{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T04:58:34Z","timestamp":1773723514280,"version":"3.50.1"},"reference-count":36,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2022,6,16]],"date-time":"2022-06-16T00:00:00Z","timestamp":1655337600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the National Key Research and Development Program","award":["2021YFC3101501"],"award-info":[{"award-number":["2021YFC3101501"]}]},{"name":"the National Key Research and Development Program","award":["41876014"],"award-info":[{"award-number":["41876014"]}]},{"name":"the National Key Research and Development Program","award":["2020TKLOMYB04"],"award-info":[{"award-number":["2020TKLOMYB04"]}]},{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation","doi-asserted-by":"publisher","award":["2021YFC3101501"],"award-info":[{"award-number":["2021YFC3101501"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation","doi-asserted-by":"publisher","award":["41876014"],"award-info":[{"award-number":["41876014"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation","doi-asserted-by":"publisher","award":["2020TKLOMYB04"],"award-info":[{"award-number":["2020TKLOMYB04"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the Open Project of Tianjin Key laboratory of Oceanic Meteorology","award":["2021YFC3101501"],"award-info":[{"award-number":["2021YFC3101501"]}]},{"name":"the Open Project of Tianjin Key laboratory of Oceanic Meteorology","award":["41876014"],"award-info":[{"award-number":["41876014"]}]},{"name":"the Open Project of Tianjin Key laboratory of Oceanic Meteorology","award":["2020TKLOMYB04"],"award-info":[{"award-number":["2020TKLOMYB04"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Mid- and long-term predictions of Arctic sea ice concentration (SIC) are important for the safety and security of the Arctic waterways. To date, SIC predictions mainly rely on numerical models, which have the disadvantages of a short prediction time and high computational complexity. Another common forecasting approach is based on a data-driven model, which is generally based on traditional statistical analysis or simple machine learning models, and achieves prediction by learning the relationships between data. Although the prediction performance of such methods has been improved in recent years, it is still difficult to find a balance between unstable model structures and complex spatio-temporal data. In this study, a classical statistical method and a deep learning model are combined to construct a data-driven rolling forecast model of SIC in the Arctic, named the EOF\u2013LSTM\u2013DNN (abbreviated as ELD) model. This model uses the empirical orthogonal function (EOF) method to extract the temporal and spatial features of the Arctic SIC, then the long short-term memory (LSTM) network is served as a feature extraction tool to effectively encode the time series, and, finally, the feature decoding is realized by the deep neural network (DNN). Comparisons of the model with climatology results, persistence predictions, other data-driven model results, and the hybrid coordinate ocean model (HYCOM) forecasts show that the ELD model has good prediction performance for the Arctic SIC on mid- and long-term time scales. When the forecast time is 100 days, the forecast root-mean-square error (RMSE), Pearson correlation coefficient (PCC), and anomaly correlation coefficient (ACC) of the ELD model are 0.2, 0.77, and 0.74, respectively.<\/jats:p>","DOI":"10.3390\/rs14122889","type":"journal-article","created":{"date-parts":[[2022,6,17]],"date-time":"2022-06-17T11:45:44Z","timestamp":1655466344000},"page":"2889","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["A Mid- and Long-Term Arctic Sea Ice Concentration Prediction Model Based on Deep Learning Technology"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6476-6927","authenticated-orcid":false,"given":"Qingyu","family":"Zheng","sequence":"first","affiliation":[{"name":"School of Marine Science and Technology, Tianjin University, Tianjin 300072, China"}]},{"given":"Wei","family":"Li","sequence":"additional","affiliation":[{"name":"School of Marine Science and Technology, Tianjin University, Tianjin 300072, China"},{"name":"Tianjin Key Laboratory for Oceanic Meteorology, Tianjin 300074, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8059-5010","authenticated-orcid":false,"given":"Qi","family":"Shao","sequence":"additional","affiliation":[{"name":"School of Marine Science and Technology, Tianjin University, Tianjin 300072, China"}]},{"given":"Guijun","family":"Han","sequence":"additional","affiliation":[{"name":"School of Marine Science and Technology, Tianjin University, Tianjin 300072, China"}]},{"given":"Xuan","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Marine Science and Technology, Tianjin University, Tianjin 300072, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,6,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"546","DOI":"10.1002\/qj.2401","article-title":"A review on Arctic sea-ice predictability and prediction on seasonal to decadal time-scales","volume":"142","author":"Guemas","year":"2016","journal-title":"Q. J. R. Meteorol. Soc."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"15919","DOI":"10.1029\/JD093iD12p15919","article-title":"A Coupled Energy Balance Climate-Sea Ice Model: Impact of Sea Ice and Leads on Climate","volume":"93","author":"Ledley","year":"1988","journal-title":"J. Geophys. Res."},{"key":"ref_3","first-page":"208","article-title":"Impacts of climate change on Arctic sea ice","volume":"2020","author":"Hwang","year":"2020","journal-title":"MCCIP Sci. Rev."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"C05S95","DOI":"10.1029\/2007JC004553","article-title":"Spatial and temporal variability of sea ice in the southern Beaufort Sea and Amundsen Gulf: 1980\u20132004","volume":"113","author":"Galley","year":"2008","journal-title":"J. Geophys. Res."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"L18501","DOI":"10.1029\/2011GL049303","article-title":"Changing seasonal sea ice predictor relationships in a changing Arctic climate","volume":"38","author":"Holland","year":"2011","journal-title":"Geophys. Res. Lett."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2411","DOI":"10.1002\/2014GL059388","article-title":"Predicting September sea ice: Ensemble skill of the SEARCH Sea Ice Outlook 2008\u20132013","volume":"41","author":"Stroeve","year":"2014","journal-title":"Geophys. Res. Lett."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"111204","DOI":"10.1016\/j.rse.2019.05.023","article-title":"Deep learning based retrieval algorithm for Arctic sea ice concentration from AMSR2 passive microwave and MODIS optical data","volume":"231","author":"Chi","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1162","DOI":"10.3189\/002214311796406095","article-title":"Sea-ice models for climate study: Retrospective and new directions","volume":"56","author":"Hunke","year":"2010","journal-title":"J. Glaciol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"659","DOI":"10.1002\/qj.2555","article-title":"Sea ice forecast verification in the Canadian Global Ice Ocean Prediction System","volume":"142","author":"Smith","year":"2016","journal-title":"Q. J. R. Meteorol. Soc."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"e2020EA001199","DOI":"10.1029\/2020EA001199","article-title":"The Navy\u2019s Earth System Prediction Capability: A New Global Coupled Atmosphere-Ocean-Sea Ice Prediction System Designed for Daily to Subseasonal Forecasting","volume":"8","author":"Barton","year":"2021","journal-title":"Earth Space Sci."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.5194\/tc-9-1735-2015","article-title":"Improving Arctic sea ice edge forecasts by assimilating high horizontal resolution sea ice concentration data into the US Navy\u2019s ice forecast systems","volume":"9","author":"Posey","year":"2015","journal-title":"Cryophere"},{"key":"ref_12","first-page":"1519","article-title":"TOPAZ4: An ocean-sea ice data assimilation system for the North Atlantic and Arctic","volume":"9","author":"Sakov","year":"2012","journal-title":"Ocean. Sci. Discuss."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1007\/s13131-014-0566-7","article-title":"Sensitivity of the Arctic sea ice concentration forecasts to different atmospheric forcing: A case study","volume":"33","author":"Yang","year":"2014","journal-title":"Acta Oceanol. Sin."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1016\/j.csda.2012.12.003","article-title":"Prediction of sea surface temperature in the tropical Atlantic by support vector machines","volume":"61","author":"Lins","year":"2013","journal-title":"Comput. Stat. Data Anal."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1007\/s10236-017-1032-9","article-title":"Prediction of daily sea surface temperature using efficient neural networks","volume":"67","author":"Patil","year":"2017","journal-title":"Ocean Dyn."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1715","DOI":"10.1175\/JTECH-D-15-0213.1","article-title":"Prediction of Sea Surface Temperature by Combining Numerical and Neural Techniques","volume":"33","author":"Patil","year":"2016","journal-title":"J. Atmos. Ocean. Technol."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1745","DOI":"10.1109\/LGRS.2017.2733548","article-title":"Prediction of Sea Surface Temperature Using Long Short-Term Memory","volume":"14","author":"Zhang","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"eaba1482","DOI":"10.1126\/sciadv.aba1482","article-title":"Purely satellite data-driven deep learning forecast of comolicated tropical instability waves","volume":"6","author":"Zheng","year":"2020","journal-title":"Sci. Adv."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"111358","DOI":"10.1016\/j.rse.2019.111358","article-title":"Short and mid-term sea surface temperature prediction using time-series satellite data and LSTM-AdaBoost combination approach","volume":"233","author":"Xiao","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"e2020EA001558","DOI":"10.1029\/2020EA001558","article-title":"Ocean Reanalysis Data-Driven Deep Learning Forecast for Sea Surface Multivariate in the South China Sea","volume":"8","author":"Shao","year":"2021","journal-title":"Earth Space Sci."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"e2021JC017515","DOI":"10.1029\/2021JC017515","article-title":"A Deep Learning Model for Forecasting Sea Surface Height Anomalies and Temperatures in the South China Sea","volume":"126","author":"Shao","year":"2021","journal-title":"J. Geophys. Res. Ocean."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1501705","DOI":"10.1109\/LGRS.2020.3042179","article-title":"Mid-Term Simultaneous Spatiotemporal Prediction of Sea Surface Height Anomaly and Sea Surface Temperature Using Satellite Data in the South China Sea","volume":"19","author":"Shao","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1510","DOI":"10.1109\/LGRS.2018.2852143","article-title":"Sea Ice Sensing From GNSS-R Data Using Convolutional Neural Networks","volume":"15","author":"Yan","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"4524","DOI":"10.1109\/TGRS.2016.2543660","article-title":"Sea Ice Concentration Estimation During Melt from Dual-Pol SAR Scenes Using Deep Convolutional Neural Networks: A Case Study","volume":"54","author":"Wang","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Chi, J., and Kim, H.-c. (2017). Prediction of Arctic Sea Ice Concentration Using a Fully Data Driven Deep Neural Network. Remote Sens., 9.","DOI":"10.3390\/rs9121305"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Wang, L., Scott, K.A., and Clausi, D.A. (2017). Sea Ice Concentration Estimation during Freeze-Up from SAR Imagery Using a Convolutional Neural Network. Remote Sens., 9.","DOI":"10.3390\/rs9050408"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Choi, M., Silva, L.W.A.D., and Yamaguchi, H. (2019). Artificial Neural Network for the Short-Term Prediction of Arctic Sea Ice Concentration. Remote Sens., 11.","DOI":"10.3390\/rs11091071"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1083","DOI":"10.5194\/tc-14-1083-2020","article-title":"Prediction of monthly Arctic sea ice concentrations using satellite and reanalysis data based on convolutional neural networks","volume":"14","author":"Kim","year":"2020","journal-title":"Cryosphere"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Liu, Q., Zhang, R., Wang, Y., Yan, H., and Hong, M. (2021). Daily Prediction of the Arctic Sea Ice Concentration Using Reanalysis Data Based on a Convolutional LSTM Network. J. Mar. Sci. Eng., 9.","DOI":"10.3390\/jmse9030330"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"699","DOI":"10.1175\/1520-0493(1982)110<0699:SEITEO>2.0.CO;2","article-title":"Sampling Errors in the Estimation of Empirical Orthogonal Functions","volume":"110","author":"North","year":"1982","journal-title":"Mon. Weather. Rev."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"879","DOI":"10.1175\/1520-0469(1984)041<0879:EOFANM>2.0.CO;2","article-title":"Empirical Orthogonal Functions and Normal Modes","volume":"41","author":"North","year":"1984","journal-title":"J. Atmos. Sci."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"2298","DOI":"10.1109\/TPAMI.2016.2646371","article-title":"An End-to-End Trainable Neural Network for Image-Based Sequence Recognition and Its Application to Scene Text Recognition","volume":"39","author":"Shi","year":"2017","journal-title":"IEEE Trans. Pattern Anal Mach. Intell."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"143807","DOI":"10.1109\/ACCESS.2021.3120749","article-title":"Unified Algorithm Framework for Nonconvex Stochastic Optimization in Deep Neural Networks","volume":"9","author":"Zhu","year":"2021","journal-title":"IEEE Access"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"809","DOI":"10.1175\/1520-0442(1996)009<0809:LLSTPU>2.0.CO;2","article-title":"Long-Lead Seasonal Temperature Prediction Using Optimal Climate Normals","volume":"9","author":"Huang","year":"1996","journal-title":"J. Clim."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"649","DOI":"10.5194\/os-5-649-2009","article-title":"Mediterranean Forecasting System: Forecast and analysis assessment through skill scores","volume":"5","author":"Tonani","year":"2009","journal-title":"Ocean Sci."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/12\/2889\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:33:23Z","timestamp":1760139203000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/12\/2889"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,16]]},"references-count":36,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2022,6]]}},"alternative-id":["rs14122889"],"URL":"https:\/\/doi.org\/10.3390\/rs14122889","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6,16]]}}}