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.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/gmd-16-199-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
06 Jan 2023
Model description paper |
| 06 Jan 2023
| 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
Damo Academy, Alibaba Group, Hangzhou 311121, China
Zhijian Ma
Damo Academy, Alibaba Group, Hangzhou 311121, China
Yichen Yao
Damo Academy, Alibaba Group, Hangzhou 311121, China
Lifei Xu
Damo Academy, Alibaba Group, Hangzhou 311121, China
Yuan Wu
Damo Academy, Alibaba Group, Hangzhou 311121, China
Zhibin Wang
CORRESPONDING AUTHOR
Damo Academy, Alibaba Group, Hangzhou 311121, China
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Cited
21 citations as recorded by crossref.
- Deep Learning‐Based Sea Surface Roughness Parameterization Scheme Improves Sea Surface Wind Forecast S. Fu et al.
- APPLICATION OF MACHINE LEARNING IN RADIATIVE HEAT TRANSFER W. Chen & T. Ren
- Different microphysics parameterizations of hydrometeor pathways in WRF simulation: A case of two high rainfall events in Nigeria A. Onyejuruwa et al.
- Applications of Machine Learning and Artificial Intelligence in Tropospheric Ozone Research S. Hickman et al.
- PHI-SMFE: spatial multi-scale feature extract neural network based on physical heterogeneous interaction for solving passive scalar advection in a 2-D unsteady flow Y. Yuan et al.
- Machine learning parameterization of the multi-scale Kain–Fritsch (MSKF) convection scheme and stable simulation coupled in the Weather Research and Forecasting (WRF) model using WRF–ML v1.0 X. Zhong et al.
- Deep Learning Emulator Towards Both Forward and Adjoint Modes of Atmospheric Gas-Phase Chemical Process Y. Liu et al.
- Impact of Horizontal Resolution on the Robustness of Radiation Emulators in a Numerical Weather Prediction Model H. Song & S. Roh
- Machine learning for numerical weather and climate modelling: a review C. de Burgh-Day & T. Leeuwenburg
- A Physics‐Incorporated Deep Learning Framework for Parameterization of Atmospheric Radiative Transfer Y. Yao et al.
- Prediction of Convective Available Potential Energy and Equivalent Potential Temperature using a Coupled WRF and Deep Learning for Typhoon Identification M. Tamamadin et al.
- The atmospheric boundary layer: a review of current challenges and a new generation of machine learning techniques L. Canché-Cab et al.
- Adaptive Voxel-Division Method of GNSS Water Vapor Tomography and Its Application in Data Assimilation Y. Ma et al.
- AI for atmosphere–ocean sciences: advancements, challenges and ways forward J. Luo et al.
- Efficient and stable coupling of the SuperdropNet deep-learning-based cloud microphysics (v0.1.0) with the ICON climate and weather model (v2.6.5) C. Arnold et al.
- Utilization of a Lightweight 3D U-Net Model for Reducing Execution Time of Numerical Weather Prediction Models H. Park & S. Chung
- Developing Intelligent Earth System Models : An AI scheme of K-profile parameterization and stable coupling into CESM with FTA B. Mu et al.
- Postprocessing of convection permitting precipitation forecast using UNets M. Esquivel-González et al.
- RTM Surrogate Modeling in Optical Remote Sensing: A Review of Emulation for Vegetation and Atmosphere Applications J. Verrelst et al.
- Developing intelligent Earth System Models: An AI framework for replacing sub-modules based on incremental learning and its application B. Mu et al.
- Source reconstruction for atmospheric radionuclide leakage: Recent advances in decoding information from atmospheric transport physics Y. Xu et al.
21 citations as recorded by crossref.
- Deep Learning‐Based Sea Surface Roughness Parameterization Scheme Improves Sea Surface Wind Forecast S. Fu et al.
- APPLICATION OF MACHINE LEARNING IN RADIATIVE HEAT TRANSFER W. Chen & T. Ren
- Different microphysics parameterizations of hydrometeor pathways in WRF simulation: A case of two high rainfall events in Nigeria A. Onyejuruwa et al.
- Applications of Machine Learning and Artificial Intelligence in Tropospheric Ozone Research S. Hickman et al.
- PHI-SMFE: spatial multi-scale feature extract neural network based on physical heterogeneous interaction for solving passive scalar advection in a 2-D unsteady flow Y. Yuan et al.
- Machine learning parameterization of the multi-scale Kain–Fritsch (MSKF) convection scheme and stable simulation coupled in the Weather Research and Forecasting (WRF) model using WRF–ML v1.0 X. Zhong et al.
- Deep Learning Emulator Towards Both Forward and Adjoint Modes of Atmospheric Gas-Phase Chemical Process Y. Liu et al.
- Impact of Horizontal Resolution on the Robustness of Radiation Emulators in a Numerical Weather Prediction Model H. Song & S. Roh
- Machine learning for numerical weather and climate modelling: a review C. de Burgh-Day & T. Leeuwenburg
- A Physics‐Incorporated Deep Learning Framework for Parameterization of Atmospheric Radiative Transfer Y. Yao et al.
- Prediction of Convective Available Potential Energy and Equivalent Potential Temperature using a Coupled WRF and Deep Learning for Typhoon Identification M. Tamamadin et al.
- The atmospheric boundary layer: a review of current challenges and a new generation of machine learning techniques L. Canché-Cab et al.
- Adaptive Voxel-Division Method of GNSS Water Vapor Tomography and Its Application in Data Assimilation Y. Ma et al.
- AI for atmosphere–ocean sciences: advancements, challenges and ways forward J. Luo et al.
- Efficient and stable coupling of the SuperdropNet deep-learning-based cloud microphysics (v0.1.0) with the ICON climate and weather model (v2.6.5) C. Arnold et al.
- Utilization of a Lightweight 3D U-Net Model for Reducing Execution Time of Numerical Weather Prediction Models H. Park & S. Chung
- Developing Intelligent Earth System Models : An AI scheme of K-profile parameterization and stable coupling into CESM with FTA B. Mu et al.
- Postprocessing of convection permitting precipitation forecast using UNets M. Esquivel-González et al.
- RTM Surrogate Modeling in Optical Remote Sensing: A Review of Emulation for Vegetation and Atmosphere Applications J. Verrelst et al.
- Developing intelligent Earth System Models: An AI framework for replacing sub-modules based on incremental learning and its application B. Mu et al.
- Source reconstruction for atmospheric radionuclide leakage: Recent advances in decoding information from atmospheric transport physics Y. Xu et al.
Saved (final revised paper)
Latest update: 20 May 2026
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.
More and more researchers use deep learning models to replace physics-based parameterizations to...
