Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 16, issue 5
Articles | Volume 16, issue 5
https://doi.org/10.5194/gmd-16-1467-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-16-1467-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 16, issue 5
Model description paper
 | 
08 Mar 2023
Model description paper |  | 08 Mar 2023

Deep learning models for generation of precipitation maps based on numerical weather prediction

Adrian Rojas-Campos, Michael Langguth, Martin Wittenbrink, and Gordon Pipa

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Short summary
Our paper presents an alternative approach for generating high-resolution precipitation maps based on the nonlinear combination of the complete set of variables of the numerical weather predictions. This process combines the super-resolution task with the bias correction in a single step, generating high-resolution corrected precipitation maps with a lead time of 3 h. We used using deep learning algorithms to combine the input information and increase the accuracy of the precipitation maps.
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