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| Hauptseite > Publikationsdatenbank > Statistical Downscaling of Surface Temperature and Precipitation with Deep Neural Networks > print |
| Hauptseite > Publikationsdatenbank > Statistical Downscaling of Surface Temperature and Precipitation with Deep Neural Networks > print |
| 001 | 908363 | ||
| 005 | 20230127125340.0 | ||
| 024 | 7 | _ | |a 2128/31419 |2 Handle |
| 037 | _ | _ | |a FZJ-2022-02565 |
| 041 | _ | _ | |a English |
| 100 | 1 | _ | |a Gong, Bing |0 P:(DE-Juel1)177767 |b 0 |e Corresponding author |u fzj |
| 111 | 2 | _ | |a Platform for Advanced Scientific Computing Conference 2022 |g PASC2022 |c Basel |d 2022-06-27 - 2022-06-30 |w Switzerland |
| 245 | _ | _ | |a Statistical Downscaling of Surface Temperature and Precipitation with Deep Neural Networks |
| 260 | _ | _ | |c 2022 |
| 336 | 7 | _ | |a Conference Paper |0 33 |2 EndNote |
| 336 | 7 | _ | |a Other |2 DataCite |
| 336 | 7 | _ | |a INPROCEEDINGS |2 BibTeX |
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| 336 | 7 | _ | |a Conference Presentation |b conf |m conf |0 PUB:(DE-HGF)6 |s 1656674930_12168 |2 PUB:(DE-HGF) |x Other |
| 520 | _ | _ | |a In light of the success of superresolution (SR) applications in computer vision, recent studies have started to develop statistical downscaling methods for meteorological data based on deep neural networks (DNNs). DNNs are attractive, because they are computationally cheap, once they are trained.In this study, deep neural networks are developed to downscale hourly 2 meter temperature and precipitation over the complex terrain of Central Europe. Our approach is based on advanced generative adversarial networks (GANs) and transformer networks. The merit of this choice is that GANs encourage the generator to preserve the strong spatial variability from the data, while the transformer can capture the temporal dependencies. The experiments are designed to generate high-resolution temperature (0.1°) from low resolution (0.8°), and time-evolving high-resolution precipitation (1 km) from low resolution (4 km/8 km). The DNNs are fed with several relevant static and dynamic predictors and comprehensively evaluated by grid point-level errors, and error metrics for spatial variability and the generated probability distribution. Our results motivate the further development of DNNs that can be potentially leveraged to downscale other challenging Earth system data such as cloud cover or wind in operational workflows. |
| 536 | _ | _ | |a 5111 - Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups (POF4-511) |0 G:(DE-HGF)POF4-5111 |c POF4-511 |f POF IV |x 0 |
| 536 | _ | _ | |a MAELSTROM - MAchinE Learning for Scalable meTeoROlogy and cliMate (955513) |0 G:(EU-Grant)955513 |c 955513 |f H2020-JTI-EuroHPC-2019-1 |x 1 |
| 536 | _ | _ | |a Earth System Data Exploration (ESDE) |0 G:(DE-Juel-1)ESDE |c ESDE |x 2 |
| 536 | _ | _ | |0 G:(DE-Juel-1)ESDE |a Earth System Data Exploration (ESDE) |c ESDE |x 3 |
| 700 | 1 | _ | |a Langguth, Michael |0 P:(DE-Juel1)180790 |b 1 |e Corresponding author |u fzj |
| 700 | 1 | _ | |a Ji, Yan |0 P:(DE-Juel1)187069 |b 2 |u fzj |
| 700 | 1 | _ | |a Mozaffari, Amirpasha |0 P:(DE-Juel1)166264 |b 3 |u fzj |
| 700 | 1 | _ | |a Mache, Karim |0 P:(DE-Juel1)187076 |b 4 |u fzj |
| 700 | 1 | _ | |a Schultz, Martin |0 P:(DE-Juel1)6952 |b 5 |u fzj |
| 856 | 4 | _ | |u https://pasc22.pasc-conference.org/program/schedule/presentation/?id=msa221&sess=sess127 |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/908363/files/2022-06-26_PASC_downscaling_Gong%2BLangguth.pptx |y OpenAccess |
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