Articles | Volume 15, issue 15
https://doi.org/10.5194/gmd-15-5967-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-15-5967-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Rad-cGAN v1.0: Radar-based precipitation nowcasting model with conditional generative adversarial networks for multiple dam domains
Suyeon Choi
Department of Civil and Environmental Engineering, Yonsei University,
Seoul 03722, Korea
Department of Civil and Environmental Engineering, Yonsei University,
Seoul 03722, Korea
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Jungho Seo, Mahdi Panahi, Ji Hyun Kim, Sayed M. Bateni, and Yeonjoo Kim
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-349, https://doi.org/10.5194/essd-2024-349, 2025
Manuscript not accepted for further review
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Short summary
Here we present the cGAN-based precipitation nowcasting model, named Rad-cGAN, trained to predict a radar reflectivity map with a lead time of 10 min. Rad-cGAN showed superior performance at a lead time of up to 90 min compared with the reference models. Furthermore, we demonstrate the successful implementation of the transfer learning strategies using pre-trained Rad-cGAN to develop the models for different dam domains.
Here we present the cGAN-based precipitation nowcasting model, named Rad-cGAN, trained to...