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

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

doi:https://doi.org/10.5194/gmd-16-1467-2023

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

Data sets

Deep learning models for generation of precipitation maps based on NWP Data Adrian Rojas-Campos https://doi.org/10.5281/zenodo.7244319

Model code and software

Deep learning models for generation of precipitation maps based on NWP Code (Version 1) Adrian Rojas-Campos https://doi.org/10.5281/zenodo.7535434

Deep learning models for generation of precipitation maps based on NWP Adrian Rojas-Campos https://github.com/DeepRainProject/models_for_radar

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.