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@ARTICLE{PatakchiYousefi:1014219,
      author       = {Patakchi Yousefi, Kaveh and Kollet, Stefan},
      title        = {{D}eep learning of model- and reanalysis-based
                      precipitation and pressure mismatches over {E}urope},
      journal      = {Frontiers in water},
      volume       = {5},
      issn         = {2624-9375},
      address      = {Lausanne},
      publisher    = {Frontiers Media},
      reportid     = {FZJ-2023-03203},
      pages        = {1178114},
      year         = {2023},
      abstract     = {Physically based numerical weather prediction and climate
                      models provide useful information for a large number of end
                      users, such as flood forecasters, water resource managers,
                      and farmers. However, due to model uncertainties arising
                      from, e.g., initial value and model errors, the simulation
                      results do not match the in situ or remotely sensed
                      observations to arbitrary accuracy. Merging model-based data
                      with observations yield promising results benefiting
                      simultaneously from the information content of the model
                      results and observations. Machine learning (ML) and/or deep
                      learning (DL) methods have been shown to be useful tools in
                      closing the gap between models and observations due to the
                      capacity in the representation of the non-linear
                      space–time correlation structure. This study focused on
                      using UNet encoder–decoder convolutional neural networks
                      (CNNs) for extracting spatiotemporal features from model
                      simulations for predicting the actual mismatches (errors)
                      between the simulation results and a reference data set.
                      Here, the climate simulations over Europe from the
                      Terrestrial Systems Modeling Platform (TSMP) were used as
                      input to the CNN. The COSMO-REA6 reanalysis data were used
                      as a reference. The proposed merging framework was applied
                      to mismatches in precipitation and surface pressure
                      representing more and less chaotic variables, respectively.
                      The merged data show a strong average improvement in mean
                      error (~ $47\%),$ correlation coefficient (~ $37\%),$ and
                      root mean square error $(~22\%).$ To highlight the
                      performance of the DL-based method, the results were
                      compared with the results obtained by a baseline method,
                      quantile mapping. The proposed DL-based merging methodology
                      can be used either during the simulation to correct model
                      forecast output online or in a post-processing step, for
                      downstream impact applications, such as flood forecasting,
                      water resources management, and agriculture.},
      cin          = {IBG-3},
      ddc          = {333.7},
      cid          = {I:(DE-Juel1)IBG-3-20101118},
      pnm          = {2173 - Agro-biogeosystems: controls, feedbacks and impact
                      (POF4-217)},
      pid          = {G:(DE-HGF)POF4-2173},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:001002189600001},
      doi          = {10.3389/frwa.2023.1178114},
      url          = {https://juser.fz-juelich.de/record/1014219},
}