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@ARTICLE{Okui:1043156,
      author       = {Okui, Haruka and Wright, Corwin J. and Berthelemy, Peter G.
                      and Hindley, Neil P. and Hoffmann, Lars and Barnes, Andrew
                      P.},
      title        = {{A} {C}onvolutional {N}eural {N}etwork for the {D}etection
                      of {G}ravity {W}aves in {S}atellite {O}bservations and
                      {N}umerical {S}imulations},
      journal      = {Geophysical research letters},
      volume       = {52},
      number       = {11},
      issn         = {0094-8276},
      address      = {Hoboken, NJ},
      publisher    = {Wiley},
      reportid     = {FZJ-2025-02772},
      pages        = {e2025GL115683},
      year         = {2025},
      abstract     = {Comparisons between observed and model-resolved gravity
                      waves (GWs) are crucial for evaluating general circulation
                      model (GCM) simulation accuracy and understanding wave
                      characteristics. However, observational noise often obscures
                      waves, complicating such comparisons. To address this, we
                      have developed a GW detection method using a convolutional
                      neural network (CNN). The CNN is trained on Atmospheric
                      Infrared Sounder (AIRS) temperatures with labels indicating
                      wave presence based on Berthelemy et al. (2025,
                      https://doi.org/10.5194/egusphere-2025-455). Their method
                      detects noise-induced pixel-to-pixel variations in
                      horizontal wavelengths; in contrast, the CNN robustly
                      identify waves even when applied to smoothly varying model
                      data. Using this method, we compare stratospheric GWs in
                      boreal winters between AIRS observations and a high-top
                      GW-permitting GCM, Japanese Atmospheric GCM for Upper
                      Atmosphere Research (JAGUAR). The results agree well and
                      exhibit similar interannual variability, with discrepancies
                      also identified, including a more zonally elongated
                      distribution of tropical GWs in JAGUAR. This method is
                      broadly applicable to the future use of satellites for
                      guiding wave-resolving atmospheric model development.},
      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)},
      pid          = {G:(DE-HGF)POF4-5111},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:001504302600001},
      doi          = {10.1029/2025GL115683},
      url          = {https://juser.fz-juelich.de/record/1043156},
}