Daily Prediction of the Arctic Sea Ice Concentration Using Reanalysis Data Based on a Convolutional LSTM Network

• Published in: Journal of Marine Science and Engineering
• Main Authors: Quanhong Liu, Ren Zhang, Yangjun Wang, Hengqian Yan, Mei Hong
• Format: Article in Journal/Newspaper
• Language: English
• Published: MDPI AG 2021
• Subjects: SIC daily prediction; ConvLSTM; CNNs; predictability; arctic; Naval architecture. Shipbuilding. Marine engineering; VM1-989; Oceanography; GC1-1581; Arctic; East Siberian Sea; Northeast Passage; Sea ice
• Online Access: https://doi.org/10.3390/jmse9030330; https://doaj.org/article/0e76309258184a289ba02a52451e5fcb

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.