Articles | Volume 23, issue 9
https://doi.org/10.5194/nhess-23-3065-2023
https://doi.org/10.5194/nhess-23-3065-2023
Research article
 | 
18 Sep 2023
Research article |  | 18 Sep 2023

Shallow and deep learning of extreme rainfall events from convective atmospheres

Gerd Bürger and Maik Heistermann

Viewed

Total article views: 1,204 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
851 316 37 1,204 125 17 22
  • HTML: 851
  • PDF: 316
  • XML: 37
  • Total: 1,204
  • Supplement: 125
  • BibTeX: 17
  • EndNote: 22
Views and downloads (calculated since 07 Nov 2022)
Cumulative views and downloads (calculated since 07 Nov 2022)

Viewed (geographical distribution)

Total article views: 1,204 (including HTML, PDF, and XML) Thereof 1,183 with geography defined and 21 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 08 May 2024
Download
Short summary
Our subject is a new catalogue of radar-based heavy rainfall events (CatRaRE) over Germany and how it relates to the concurrent atmospheric circulation. We classify reanalyzed daily atmospheric fields of convective indices according to CatRaRE, using conventional statistical and more recent machine learning algorithms, and apply them to present and future atmospheres. Increasing trends are projected for CatRaRE-type probabilities, from reanalyzed as well as from simulated atmospheric fields.
Altmetrics
Final-revised paper
Preprint