Gast ::
| Hauptseite > Publikationsdatenbank > DownscaleBench: A benchmark dataset for statisticaldownscaling of meteorological fields > print |
| Hauptseite > Publikationsdatenbank > DownscaleBench: A benchmark dataset for statisticaldownscaling of meteorological fields > print |
| 001 | 1034626 | ||
| 005 | 20250110192855.0 | ||
| 024 | 7 | _ | |a 10.34734/FZJ-2024-07387 |2 datacite_doi |
| 037 | _ | _ | |a FZJ-2024-07387 |
| 041 | _ | _ | |a English |
| 100 | 1 | _ | |a Langguth, Michael |0 P:(DE-Juel1)180790 |b 0 |e Corresponding author |
| 111 | 2 | _ | |a Workshop on Large-Scale Deep Learning for the Earth System |g LSDL4ES 2024 |c Bonn |d 2024-08-29 - 2024-08-30 |w Germany |
| 245 | _ | _ | |a DownscaleBench: A benchmark dataset for statisticaldownscaling of meteorological fields |
| 260 | _ | _ | |c 2024 |
| 336 | 7 | _ | |a Conference Paper |0 33 |2 EndNote |
| 336 | 7 | _ | |a Other |2 DataCite |
| 336 | 7 | _ | |a INPROCEEDINGS |2 BibTeX |
| 336 | 7 | _ | |a conferenceObject |2 DRIVER |
| 336 | 7 | _ | |a LECTURE_SPEECH |2 ORCID |
| 336 | 7 | _ | |a Conference Presentation |b conf |m conf |0 PUB:(DE-HGF)6 |s 1736508388_6153 |2 PUB:(DE-HGF) |x Other |
| 520 | _ | _ | |a Statistical 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. |
| 536 | _ | _ | |a 5111 - Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups (POF4-511) |0 G:(DE-HGF)POF4-5111 |c POF4-511 |f POF IV |x 0 |
| 536 | _ | _ | |a MAELSTROM - MAchinE Learning for Scalable meTeoROlogy and cliMate (955513) |0 G:(EU-Grant)955513 |c 955513 |f H2020-JTI-EuroHPC-2019-1 |x 1 |
| 536 | _ | _ | |a Verbundprojekt: MAELSTROM - Skalierbarkeit von Anwendungen des Maschinellen Lernens in den Bereichen Wetter und Klimawissenschaften für das zukünftige Supercomputing (16HPC029) |0 G:(BMBF)16HPC029 |c 16HPC029 |x 2 |
| 536 | _ | _ | |a Earth System Data Exploration (ESDE) |0 G:(DE-Juel-1)ESDE |c ESDE |x 3 |
| 700 | 1 | _ | |a Lehner, Sebastian |0 P:(DE-HGF)0 |b 1 |
| 700 | 1 | _ | |a Harder, Paula |0 P:(DE-HGF)0 |b 2 |
| 700 | 1 | _ | |a Patnala, Ankit |0 P:(DE-Juel1)186635 |b 3 |
| 700 | 1 | _ | |a Schicker, Irene |0 P:(DE-HGF)0 |b 4 |
| 700 | 1 | _ | |a Dabernig, Markus |0 P:(DE-HGF)0 |b 5 |
| 700 | 1 | _ | |a Schultz, Martin |0 P:(DE-Juel1)6952 |b 6 |u fzj |
| 856 | 4 | _ | |u https://cesoc.net/lsdles-workshop-abstracts/#:~:text=DownscaleBench%3A%20A%20benchmark%20dataset%20for%20statistical%20downscaling%20of%20meteorological%20fields |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/1034626/files/Langguth_DownscleBench_LSDLES_2024-08-29.pdf |y OpenAccess |
| 909 | C | O | |o oai:juser.fz-juelich.de:1034626 |p openaire |p open_access |p VDB |p driver |p ec_fundedresources |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 0 |6 P:(DE-Juel1)180790 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 3 |6 P:(DE-Juel1)186635 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 6 |6 P:(DE-Juel1)6952 |
| 913 | 1 | _ | |a DE-HGF |b Key Technologies |l Engineering Digital Futures – Supercomputing, Data Management and Information Security for Knowledge and Action |1 G:(DE-HGF)POF4-510 |0 G:(DE-HGF)POF4-511 |3 G:(DE-HGF)POF4 |2 G:(DE-HGF)POF4-500 |4 G:(DE-HGF)POF |v Enabling Computational- & Data-Intensive Science and Engineering |9 G:(DE-HGF)POF4-5111 |x 0 |
| 914 | 1 | _ | |y 2024 |
| 915 | _ | _ | |a OpenAccess |0 StatID:(DE-HGF)0510 |2 StatID |
| 920 | _ | _ | |l yes |
| 920 | 1 | _ | |0 I:(DE-Juel1)JSC-20090406 |k JSC |l Jülich Supercomputing Center |x 0 |
| 980 | _ | _ | |a conf |
| 980 | _ | _ | |a VDB |
| 980 | _ | _ | |a UNRESTRICTED |
| 980 | _ | _ | |a I:(DE-Juel1)JSC-20090406 |
| 980 | 1 | _ | |a FullTexts |
| Library | Collection | CLSMajor | CLSMinor | Language | Author |
|---|