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

Bodini, Nicola; Lundquist, Julie K.; Optis, Mike

doi:https://doi.org/10.5194/gmd-13-4271-2020

Articles | Volume 13, issue 9

https://doi.org/10.5194/gmd-13-4271-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/gmd-13-4271-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 13, issue 9

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, Julie K. Lundquist, and Mike Optis

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Final revised paper (published on 16 Sep 2020)
Supplement to the final revised paper
Preprint (discussion started on 21 Apr 2020)
Supplement to the preprint

Interactive discussion

Status: closed

AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

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- Supplement

RC1: 'Review of “Can machine learning improve the model representation of TKE dissipation rate in the boundary layer for complex terrain?” by N. Bodini, J. K. Lundquist, M. Optis', Ivana Stiperski, 10 May 2020
- AC1: 'Response to review by Dr. Ivana Stiperski', Nicola Bodini, 04 Jun 2020
RC2: 'Paper review', Anonymous Referee #2, 24 May 2020
- AC2: 'Response to review by Anonymous Referee #2', Nicola Bodini, 04 Jun 2020

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

AR by Nicola Bodini on behalf of the Authors (30 Jul 2020)

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.