Articles | Volume 15, issue 2
Geosci. Model Dev., 15, 731–744, 2022
https://doi.org/10.5194/gmd-15-731-2022

Special issue: Benchmark datasets and machine learning algorithms for Earth...

Geosci. Model Dev., 15, 731–744, 2022
https://doi.org/10.5194/gmd-15-731-2022
Model evaluation paper
27 Jan 2022
Model evaluation paper | 27 Jan 2022

EuLerian Identification of ascending AirStreams (ELIAS 2.0) in numerical weather prediction and climate models – Part 2: Model application to different datasets

Julian F. Quinting et al.

Viewed

Total article views: 1,707 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,390 272 45 1,707 19 10
  • HTML: 1,390
  • PDF: 272
  • XML: 45
  • Total: 1,707
  • BibTeX: 19
  • EndNote: 10
Views and downloads (calculated since 28 Sep 2021)
Cumulative views and downloads (calculated since 28 Sep 2021)

Viewed (geographical distribution)

Total article views: 1,707 (including HTML, PDF, and XML) Thereof 1,586 with geography defined and 121 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 28 Jan 2023
Short summary
This study applies novel artificial-intelligence-based models that allow the identification of one specific weather system which affects the midlatitude circulation. We show that the models yield similar results as their trajectory-based counterpart, which requires data at higher spatiotemporal resolution and is computationally more expensive. Overall, we aim to show how deep learning methods can be used efficiently to support process understanding of biases in weather prediction models.