Hydrology and Earth System Sciences
Hydrology and Earth System Sciences
HESS
Articles | Volume 29, issue 15
Articles | Volume 29, issue 15
https://doi.org/10.5194/hess-29-3687-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-29-3687-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 29, issue 15
Research article
 | 
12 Aug 2025
Research article |  | 12 Aug 2025

Infilling of missing rainfall radar data with a memory-assisted deep learning approach

Johannes Meuer, Laurens M. Bouwer, Frank Kaspar, Roman Lehmann, Wolfgang Karl, Thomas Ludwig, and Christopher Kadow

Viewed

Total article views: 3,156 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
2,666 396 94 3,156 100 119
  • HTML: 2,666
  • PDF: 396
  • XML: 94
  • Total: 3,156
  • BibTeX: 100
  • EndNote: 119
Views and downloads (calculated since 22 Jul 2024)
Cumulative views and downloads (calculated since 22 Jul 2024)

Viewed (geographical distribution)

Total article views: 3,156 (including HTML, PDF, and XML) Thereof 3,076 with geography defined and 80 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 10 Mar 2026
Download
Short summary
Our study focuses on filling in missing precipitation data using an advanced neural network model. Traditional methods for estimating missing climate information often struggle in large regions where data are scarce. Our solution, which incorporates recent advances in machine learning, captures the intricate patterns of precipitation over time, especially during extreme weather events. Our model shows good performance in reconstructing large regions of missing rainfall radar data.
Read more
Share