WRF–ML v1.0: a bridge between WRF v4.3 and machine learning parameterizations and its application to atmospheric radiative transfer

Zhong, Xiaohui; Ma, Zhijian; Yao, Yichen; Xu, Lifei; Wu, Yuan; Wang, Zhibin

doi:https://doi.org/10.5194/gmd-16-199-2023

Articles | Volume 16, issue 1

https://doi.org/10.5194/gmd-16-199-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Special issue:

Model infrastructure integration and interoperability (MI3)

https://doi.org/10.5194/gmd-16-199-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 16, issue 1

Model description paper

|

06 Jan 2023

Model description paper |

| 06 Jan 2023

WRF–ML v1.0: a bridge between WRF v4.3 and machine learning parameterizations and its application to atmospheric radiative transfer

Xiaohui Zhong, Zhijian Ma, Yichen Yao, Lifei Xu, Yuan Wu, and Zhibin Wang

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Final revised paper (published on 06 Jan 2023)
Preprint (discussion started on 23 Sep 2022)

Interactive discussion

Status: closed

RC1:
'Comment on egusphere-2022-866', Sergey Osipov, 05 Oct 2022

The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2022/egusphere-2022-866/egusphere-2022-866-RC1-supplement.pdf

Citation: https://doi.org/10.5194/egusphere-2022-866-RC1
- AC1: 'Reply on RC1', Xiaohui Zhong, 10 Oct 2022
  
  Dear Reviewer,
  
  please see attached document pages 1--2.
  
  Thanks
  
  Citation: https://doi.org/10.5194/egusphere-2022-866-RC1
  
  Citation: https://doi.org/10.5194/egusphere-2022-866-AC1
RC2:
'Comment on egusphere-2022-866', Peter Ukkonen, 07 Oct 2022

I have attached my review as a supplement.

Best regards,

Peter Ukkonen

Citation: https://doi.org/10.5194/egusphere-2022-866-RC2
- AC2:
  'Reply on RC2', Xiaohui Zhong, 10 Oct 2022
  
  Dear Reviewer,
  
  please see attached document pages 3--7.
  
  Thanks
  
  Citation: https://doi.org/10.5194/egusphere-2022-866-AC2
  - RC3: 'Reply on AC2', Peter Ukkonen, 19 Oct 2022
    
    In the revised paper, may I suggest you use "prognostic validation" instead of "quantitative estimation" in 5.2 as the former is more appropriate.
    
    Citation: https://doi.org/10.5194/egusphere-2022-866-RC3
    
    AC3: 'Reply on RC3', Xiaohui Zhong, 20 Oct 2022
    
    Dear Reviewer,
    We have changed the title of subsection 5.2 from "Quantitative estimation" to "Prognostic validation" as you suggested.
    Thanks
    
    Citation: https://doi.org/10.5194/egusphere-2022-866-AC3

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload

AR by Xiaohui Zhong on behalf of the Authors (21 Nov 2022) Author's response Author's tracked changes Manuscript

ED: Referee Nomination & Report Request started (23 Nov 2022) by Klaus Klingmüller

RR by Peter Ukkonen (05 Dec 2022)

ED: Publish subject to minor revisions (review by editor) (06 Dec 2022) by Klaus Klingmüller

AR by Xiaohui Zhong on behalf of the Authors (07 Dec 2022) Author's response Author's tracked changes Manuscript

ED: Publish as is (07 Dec 2022) by Klaus Klingmüller

AR by Xiaohui Zhong on behalf of the Authors (08 Dec 2022) Manuscript

Short summary

More and more researchers use deep learning models to replace physics-based parameterizations to accelerate weather simulations. However, embedding the ML models within the weather models is difficult as they are implemented in different languages. This work proposes a coupling framework to allow ML-based parameterizations to be coupled with the Weather Research and Forecasting (WRF) model. We also demonstrate using the coupler to couple the ML-based radiation schemes with the WRF model.