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001034629 0247_ $$2datacite_doi$$a10.34734/FZJ-2024-07390
001034629 037__ $$aFZJ-2024-07390
001034629 041__ $$aEnglish
001034629 1001_ $$0P:(DE-Juel1)180790$$aLangguth, Michael$$b0$$ufzj
001034629 1112_ $$aInternational Conference on Learning Representations$$cVienna$$d2024-05-07 - 2024-05-11$$gICLR 2024$$wAustria
001034629 245__ $$aA Benchmark Dataset for Meteorological Downscaling
001034629 260__ $$c2024
001034629 3367_ $$033$$2EndNote$$aConference Paper
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001034629 520__ $$aHigh spatial resolution in atmospheric representations is crucial across Earth science domains, but global reanalysis datasets like ERA5 often lack the detail to capture local phenomena due to their coarse resolution. Recent efforts have leveraged deep neural networks from computer vision to enhance the spatial resolution of meteorological data, showing promise for statistical downscaling. However, methodological diversity and insufficient comparisons with traditional downscaling techniques challenge these advancements. Our study introduces a benchmark dataset for statistical downscaling, utilizing ERA5 and the finer-resolution COSMO-REA6, to facilitate direct comparisons of downscaling methods for 2m temperature, global (solar) irradiance and 100m wind fields. Accompanying U-Net, GAN, and transformer models with a suite of evaluation metrics aim to standardize assessments and promote transparency and confidence in applying deep learning to meteorological downscaling.
001034629 536__ $$0G:(DE-HGF)POF4-5111$$a5111 - Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups (POF4-511)$$cPOF4-511$$fPOF IV$$x0
001034629 536__ $$0G:(EU-Grant)955513$$aMAELSTROM - MAchinE Learning for Scalable meTeoROlogy and cliMate (955513)$$c955513$$fH2020-JTI-EuroHPC-2019-1$$x1
001034629 536__ $$0G:(BMBF)16HPC029$$aVerbundprojekt: MAELSTROM - Skalierbarkeit von Anwendungen des Maschinellen Lernens in den Bereichen Wetter und Klimawissenschaften für das zukünftige Supercomputing (16HPC029)$$c16HPC029$$x2
001034629 536__ $$0G:(DE-Juel-1)ESDE$$aEarth System Data Exploration (ESDE)$$cESDE$$x3
001034629 7001_ $$0P:(DE-Juel1)186635$$aPatnala, Ankit$$b1$$ufzj
001034629 7001_ $$0P:(DE-HGF)0$$aLehner, Sebastian$$b2
001034629 7001_ $$0P:(DE-HGF)0$$aDabernig, Markus$$b3
001034629 7001_ $$0P:(DE-HGF)0$$aMayer, Konrad$$b4
001034629 7001_ $$0P:(DE-HGF)0$$aSchicker, Irene$$b5
001034629 7001_ $$0P:(DE-HGF)0$$aHarder, Paula$$b6$$eCorresponding author
001034629 8564_ $$uhttps://www.climatechange.ai/papers/iclr2024/71/poster.pdf
001034629 8564_ $$uhttps://juser.fz-juelich.de/record/1034629/files/poster_iclr.pdf$$yOpenAccess
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001034629 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)180790$$aForschungszentrum Jülich$$b0$$kFZJ
001034629 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)186635$$aForschungszentrum Jülich$$b1$$kFZJ
001034629 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$$x0
001034629 9141_ $$y2024
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001034629 9201_ $$0I:(DE-Juel1)JSC-20090406$$kJSC$$lJülich Supercomputing Center$$x0
001034629 980__ $$aposter
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