TY  - CONF
AU  - Gong, Bing
AU  - Stadtler, Scarlet
AU  - Langguth, Michael
AU  - Mozaffari, Amirpasha
AU  - Vogelsang, Jan
AU  - Schultz, Martin
TI  - Deep learning for short-term temperature forecasts with video prediction methods
M1  - FZJ-2021-00761
PY  - 2020
AB  - 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.
T2  - European Geosciences Union 2020
CY  - 4 May 2020 - 8 May 2020, Virtual (Austria)
Y2  - 4 May 2020 - 8 May 2020
M2  - Virtual, Austria
LB  - PUB:(DE-HGF)1
UR  - https://juser.fz-juelich.de/record/890172
ER  -