Next Article in Journal
Offshore Oil and Gas Safety: Protection against Explosions
Previous Article in Journal
Preferential Treatment as a Tool for Managing the Coastal Area Sustainable Development: The Case of the Vladivostok Free Port
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
JMSE
Volume 9
Issue 3
10.3390/jmse9030330
jmse-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

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

by
Quanhong Liu
,
Ren Zhang
*,
Yangjun Wang
,
Hengqian Yan
and
Mei Hong
Institute of Meteorology and Oceanology, National University of Defense Technology, Nanjing 211101, China
*
Author to whom correspondence should be addressed.
J. Mar. Sci. Eng. 2021, 9(3), 330; https://doi.org/10.3390/jmse9030330
Submission received: 8 February 2021 / Revised: 7 March 2021 / Accepted: 14 March 2021 / Published: 16 March 2021
(This article belongs to the Section Physical Oceanography)
Browse Figures
Versions Notes

Abstract

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.
Keywords: SIC daily prediction; ConvLSTM; CNNs; predictability; arctic SIC daily prediction; ConvLSTM; CNNs; predictability; arctic

Share and Cite

MDPI and ACS Style

Liu, Q.; Zhang, R.; Wang, Y.; Yan, H.; Hong, M. Daily Prediction of the Arctic Sea Ice Concentration Using Reanalysis Data Based on a Convolutional LSTM Network. J. Mar. Sci. Eng. 2021, 9, 330. https://doi.org/10.3390/jmse9030330

AMA Style

Liu Q, Zhang R, Wang Y, Yan H, Hong M. Daily Prediction of the Arctic Sea Ice Concentration Using Reanalysis Data Based on a Convolutional LSTM Network. Journal of Marine Science and Engineering. 2021; 9(3):330. https://doi.org/10.3390/jmse9030330

Chicago/Turabian Style

Liu, Quanhong, Ren Zhang, Yangjun Wang, Hengqian Yan, and Mei Hong. 2021. "Daily Prediction of the Arctic Sea Ice Concentration Using Reanalysis Data Based on a Convolutional LSTM Network" Journal of Marine Science and Engineering 9, no. 3: 330. https://doi.org/10.3390/jmse9030330

APA Style

Liu, Q., Zhang, R., Wang, Y., Yan, H., & Hong, M. (2021). Daily Prediction of the Arctic Sea Ice Concentration Using Reanalysis Data Based on a Convolutional LSTM Network. Journal of Marine Science and Engineering, 9(3), 330. https://doi.org/10.3390/jmse9030330

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop