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.
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.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
22 Oct 2021
Development and technical paper |
| 22 Oct 2021
| 22 Oct 2021
Fast and accurate learned multiresolution dynamical downscaling for precipitation
Jiali Wang
Environmental Science Division, Argonne National Laboratory, Lemont, IL, USA
Data Science and Learning Division, Argonne National Laboratory, Lemont, IL, USA
Ian Foster
Data Science and Learning Division, Argonne National Laboratory, Lemont, IL, USA
Won Chang
Division of Statistics and Data Science, University of Cincinnati, Cincinnati, OH, USA
Rajkumar Kettimuthu
Data Science and Learning Division, Argonne National Laboratory, Lemont, IL, USA
Environmental Science Division, Argonne National Laboratory, Lemont, IL, USA
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- Assimilation of surface soil moisture jointly retrieved by multiple microwave satellites into the WRF-Hydro model in ungauged regions: Towards a robust flood simulation and forecasting L. Chao et al. 10.1016/j.envsoft.2022.105421
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Latest update: 29 Mar 2025
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.
Downscaling, the process of generating a higher spatial or time dataset from a coarser...
