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@ARTICLE{RojasCampos:1037646,
      author       = {Rojas-Campos, Adrian and Langguth, Michael and Wittenbrink,
                      Martin and Pipa, Gordon},
      title        = {{D}eep learning models for generation of precipitation maps
                      based on numerical weather prediction},
      journal      = {Geoscientific model development},
      volume       = {16},
      number       = {5},
      issn         = {1991-959X},
      address      = {Katlenburg-Lindau},
      publisher    = {Copernicus},
      reportid     = {FZJ-2025-00811},
      pages        = {1467 - 1480},
      year         = {2023},
      abstract     = {Numerical weather prediction (NWP) models are atmospheric
                      simulations that imitate the dynamics of the atmosphere and
                      provide high-quality forecasts. One of the most significant
                      limitations of NWP is the elevated amount of computational
                      resources required for its functioning, which limits the
                      spatial and temporal resolution of the outputs. Traditional
                      meteorological techniques to increase the resolution are
                      uniquely based on information from a limited group of
                      interest variables. In this study, we offer an alternative
                      approach to the task where we generate precipitation maps
                      based on the complete set of variables of the NWP to
                      generate high-resolution and short-time precipitation
                      predictions. To achieve this, five different deep learning
                      models were trained and evaluated: a baseline, U-Net, two
                      deconvolution networks and one conditional generative model
                      (Conditional Generative Adversarial Network; CGAN). A total
                      of 20 independent random initializations were performed for
                      each of the models. The predictions were evaluated using
                      skill scores based on mean absolute error (MAE) and linear
                      error in probability space (LEPS), equitable threat score
                      (ETS), critical success index (CSI) and frequency bias after
                      applying several thresholds. The models showed a significant
                      improvement in predicting precipitation, showing the
                      benefits of including the complete information from the NWP.
                      The algorithms doubled the resolution of the predictions and
                      corrected an over-forecast bias from the input information.
                      However, some new models presented new types of bias: U-Net
                      tended to mid-range precipitation events, and the
                      deconvolution models favored low rain events and generated
                      some spatial smoothing. The CGAN offered the highest-quality
                      precipitation forecast, generating realistic outputs and
                      indicating possible future research paths.},
      cin          = {JSC},
      ddc          = {550},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {5111 - Domain-Specific Simulation $\&$ Data Life Cycle Labs
                      (SDLs) and Research Groups (POF4-511) / Earth System Data
                      Exploration (ESDE) / Verbundprojekt DeepRain: Effiziente
                      Lokale Niederschlagsvorhersage durch Maschinelles Lernen
                      (01IS18047A)},
      pid          = {G:(DE-HGF)POF4-5111 / G:(DE-Juel-1)ESDE /
                      G:(BMBF)01IS18047A},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000945377600001},
      doi          = {10.5194/gmd-16-1467-2023},
      url          = {https://juser.fz-juelich.de/record/1037646},
}