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@ARTICLE{Kurtz:820932,
      author       = {Kurtz, Wolfgang and He, Guowei and Kollet, Stefan and
                      Maxwell, Reed M. and Vereecken, Harry and
                      Hendricks-Franssen, Harrie-Jan},
      title        = {{T}err{S}ys{MP}–{PDAF} (version 1.0): a modular
                      high-performance data assimilation framework for an
                      integrated land surface–subsurface model},
      journal      = {Geoscientific model development},
      volume       = {9},
      number       = {4},
      issn         = {1991-9603},
      address      = {Katlenburg-Lindau},
      publisher    = {Copernicus},
      reportid     = {FZJ-2016-06196},
      pages        = {1341 - 1360},
      year         = {2016},
      abstract     = {Modelling of terrestrial systems is continuously moving
                      towards more integrated modelling approaches, where
                      different terrestrial compartment models are combined in
                      order to realise a more sophisticated physical description
                      of water, energy and carbon fluxes across compartment
                      boundaries and to provide a more integrated view on
                      terrestrial processes. While such models can effectively
                      reduce certain parameterisation errors of single compartment
                      models, model predictions are still prone to uncertainties
                      regarding model input variables. The resulting uncertainties
                      of model predictions can be effectively tackled by data
                      assimilation techniques, which allow one to correct model
                      predictions with observations taking into account both the
                      model and measurement uncertainties. The steadily increasing
                      availability of computational resources makes it now
                      increasingly possible to perform data assimilation also for
                      computationally highly demanding integrated terrestrial
                      system models. However, as the computational burden for
                      integrated models as well as data assimilation techniques is
                      quite large, there is an increasing need to provide
                      computationally efficient data assimilation frameworks for
                      integrated models that allow one to run on and to make
                      efficient use of massively parallel computational resources.
                      In this paper we present a data assimilation framework for
                      the land surface–subsurface part of the Terrestrial System
                      Modelling Platform (TerrSysMP). TerrSysMP is connected via a
                      memory-based coupling approach with the pre-existing
                      parallel data assimilation library PDAF (Parallel Data
                      Assimilation Framework). This framework provides a fully
                      parallel modular environment for performing data
                      assimilation for the land surface and the subsurface
                      compartment. A simple synthetic case study for a land
                      surface–subsurface system (0.8 million unknowns) is used
                      to demonstrate the effects of data assimilation in the
                      integrated model TerrSysMP and to assess the scaling
                      behaviour of the data assimilation system. Results show that
                      data assimilation effectively corrects model states and
                      parameters of the integrated model towards the reference
                      values. Scaling tests provide evidence that the data
                      assimilation system for TerrSysMP can make efficient use of
                      parallel computational resources for  > 30 k
                      processors. Simulations with a large problem size (20
                      million unknowns) for the forward model were also
                      efficiently handled by the data assimilation system. The
                      proposed data assimilation framework is useful in simulating
                      and estimating uncertainties in predicted states and fluxes
                      of the terrestrial system over large spatial scales at high
                      resolution utilising integrated models.},
      cin          = {IBG-3},
      ddc          = {910},
      cid          = {I:(DE-Juel1)IBG-3-20101118},
      pnm          = {255 - Terrestrial Systems: From Observation to Prediction
                      (POF3-255) / High-resolution conditional stochastic
                      modelling of subsurface- land surface interactions
                      $(jibg30_20121101)$},
      pid          = {G:(DE-HGF)POF3-255 / $G:(DE-Juel1)jibg30_20121101$},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000376936200003},
      doi          = {10.5194/gmd-9-1341-2016},
      url          = {https://juser.fz-juelich.de/record/820932},
}