Articles | Volume 13, issue 9
Geosci. Model Dev., 13, 4271–4285, 2020
https://doi.org/10.5194/gmd-13-4271-2020
Geosci. Model Dev., 13, 4271–4285, 2020
https://doi.org/10.5194/gmd-13-4271-2020

Development and technical paper 16 Sep 2020

Development and technical paper | 16 Sep 2020

Can machine learning improve the model representation of turbulent kinetic energy dissipation rate in the boundary layer for complex terrain?

Nicola Bodini et al.

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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Nicola Bodini on behalf of the Authors (17 Jun 2020)  Author's response    Manuscript
ED: Referee Nomination & Report Request started (19 Jun 2020) by Lutz Gross
RR by Ivana Stiperski (03 Jul 2020)
RR by Anonymous Referee #3 (16 Jul 2020)
ED: Publish subject to minor revisions (review by editor) (17 Jul 2020) by Lutz Gross
AR by Nicola Bodini on behalf of the Authors (28 Jul 2020)  Author's response    Manuscript
ED: Publish as is (30 Jul 2020) by Lutz Gross
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Short summary
While turbulence dissipation rate (ε) is an essential parameter for the prediction of wind speed, its current representation in weather prediction models is inaccurate, especially in complex terrain. In this study, we leverage the potential of machine-learning techniques to provide a more accurate representation of turbulence dissipation rate. Our results show a 30 % reduction in the average error compared to the current model representation of ε and a total elimination of its average bias.