A Data-Driven Deep Learning Model for Weekly Sea Ice Concentration Prediction of the Pan-Arctic During the Melting Season
文献类型：期刊论文
| 作者 | Ren, Yibin1,2; Li, Xiaofeng1,2; Zhang, Wenhao1,2 |
| 刊名 | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
 |
| 出版日期 | 2022 |
| 卷号 | 60页码:19 |
| ISSN号 | 0196-2892 |
| 关键词 | Deep fully convolutional networks (FCNs) recursively predicting satellite-derived sea ice concentration (SIC) SIC prediction temporal-spatial attention |
| DOI | 10.1109/TGRS.2022.3177600 |
| 通讯作者 | Li, Xiaofeng(xiaofeng.li@ieee.org) |
| 英文摘要 | This study proposes a purely data-driven model for the weekly prediction of daily sea ice concentration (SIC) of the pan-Arctic (90 N, 45 N, 180 E, 180 W) during the melting season. The model, SICNet, adopts an encoder-decoder framework with fully convolutional networks (FCNs) and can predict the SIC (covering 320 x 224 grids, each with a resolution of 25 km) one-week lead with high accuracy. We design a temporal-spatial attention module (TSAM) to help SICNet capture spatiotemporal dependencies from SIC sequences. The satellite-derived SIC data of 33 years (1988-2020) from the National Snow and Ice Data Center (NSIDC) are employed to train and test the model, 1988-2015 for training, and 2016-2020 for testing. SICNet achieves the mean absolute error (MAE) of 2.67%, the mean absolute percentage error (MAPE) of 8.67%, and the Nash-Sutcliffe efficiency (NSE) of 0.9784 in weekly predicting of SIC during the melting season. SICNet achieves better performance than existing deep-learning-based models. The TSAM reduced the MAE from 2.73% to 2.67%. We evaluate the model's performance by recursively predicting, from seven- to 28-day leads. We employ the binary accuracy (BACC) metric to measure the accuracy of the predicted sea ice extent (SIE) and compare SICNet with the anomaly persistence (Persist). SICNet shows better performance than Persist with an average BACC on the 28th day of 2016-2019 over 90% (90.17%). For the 28-day lead predictions of three extreme minimum SIE in September 2007, 2012, and 2020, SICNet outperforms Persist with an average improvement of 1.84% in BACC and 0.16 milkm(2) in the SIE error. |
| 资助项目 | Strategic Priority Research Program of the Chinese Academy of Sciences (CAS)[XDA19060101] ; Strategic Priority Research Program of the Chinese Academy of Sciences (CAS)[XDB42040401] ; Key Research and Development Project of Shandong Province[2019JZZY010102] ; Key Deployment Project of Centre for Ocean Mega-Science through the CAS Programs[COMS2019R02 Y9KY04101L] ; China Postdoctoral Science Foundation[2019M662452] ; National Natural Science Foundation of China[U2006211] |
| 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:000809416400026 |
| 源URL | [http://ir.qdio.ac.cn/handle/337002/179154]  |
| 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, Inst Oceanol, Key Lab Ocean Circulat & Waves, Qingdao 266071, Peoples R China 2.Chinese Acad Sci, Ctr Ocean Mega Sci, Qingdao 266071, Peoples R China |
| 推荐引用方式 GB/T 7714 | Ren, Yibin,Li, Xiaofeng,Zhang, Wenhao. A Data-Driven Deep Learning Model for Weekly Sea Ice Concentration Prediction of the Pan-Arctic During the Melting Season[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2022,60:19. |
| APA | Ren, Yibin,Li, Xiaofeng,&Zhang, Wenhao.(2022).A Data-Driven Deep Learning Model for Weekly Sea Ice Concentration Prediction of the Pan-Arctic During the Melting Season.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,60,19. |
| MLA | Ren, Yibin,et al."A Data-Driven Deep Learning Model for Weekly Sea Ice Concentration Prediction of the Pan-Arctic During the Melting Season".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 60(2022):19. |
入库方式： OAI收割
来源：海洋研究所
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