Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 14, issue 10
Articles | Volume 14, issue 10
https://doi.org/10.5194/gmd-14-6355-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-14-6355-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 14, issue 10
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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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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