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| Home > Publications database > HClimRep: AI Climate Model for Capturing the Atmosphere, Ocean, and Sea Ice Interactions > print |
| 001 | 1049641 | ||
| 005 | 20260119203213.0 | ||
| 037 | _ | _ | |a FZJ-2025-05426 |
| 041 | _ | _ | |a English |
| 100 | 1 | _ | |a Melidonis, Savvas |0 P:(DE-Juel1)207675 |b 0 |e Corresponding author |u fzj |
| 111 | 2 | _ | |a AI in Science Summit |c Copenhagen |d 2025-11-03 - 2025-11-04 |w Denmark |
| 245 | _ | _ | |a HClimRep: AI Climate Model for Capturing the Atmosphere, Ocean, and Sea Ice Interactions |
| 260 | _ | _ | |c 2025 |
| 336 | 7 | _ | |a Conference Paper |0 33 |2 EndNote |
| 336 | 7 | _ | |a INPROCEEDINGS |2 BibTeX |
| 336 | 7 | _ | |a conferenceObject |2 DRIVER |
| 336 | 7 | _ | |a CONFERENCE_POSTER |2 ORCID |
| 336 | 7 | _ | |a Output Types/Conference Poster |2 DataCite |
| 336 | 7 | _ | |a Poster |b poster |m poster |0 PUB:(DE-HGF)24 |s 1768826947_9171 |2 PUB:(DE-HGF) |x After Call |
| 520 | _ | _ | |a Climate change poses a significant threat to ecosystems and human society. Accurate climate projections are crucial for developing effective policies for mitigating extreme weather events that are expected to increase due to global warming. However, traditional climate models have limitations, including biases and high computational costs. Under the Helmholtz Foundation Model Initiative (HFMI), we propose a new data-driven climate model, namely HClimRep, which uses foundation model principles and machine learning to analyze diverse climate datasets. This approach enables flexible and customizable outputs, providing a versatile tool for climate applications. Keywords: Foundation Model, AI Model, Deep Learning, Climate Modelling, Climate Simulations |
| 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 Earth System Data Exploration (ESDE) |0 G:(DE-Juel-1)ESDE |c ESDE |x 1 |
| 536 | _ | _ | |a HClimRep2024050120270431 - Helmholtz Representation Model for Climate Science (HClimRep) (HClimRep2024050120270431) |0 G:(DE-HGF)HClimRep2024050120270431 |c HClimRep2024050120270431 |x 2 |
| 700 | 1 | _ | |a Patnala, Ankit |0 P:(DE-Juel1)186635 |b 1 |u fzj |
| 700 | 1 | _ | |a Semcheddine, Asma |0 P:(DE-Juel1)203330 |b 2 |u fzj |
| 700 | 1 | _ | |a Schultz, Martin |0 P:(DE-Juel1)6952 |b 3 |u fzj |
| 700 | 1 | _ | |a Polz, Julius |0 P:(DE-HGF)0 |b 4 |
| 700 | 1 | _ | |a Nowak, Kacper |0 P:(DE-HGF)0 |b 5 |
| 856 | 4 | _ | |u https://cdn.prod.website-files.com/68a7113a28bc36a9033775bf/6903613bfba9f2d65bde4275_32.pdf |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/1049641/files/AIS2025_paper_0024.pdf |y Restricted |
| 909 | C | O | |o oai:juser.fz-juelich.de:1049641 |p VDB |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 0 |6 P:(DE-Juel1)207675 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 1 |6 P:(DE-Juel1)186635 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 2 |6 P:(DE-Juel1)203330 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 3 |6 P:(DE-Juel1)6952 |
| 910 | 1 | _ | |a Karlsruher Institut für Technologie |0 I:(DE-HGF)0 |b 4 |6 P:(DE-HGF)0 |
| 910 | 1 | _ | |a Alfred Wegener Institute |0 I:(DE-HGF)0 |b 5 |6 P:(DE-HGF)0 |
| 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 2025 |
| 920 | _ | _ | |l yes |
| 920 | 1 | _ | |0 I:(DE-Juel1)JSC-20090406 |k JSC |l Jülich Supercomputing Center |x 0 |
| 980 | _ | _ | |a poster |
| 980 | _ | _ | |a VDB |
| 980 | _ | _ | |a I:(DE-Juel1)JSC-20090406 |
| 980 | _ | _ | |a UNRESTRICTED |
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