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.

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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2021-278', Anonymous Referee #1, 30 Oct 2021
  • RC2: 'Comment on gmd-2021-278', Anonymous Referee #2, 30 Nov 2021
  • AC1: 'Comment on gmd-2021-278', Julian Quinting, 10 Dec 2021

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Julian Quinting on behalf of the Authors (10 Dec 2021)  Author's response    Author's tracked changes    Manuscript
ED: Publish as is (20 Dec 2021) by Travis O'Brien
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.