Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 16, issue 2
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.
https://doi.org/10.5194/gmd-16-535-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 16, issue 2
Development and technical paper
 | 
25 Jan 2023
Development and technical paper |  | 25 Jan 2023

Customized deep learning for precipitation bias correction and downscaling

Fang Wang, Di Tian, and Mark Carroll

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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.
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