文章基本信息

标题：Daily Prediction of the Arctic Sea Ice Concentration Using Reanalysis Data Based on a Convolutional LSTM Network
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作者：Quanhong Liu ; Ren Zhang ; Yangjun Wang 等
期刊名称：Journal of Marine Science and Engineering
电子版ISSN：2077-1312
出版年度：2021
卷号：9
期号：3
页码：330
DOI：10.3390/jmse9030330
语种：English
出版社：MDPI AG
摘要：To meet the increasing sailing demand of the Northeast Passage of the Arctic, a daily prediction model of sea ice concentration (SIC) based on the convolutional long short-term memory network (ConvLSTM) algorithm was proposed in this study. Previously, similar deep learning algorithms (such as convolutional neural networks; CNNs) were frequently used to predict monthly changes in sea ice. To verify the validity of the model, the ConvLSTM and CNNs models were compared based on their spatiotemporal scale by calculating the spatial structure similarity, root-mean-square-error, and correlation coefficient. The results show that in the entire test set, the single prediction effect of ConvLSTM was better than that of CNNs. Taking 15 December 2018 as an example, ConvLSTM was superior to CNNs in simulating the local variations in the sea ice concentration in the Northeast Passage, particularly in the vicinity of the East Siberian Sea. Finally, the predictability of ConvLSTM and CNNs was analysed following the iteration prediction method, demonstrating that the predictability of ConvLSTM was better than that of CNNs.