Predicting the Daily Sea Ice Concentration on a Subseasonal Scale of the Pan-Arctic During the Melting Season by a Deep Learning Model

doi:10.1109/TGRS.2023.3279089

IOCAS-IR > 海洋环流与波动重点实验室

	Predicting the Daily Sea Ice Concentration on a Subseasonal Scale of the Pan-Arctic During the Melting Season by a Deep Learning Model
	Ren, Yibin 1,2; Li, Xiaofeng 1,2
	2023
发表期刊	IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
ISSN	0196-2892
卷号	61 页码:15
通讯作者	Li, Xiaofeng([email protected])
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.
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
收录类别	SCI
语种	英语
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
WOS类目	Geochemistry & Geophysics ; Engineering, Electrical & Electronic ; Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号	WOS:001005737500023
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS关键词	PREDICTABILITY ; FORECAST ; VARIABILITY ; THICKNESS ; ENSEMBLE ; DRIVEN ; EXTENT
引用统计	被引频次：13[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/337002/182434
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.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
第一作者单位	海洋环流与波动重点实验室
通讯作者单位	海洋环流与波动重点实验室
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.