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

Data sets

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 https://doi.org/10.5281/zenodo.7407487

Model code and software

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 https://doi.org/10.5281/zenodo.7407487

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.