Articles | Volume 12, issue 10
https://doi.org/10.5194/gmd-12-4261-2019
© Author(s) 2019. 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-12-4261-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
10 Oct 2019
Development and technical paper |
| 10 Oct 2019
| 10 Oct 2019
Fast domain-aware neural network emulation of a planetary boundary layer parameterization in a numerical weather forecast model
Jiali Wang
Environmental Science Division, Argonne National Laboratory, 9700
South Cass Avenue,Lemont, IL 60439, USA
Prasanna Balaprakash
Mathematics and Computer Science Division, Argonne National
Laboratory, 9700 South Cass Avenue, Lemont, IL 60439, USA
Environmental Science Division, Argonne National Laboratory, 9700
South Cass Avenue,Lemont, IL 60439, USA
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- Artificial Intelligence and Its Application in Numerical Weather Prediction S. Soldatenko 10.3103/S1068373924040010
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- Machine learning-enabled weather forecasting for real-time radioactive transport and contamination prediction A. Ayoub et al. 10.1016/j.pnucene.2024.105255
- Physics-informed neural networks as surrogate models of hydrodynamic simulators J. Donnelly et al. 10.1016/j.scitotenv.2023.168814
- A Neural Network‐Based Scale‐Adaptive Cloud‐Fraction Scheme for GCMs G. Chen et al. 10.1029/2022MS003415
- Improved Weather Forecasting Using Neural Network Emulation for Radiation Parameterization H. Song & S. Roh 10.1029/2021MS002609
- 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
- Evaluation of Neural Network Emulations for Radiation Parameterization in Cloud Resolving Model S. Roh & H. Song 10.1029/2020GL089444
- A radiative transfer deep learning model coupled into WRF with a generic fortran torch adaptor B. Mu et al. 10.3389/feart.2023.1149566
- SmartAirQ: A Big Data Governance Framework for Urban Air Quality Management in Smart Cities A. Kaginalkar et al. 10.3389/fenvs.2022.785129
- Short‐Term Precipitation Prediction for Contiguous United States Using Deep Learning G. Chen & W. Wang 10.1029/2022GL097904
- Machine Learning in Tropical Cyclone Forecast Modeling: A Review R. Chen et al. 10.3390/atmos11070676
- Application of Deep Learning to Estimate Atmospheric Gravity Wave Parameters in Reanalysis Data Sets D. Matsuoka et al. 10.1029/2020GL089436
- Exploring the potential of machine learning for simulations of urban ozone variability N. Ojha et al. 10.1038/s41598-021-01824-z
- Challenges and opportunities for a hybrid modelling approach to earth system science S. See & J. Adie 10.1007/s42514-021-00071-y
- Machine Learning Emulation of Spatial Deposition from a Multi-Physics Ensemble of Weather and Atmospheric Transport Models N. Gunawardena et al. 10.3390/atmos12080953
Latest update: 30 Dec 2024
Short summary
Parameterizations are frequently used in models representing physical phenomena and are often the computationally expensive portions of the code. Using model output from simulations performed using a weather model, we train deep neural networks to provide an accurate alternative to a physics-based parameterization. We demonstrate that a domain-aware deep neural network can successfully simulate the entire diurnal cycle of the boundary layer physics and the results are transferable.
Parameterizations are frequently used in models representing physical phenomena and are often...
