Institutional Repository of Key Laboratory of Ocean Circulation and Wave Studies, Institute of Oceanology, Chinese Academy of Sciences
| Combination of Satellite Observations and Machine Learning Method for Internal Wave Forecast in the Sulu and Celebes Seas | |
| Zhang, Xudong1,2; Li, Xiaofeng1,2 | |
| 2021-04-01 | |
| Source Publication | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
 |
| ISSN | 0196-2892 |
| Volume | 59Issue:4Pages:2822-2832 |
| Corresponding Author | Li, Xiaofeng(xiaofeng.li@ieee.org) |
| Abstract | 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. |
| Keyword | Oceans Predictive models Satellite broadcasting MODIS Satellites Machine learning Spatial resolution Celebes sea internal wave (IW) machine learning Sulu sea |
| DOI | 10.1109/TGRS.2020.3008067 |
| Indexed By | SCI |
| Language | 英语 |
| Funding Project | 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 Research Area | Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology |
| WOS Subject | Geochemistry & Geophysics ; Engineering, Electrical & Electronic ; Remote Sensing ; Imaging Science & Photographic Technology |
| WOS ID | WOS:000633493700008 |
| Publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.qdio.ac.cn/handle/337002/170827 |
| Collection | 海洋环流与波动重点实验室 |
| Corresponding Author | Li, Xiaofeng |
| Affiliation | 1.Chinese Acad Sci, Inst Oceanol, CAS Key Lab Ocean Circulat & Waves, Qingdao 266071, Peoples R China 2.Chinese Acad Sci, Ctr Ocean Mega Sci, Qingdao 266071, Peoples R China |
| First Author Affilication | Institute of Oceanology, Chinese Academy of Sciences |
| Corresponding Author Affilication | Institute of Oceanology, Chinese Academy of Sciences |
| Recommended Citation 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. |
| Files in This Item: | ||||||
| File Name/Size | DocType | Version | Access | License | ||
| 09142440.pdf（28337KB） | 期刊论文 | 出版稿 | 延迟开放 | CC BY-NC-SA | View 2023-7-1后可获取 | |
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