Articles | Volume 16, issue 2
https://doi.org/10.5194/gmd-16-535-2023
© Author(s) 2023. 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-16-535-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
25 Jan 2023
Development and technical paper |
| 25 Jan 2023
| 25 Jan 2023
Customized deep learning for precipitation bias correction and downscaling
Fang Wang
Department of Crop, Soil, and Environmental Sciences, Auburn
University, Auburn, AL 36849, USA
Department of Crop, Soil, and Environmental Sciences, Auburn
University, Auburn, AL 36849, USA
Mark Carroll
Computational and Information Science Technology Office, NASA Goddard
Space Flight Center, Greenbelt, MD 20771, USA
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Short summary
Gridded precipitation datasets suffer from biases and coarse resolutions. We developed a customized deep learning (DL) model to bias-correct and downscale gridded precipitation data using radar observations. The results showed that the customized DL model can generate improved precipitation at fine resolutions where regular DL and statistical methods experience challenges. The new model can be used to improve precipitation estimates, especially for capturing extremes at smaller scales.
Gridded precipitation datasets suffer from biases and coarse resolutions. We developed a...
