Institutional Repository of Key Laboratory of Ocean Circulation and Wave Studies, Institute of Oceanology, Chinese Academy of Sciences
| Purely satellite data-driven deep learning forecast of complicated tropical instability waves | |
Zheng, Gang1; Li, Xiaofeng2,3; Zhang, Rong-Hua2,3 ; Liu, Bin4
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| 2020-07-01 | |
| Source Publication | SCIENCE ADVANCES
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| ISSN | 2375-2548 |
| Volume | 6Issue:29Pages:9 |
| Corresponding Author | Li, Xiaofeng(lixf@qdio.ac.cn) |
| Abstract | Forecasting fields of oceanic phenomena has long been dependent on physical equation-based numerical models. The challenge is that many natural processes need to be considered for understanding complicated phenomena. In contrast, rules of the processes are already embedded in the time-series observation itself. Thus, inspired by largely available satellite remote sensing data and the advance of deep learning technology, we developed a purely satellite data-driven deep learning model for forecasting the sea surface temperature evolution associated with a typical phenomenon: a tropical instability wave. During the testing period of 9 years (2010-2019), our model accurately and efficiently forecasts the sea surface temperature field. This study demonstrates the strong potential of the satellite data-driven deep learning model as an alternative to traditional numerical models for forecasting oceanic phenomena. |
| DOI | 10.1126/sciadv.aba1482 |
| Indexed By | SCI |
| Language | 英语 |
| Funding Project | Strategic Priority Research Program of the Chinese Academy of Sciences[XDB42000000] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDA19090103] ; Key R&D Project of Shandong Province[2019JZZY010102] ; National Natural Science Foundation of China[41676167] ; National Natural Science Foundation of China[41776183] ; Key Deployment Project of Center for Ocean Mega-Science, CAS[COMS2019R02] ; CAS Program[Y9KY04101L] ; Project of State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography[SOEDZZ2003] |
| WOS Research Area | Science & Technology - Other Topics |
| WOS Subject | Multidisciplinary Sciences |
| WOS ID | WOS:000552227800010 |
| Publisher | AMER ASSOC ADVANCEMENT SCIENCE |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.qdio.ac.cn/handle/337002/167997 |
| Collection | 海洋环流与波动重点实验室 |
| Corresponding Author | Li, Xiaofeng |
| Affiliation | 1.Minist Nat Resources, State Key Lab Satellite Ocean Environm Dynam, Inst Oceanog 2, Hangzhou 310012, Peoples R China 2.Chinese Acad Sci, Big Data Ctr, Inst Oceanol, CAS Key Lab Ocean Circulat & Waves, Qingdao 266071, Peoples R China 3.Chinese Acad Sci, Ctr Ocean Megasci, Qingdao 266071, Peoples R China 4.Shanghai Ocean Univ, Coll Marine Sci, Shanghai 201306, Peoples R China |
| Corresponding Author Affilication | Institute of Oceanology, Chinese Academy of Sciences; Center for Ocean Mega-Science, Chinese Academy of Sciences |
| Recommended Citation GB/T 7714 | Zheng, Gang,Li, Xiaofeng,Zhang, Rong-Hua,et al. Purely satellite data-driven deep learning forecast of complicated tropical instability waves[J]. SCIENCE ADVANCES,2020,6(29):9. |
| APA | Zheng, Gang,Li, Xiaofeng,Zhang, Rong-Hua,&Liu, Bin.(2020).Purely satellite data-driven deep learning forecast of complicated tropical instability waves.SCIENCE ADVANCES,6(29),9. |
| MLA | Zheng, Gang,et al."Purely satellite data-driven deep learning forecast of complicated tropical instability waves".SCIENCE ADVANCES 6.29(2020):9. |
| Files in This Item: | ||||||
| File Name/Size | DocType | Version | Access | License | ||
| Purely satellite dat（7758KB） | 期刊论文 | 出版稿 | 开放获取 | CC BY-NC-SA | View | |
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