Purely satellite data-driven deep learning forecast of complicated tropical instability waves

doi:10.1126/sciadv.aba1482

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, Gang 1; Li, Xiaofeng 2,3; Zhang, Rong-Hua2,3 ; Liu, Bin 4
	2020-07-01
发表期刊	SCIENCE ADVANCES
ISSN	2375-2548
卷号	6 期号:29 页码:9
通讯作者	Li, Xiaofeng([email protected])
摘要	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
收录类别	SCI
语种	英语
资助项目	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研究方向	Science & Technology - Other Topics
WOS类目	Multidisciplinary Sciences
WOS记录号	WOS:000552227800010
出版者	AMER ASSOC ADVANCEMENT SCIENCE
引用统计	被引频次：143[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.qdio.ac.cn/handle/337002/167997
专题	海洋环流与波动重点实验室
通讯作者	Li, Xiaofeng
作者单位	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
通讯作者单位	中国科学院海洋研究所; 中国科学院海洋大科学研究中心
推荐引用方式 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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文件名称/大小	文献类型	版本类型	开放类型	使用许可
Purely satellite dat（7758KB）	期刊论文	出版稿	限制开放	CC BY-NC-SA	浏览