This title appears in the Scientific Report :
2020
Deep learning for short-term temperature forecasts with video prediction methods
Deep learning for short-term temperature forecasts with video prediction methods
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...
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Personal Name(s): | Gong, Bing (Corresponding author) |
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Stadtler, Scarlet / Langguth, Michael / Mozaffari, Amirpasha / Vogelsang, Jan / Schultz, Martin | |
Contributing Institute: |
Jülich Supercomputing Center; JSC |
Imprint: |
2020
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Conference: | European Geosciences Union 2020, Virtual (Austria), 2020-05-04 - 2020-05-08 |
Document Type: |
Abstract |
Research Program: |
Earth System Data Exploration Artificial Intelligence for Air Quality Data-Intensive Science and Federated Computing |
Publikationsportal JuSER |
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. |