Articles | Volume 14, issue 10
https://doi.org/10.5194/gmd-14-6355-2021
https://doi.org/10.5194/gmd-14-6355-2021
Development and technical paper
 | 
22 Oct 2021
Development and technical paper |  | 22 Oct 2021

Fast and accurate learned multiresolution dynamical downscaling for precipitation

Jiali Wang, Zhengchun Liu, Ian Foster, Won Chang, Rajkumar Kettimuthu, and V. Rao Kotamarthi

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Cited articles

Abeykoon, V., Liu, Z., Kettimuthu, R., Fox, G., and Foster, I.: Scientific image restoration anywhere, in: 2019 IEEE/ACM 1st Annual Workshop on Large-scale Experiment-in-the-Loop Computing (XLOOP), Denver, Colorado, November 2019, IEEE, 8–13, 2019. a
Barrett, A. I., Wellmann, C., Seifert, A., Hoose, C., Vogel, B., and Kunz, M.: One step at a time: How model time step significantly affects convection-permitting simulations, J. Adv. Model. Earth Sy., 11, 641–658, 2019. a
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Chang, W., Stein, M. L., Wang, J., Kotamarthi, V. R., and Moyer, E. J.: Changes in spatiotemporal precipitation patterns in changing climate conditions, J. Climate, 29, 8355–8376, 2016. a
Chang, W., Wang, J., Marohnic, J., Kotamarthi, V. R., and Moyer, E. J.: Diagnosing added value of convection-permitting regional models using precipitation event identification and tracking, Clim. Dynam., 55, 175–192, 2020. a
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Short summary
Downscaling, the process of generating a higher spatial or time dataset from a coarser observational or model dataset, is a widely used technique. Two common methodologies for performing downscaling are to use either dynamic (physics-based) or statistical (empirical). Here we develop a novel methodology, using a conditional generative adversarial network (CGAN), to perform the downscaling of a model's precipitation forecasts and describe the advantages of this method compared to the others.
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