Model experiment description paper |
| 23 May 2023
CLGAN: a generative adversarial network (GAN)-based video prediction model for precipitation nowcasting
Yan Ji,Bing Gong,Michael Langguth,Amirpasha Mozaffari,and Xiefei Zhi
Yan Ji
Key Laboratory of Meteorological Disaster, Ministry of Education (KLME), Nanjing University of Information Science and Technology (NUIST), Nanjing 210044, China
Jülich Supercomputing Centre, Forschungszentrum Jülich, 52425 Jülich, Germany
Jülich Supercomputing Centre, Forschungszentrum Jülich, 52425 Jülich, Germany
Xiefei Zhi
Key Laboratory of Meteorological Disaster, Ministry of Education (KLME), Nanjing University of Information Science and Technology (NUIST), Nanjing 210044, China
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Viewed (geographical distribution)
Total article views: 5,668 (including HTML, PDF, and XML)
Thereof 5,540 with geography defined
and 128 with unknown origin.
Total article views: 4,517 (including HTML, PDF, and XML)
Thereof 4,427 with geography defined
and 90 with unknown origin.
Total article views: 1,151 (including HTML, PDF, and XML)
Thereof 1,113 with geography defined
and 38 with unknown origin.
Formulating short-term precipitation forecasting as a video prediction task, a novel deep learning architecture (convolutional long short-term memory generative adversarial network, CLGAN) is proposed. A benchmark dataset is built on minute-level precipitation measurements. Results show that with the GAN component the model generates predictions sharing statistical properties with observations, resulting in it outperforming the baseline in dichotomous and spatial scores for heavy precipitation.
Formulating short-term precipitation forecasting as a video prediction task, a novel deep...
