Neural Network Parameterization of Subgrid‐Scale Physics From a Realistic Geography Global Storm‐Resolving Simulation
Abstract Parameterization of subgrid‐scale processes is a major source of uncertainty in global atmospheric model simulations. Global storm‐resolving simulations use a finer grid (less than 5 km) to reduce this uncertainty by explicitly resolving deep convection and details of orography. This study...
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| Format: | Article |
| Language: | English |
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American Geophysical Union (AGU)
2024-02-01
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| Series: | Journal of Advances in Modeling Earth Systems |
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| Online Access: | https://doi.org/10.1029/2023MS003668 |
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| author | Oliver Watt‐Meyer Noah D. Brenowitz Spencer K. Clark Brian Henn Anna Kwa Jeremy McGibbon W. Andre Perkins Lucas Harris Christopher S. Bretherton |
| author_facet | Oliver Watt‐Meyer Noah D. Brenowitz Spencer K. Clark Brian Henn Anna Kwa Jeremy McGibbon W. Andre Perkins Lucas Harris Christopher S. Bretherton |
| author_sort | Oliver Watt‐Meyer |
| collection | DOAJ |
| description | Abstract Parameterization of subgrid‐scale processes is a major source of uncertainty in global atmospheric model simulations. Global storm‐resolving simulations use a finer grid (less than 5 km) to reduce this uncertainty by explicitly resolving deep convection and details of orography. This study uses machine learning to replace the physical parameterizations of heating and moistening rates, but not wind tendencies, in a coarse‐grid (200 km) global atmosphere model, using training data obtained by spatially coarse‐graining a 40‐day realistic geography global storm‐resolving simulation. The training targets are the three‐dimensional fields of effective heating and moistening rates, including the effect of grid‐scale motions that are resolved but imperfectly simulated by the coarse model. A neural network is trained to predict the time‐dependent heating and moistening rates in each grid column using the coarse‐grained temperature, specific humidity, surface turbulent heat fluxes, cosine of solar zenith angle, land‐sea mask and surface geopotential of that grid column as inputs. The coefficient of determination R2 for offline prediction ranges from 0.4 to 0.8 at most vertical levels and latitudes. Online, we achieve stable 35‐day simulations, with metrics of skill such as the time‐mean pattern of near‐surface temperature and precipitation comparable or slightly better than a baseline simulation with conventional physical parameterizations. However, the structure of tropical circulation and relative humidity in the upper troposphere are unrealistic. Overall, this study shows potential for the replacement of human‐designed parameterizations with data‐driven ones in a realistic setting. |
| format | Article |
| id | doaj-art-e8eeeac9be0a41338c896fabcc34c313 |
| institution | OA Journals |
| issn | 1942-2466 |
| language | English |
| publishDate | 2024-02-01 |
| publisher | American Geophysical Union (AGU) |
| record_format | Article |
| series | Journal of Advances in Modeling Earth Systems |
| spelling | doaj-art-e8eeeac9be0a41338c896fabcc34c3132025-08-20T02:20:37ZengAmerican Geophysical Union (AGU)Journal of Advances in Modeling Earth Systems1942-24662024-02-01162n/an/a10.1029/2023MS003668Neural Network Parameterization of Subgrid‐Scale Physics From a Realistic Geography Global Storm‐Resolving SimulationOliver Watt‐Meyer0Noah D. Brenowitz1Spencer K. Clark2Brian Henn3Anna Kwa4Jeremy McGibbon5W. Andre Perkins6Lucas Harris7Christopher S. Bretherton8Allen Institute for Artificial Intelligence Seattle WA USANVIDIA Corporation Santa Clara CA USAAllen Institute for Artificial Intelligence Seattle WA USAAllen Institute for Artificial Intelligence Seattle WA USAAllen Institute for Artificial Intelligence Seattle WA USAAllen Institute for Artificial Intelligence Seattle WA USAAllen Institute for Artificial Intelligence Seattle WA USAGeophysical Fluid Dynamics Laboratory NOAA Princeton NJ USAAllen Institute for Artificial Intelligence Seattle WA USAAbstract Parameterization of subgrid‐scale processes is a major source of uncertainty in global atmospheric model simulations. Global storm‐resolving simulations use a finer grid (less than 5 km) to reduce this uncertainty by explicitly resolving deep convection and details of orography. This study uses machine learning to replace the physical parameterizations of heating and moistening rates, but not wind tendencies, in a coarse‐grid (200 km) global atmosphere model, using training data obtained by spatially coarse‐graining a 40‐day realistic geography global storm‐resolving simulation. The training targets are the three‐dimensional fields of effective heating and moistening rates, including the effect of grid‐scale motions that are resolved but imperfectly simulated by the coarse model. A neural network is trained to predict the time‐dependent heating and moistening rates in each grid column using the coarse‐grained temperature, specific humidity, surface turbulent heat fluxes, cosine of solar zenith angle, land‐sea mask and surface geopotential of that grid column as inputs. The coefficient of determination R2 for offline prediction ranges from 0.4 to 0.8 at most vertical levels and latitudes. Online, we achieve stable 35‐day simulations, with metrics of skill such as the time‐mean pattern of near‐surface temperature and precipitation comparable or slightly better than a baseline simulation with conventional physical parameterizations. However, the structure of tropical circulation and relative humidity in the upper troposphere are unrealistic. Overall, this study shows potential for the replacement of human‐designed parameterizations with data‐driven ones in a realistic setting.https://doi.org/10.1029/2023MS003668machine learningparameterizationatmospheric modelingglobal storm‐resolving simulations |
| spellingShingle | Oliver Watt‐Meyer Noah D. Brenowitz Spencer K. Clark Brian Henn Anna Kwa Jeremy McGibbon W. Andre Perkins Lucas Harris Christopher S. Bretherton Neural Network Parameterization of Subgrid‐Scale Physics From a Realistic Geography Global Storm‐Resolving Simulation Journal of Advances in Modeling Earth Systems machine learning parameterization atmospheric modeling global storm‐resolving simulations |
| title | Neural Network Parameterization of Subgrid‐Scale Physics From a Realistic Geography Global Storm‐Resolving Simulation |
| title_full | Neural Network Parameterization of Subgrid‐Scale Physics From a Realistic Geography Global Storm‐Resolving Simulation |
| title_fullStr | Neural Network Parameterization of Subgrid‐Scale Physics From a Realistic Geography Global Storm‐Resolving Simulation |
| title_full_unstemmed | Neural Network Parameterization of Subgrid‐Scale Physics From a Realistic Geography Global Storm‐Resolving Simulation |
| title_short | Neural Network Parameterization of Subgrid‐Scale Physics From a Realistic Geography Global Storm‐Resolving Simulation |
| title_sort | neural network parameterization of subgrid scale physics from a realistic geography global storm resolving simulation |
| topic | machine learning parameterization atmospheric modeling global storm‐resolving simulations |
| url | https://doi.org/10.1029/2023MS003668 |
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