IOCAS-IR  > 海洋环流与波动重点实验室
Purely satellite data-driven deep learning forecast of complicated tropical instability waves
Zheng, Gang1; Li, Xiaofeng2,3; Zhang, Rong-Hua2,3; Liu, Bin4
2020-07-01
Source PublicationSCIENCE ADVANCES
ISSN2375-2548
Volume6Issue:29Pages:9
Corresponding AuthorLi, Xiaofeng(lixf@qdio.ac.cn)
AbstractForecasting 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.
DOI10.1126/sciadv.aba1482
Indexed BySCI
Language英语
Funding ProjectStrategic 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 AreaScience & Technology - Other Topics
WOS SubjectMultidisciplinary Sciences
WOS IDWOS:000552227800010
PublisherAMER ASSOC ADVANCEMENT SCIENCE
Citation statistics
Document Type期刊论文
Identifierhttp://ir.qdio.ac.cn/handle/337002/167997
Collection海洋环流与波动重点实验室
Corresponding AuthorLi, Xiaofeng
Affiliation1.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 AffilicationInstitute 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.
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