-dimensional models in practical
                      applications, we derive an ensemble based version of the
                      approach and give several elementary experiments for
                      illustrations.},
      cin          = {IEK-8},
      ddc          = {550},
      cid          = {I:(DE-Juel1)IEK-8-20101013},
      pnm          = {2111 - Air Quality (POF4-211)},
      pid          = {G:(DE-HGF)POF4-2111},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000711789700001},
      doi          = {10.1007/s00477-021-02113-x},
      url          = {https://juser.fz-juelich.de/record/902298},
}