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@ARTICLE{Han:819314,
      author       = {Han, X. and He, G. and Kumbhar, P. and Montzka, C. and
                      Kollet, S. and Miyoshi, T. and Rosolem, R. and Vereecken, H.
                      and Franssen, H.-J. H. and Li, X. and Zhang, Y.},
      title        = {{D}as{P}y 1.0 $\–$ the {O}pen {S}ource {M}ultivariate
                      {L}and {D}ata {A}ssimilation {F}ramework in combination with
                      the {C}ommunity {L}and {M}odel 4.5},
      journal      = {Geoscientific model development discussions},
      volume       = {8},
      number       = {8},
      issn         = {1991-962X},
      address      = {Katlenburg-Lindau},
      publisher    = {Copernicus},
      reportid     = {FZJ-2016-05015},
      pages        = {7395 - 7444},
      year         = {2015},
      abstract     = {Data assimilation has become a popular method to integrate
                      observations from multiple sources with land surface models
                      to improve predictions of the water and energy cycles of the
                      soil-vegetation-atmosphere continuum. Multivariate data
                      assimilation refers to the simultaneous assimilation of
                      observation data from multiple model state variables into a
                      simulation model. In recent years, several land data
                      assimilation systems have been developed in different
                      research agencies. Because of the software availability or
                      adaptability, these systems are not easy to apply for the
                      purpose of multivariate land data assimilation research. We
                      developed an open source multivariate land data assimilation
                      framework (DasPy) which is implemented using the Python
                      script language mixed with the C++ and Fortran programming
                      languages. LETKF (Local Ensemble Transform Kalman Filter) is
                      implemented as the main data assimilation algorithm, and
                      uncertainties in the data assimilation can be introduced by
                      perturbed atmospheric forcing data, and represented by
                      perturbed soil and vegetation parameters and model initial
                      conditions. The Community Land Model (CLM) was integrated as
                      the model operator. The implementation allows also parameter
                      estimation (soil properties and/or leaf area index) on the
                      basis of the joint state and parameter estimation approach.
                      The Community Microwave Emission Modelling platform (CMEM),
                      COsmic-ray Soil Moisture Interaction Code (COSMIC) and the
                      Two-Source Formulation (TSF) were integrated as observation
                      operators for the assimilation of L-band passive microwave,
                      cosmic-ray soil moisture probe and land surface temperature
                      measurements, respectively. DasPy has been evaluated in
                      several assimilation studies of neutron count intensity
                      (soil moisture), L-band brightness temperature and land
                      surface temperature. DasPy is parallelized using the hybrid
                      Message Passing Interface and Open Multi-Processing
                      techniques. All the input and output data flows are
                      organized efficiently using the commonly used NetCDF file
                      format. Online 1-D and 2-D visualization of data
                      assimilation results is also implemented to facilitate the
                      post simulation analysis. In summary, DasPy is a ready to
                      use open source parallel multivariate land data assimilation
                      framework.},
      cin          = {IBG-3 / NIC},
      ddc          = {910},
      cid          = {I:(DE-Juel1)IBG-3-20101118 / I:(DE-Juel1)NIC-20090406},
      pnm          = {255 - Terrestrial Systems: From Observation to Prediction
                      (POF3-255) / Data Assimilation for Improved Characterization
                      of Fluxes Across Compartmental Interfaces
                      $(hbn29_20140501)$},
      pid          = {G:(DE-HGF)POF3-255 / $G:(DE-Juel1)hbn29_20140501$},
      typ          = {PUB:(DE-HGF)16},
      doi          = {10.5194/gmdd-8-7395-2015},
      url          = {https://juser.fz-juelich.de/record/819314},
}