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Deep learning for multi-year ENSO forecasts
文献类型：期刊论文
| 作者 | Ham, Yoo-Geun1; Kim, Jeong-Hwan1; Luo, Jing-Jia2,3 |
| 刊名 | NATURE
 |
| 出版日期 | 2019-09-26 |
| 卷号 | 573期号:7775页码:568-+ |
| ISSN号 | 0028-0836 |
| DOI | 10.1038/s41586-019-1559-7 |
| 通讯作者 | Ham, Yoo-Geun(ygham@jnu.ac.kr) |
| 英文摘要 | Variations in the El Nino/Southern Oscillation (ENSO) are associated with a wide array of regional climate extremes and ecosystem impacts(1). Robust, long-lead forecasts would therefore be valuable for managing policy responses. But despite decades of effort, forecasting ENSO events at lead times of more than one year remains problematic(2). Here we show that a statistical forecast model employing a deep-learning approach produces skilful ENSO forecasts for lead times of up to one and a half years. To circumvent the limited amount of observation data, we use transfer learning to train a convolutional neural network (CNN) first on historical simulations(3) and subsequently on reanalysis from 1871 to 1973. During the validation period from 1984 to 2017, the all-season correlation skill of the Nino3.4 index of the CNN model is much higher than those of current state-of-the-art dynamical forecast systems. The CNN model is also better at predicting the detailed zonal distribution of sea surface temperatures, overcoming a weakness of dynamical forecast models. A heat map analysis indicates that the CNN model predicts ENSO events using physically reasonable precursors. The CNN model is thus a powerful tool for both the prediction of ENSO events and for the analysis of their associated complex mechanisms. |
| WOS关键词 | EL-NINO ; INDIAN-OCEAN ; PREDICTION ; FLAVORS |
| 资助项目 | Korea Meteorological Administration Research and Development Program[KMI2018-03214] ; Basic Science Research Program through the National Research Foundation of Korea (NRF) - Ministry of Education[NRF-2016R1A6A1A03012647] ; 'The Startup Foundation for Introducing Talent' of NUIST |
| WOS研究方向 | Science & Technology - Other Topics |
| 语种 | 英语 |
| WOS记录号 | WOS:000488247600055 |
| 出版者 | NATURE PUBLISHING GROUP |
| 资助机构 | Korea Meteorological Administration Research and Development Program ; Basic Science Research Program through the National Research Foundation of Korea (NRF) - Ministry of Education ; 'The Startup Foundation for Introducing Talent' of NUIST |
| 源URL | [http://ir.ieecas.cn/handle/361006/13360]  |
| 专题 | 地球环境研究所_黄土与第四纪地质国家重点实验室（2010~） |
| 通讯作者 | Ham, Yoo-Geun |
| 作者单位 | 1.Chonnam Natl Univ, Dept Oceanog, Gwangju, South Korea 2.Chinese Acad Sci, Inst Earth Environm, SKLLQG, Xian, Shaanxi, Peoples R China 3.Nanjing Univ Informat Sci & Technol, Inst Climate & Applicat Res ICAR CICFEM KLME ILCE, Nanjing, Jiangsu, Peoples R China |
| 推荐引用方式 GB/T 7714 | Ham, Yoo-Geun,Kim, Jeong-Hwan,Luo, Jing-Jia. Deep learning for multi-year ENSO forecasts[J]. NATURE,2019,573(7775):568-+. |
| APA | Ham, Yoo-Geun,Kim, Jeong-Hwan,&Luo, Jing-Jia.(2019).Deep learning for multi-year ENSO forecasts.NATURE,573(7775),568-+. |
| MLA | Ham, Yoo-Geun,et al."Deep learning for multi-year ENSO forecasts".NATURE 573.7775(2019):568-+. |
入库方式： OAI收割
来源：地球环境研究所
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