001041706 001__ 1041706
001041706 005__ 20250430202312.0
001041706 0247_ $$2datacite_doi$$a10.34734/FZJ-2025-02391
001041706 037__ $$aFZJ-2025-02391
001041706 041__ $$aEnglish
001041706 1001_ $$0P:(DE-Juel1)190266$$aNobre Wittwer, Nils$$b0$$eCorresponding author$$ufzj
001041706 245__ $$aDeep Leaning based data compression for climate data$$f2024-07-10 - 2024-09-11
001041706 260__ $$c2024
001041706 300__ $$a44p
001041706 3367_ $$2DRIVER$$abachelorThesis
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001041706 3367_ $$2BibTeX$$aMASTERSTHESIS
001041706 3367_ $$2ORCID$$aSUPERVISED_STUDENT_PUBLICATION
001041706 502__ $$aBachelorarbeit, FH Aachen, 2024$$bBachelorarbeit$$cFH Aachen$$d2024
001041706 520__ $$aWith 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.
001041706 536__ $$0G:(DE-HGF)POF4-5111$$a5111 - Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups (POF4-511)$$cPOF4-511$$fPOF IV$$x0
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