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@ARTICLE{Gong:917525,
      author       = {Gong, Bing and Langguth, Michael and Ji, Yan and Mozaffari,
                      Amirpasha and Stadtler, Scarlet and Mache, Karim and
                      Schultz, Martin G.},
      title        = {{T}emperature forecasting by deep learning methods},
      journal      = {Geoscientific model development},
      volume       = {15},
      number       = {23},
      issn         = {1991-959X},
      address      = {Katlenburg-Lindau},
      publisher    = {Copernicus},
      reportid     = {FZJ-2023-00738},
      pages        = {8931 - 8956},
      year         = {2022},
      abstract     = {Numerical weather prediction (NWP) models solve a system of
                      partial differential equations based on physical laws to
                      forecast the future state of the atmosphere. These models
                      are deployed operationally, but they are computationally
                      very expensive. Recently, the potential of deep neural
                      networks to generate bespoke weather forecasts has been
                      explored in a couple of scientific studies inspired by the
                      success of video frame prediction models in computer vision.
                      In this study, a simple recurrent neural network with
                      convolutional filters, called ConvLSTM, and an advanced
                      generative network, the Stochastic Adversarial Video
                      Prediction (SAVP) model, are applied to create hourly
                      forecasts of the 2 m temperature for the next 12 h over
                      Europe. We make use of 13 years of data from the ERA5
                      reanalysis, of which 11 years are utilized for training and
                      1 year each is used for validating and testing. We choose
                      the 2 m temperature, total cloud cover, and the 850 hPa
                      temperature as predictors and show that both models attain
                      predictive skill by outperforming persistence forecasts.
                      SAVP is superior to ConvLSTM in terms of several evaluation
                      metrics, confirming previous results from computer vision
                      that larger, more complex networks are better suited to
                      learn complex features and to generate better predictions.
                      The 12 h forecasts of SAVP attain a mean squared error
                      (MSE) of about 2.3 K2, an anomaly correlation coefficient
                      (ACC) larger than 0.85, a structural similarity index (SSIM)
                      of around 0.72, and a gradient ratio (rG) of about 0.82. The
                      ConvLSTM yields a higher MSE (3.6 K2), a smaller ACC
                      (0.80) and SSIM (0.65), and a slightly larger rG (0.84). The
                      superior performance of SAVP in terms of MSE, ACC, and SSIM
                      can be largely attributed to the generator. A sensitivity
                      study shows that a larger weight of the generative
                      adversarial network (GAN) component in the SAVP loss leads
                      to even better preservation of spatial variability at the
                      cost of a somewhat increased MSE (2.5 K2). Including the
                      850 hPa temperature as an additional predictor enhances
                      the forecast quality, and the model also benefits from a
                      larger spatial domain. By contrast, adding the total cloud
                      cover as predictor or reducing the amount of training data
                      to 8 years has only small effects. Although the temperature
                      forecasts obtained in this way are still less powerful than
                      contemporary NWP models, this study demonstrates that
                      sophisticated deep neural networks may achieve considerable
                      forecast quality beyond the nowcasting range in a purely
                      data-driven way.},
      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) / MAELSTROM - MAchinE
                      Learning for Scalable meTeoROlogy and cliMate (955513) /
                      IntelliAQ - Artificial Intelligence for Air Quality (787576)
                      / Earth System Data Exploration (ESDE)},
      pid          = {G:(DE-HGF)POF4-5111 / G:(EU-Grant)955513 /
                      G:(EU-Grant)787576 / G:(DE-Juel-1)ESDE},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000898541700001},
      doi          = {10.5194/gmd-15-8931-2022},
      url          = {https://juser.fz-juelich.de/record/917525},
}