Preprints
https://doi.org/10.5194/egusphere-2022-866
https://doi.org/10.5194/egusphere-2022-866
 
23 Sep 2022
23 Sep 2022

WRF-DL v1.0: A Bridge between WRF v4.3 and Deep Learning Parameterizations and its Application to Atmospheric Radiative Transfer

Xiaohui Zhong, Zhijian Ma, Yichen Yao, Lifei Xu, Yuan Wu, and Zhibin Wang Xiaohui Zhong et al.
  • Damo Academy, Alibaba Group, Hangzhou 311121, China

Abstract. In numerical weather prediction (NWP) models, physical parameterization schemes are the most computationally expensive components, despite being greatly simplified. In the past few years, an increasing number of studies have demonstrated that deep learning (DL) parameterizations of subgrid physics have the potential to accelerate and even outperform conventional physic-based schemes. However, as the DL models are commonly implemented using the DL libraries written in Python, very few DL-based parameterizations have been successfully integrated with NWP models due to the difficulty of embedding Python functions into Fortran-based NWP models. To address this issue, we developed a coupler to allow the DL-based parameterizations to be coupled with a widely used NWP model, i.e., the Weather and Research Forecasting (WRF) model. Similar to the WRF I/O methodologies, the coupler provides the options to run the DL model inference with exclusive processors or the same processors for WRF calculations. In addition, to demonstrate the effectiveness of the coupler, the DL-based radiation emulators are trained and coupled with the WRF model successfully.

Journal article(s) based on this preprint

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
Geosci. Model Dev., 16, 199–209, https://doi.org/10.5194/gmd-16-199-2023,https://doi.org/10.5194/gmd-16-199-2023, 2023
Short summary

Xiaohui Zhong et al.

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2022-866', Sergey Osipov, 05 Oct 2022
    • AC1: 'Reply on RC1', Xiaohui Zhong, 10 Oct 2022
  • RC2: 'Comment on egusphere-2022-866', Peter Ukkonen, 07 Oct 2022
    • AC2: 'Reply on RC2', Xiaohui Zhong, 10 Oct 2022
      • RC3: 'Reply on AC2', Peter Ukkonen, 19 Oct 2022
        • AC3: 'Reply on RC3', Xiaohui Zhong, 20 Oct 2022

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2022-866', Sergey Osipov, 05 Oct 2022
    • AC1: 'Reply on RC1', Xiaohui Zhong, 10 Oct 2022
  • RC2: 'Comment on egusphere-2022-866', Peter Ukkonen, 07 Oct 2022
    • AC2: 'Reply on RC2', Xiaohui Zhong, 10 Oct 2022
      • RC3: 'Reply on AC2', Peter Ukkonen, 19 Oct 2022
        • AC3: 'Reply on RC3', Xiaohui Zhong, 20 Oct 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
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

Journal article(s) based on this preprint

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
Geosci. Model Dev., 16, 199–209, https://doi.org/10.5194/gmd-16-199-2023,https://doi.org/10.5194/gmd-16-199-2023, 2023
Short summary

Xiaohui Zhong et al.

Model code and software

A Bridge between WRF and Deep Learning Parameterization and Application on Deep Learning Parameterization of Atmospheric Radiative Transfer Zhong, Xiaohui and Ma, Zhijian and Yao, Yichen and Xue, Lifei and Wu, Yuan and Wang, Zhibin https://doi.org/10.5281/zenodo.7056166

Xiaohui Zhong et al.

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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.

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