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@MASTERSTHESIS{NobreWittwer:1041706,
      author       = {Nobre Wittwer, Nils},
      title        = {{D}eep {L}eaning based data compression for climate data},
      school       = {FH Aachen},
      type         = {Bachelorarbeit},
      reportid     = {FZJ-2025-02391},
      pages        = {44p},
      year         = {2024},
      note         = {Bachelorarbeit, FH Aachen, 2024},
      abstract     = {With the amount of data generated in climate science from
                      scientific models reachingmultiple terabytes per simulation,
                      it becomes difficult to provide sufficient storagespace and
                      achieve satisfactory data transfer rates. In order to be
                      able to store moreinformation, data compression becomes
                      increasingly relevant, as it allows the size ofthe data to
                      be decreased significantly.In this work a novel approach to
                      lossy data compression for climate data by usingthe deep
                      learning model AtmoRep is being analysed. AtmoRep uses
                      large-scalerepresentation learning to determine a general
                      description of the highly complex,stochastic dynamics of the
                      atmosphere which can be used to achieve a varietyof
                      different tasks. Specifically, I want to test the accuracy
                      of data reconstructionthat can be achieved with this model.
                      This reconstructive ability can be used toefficiently
                      compress data by purposefully removing parts of the data,
                      which can bereconstructed using AtmoRep.To determine an
                      optimal configuration of the AtmoRep model, this thesis
                      proposesa series of different experiments where portions of
                      temperature data are completelyremoved from the dataset at
                      varying temporal positions and then reconstructedusing
                      AtmoRep. The reconstructed data is subsequently analyzed and
                      compared tothe original data to determine the quality of the
                      data in relation to the compressionratio.For comparison, the
                      results of AtmoRep are being compared to those of
                      commonlyused lossless and lossy data compression algorithms
                      for climate data.},
      cin          = {JSC},
      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)2},
      doi          = {10.34734/FZJ-2025-02391},
      url          = {https://juser.fz-juelich.de/record/1041706},
}