Articles | Volume 15, issue 2
https://doi.org/10.5194/gmd-15-731-2022
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, Christian M. Grams, Annika Oertel, and Moritz Pickl

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Cited articles

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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.
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