Articles | Volume 13, issue 9
https://doi.org/10.5194/gmd-13-4399-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-13-4399-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
21 Sep 2020
Model description paper |
| 21 Sep 2020
| 21 Sep 2020
RadNet 1.0: exploring deep learning architectures for longwave radiative transfer
Ying Liu
CORRESPONDING AUTHOR
Department of Meteorology, Stockholm University, Stockholm, Sweden
Rodrigo Caballero
Department of Meteorology, Stockholm University, Stockholm, Sweden
Joy Merwin Monteiro
Department of Meteorology, Stockholm University, Stockholm, Sweden
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Cited
17 citations as recorded by crossref.
- 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
- Benefits of Stochastic Weight Averaging in Developing Neural Network Radiation Scheme for Numerical Weather Prediction H. Song et al. 10.1029/2021MS002921
- Streamlining hyperparameter optimization for radiation emulator training with automated Sherpa S. Roh et al. 10.1186/s40562-024-00336-8
- A Physics‐Incorporated Deep Learning Framework for Parameterization of Atmospheric Radiative Transfer Y. Yao et al. 10.1029/2022MS003445
- Usefulness of Automatic Hyperparameter Optimization in Developing Radiation Emulator in a Numerical Weather Prediction Model P. Kim & H. Song 10.3390/atmos13050721
- Compound Parameterization to Improve the Accuracy of Radiation Emulator in a Numerical Weather Prediction Model H. Song et al. 10.1029/2021GL095043
- Impact of Horizontal Resolution on the Robustness of Radiation Emulators in a Numerical Weather Prediction Model H. Song & S. Roh 10.3390/rs15102637
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- Implementation of a machine-learned gas optics parameterization in the ECMWF Integrated Forecasting System: RRTMGP-NN 2.0 P. Ukkonen & R. Hogan 10.5194/gmd-16-3241-2023
- Applying Deep Learning to Clear-Sky Radiance Simulation for VIIRS with Community Radiative Transfer Model—Part 2: Model Architecture and Assessment X. Liang & Q. Liu 10.3390/rs12223825
- A radiative transfer deep learning model coupled into WRF with a generic fortran torch adaptor B. Mu et al. 10.3389/feart.2023.1149566
- Improved Weather Forecasting Using Neural Network Emulation for Radiation Parameterization H. Song & S. Roh 10.1029/2021MS002609
- Surrogate models of radiative transfer codes for atmospheric trace gas retrievals from satellite observations J. Brence et al. 10.1007/s10994-022-06155-2
- Identifying Structural Priors in a Hybrid Differentiable Model for Stream Water Temperature Modeling F. Rahmani et al. 10.1029/2023WR034420
- A daily 5-km all-sky sea-surface longwave radiation product based on statistically modified deep neural network and spatiotemporal analysis for 1981–2018 J. Xu et al. 10.1016/j.rse.2023.113550
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- A physics-inspired neural network for short-wave radiation parameterization N. Yavich et al. 10.1515/jiip-2023-0075
17 citations as recorded by crossref.
- 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
- Benefits of Stochastic Weight Averaging in Developing Neural Network Radiation Scheme for Numerical Weather Prediction H. Song et al. 10.1029/2021MS002921
- Streamlining hyperparameter optimization for radiation emulator training with automated Sherpa S. Roh et al. 10.1186/s40562-024-00336-8
- A Physics‐Incorporated Deep Learning Framework for Parameterization of Atmospheric Radiative Transfer Y. Yao et al. 10.1029/2022MS003445
- Usefulness of Automatic Hyperparameter Optimization in Developing Radiation Emulator in a Numerical Weather Prediction Model P. Kim & H. Song 10.3390/atmos13050721
- Compound Parameterization to Improve the Accuracy of Radiation Emulator in a Numerical Weather Prediction Model H. Song et al. 10.1029/2021GL095043
- Impact of Horizontal Resolution on the Robustness of Radiation Emulators in a Numerical Weather Prediction Model H. Song & S. Roh 10.3390/rs15102637
- BHCNet: Neural Network-Based Brain Hemorrhage Classification Using Head CT Scan M. Mushtaq et al. 10.1109/ACCESS.2021.3102740
- Implementation of a machine-learned gas optics parameterization in the ECMWF Integrated Forecasting System: RRTMGP-NN 2.0 P. Ukkonen & R. Hogan 10.5194/gmd-16-3241-2023
- Applying Deep Learning to Clear-Sky Radiance Simulation for VIIRS with Community Radiative Transfer Model—Part 2: Model Architecture and Assessment X. Liang & Q. Liu 10.3390/rs12223825
- A radiative transfer deep learning model coupled into WRF with a generic fortran torch adaptor B. Mu et al. 10.3389/feart.2023.1149566
- Improved Weather Forecasting Using Neural Network Emulation for Radiation Parameterization H. Song & S. Roh 10.1029/2021MS002609
- Surrogate models of radiative transfer codes for atmospheric trace gas retrievals from satellite observations J. Brence et al. 10.1007/s10994-022-06155-2
- Identifying Structural Priors in a Hybrid Differentiable Model for Stream Water Temperature Modeling F. Rahmani et al. 10.1029/2023WR034420
- A daily 5-km all-sky sea-surface longwave radiation product based on statistically modified deep neural network and spatiotemporal analysis for 1981–2018 J. Xu et al. 10.1016/j.rse.2023.113550
- NeuralMie (v1.0): an aerosol optics emulator A. Geiss & P. Ma 10.5194/gmd-18-1809-2025
- A physics-inspired neural network for short-wave radiation parameterization N. Yavich et al. 10.1515/jiip-2023-0075
Latest update: 11 Jul 2025
Short summary
The calculation of atmospheric radiative transfer is the most computationally expensive part of climate models. Reducing this computational burden could potentially improve our ability to simulate the earth's climate at finer scales. We propose using a statistical model – a deep neural network – to compute approximate radiative transfer in the earth's atmosphere. We demonstrate a significant reduction in computational burden as compared to a traditional scheme, especially when using GPUs.
The calculation of atmospheric radiative transfer is the most computationally expensive part of...
