Articles | Volume 15, issue 2
https://doi.org/10.5194/gmd-15-715-2022
https://doi.org/10.5194/gmd-15-715-2022
Model description paper
 | 
27 Jan 2022
Model description paper |  | 27 Jan 2022

EuLerian Identification of ascending AirStreams (ELIAS 2.0) in numerical weather prediction and climate models – Part 1: Development of deep learning model

Julian F. Quinting and Christian M. Grams

Viewed

Total article views: 3,716 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
2,983 652 81 3,716 62 45
  • HTML: 2,983
  • PDF: 652
  • XML: 81
  • Total: 3,716
  • BibTeX: 62
  • EndNote: 45
Views and downloads (calculated since 22 Sep 2021)
Cumulative views and downloads (calculated since 22 Sep 2021)

Viewed (geographical distribution)

Total article views: 3,716 (including HTML, PDF, and XML) Thereof 3,497 with geography defined and 219 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 17 Jul 2024
Short summary
Physical processes in weather systems importantly affect the midlatitude large-scale circulation. This study introduces an artificial-intelligence-based framework which allows the identification of an important weather system – the so-called warm conveyor belt (WCB) – at comparably low computational costs and from data at low spatial and temporal resolution. The framework thus newly enables the systematic investigation of WCBs in large data sets such as climate model projections.