Predicting the Daily Sea Ice Concentration on a Subseasonal Scale of the Pan-Arctic During the Melting Season by a Deep Learning Model
文献类型：期刊论文
| 作者 | Ren, Yibin1,2; Li, Xiaofeng1,2 |
| 刊名 | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
 |
| 出版日期 | 2023 |
| 卷号 | 61页码:15 |
| ISSN号 | 0196-2892 |
| 关键词 | Predictive models Atmospheric modeling Sea ice Numerical models Arctic Ocean temperature Data models Deep learning Pan-Arctic physically constrained loss function sea ice concentration (SIC) prediction subseasonal scale |
| DOI | 10.1109/TGRS.2023.3279089 |
| 通讯作者 | Li, Xiaofeng(xiaofeng.li@ieee.org) |
| 英文摘要 | During the melting season, predicting the daily sea ice concentration (SIC) of the Pan-Arctic at a subseasonal scale is strongly required for economic activities and a challenging task for current studies. We propose a deep-learning-based data driven model to predict the 90 days SIC of the Pan-Arctic, named SICNet90. SICNet90 takes the historical 60 days' SIC and its anomaly and outputs the SIC of the next 90 days. We design a physically constrained loss function, normalized integrated ice-edge error (NIIEE), to constrain the SICNet(90's) optimization by the spatial morphology of SIC. The satellite-observed SIC trains (1991-2011/1997-2017) and tests the model (2012/2018-2020). For each test year, a 90-day SIC prediction is made daily from May 1 to July 2. The binary accuracy (BACC) of sea ice extent (SIC > 15%) and the mean absolute error (MAE) are evaluation metrics. Experiments show that SICNet90 significantly outperforms the Climatology benchmark on 90 days prediction, with a BACC/MAE improvement/reduction of 5.41%/1.35%. The data-driven model shows a late-spring-early-summer predictability barrier (around June 20) and a prediction challenge (around July 10), consistent with SIC's autocorrelation. The NIIEE loss optimizes the predictability barrier/challenge with a BACC increase of 4%. Using a 60 days historical SIC to predict 90 days SIC is better than a historical SIC of 30/90 days. The historical 2-m surface air temperature shows positive contributions to the prediction made from May 1 to mid-June, but negative contributions to the prediction made after mid-June. The historical sea surface temperature and 500 hp geopotential height show negative contributions. |
| WOS关键词 | PREDICTABILITY ; FORECAST ; VARIABILITY ; THICKNESS ; ENSEMBLE ; DRIVEN ; EXTENT |
| 资助项目 | National Natural Science Foundation for Young Scientists of China[42206202] ; National Natural Science Foundation of China[U2006211] ; Chinese Academy of Sciences (CAS) through the Strategic Priority Research Program[XDB42000000] ; Chinese Academy of Sciences (CAS) through the Strategic Priority Research Program[XDA19060101] ; Key Project of Centre for Ocean Mega-Science through the CAS[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:001005737500023 |
| 源URL | [http://ir.qdio.ac.cn/handle/337002/182433]  |
| 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, Key Lab Ocean Circulat & Waves, Qingdao 266071, Peoples R China |
| 推荐引用方式 GB/T 7714 | Ren, Yibin,Li, Xiaofeng. Predicting the Daily Sea Ice Concentration on a Subseasonal Scale of the Pan-Arctic During the Melting Season by a Deep Learning Model[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2023,61:15. |
| APA | Ren, Yibin,&Li, Xiaofeng.(2023).Predicting the Daily Sea Ice Concentration on a Subseasonal Scale of the Pan-Arctic During the Melting Season by a Deep Learning Model.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,61,15. |
| MLA | Ren, Yibin,et al."Predicting the Daily Sea Ice Concentration on a Subseasonal Scale of the Pan-Arctic During the Melting Season by a Deep Learning Model".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 61(2023):15. |
入库方式： OAI收割
来源：海洋研究所
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