Conference Presentation (After Call)

FZJ-2021-00757

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Deep learning for short-term temperature forecasts with video prediction methods

Gong, B. (Corresponding author)FZJ* ; Stadtler, S.FZJ* ; Langguth, M.FZJ* ; Mozaffari, A.FZJ* ; Vogelsang, J.FZJ* ; Schultz, M.FZJ*

2020

Please use a persistent id in citations: http://hdl.handle.net/2128/27011

Abstract: 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

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Contributing Institute(s):

1. Jülich Supercomputing Center (JSC)

Research Program(s):

1. 512 - Data-Intensive Science and Federated Computing (POF3-512) (POF3-512)
2. IntelliAQ - Artificial Intelligence for Air Quality (787576) (787576)
3. Earth System Data Exploration (ESDE) (ESDE)

Appears in the scientific report 2020

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Database coverage:

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