001034626 001__ 1034626
001034626 005__ 20250110192855.0
001034626 0247_ $$2datacite_doi$$a10.34734/FZJ-2024-07387
001034626 037__ $$aFZJ-2024-07387
001034626 041__ $$aEnglish
001034626 1001_ $$0P:(DE-Juel1)180790$$aLangguth, Michael$$b0$$eCorresponding author
001034626 1112_ $$aWorkshop on Large-Scale Deep Learning for the Earth System$$cBonn$$d2024-08-29 - 2024-08-30$$gLSDL4ES 2024$$wGermany
001034626 245__ $$aDownscaleBench: A benchmark dataset for statisticaldownscaling of meteorological fields
001034626 260__ $$c2024
001034626 3367_ $$033$$2EndNote$$aConference Paper
001034626 3367_ $$2DataCite$$aOther
001034626 3367_ $$2BibTeX$$aINPROCEEDINGS
001034626 3367_ $$2DRIVER$$aconferenceObject
001034626 3367_ $$2ORCID$$aLECTURE_SPEECH
001034626 3367_ $$0PUB:(DE-HGF)6$$2PUB:(DE-HGF)$$aConference Presentation$$bconf$$mconf$$s1736508388_6153$$xOther
001034626 520__ $$aStatistical downscaling with deep neural networks has recently gained a lot of momentum in the meteorological community. While several studies show promising results, direct comparison between different approaches is often impeded due to a large variety in the related downscaling task (target quantity), the deployed dataset and the evaluation framework. Furthermore, the performance of deep neural networks for statistical downscaling is barely compared to classical downscaling methods that have been established in the meteorological domain over the last decades. To enable fair comparison between different downscaling techniques, we suggest DownscaleBench, a benchmark dataset based on the coarse-grained ERA5 and the high-resolved COSMO REA6-dataset. DownscaleBench provides a standardized set of predictors for three (deterministic) downscaling tasks, that are downscaling of 2m temperature, 100m wind and solar irradiance. The ready-to-use datasets are complemented by a set of baseline models (standard deep neural networks and a classical method) whose performance is accessed in a task-specific, but standardized evaluation framework.
001034626 536__ $$0G:(DE-HGF)POF4-5111$$a5111 - Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups (POF4-511)$$cPOF4-511$$fPOF IV$$x0
001034626 536__ $$0G:(EU-Grant)955513$$aMAELSTROM - MAchinE Learning for Scalable meTeoROlogy and cliMate (955513)$$c955513$$fH2020-JTI-EuroHPC-2019-1$$x1
001034626 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
001034626 536__ $$0G:(DE-Juel-1)ESDE$$aEarth System Data Exploration (ESDE)$$cESDE$$x3
001034626 7001_ $$0P:(DE-HGF)0$$aLehner, Sebastian$$b1
001034626 7001_ $$0P:(DE-HGF)0$$aHarder, Paula$$b2
001034626 7001_ $$0P:(DE-Juel1)186635$$aPatnala, Ankit$$b3
001034626 7001_ $$0P:(DE-HGF)0$$aSchicker, Irene$$b4
001034626 7001_ $$0P:(DE-HGF)0$$aDabernig, Markus$$b5
001034626 7001_ $$0P:(DE-Juel1)6952$$aSchultz, Martin$$b6$$ufzj
001034626 8564_ $$uhttps://cesoc.net/lsdles-workshop-abstracts/#:~:text=DownscaleBench%3A%20A%20benchmark%20dataset%20for%20statistical%20downscaling%20of%20meteorological%20fields
001034626 8564_ $$uhttps://juser.fz-juelich.de/record/1034626/files/Langguth_DownscleBench_LSDLES_2024-08-29.pdf$$yOpenAccess
001034626 909CO $$ooai:juser.fz-juelich.de:1034626$$popenaire$$popen_access$$pVDB$$pdriver$$pec_fundedresources
001034626 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)180790$$aForschungszentrum Jülich$$b0$$kFZJ
001034626 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)186635$$aForschungszentrum Jülich$$b3$$kFZJ
001034626 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)6952$$aForschungszentrum Jülich$$b6$$kFZJ
001034626 9131_ $$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
001034626 9141_ $$y2024
001034626 915__ $$0StatID:(DE-HGF)0510$$2StatID$$aOpenAccess
001034626 920__ $$lyes
001034626 9201_ $$0I:(DE-Juel1)JSC-20090406$$kJSC$$lJülich Supercomputing Center$$x0
001034626 980__ $$aconf
001034626 980__ $$aVDB
001034626 980__ $$aUNRESTRICTED
001034626 980__ $$aI:(DE-Juel1)JSC-20090406
001034626 9801_ $$aFullTexts