14 citations as recorded by crossref.

1. 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
2. Forecasting the June Ridge Line of the Western Pacific Subtropical High with a Machine Learning Method C. Sun et al. 10.3390/atmos13050660
3. A Physics‐Incorporated Deep Learning Framework for Parameterization of Atmospheric Radiative Transfer Y. Yao et al. 10.1029/2022MS003445
4. Impact of Horizontal Resolution on the Robustness of Radiation Emulators in a Numerical Weather Prediction Model H. Song & S. Roh 10.3390/rs15102637
5. Improved Weather Forecasting Using Neural Network Emulation for Radiation Parameterization H. Song & S. Roh 10.1029/2021MS002609
6. Applying Machine Learning in Numerical Weather and Climate Modeling Systems V. Krasnopolsky 10.3390/cli12060078
7. Usefulness of Automatic Hyperparameter Optimization in Developing Radiation Emulator in a Numerical Weather Prediction Model P. Kim & H. Song 10.3390/atmos13050721
8. MieAI: a neural network for calculating optical properties of internally mixed aerosol in atmospheric models P. Kumar et al. 10.1038/s41612-024-00652-y
9. WRF–ML v1.0: a bridge between WRF v4.3 and machine learning parameterizations and its application to atmospheric radiative transfer X. Zhong et al. 10.5194/gmd-16-199-2023
10. Benefits of Stochastic Weight Averaging in Developing Neural Network Radiation Scheme for Numerical Weather Prediction H. Song et al. 10.1029/2021MS002921
11. Neural‐Network Parameterization of Subgrid Momentum Transport in the Atmosphere J. Yuval & P. O’Gorman 10.1029/2023MS003606
12. A radiative transfer deep learning model coupled into WRF with a generic fortran torch adaptor B. Mu et al. 10.3389/feart.2023.1149566
13. Robustness of neural network emulations of radiative transfer parameterizations in a state-of-the-art general circulation model A. Belochitski & V. Krasnopolsky 10.5194/gmd-14-7425-2021
14. Effects of Cloud Microphysics on the Universal Performance of Neural Network Radiation Scheme H. Song & P. Kim 10.1029/2022GL098601