Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1029/2018GL080704 |
Toward Data-Driven Weather and Climate Forecasting: Approximating a Simple General Circulation Model With Deep Learning | |
Scher, S.1,2 | |
2018-11-28 | |
发表期刊 | GEOPHYSICAL RESEARCH LETTERS |
ISSN | 0094-8276 |
EISSN | 1944-8007 |
出版年 | 2018 |
卷号 | 45期号:22页码:12616-12622 |
文章类型 | Article |
语种 | 英语 |
国家 | Sweden |
英文摘要 | It is shown that it is possible to emulate the dynamics of a simple general circulation model with a deep neural network. After being trained on the model, the network can predict the complete model state several time steps aheadwhich conceptually is making weather forecasts in the model world. Additionally, after being initialized with an arbitrary model state, the network can through repeatedly feeding back its predictions into its inputs create a climate run, which has similar climate statistics to the climate of the general circulation model. This network climate run shows no long-term drift, even though no conservation properties were explicitly designed into the network. Plain Language Summary Numerical weather prediction and climate models are complex computer programs that represent the physics of the atmosphere. They are essential tools for predicting the weather and for studying the Earth's climate. Recently, a lot of progress has been made in machine learning methods. These are data-driven algorithms that learn from existing data. We show that it is possible that such an algorithm learns the dynamics of a simple climate model. After being presented with enough data from the climate model, the network can successfully predict the time evolution of the model's state, thus replacing the dynamics of the model. This finding is an important step toward purely data-driven weather forecastingthus weather forecasting without the use of traditional numerical models and also opens up new possibilities for climate modeling. |
英文关键词 | machine learning weather prediction neural networks deep learning climate models |
领域 | 气候变化 |
收录类别 | SCI-E |
WOS记录号 | WOS:000453250000058 |
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/25647 |
专题 | 气候变化 |
作者单位 | 1.Stockholm Univ, Dept Meteorol, Stockholm, Sweden; 2.Stockholm Univ, Bolin Ctr Climate Res, Stockholm, Sweden |
推荐引用方式 GB/T 7714 | Scher, S.. Toward Data-Driven Weather and Climate Forecasting: Approximating a Simple General Circulation Model With Deep Learning[J]. GEOPHYSICAL RESEARCH LETTERS,2018,45(22):12616-12622. |
APA | Scher, S..(2018).Toward Data-Driven Weather and Climate Forecasting: Approximating a Simple General Circulation Model With Deep Learning.GEOPHYSICAL RESEARCH LETTERS,45(22),12616-12622. |
MLA | Scher, S.."Toward Data-Driven Weather and Climate Forecasting: Approximating a Simple General Circulation Model With Deep Learning".GEOPHYSICAL RESEARCH LETTERS 45.22(2018):12616-12622. |
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