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001     890172
005     20230127125338.0
037 _ _ |a FZJ-2021-00761
041 _ _ |a English
100 1 _ |a Gong, Bing
|0 P:(DE-Juel1)177767
|b 0
|e Corresponding author
|u fzj
111 2 _ |a European Geosciences Union 2020
|g EGU2020
|c Virtual
|d 2020-05-04 - 2020-05-08
|w Austria
245 _ _ |a Deep learning for short-term temperature forecasts with video prediction methods
260 _ _ |c 2020
336 7 _ |a Abstract
|b abstract
|m abstract
|0 PUB:(DE-HGF)1
|s 1611580296_25895
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336 7 _ |a Conference Paper
|0 33
|2 EndNote
336 7 _ |a INPROCEEDINGS
|2 BibTeX
336 7 _ |a conferenceObject
|2 DRIVER
336 7 _ |a Output Types/Conference Abstract
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336 7 _ |a OTHER
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520 _ _ |a This study explores the adaptation of state-of-the-art deep learning architectures for video frame prediction in the context of weather and climate applications. A proof-of-concept case study was performed to predict surface temperature fields over Europe for up to 20 hours based on ERA5 reanalyses weather data. Initial results have been achieved with a PredNet and a GAN-based architecture by using various combinations of temperature, surface pressure, and 500 hPa geopotential as inputs. The results show that the GAN-based architecture outperforms the PredNet. To facilitate the massive data processing and testing of various deep learning architectures, we have developed a containerized parallel workflow for the full life-cycle of the application, which consists of data extraction, data pre-processing, training, post-processing and visualisation of results. The training for PredNet was parallelized on JUWELS supercomputer at JSC, and the training scalability performance was also evaluated.
536 _ _ |a 512 - Data-Intensive Science and Federated Computing (POF3-512)
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|c POF3-512
|f POF III
|x 0
536 _ _ |a IntelliAQ - Artificial Intelligence for Air Quality (787576)
|0 G:(EU-Grant)787576
|c 787576
|f ERC-2017-ADG
|x 1
536 _ _ |0 G:(DE-Juel-1)ESDE
|a Earth System Data Exploration (ESDE)
|c ESDE
|x 2
700 1 _ |a Stadtler, Scarlet
|0 P:(DE-Juel1)180752
|b 1
|u fzj
700 1 _ |a Langguth, Michael
|0 P:(DE-Juel1)180790
|b 2
|u fzj
700 1 _ |a Mozaffari, Amirpasha
|0 P:(DE-Juel1)166264
|b 3
|u fzj
700 1 _ |a Vogelsang, Jan
|0 P:(DE-Juel1)173676
|b 4
|u fzj
700 1 _ |a Schultz, Martin
|0 P:(DE-Juel1)6952
|b 5
|u fzj
909 C O |o oai:juser.fz-juelich.de:890172
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910 1 _ |a Forschungszentrum Jülich
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913 1 _ |a DE-HGF
|b Key Technologies
|l Supercomputing & Big Data
|1 G:(DE-HGF)POF3-510
|0 G:(DE-HGF)POF3-512
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914 1 _ |y 2020
920 _ _ |l yes
920 1 _ |0 I:(DE-Juel1)JSC-20090406
|k JSC
|l Jülich Supercomputing Center
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980 _ _ |a abstract
980 _ _ |a VDB
980 _ _ |a I:(DE-Juel1)JSC-20090406
980 _ _ |a UNRESTRICTED

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