Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1029/2018GL081646 |
Using Deep Neural Networks as Cost-Effective Surrogate Models for Super-Parameterized E3SM Radiative Transfer | |
Pal, Anikesh1; Mahajan, Salil2; Norman, Matthew R.1 | |
2019-06-16 | |
发表期刊 | GEOPHYSICAL RESEARCH LETTERS
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ISSN | 0094-8276 |
EISSN | 1944-8007 |
出版年 | 2019 |
卷号 | 46期号:11页码:6069-6079 |
文章类型 | Article |
语种 | 英语 |
国家 | USA |
英文摘要 | Deep neural networks (DNNs) are implemented in Super-Parameterized Energy Exascale Earth System Model (SP-E3SM) to imitate the shortwave and longwave radiative transfer calculations. These DNNs were able to emulate the radiation parameters with an accuracy of 90-95% at a cost of 8-10 times cheaper than the original radiation parameterization. A comparison of time-averaged radiative fluxes and the prognostic variables manifested qualitative and quantitative similarity between the DNN emulation and the original parameterization. It has also been found that the differences between the DNN emulation and the original parameterization are comparable to the internal variability of the original parameterization. Although the DNNs developed in this investigation emulate the radiation parameters for a specific set of initial conditions, the results justify the need of further research to generalize the use of DNNs for the emulations of full model radiation and other parameterization for seasonal predictions and climate simulations. |
英文关键词 | deep neural networks radiation models general circulation models |
领域 | 气候变化 |
收录类别 | SCI-E |
WOS记录号 | WOS:000477616200048 |
WOS关键词 | CLIMATE SIMULATIONS ; ACCURATE ; LONGWAVE ; SUPERPARAMETERIZATION ; CONVECTION ; EMULATION ; ROBUST |
WOS类目 | Geosciences, Multidisciplinary |
WOS研究方向 | Geology |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/184112 |
专题 | 气候变化 |
作者单位 | 1.Oak Ridge Natl Lab, Natl Ctr Computat Sci, Oak Ridge, TN 37830 USA; 2.Oak Ridge Natl Lab, Computat Earth Sci, Oak Ridge, TN USA |
推荐引用方式 GB/T 7714 | Pal, Anikesh,Mahajan, Salil,Norman, Matthew R.. Using Deep Neural Networks as Cost-Effective Surrogate Models for Super-Parameterized E3SM Radiative Transfer[J]. GEOPHYSICAL RESEARCH LETTERS,2019,46(11):6069-6079. |
APA | Pal, Anikesh,Mahajan, Salil,&Norman, Matthew R..(2019).Using Deep Neural Networks as Cost-Effective Surrogate Models for Super-Parameterized E3SM Radiative Transfer.GEOPHYSICAL RESEARCH LETTERS,46(11),6069-6079. |
MLA | Pal, Anikesh,et al."Using Deep Neural Networks as Cost-Effective Surrogate Models for Super-Parameterized E3SM Radiative Transfer".GEOPHYSICAL RESEARCH LETTERS 46.11(2019):6069-6079. |
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