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001034617 005__ 20250110192855.0
001034617 0247_ $$2datacite_doi$$a10.34734/FZJ-2024-07378
001034617 037__ $$aFZJ-2024-07378
001034617 041__ $$aEnglish
001034617 1001_ $$0P:(DE-Juel1)180790$$aLangguth, Michael$$b0$$eCorresponding author$$ufzj
001034617 1112_ $$aInternational Conference on Learning Representations$$cVienna$$d2024-05-07 - 2024-05-11$$gICLR 2024$$wAustria
001034617 245__ $$aA Benchmark Dataset for Meteorological Downscaling
001034617 260__ $$c2024
001034617 300__ $$aN/A
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001034617 520__ $$aHigh spatial resolution in atmospheric representations is crucial across Earth science domains, but global reanalysis datasets like ERA5 often lack the detail to capture local phenomena due to their coarse resolution. Recent efforts have leveraged deep neural networks from computer vision to enhance the spatial resolution of meteorological data, showing promise for statistical downscaling. However, methodological diversity and insufficient comparisons with traditional downscaling techniques challenge these advancements. Our study introduces a benchmark dataset for statistical downscaling, utilizing ERA5 and the finer-resolution COSMO-REA6, to facilitate direct comparisons of downscaling methods for 2m temperature, global (solar) irradiance and 100m wind fields. Accompanying U-Net, GAN, and transformer models with a suite of evaluation metrics aim to standardize assessments and promote transparency and confidence in applying deep learning to meteorological downscaling.
001034617 536__ $$0G:(DE-HGF)POF4-5111$$a5111 - Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups (POF4-511)$$cPOF4-511$$fPOF IV$$x0
001034617 536__ $$0G:(EU-Grant)955513$$aMAELSTROM - MAchinE Learning for Scalable meTeoROlogy and cliMate (955513)$$c955513$$fH2020-JTI-EuroHPC-2019-1$$x1
001034617 536__ $$0G:(BMBF)16HPC029$$aVerbundprojekt: MAELSTROM - Skalierbarkeit von Anwendungen des Maschinellen Lernens in den Bereichen Wetter und Klimawissenschaften für das zukünftige Supercomputing (16HPC029)$$c16HPC029$$x2
001034617 536__ $$0G:(DE-Juel-1)ESDE$$aEarth System Data Exploration (ESDE)$$cESDE$$x3
001034617 7001_ $$0P:(DE-HGF)0$$aHarder, Paula$$b1
001034617 7001_ $$0P:(DE-HGF)0$$aSchicker, Irene$$b2
001034617 7001_ $$0P:(DE-Juel1)186635$$aPatnala, Ankit$$b3$$ufzj
001034617 7001_ $$0P:(DE-HGF)0$$aLehner, Sebastian$$b4
001034617 7001_ $$0P:(DE-HGF)0$$aDabernig, Markus$$b5
001034617 7001_ $$0P:(DE-HGF)0$$aMayer, Konrad$$b6
001034617 8564_ $$uhttps://www.climatechange.ai/papers/iclr2024/71
001034617 8564_ $$uhttps://juser.fz-juelich.de/record/1034617/files/paper.pdf$$yOpenAccess
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001034617 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)180790$$aForschungszentrum Jülich$$b0$$kFZJ
001034617 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)186635$$aForschungszentrum Jülich$$b3$$kFZJ
001034617 9131_ $$0G:(DE-HGF)POF4-511$$1G:(DE-HGF)POF4-510$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5111$$aDE-HGF$$bKey Technologies$$lEngineering Digital Futures – Supercomputing, Data Management and Information Security for Knowledge and Action$$vEnabling Computational- & Data-Intensive Science and Engineering$$x0
001034617 9141_ $$y2024
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001034617 9201_ $$0I:(DE-Juel1)JSC-20090406$$kJSC$$lJülich Supercomputing Center$$x0
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