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@INPROCEEDINGS{Ji:905743,
      author       = {Ji, Yan and Gong, Bing and Langguth, Michael and Mozaffari,
                      Amirpasha and Mache, Karim and Schultz, Martin},
      title        = {{D}eep {L}earning for weather forecasts},
      reportid     = {FZJ-2022-00967},
      year         = {2021},
      abstract     = {Accurate weather predictions are highly demanded by
                      society. This study explores the adaptation of
                      state-of-the-art deep learning architectures for video frame
                      prediction in the context of weather applications.
                      Proof-of-concept case studies are performed to 2m
                      temperature forecasts up to 12 hours over central Europe,
                      and precipitation nowcasting up to 2 hours over south China.
                      The pixel-wise loss-based convolutional Long Short Term
                      Memory architectures (ConvLSTM) and GAN’s variant
                      architecture, stochastic adversarial video prediction
                      (SAVP), are used and compared with standard persistent
                      forecasts for 2m temperature, and traditional optical flow
                      method for precipitation, respectively. Mean square error
                      (MSE), anomaly correlation coefficient (ACC), and Structural
                      Similarity Index (SSIM) are utilized to evaluate the 2m
                      temperature forecast. The method of object-based diagnostic
                      evaluation (MODE) was particularly adopted for precipitation
                      nowcasting to evaluate the attributions of rain events in
                      terms of centroid, intensity, and shape. Finally, the
                      sensitivity was performed to test the models' robustness to
                      input variables, target regions, and the number of training
                      samples.},
      month         = {Oct},
      date          = {2021-10-11},
      organization  = {Second Symposium on Artificial
                       Intelligence for Science, Industry and
                       Society, online (Mexico), 11 Oct 2021 -
                       15 Oct 2021},
      subtyp        = {After Call},
      cin          = {JSC},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {5111 - Domain-Specific Simulation $\&$ Data Life Cycle Labs
                      (SDLs) and Research Groups (POF4-511) / Verbundprojekt
                      DeepRain: Effiziente Lokale Niederschlagsvorhersage durch
                      Maschinelles Lernen (01IS18047A) / IntelliAQ - Artificial
                      Intelligence for Air Quality (787576) / MAELSTROM - MAchinE
                      Learning for Scalable meTeoROlogy and cliMate (955513) /
                      Earth System Data Exploration (ESDE)},
      pid          = {G:(DE-HGF)POF4-5111 / G:(BMBF)01IS18047A /
                      G:(EU-Grant)787576 / G:(EU-Grant)955513 / G:(DE-Juel-1)ESDE},
      typ          = {PUB:(DE-HGF)6},
      url          = {https://juser.fz-juelich.de/record/905743},
}