Use of Neural Networks for Stable, Accurate and Physically Consistent Parameterization of Subgrid Atmospheric Processes With Good Performance at Reduced Precision
Abstract A promising approach to improve climate‐model simulations is to replace traditional subgrid parameterizations based on simplified physical models by machine learning algorithms that are data‐driven. However, neural networks (NNs) often lead to instabilities and climate drift when coupled to...
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| Format: | Article |
| Language: | English |
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Wiley
2021-03-01
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| Series: | Geophysical Research Letters |
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| Online Access: | https://doi.org/10.1029/2020GL091363 |
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| author | Janni Yuval Paul A. O'Gorman Chris N. Hill |
| author_facet | Janni Yuval Paul A. O'Gorman Chris N. Hill |
| author_sort | Janni Yuval |
| collection | DOAJ |
| description | Abstract A promising approach to improve climate‐model simulations is to replace traditional subgrid parameterizations based on simplified physical models by machine learning algorithms that are data‐driven. However, neural networks (NNs) often lead to instabilities and climate drift when coupled to an atmospheric model. Here, we learn an NN parameterization from a high‐resolution atmospheric simulation in an idealized domain by accurately calculating subgrid terms through coarse graining. The NN parameterization has a structure that ensures physical constraints are respected, such as by predicting subgrid fluxes instead of tendencies. The NN parameterization leads to stable simulations that replicate the climate of the high‐resolution simulation with similar accuracy to a successful random‐forest parameterization while needing far less memory. We find that the simulations are stable for different horizontal resolutions and a variety of NN architectures, and that an NN with substantially reduced numerical precision could decrease computational costs without affecting the quality of simulations. |
| format | Article |
| id | doaj-art-a8022d9b88a14cd08c85a7a910447dd2 |
| institution | OA Journals |
| issn | 0094-8276 1944-8007 |
| language | English |
| publishDate | 2021-03-01 |
| publisher | Wiley |
| record_format | Article |
| series | Geophysical Research Letters |
| spelling | doaj-art-a8022d9b88a14cd08c85a7a910447dd22025-08-20T01:48:15ZengWileyGeophysical Research Letters0094-82761944-80072021-03-01486n/an/a10.1029/2020GL091363Use of Neural Networks for Stable, Accurate and Physically Consistent Parameterization of Subgrid Atmospheric Processes With Good Performance at Reduced PrecisionJanni Yuval0Paul A. O'Gorman1Chris N. Hill2Department of Earth, Atmospheric and Planetary Sciences Massachusetts Institute of Technology Cambridge MA USADepartment of Earth, Atmospheric and Planetary Sciences Massachusetts Institute of Technology Cambridge MA USADepartment of Earth, Atmospheric and Planetary Sciences Massachusetts Institute of Technology Cambridge MA USAAbstract A promising approach to improve climate‐model simulations is to replace traditional subgrid parameterizations based on simplified physical models by machine learning algorithms that are data‐driven. However, neural networks (NNs) often lead to instabilities and climate drift when coupled to an atmospheric model. Here, we learn an NN parameterization from a high‐resolution atmospheric simulation in an idealized domain by accurately calculating subgrid terms through coarse graining. The NN parameterization has a structure that ensures physical constraints are respected, such as by predicting subgrid fluxes instead of tendencies. The NN parameterization leads to stable simulations that replicate the climate of the high‐resolution simulation with similar accuracy to a successful random‐forest parameterization while needing far less memory. We find that the simulations are stable for different horizontal resolutions and a variety of NN architectures, and that an NN with substantially reduced numerical precision could decrease computational costs without affecting the quality of simulations.https://doi.org/10.1029/2020GL091363Atmospheric modelingConvectionmachine learningparameterizationsubgrid physics |
| spellingShingle | Janni Yuval Paul A. O'Gorman Chris N. Hill Use of Neural Networks for Stable, Accurate and Physically Consistent Parameterization of Subgrid Atmospheric Processes With Good Performance at Reduced Precision Geophysical Research Letters Atmospheric modeling Convection machine learning parameterization subgrid physics |
| title | Use of Neural Networks for Stable, Accurate and Physically Consistent Parameterization of Subgrid Atmospheric Processes With Good Performance at Reduced Precision |
| title_full | Use of Neural Networks for Stable, Accurate and Physically Consistent Parameterization of Subgrid Atmospheric Processes With Good Performance at Reduced Precision |
| title_fullStr | Use of Neural Networks for Stable, Accurate and Physically Consistent Parameterization of Subgrid Atmospheric Processes With Good Performance at Reduced Precision |
| title_full_unstemmed | Use of Neural Networks for Stable, Accurate and Physically Consistent Parameterization of Subgrid Atmospheric Processes With Good Performance at Reduced Precision |
| title_short | Use of Neural Networks for Stable, Accurate and Physically Consistent Parameterization of Subgrid Atmospheric Processes With Good Performance at Reduced Precision |
| title_sort | use of neural networks for stable accurate and physically consistent parameterization of subgrid atmospheric processes with good performance at reduced precision |
| topic | Atmospheric modeling Convection machine learning parameterization subgrid physics |
| url | https://doi.org/10.1029/2020GL091363 |
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