Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 15, issue 2
Articles | Volume 15, issue 2
https://doi.org/10.5194/gmd-15-731-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/gmd-15-731-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 15, issue 2
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, Christian M. Grams, Annika Oertel, and Moritz Pickl

Viewed

Total article views: 2,558 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,971 508 79 2,558 51 43
  • HTML: 1,971
  • PDF: 508
  • XML: 79
  • Total: 2,558
  • BibTeX: 51
  • EndNote: 43
Views and downloads (calculated since 28 Sep 2021)
Cumulative views and downloads (calculated since 28 Sep 2021)

Viewed (geographical distribution)

Total article views: 2,558 (including HTML, PDF, and XML) Thereof 2,421 with geography defined and 137 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 20 Nov 2024
Download
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.
Read more
Special issue
Benchmark datasets and machine learning algorithms for Earth...