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

Viewed

Total article views: 3,342 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
2,534 733 75 3,342 68 52
  • HTML: 2,534
  • PDF: 733
  • XML: 75
  • Total: 3,342
  • BibTeX: 68
  • EndNote: 52
Views and downloads (calculated since 04 Aug 2022)
Cumulative views and downloads (calculated since 04 Aug 2022)

Viewed (geographical distribution)

Total article views: 3,342 (including HTML, PDF, and XML) Thereof 3,231 with geography defined and 111 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 13 Dec 2024
Download
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.
Read more