Combination of Satellite Observations and Machine Learning Method for Internal Wave Forecast in the Sulu and Celebes Seas

doi:10.1109/TGRS.2020.3008067

CORC > 海洋研究所 > 中国科学院海洋研究所 > 海洋环流与波动重点实验室

	Combination of Satellite Observations and Machine Learning Method for Internal Wave Forecast in the Sulu and Celebes Seas
	Zhang, Xudong 1,2; Li, Xiaofeng 1,2
刊名	IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
	2021-04-01
卷号	59 期号:4 页码:2822-2832
关键词	Oceans Predictive models Satellite broadcasting MODIS Satellites Machine learning Spatial resolution Celebes sea internal wave (IW) machine learning Sulu sea
ISSN号	0196-2892
DOI	10.1109/TGRS.2020.3008067
通讯作者	Li, Xiaofeng(xiaofeng.li@ieee.org)
英文摘要	Internal waves (IWs), observed in the world oceans, have significant impacts on ocean engineering and environments. In this study, we collected satellite images from Moderate-Resolution Imaging Spectroradiometer and Visible Infrared Imaging Radiometer Suite sensors in the Sulu-Celebes Sea from 2016 to 2019 to understand the IW generation and propagation. Satellite observations show a coherent IW phase difference in both seas, indicating that the IWs are alternatively generated when the tidal currents oscillate back and forth in the Sulu Archipelago, which separates two seas. A new generation site is found for occasionally observed long IWs in the eastern Sulu Sea. To understand the IW propagation characteristics, we developed a machine-learning-based forecast model. We trained the model with both IW wave crest curves extracted from satellite images and published climatological ocean temperaturesalinity profiles. Since many satellite images contain IW packets generated at two or three tidal cycles, we can validate the model performance by matching the model prediction after one or two tidal cycles with the second or third wave crests in satellite images. Three factors are adopted to evaluate the forecast results: the root-mean-square error (RMSE), the Frchet distance (FD), and the correlation coefficient (CC). The forecast model has an average error with an RMSE of 12.92 km, an FD of 18.73 km, and a CC of 0.98. Analysis shows that a smaller time step is preferred in regions where the water depth changes significantly. Comparison with the Kortewegde Vries equation solutions shows that the developed forecast model is more robust when errors introduced to the model inputs.
资助项目	Strategic Priority Research Program of the Chinese Academy of Sciences (CAS)[XDA19090103] ; Strategic Priority Research Program of the Chinese Academy of Sciences (CAS)[XDB42000000] ; Strategic Priority Research Program of the Chinese Academy of Sciences (CAS)[XDA19060101] ; National Natural Science Foundation for Young Scientists of China[41906157] ; National Natural Science Foundation of China[41776183] ; Major Scientific and Technological Innovation Projects in Shandong Province[2019JZZY010102] ; Key Project of Center for Ocean Mega-Science, Chinese Academy of Sciences[COMS2019R02] ; CAS Program[Y9KY04101L]
WOS研究方向	Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000633493700008
内容类型	期刊论文
源URL	[http://ir.qdio.ac.cn/handle/337002/170826]
专题	海洋研究所_海洋环流与波动重点实验室
通讯作者	Li, Xiaofeng
作者单位	1.Chinese Acad Sci, Ctr Ocean Mega Sci, Qingdao 266071, Peoples R China 2.Chinese Acad Sci, Inst Oceanol, CAS Key Lab Ocean Circulat & Waves, Qingdao 266071, Peoples R China
推荐引用方式 GB/T 7714	Zhang, Xudong,Li, Xiaofeng. Combination of Satellite Observations and Machine Learning Method for Internal Wave Forecast in the Sulu and Celebes Seas[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2021,59(4):2822-2832.
APA	Zhang, Xudong,&Li, Xiaofeng.(2021).Combination of Satellite Observations and Machine Learning Method for Internal Wave Forecast in the Sulu and Celebes Seas.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,59(4),2822-2832.
MLA	Zhang, Xudong,et al."Combination of Satellite Observations and Machine Learning Method for Internal Wave Forecast in the Sulu and Celebes Seas".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 59.4(2021):2822-2832.