12 citations as recorded by crossref.

1. Deep Learning‐Based Sea Surface Roughness Parameterization Scheme Improves Sea Surface Wind Forecast S. Fu et al. 10.1029/2023GL106580
2. Different microphysics parameterizations of hydrometeor pathways in WRF simulation: A case of two high rainfall events in Nigeria A. Onyejuruwa et al. 10.1016/j.jastp.2025.106455
3. 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. 10.3389/fmars.2023.1276869
4. 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. 10.5194/gmd-17-3667-2024
5. Impact of Horizontal Resolution on the Robustness of Radiation Emulators in a Numerical Weather Prediction Model H. Song & S. Roh 10.3390/rs15102637
6. Machine learning for numerical weather and climate modelling: a review C. de Burgh-Day & T. Leeuwenburg 10.5194/gmd-16-6433-2023
7. A Physics‐Incorporated Deep Learning Framework for Parameterization of Atmospheric Radiative Transfer Y. Yao et al. 10.1029/2022MS003445
8. Prediction of Convective Available Potential Energy and Equivalent Potential Temperature using a Coupled WRF and Deep Learning for Typhoon Identification M. Tamamadin et al. 10.1088/1755-1315/1245/1/012034
9. The atmospheric boundary layer: a review of current challenges and a new generation of machine learning techniques L. Canché-Cab et al. 10.1007/s10462-024-10962-5
10. 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. 10.5194/gmd-17-4017-2024
11. Utilization of a Lightweight 3D U-Net Model for Reducing Execution Time of Numerical Weather Prediction Models H. Park & S. Chung 10.3390/atmos16010060
12. Developing intelligent Earth System Models: An AI framework for replacing sub-modules based on incremental learning and its application B. Mu et al. 10.1016/j.atmosres.2024.107306