A Data-Driven Deep Learning Model for Weekly Sea Ice Concentration Prediction of the Pan-Arctic During the Melting Season

doi:10.1109/TGRS.2022.3177600

IOCAS-IR > 海洋环流与波动重点实验室

	A Data-Driven Deep Learning Model for Weekly Sea Ice Concentration Prediction of the Pan-Arctic During the Melting Season
	Ren, Yibin 1,2; Li, Xiaofeng 1,2; Zhang, Wenhao 1,2
	2022
发表期刊	IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
ISSN	0196-2892
卷号	60 页码:19
通讯作者	Li, Xiaofeng([email protected])
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.
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
收录类别	SCI
语种	英语
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
WOS类目	Geochemistry & Geophysics ; Engineering, Electrical & Electronic ; Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号	WOS:000809416400026
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：31[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/337002/179155
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,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.

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