Articles | Volume 15, issue 15
https://doi.org/10.5194/gmd-15-5967-2022
https://doi.org/10.5194/gmd-15-5967-2022
Model description paper
 | 
01 Aug 2022
Model description paper |  | 01 Aug 2022

Rad-cGAN v1.0: Radar-based precipitation nowcasting model with conditional generative adversarial networks for multiple dam domains

Suyeon Choi and Yeonjoo Kim

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Cited articles

Agrawal, S., Barrington, L., Bromberg, C., Burge, J., Gazen, C., and Hickey, J.: Machine learning for precipitation nowcasting from radar images, arXiv [preprint], https://doi.org/10.48550/arXiv.1912.12132, 2019. 
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Ayzel, G., Scheffer, T., and Heistermann, M.: RainNet v1.0: a convolutional neural network for radar-based precipitation nowcasting, Geosci. Model Dev., 13, 2631–2644, https://doi.org/10.5194/gmd-13-2631-2020, 2020. 
Basist, A., Bell, G. D., and Meentemeyer, V.: Statistical relationships between topography and precipitation patterns, J. climate, 7, 1305–1315, https://doi.org/10.1175/1520-0442(1994)007<1305:SRBTAP>2.0.CO;2, 1994. 
Berenguer, M., Surcel, M., Zawadzki, I., Xue, M., and Kong, F.: The Diurnal Cycle of Precipitation from Continental Radar Mosaics and Numerical Weather Prediction Models. Part II: Intercomparison among Numerical Models and with Nowcasting, Mon. Weather Rev., 140, 2689–2705, https://doi.org/10.1175/MWR-D-11-00181.1, 2012. 
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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.