Articles | Volume 14, issue 3
https://doi.org/10.5194/tc-14-1083-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-14-1083-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
25 Mar 2020
Research article |
| 25 Mar 2020
| 25 Mar 2020
Prediction of monthly Arctic sea ice concentrations using satellite and reanalysis data based on convolutional neural networks
Young Jun Kim
School of Urban and Environmental Engineering, Ulsan National
Institute of Science and Technology, Ulsan, South Korea
Hyun-Cheol Kim
Unit of Arctic Sea-Ice Prediction, Korea Polar Research Institute,
Incheon, South Korea
Daehyeon Han
School of Urban and Environmental Engineering, Ulsan National
Institute of Science and Technology, Ulsan, South Korea
Sanggyun Lee
Centre for Polar Observation and Modelling, University College London, London, UK
School of Urban and Environmental Engineering, Ulsan National
Institute of Science and Technology, Ulsan, South Korea
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Cited
78 citations as recorded by crossref.
- A Spatiotemporal Multiscale Deep Learning Model for Subseasonal Prediction of Arctic Sea Ice Q. Zheng et al. https://doi.org/10.1109/TGRS.2024.3355238
- A Multi-Mode Convolutional Neural Network to reconstruct satellite-derived chlorophyll-a time series in the global ocean from physical drivers J. Roussillon et al. https://doi.org/10.3389/fmars.2023.1077623
- A novel sea ice floe fragmentation index using Sentinel-2 and AMSR2 satellite data based on machine learning W. Kim et al. https://doi.org/10.1016/j.jag.2025.104911
- Seasonal prediction and possible causes of sudden losses of sea-ice in the Weddell Sea in recent years based on potential oceanic and atmospheric factors H. Zhao et al. https://doi.org/10.3389/fenvs.2023.1135165
- Monthly Arctic sea ice prediction based on a data-driven deep learning model X. Huan et al. https://doi.org/10.1088/2515-7620/acffb2
- Seasonal forecasting of Pan-Arctic sea ice with state space model W. Wang et al. https://doi.org/10.1038/s41612-025-01058-0
- DB-SICNet: A dual-branch model for predicting Arctic sea ice concentration L. Tan et al. https://doi.org/10.1016/j.ocemod.2025.102658
- A Bayesian Logistic Regression for Probabilistic Forecasts of the Minimum September Arctic Sea Ice Cover S. Horvath et al. https://doi.org/10.1029/2020EA001176
- Improving the accuracy of global ECMWF wave height forecasts with machine learning S. Zhou et al. https://doi.org/10.1016/j.ocemod.2024.102450
- An Explainable Deep Learning Model for Daily Sea Ice Concentration Forecast Y. Li et al. https://doi.org/10.1109/TGRS.2024.3386930
- Assessments of Data-Driven Deep Learning Models on One-Month Predictions of Pan-Arctic Sea Ice Thickness C. Song et al. https://doi.org/10.1007/s00376-023-3259-3
- Advancing Arctic Sea Ice Remote Sensing with AI and Deep Learning: Opportunities and Challenges W. Li et al. https://doi.org/10.3390/rs16203764
- Two-Stream Convolutional Long- and Short-Term Memory Model Using Perceptual Loss for Sequence-to-Sequence Arctic Sea Ice Prediction J. Chi et al. https://doi.org/10.3390/rs13173413
- STDNet: Spatio-Temporal Decompose Network for Predicting Arctic Sea Ice Concentration X. Zhu et al. https://doi.org/10.3390/rs16234534
- Assessment of High‐Resolution Dynamical and Machine Learning Models for Prediction of Sea Ice Concentration in a Regional Application S. Fritzner et al. https://doi.org/10.1029/2020JC016277
- On Mathews Correlation Coefficient and Improved Distance Map Loss for Automatic Glacier Calving Front Segmentation in SAR Imagery A. Davari et al. https://doi.org/10.1109/TGRS.2021.3115883
- CoCluster-DAGCN: a dynamic aggregate graph convolution network by a co-attention LSTM cluster for ocean temperature predictions Y. Chen et al. https://doi.org/10.1007/s11042-023-15768-1
- Mapping Potential Plant Species Richness over Large Areas with Deep Learning, MODIS, and Species Distribution Models H. Choe et al. https://doi.org/10.3390/rs13132490
- Artificial intelligence for ocean phenomena forecasting X. Li et al. https://doi.org/10.1016/j.scib.2026.04.002
- Machine Learning-Based Image Processing for Ice Concentration during Chukchi and Beaufort Sea Trials H. Kim et al. https://doi.org/10.3390/jmse11122281
- Long-term prediction of sea ice concentration with convolutional long short-term memory Ö. AK et al. https://doi.org/10.1007/s12145-026-02125-7
- Probabilistic spatiotemporal seasonal sea ice presence forecasting using sequence-to-sequence learning and ERA5 data in the Hudson Bay region N. Asadi et al. https://doi.org/10.5194/tc-16-3753-2022
- Antarctic sea ice prediction with A convolutional long short-term memory network X. Dong et al. https://doi.org/10.1016/j.ocemod.2024.102386
- PDED-ConvLSTM: Pyramid Dilated Deeper Encoder–Decoder Convolutional LSTM for Arctic Sea Ice Concentration Prediction D. Zhang et al. https://doi.org/10.3390/app14083278
- Arctic Sea Ice Concentration Prediction Using Spatial Attention Deep Learning H. Gu et al. https://doi.org/10.1109/JSTARS.2024.3486187
- Data-Driven Short-Term Daily Operational Sea Ice Regional Forecasting T. Grigoryev et al. https://doi.org/10.3390/rs14225837
- Predicting the Daily Sea Ice Concentration on a Subseasonal Scale of the Pan-Arctic During the Melting Season by a Deep Learning Model Y. Ren & X. Li https://doi.org/10.1109/TGRS.2023.3279089
- SICFormer: A 3D-Swin Transformer for Sea Ice Concentration Prediction Z. Jiang et al. https://doi.org/10.3390/jmse12081424
- Self-Attention Convolutional Long Short-Term Memory for Short-Term Arctic Sea Ice Motion Prediction Using Advanced Microwave Scanning Radiometer Earth Observing System 36.5 GHz Data D. Zhong et al. https://doi.org/10.3390/rs15235437
- Extended seasonal prediction of Antarctic sea ice concentration using ANTSIC-UNet Z. Yang et al. https://doi.org/10.5194/tc-19-6381-2025
- ConvLSTM-Based Wave Forecasts in the South and East China Seas S. Zhou et al. https://doi.org/10.3389/fmars.2021.680079
- Deep Learning‐Based Seasonal Forecast of Sea Ice Considering Atmospheric Conditions Y. Zhu et al. https://doi.org/10.1029/2023JD039521
- STEPNet: A Spatial and Temporal Encoding Pipeline to Handle Temporal Heterogeneity in Climate Modeling Using AI: A Use Case of Sea Ice Forecasting S. Wang et al. https://doi.org/10.1109/JSTARS.2025.3532219
- Daily-Scale Prediction of Arctic Sea Ice Concentration Based on Recurrent Neural Network Models J. Feng et al. https://doi.org/10.3390/jmse11122319
- Physically Constrained Spatiotemporal Deep Learning Model for Fine-Scale, Long-Term Arctic Sea Ice Concentration Prediction J. He et al. https://doi.org/10.1109/TGRS.2025.3549823
- AI for atmosphere–ocean sciences: advancements, challenges and ways forward J. Luo et al. https://doi.org/10.1093/nsr/nwag063
- Data-driven surrogate modeling of high-resolution sea-ice thickness in the Arctic C. Durand et al. https://doi.org/10.5194/tc-18-1791-2024
- Hierarchical Information-Sharing Convolutional Neural Network for the Prediction of Arctic Sea Ice Concentration and Velocity Y. Koo & M. Rahnemoonfar https://doi.org/10.1109/TGRS.2024.3501094
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- A time series-based hybrid model for daily Arctic sea ice forecasting years in advance C. Hu et al. https://doi.org/10.1016/j.envsoft.2025.106768
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- Sea Ice Edge Constraint Improves Antarctic Sea Ice Seasonal Prediction in Deep Learning Models H. Wang et al. https://doi.org/10.1007/s00376-025-5024-2
- Neural Network Prediction for Ice Shapes on Airfoils Using iceFoam Simulations S. Strijhak et al. https://doi.org/10.3390/aerospace9020096
- Key factors for quantitative precipitation nowcasting using ground weather radar data based on deep learning D. Han et al. https://doi.org/10.5194/gmd-16-5895-2023
- Sea ice concentration estimation via physical information-guided multi-source data fusion and spatial continuity preservation X. Liu et al. https://doi.org/10.1016/j.jag.2026.105339
- Exploring research themes and trends in arctic shipping using latent dirichlet allocation B. Sağlam et al. https://doi.org/10.1080/03088839.2025.2574683
- Semantic image segmentation for sea ice parameters recognition using deep convolutional neural networks C. Zhang et al. https://doi.org/10.1016/j.jag.2022.102885
- Optimization of the k-nearest-neighbors model for summer Arctic Sea ice prediction Y. Lin et al. https://doi.org/10.3389/fmars.2023.1260047
- Estimation of Daily Arctic Winter Sea Ice Thickness from Thermodynamic Parameters Using a Self-Attention Convolutional Neural Network Z. Liang et al. https://doi.org/10.3390/rs15071887
- Retrieval of Summer Sea Ice Concentration in the Pacific Arctic Ocean from AMSR2 Observations and Numerical Weather Data Using Random Forest Regression H. Han et al. https://doi.org/10.3390/rs13122283
- Short-Term Daily Prediction of Sea Ice Concentration Based on Deep Learning of Gradient Loss Function Q. Liu et al. https://doi.org/10.3389/fmars.2021.736429
- Research Progress of Deep Learning in Sea Ice Prediction J. Ran et al. https://doi.org/10.3390/rs18030419
- Mechanisms and Predictability of Beaufort Sea Ice Retreat Revealed by Coupled Modeling and Remote Sensing Data H. Nie et al. https://doi.org/10.3390/rs17193286
- Improving short-term sea ice concentration forecasts using deep learning C. Palerme et al. https://doi.org/10.5194/tc-18-2161-2024
- Daily Prediction of the Arctic Sea Ice Concentration Using Reanalysis Data Based on a Convolutional LSTM Network Q. Liu et al. https://doi.org/10.3390/jmse9030330
- Prediction of Pan-Arctic Sea Ice Using Attention-Based LSTM Neural Networks J. Wei et al. https://doi.org/10.3389/fmars.2022.860403
- Comparative Research on Vessel Navigability on the Northern Sea Route Based on the NSR Admission Criteria and POLARIS Methodology L. Ma et al. https://doi.org/10.3390/jmse13071282
- Seasonal Arctic sea ice forecasting with probabilistic deep learning T. Andersson et al. https://doi.org/10.1038/s41467-021-25257-4
- Snow effects on altimeter waveforms over sea ice in the Weddell Sea — Part I: Radar waveform decomposition L. Zhou et al. https://doi.org/10.1016/j.rse.2025.115112
- Recent developments of artificial intelligence methods for sea ice concentration monitoring using high-resolution imaging datasets M. Stofa et al. https://doi.org/10.1016/j.ecoinf.2025.103132
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- Neural Network Approaches to Reconstruct Phytoplankton Time-Series in the Global Ocean E. Martinez et al. https://doi.org/10.3390/rs12244156
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- The Challenge of Arctic Sea Ice Thickness Prediction by ECMWF on Subseasonal Time Scales Y. Xiu et al. https://doi.org/10.1029/2021GL097476
- A Data-Driven Deep Learning Model for Weekly Sea Ice Concentration Prediction of the Pan-Arctic During the Melting Season Y. Ren et al. https://doi.org/10.1109/TGRS.2022.3177600
- SICNetseason V1.0: a transformer-based deep learning model for seasonal Arctic sea ice prediction by incorporating sea ice thickness data Y. Ren et al. https://doi.org/10.5194/gmd-18-2665-2025
- Satellite image processing in the circumpolar north: Understanding climate crisis by predicting sea ice extent in the arctic I. Namir et al. https://doi.org/10.1016/j.rsase.2025.101797
- Predicting Sea Ice Concentration With Uncertainty Quantification Using Passive Microwave and Reanalysis Data: A Case Study in Baffin Bay X. Chen et al. https://doi.org/10.1109/TGRS.2023.3250164
- Long-term prediction of Arctic sea ice concentrations using deep learning: Effects of surface temperature, radiation, and wind conditions Y. Kim et al. https://doi.org/10.1016/j.rse.2024.114568
- Frequency-Compensated Network for Daily Arctic Sea Ice Concentration Prediction J. Zhang et al. https://doi.org/10.1109/TGRS.2025.3564457
- Subseasonal Prediction of Regional Antarctic Sea Ice by a Deep Learning Model Y. Wang et al. https://doi.org/10.1029/2023GL104347
- Statistical seasonal prediction of Arctic sea ice concentration based on spatiotemporal anomaly persistent method G. Lee et al. https://doi.org/10.1088/1748-9326/ad7d1f
- Comparative analysis of sea ice conditions in five reanalysis datasets along the Northern Sea Route Z. Xiong et al. https://doi.org/10.1007/s10236-025-01684-5
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- A Mid- and Long-Term Arctic Sea Ice Concentration Prediction Model Based on Deep Learning Technology Q. Zheng et al. https://doi.org/10.3390/rs14122889
- Forecasting of Sea Ice Concentration using CNN, PDE discovery and Bayesian Networks J. Borisova et al. https://doi.org/10.1016/j.procs.2023.12.019
78 citations as recorded by crossref.
- A Spatiotemporal Multiscale Deep Learning Model for Subseasonal Prediction of Arctic Sea Ice Q. Zheng et al. https://doi.org/10.1109/TGRS.2024.3355238
- A Multi-Mode Convolutional Neural Network to reconstruct satellite-derived chlorophyll-a time series in the global ocean from physical drivers J. Roussillon et al. https://doi.org/10.3389/fmars.2023.1077623
- A novel sea ice floe fragmentation index using Sentinel-2 and AMSR2 satellite data based on machine learning W. Kim et al. https://doi.org/10.1016/j.jag.2025.104911
- Seasonal prediction and possible causes of sudden losses of sea-ice in the Weddell Sea in recent years based on potential oceanic and atmospheric factors H. Zhao et al. https://doi.org/10.3389/fenvs.2023.1135165
- Monthly Arctic sea ice prediction based on a data-driven deep learning model X. Huan et al. https://doi.org/10.1088/2515-7620/acffb2
- Seasonal forecasting of Pan-Arctic sea ice with state space model W. Wang et al. https://doi.org/10.1038/s41612-025-01058-0
- DB-SICNet: A dual-branch model for predicting Arctic sea ice concentration L. Tan et al. https://doi.org/10.1016/j.ocemod.2025.102658
- A Bayesian Logistic Regression for Probabilistic Forecasts of the Minimum September Arctic Sea Ice Cover S. Horvath et al. https://doi.org/10.1029/2020EA001176
- Improving the accuracy of global ECMWF wave height forecasts with machine learning S. Zhou et al. https://doi.org/10.1016/j.ocemod.2024.102450
- An Explainable Deep Learning Model for Daily Sea Ice Concentration Forecast Y. Li et al. https://doi.org/10.1109/TGRS.2024.3386930
- Assessments of Data-Driven Deep Learning Models on One-Month Predictions of Pan-Arctic Sea Ice Thickness C. Song et al. https://doi.org/10.1007/s00376-023-3259-3
- Advancing Arctic Sea Ice Remote Sensing with AI and Deep Learning: Opportunities and Challenges W. Li et al. https://doi.org/10.3390/rs16203764
- Two-Stream Convolutional Long- and Short-Term Memory Model Using Perceptual Loss for Sequence-to-Sequence Arctic Sea Ice Prediction J. Chi et al. https://doi.org/10.3390/rs13173413
- STDNet: Spatio-Temporal Decompose Network for Predicting Arctic Sea Ice Concentration X. Zhu et al. https://doi.org/10.3390/rs16234534
- Assessment of High‐Resolution Dynamical and Machine Learning Models for Prediction of Sea Ice Concentration in a Regional Application S. Fritzner et al. https://doi.org/10.1029/2020JC016277
- On Mathews Correlation Coefficient and Improved Distance Map Loss for Automatic Glacier Calving Front Segmentation in SAR Imagery A. Davari et al. https://doi.org/10.1109/TGRS.2021.3115883
- CoCluster-DAGCN: a dynamic aggregate graph convolution network by a co-attention LSTM cluster for ocean temperature predictions Y. Chen et al. https://doi.org/10.1007/s11042-023-15768-1
- Mapping Potential Plant Species Richness over Large Areas with Deep Learning, MODIS, and Species Distribution Models H. Choe et al. https://doi.org/10.3390/rs13132490
- Artificial intelligence for ocean phenomena forecasting X. Li et al. https://doi.org/10.1016/j.scib.2026.04.002
- Machine Learning-Based Image Processing for Ice Concentration during Chukchi and Beaufort Sea Trials H. Kim et al. https://doi.org/10.3390/jmse11122281
- Long-term prediction of sea ice concentration with convolutional long short-term memory Ö. AK et al. https://doi.org/10.1007/s12145-026-02125-7
- Probabilistic spatiotemporal seasonal sea ice presence forecasting using sequence-to-sequence learning and ERA5 data in the Hudson Bay region N. Asadi et al. https://doi.org/10.5194/tc-16-3753-2022
- Antarctic sea ice prediction with A convolutional long short-term memory network X. Dong et al. https://doi.org/10.1016/j.ocemod.2024.102386
- PDED-ConvLSTM: Pyramid Dilated Deeper Encoder–Decoder Convolutional LSTM for Arctic Sea Ice Concentration Prediction D. Zhang et al. https://doi.org/10.3390/app14083278
- Arctic Sea Ice Concentration Prediction Using Spatial Attention Deep Learning H. Gu et al. https://doi.org/10.1109/JSTARS.2024.3486187
- Data-Driven Short-Term Daily Operational Sea Ice Regional Forecasting T. Grigoryev et al. https://doi.org/10.3390/rs14225837
- Predicting the Daily Sea Ice Concentration on a Subseasonal Scale of the Pan-Arctic During the Melting Season by a Deep Learning Model Y. Ren & X. Li https://doi.org/10.1109/TGRS.2023.3279089
- SICFormer: A 3D-Swin Transformer for Sea Ice Concentration Prediction Z. Jiang et al. https://doi.org/10.3390/jmse12081424
- Self-Attention Convolutional Long Short-Term Memory for Short-Term Arctic Sea Ice Motion Prediction Using Advanced Microwave Scanning Radiometer Earth Observing System 36.5 GHz Data D. Zhong et al. https://doi.org/10.3390/rs15235437
- Extended seasonal prediction of Antarctic sea ice concentration using ANTSIC-UNet Z. Yang et al. https://doi.org/10.5194/tc-19-6381-2025
- ConvLSTM-Based Wave Forecasts in the South and East China Seas S. Zhou et al. https://doi.org/10.3389/fmars.2021.680079
- Deep Learning‐Based Seasonal Forecast of Sea Ice Considering Atmospheric Conditions Y. Zhu et al. https://doi.org/10.1029/2023JD039521
- STEPNet: A Spatial and Temporal Encoding Pipeline to Handle Temporal Heterogeneity in Climate Modeling Using AI: A Use Case of Sea Ice Forecasting S. Wang et al. https://doi.org/10.1109/JSTARS.2025.3532219
- Daily-Scale Prediction of Arctic Sea Ice Concentration Based on Recurrent Neural Network Models J. Feng et al. https://doi.org/10.3390/jmse11122319
- Physically Constrained Spatiotemporal Deep Learning Model for Fine-Scale, Long-Term Arctic Sea Ice Concentration Prediction J. He et al. https://doi.org/10.1109/TGRS.2025.3549823
- AI for atmosphere–ocean sciences: advancements, challenges and ways forward J. Luo et al. https://doi.org/10.1093/nsr/nwag063
- Data-driven surrogate modeling of high-resolution sea-ice thickness in the Arctic C. Durand et al. https://doi.org/10.5194/tc-18-1791-2024
- Hierarchical Information-Sharing Convolutional Neural Network for the Prediction of Arctic Sea Ice Concentration and Velocity Y. Koo & M. Rahnemoonfar https://doi.org/10.1109/TGRS.2024.3501094
- Calibration of sea ice drift forecasts using random forest algorithms C. Palerme & M. Müller https://doi.org/10.5194/tc-15-3989-2021
- A time series-based hybrid model for daily Arctic sea ice forecasting years in advance C. Hu et al. https://doi.org/10.1016/j.envsoft.2025.106768
- Incorporating physical constraints in a deep learning framework for short-term daily prediction of sea ice concentration Q. Liu et al. https://doi.org/10.1016/j.apor.2024.104007
- Sea Ice Edge Constraint Improves Antarctic Sea Ice Seasonal Prediction in Deep Learning Models H. Wang et al. https://doi.org/10.1007/s00376-025-5024-2
- Neural Network Prediction for Ice Shapes on Airfoils Using iceFoam Simulations S. Strijhak et al. https://doi.org/10.3390/aerospace9020096
- Key factors for quantitative precipitation nowcasting using ground weather radar data based on deep learning D. Han et al. https://doi.org/10.5194/gmd-16-5895-2023
- Sea ice concentration estimation via physical information-guided multi-source data fusion and spatial continuity preservation X. Liu et al. https://doi.org/10.1016/j.jag.2026.105339
- Exploring research themes and trends in arctic shipping using latent dirichlet allocation B. Sağlam et al. https://doi.org/10.1080/03088839.2025.2574683
- Semantic image segmentation for sea ice parameters recognition using deep convolutional neural networks C. Zhang et al. https://doi.org/10.1016/j.jag.2022.102885
- Optimization of the k-nearest-neighbors model for summer Arctic Sea ice prediction Y. Lin et al. https://doi.org/10.3389/fmars.2023.1260047
- Estimation of Daily Arctic Winter Sea Ice Thickness from Thermodynamic Parameters Using a Self-Attention Convolutional Neural Network Z. Liang et al. https://doi.org/10.3390/rs15071887
- Retrieval of Summer Sea Ice Concentration in the Pacific Arctic Ocean from AMSR2 Observations and Numerical Weather Data Using Random Forest Regression H. Han et al. https://doi.org/10.3390/rs13122283
- Short-Term Daily Prediction of Sea Ice Concentration Based on Deep Learning of Gradient Loss Function Q. Liu et al. https://doi.org/10.3389/fmars.2021.736429
- Research Progress of Deep Learning in Sea Ice Prediction J. Ran et al. https://doi.org/10.3390/rs18030419
- Mechanisms and Predictability of Beaufort Sea Ice Retreat Revealed by Coupled Modeling and Remote Sensing Data H. Nie et al. https://doi.org/10.3390/rs17193286
- Improving short-term sea ice concentration forecasts using deep learning C. Palerme et al. https://doi.org/10.5194/tc-18-2161-2024
- Daily Prediction of the Arctic Sea Ice Concentration Using Reanalysis Data Based on a Convolutional LSTM Network Q. Liu et al. https://doi.org/10.3390/jmse9030330
- Prediction of Pan-Arctic Sea Ice Using Attention-Based LSTM Neural Networks J. Wei et al. https://doi.org/10.3389/fmars.2022.860403
- Comparative Research on Vessel Navigability on the Northern Sea Route Based on the NSR Admission Criteria and POLARIS Methodology L. Ma et al. https://doi.org/10.3390/jmse13071282
- Seasonal Arctic sea ice forecasting with probabilistic deep learning T. Andersson et al. https://doi.org/10.1038/s41467-021-25257-4
- Snow effects on altimeter waveforms over sea ice in the Weddell Sea — Part I: Radar waveform decomposition L. Zhou et al. https://doi.org/10.1016/j.rse.2025.115112
- Recent developments of artificial intelligence methods for sea ice concentration monitoring using high-resolution imaging datasets M. Stofa et al. https://doi.org/10.1016/j.ecoinf.2025.103132
- IceTFT v1.0.0: interpretable long-term prediction of Arctic sea ice extent with deep learning B. Mu et al. https://doi.org/10.5194/gmd-16-4677-2023
- Multivariate deep learning approach to forecast of an ice-free Arctic summer under the SSP2-4.5 and SSP5-8.5 scenarios M. Bilgili https://doi.org/10.1016/j.polar.2025.101308
- Neural Network Approaches to Reconstruct Phytoplankton Time-Series in the Global Ocean E. Martinez et al. https://doi.org/10.3390/rs12244156
- Sea Ice Extraction via Remote Sensing Imagery: Algorithms, Datasets, Applications and Challenges W. Huang et al. https://doi.org/10.3390/rs16050842
- The Challenge of Arctic Sea Ice Thickness Prediction by ECMWF on Subseasonal Time Scales Y. Xiu et al. https://doi.org/10.1029/2021GL097476
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Saved (final revised paper)
Latest update: 28 May 2026
Short summary
In this study, we proposed a novel 1-month sea ice concentration (SIC) prediction model with eight predictors using a deep-learning approach, convolutional neural networks (CNNs). The proposed CNN model was evaluated and compared with the two baseline approaches, random-forest and simple-regression models, resulting in better performance. This study also examined SIC predictions for two extreme cases in 2007 and 2012 in detail and the influencing factors through a sensitivity analysis.
In this study, we proposed a novel 1-month sea ice concentration (SIC) prediction model with...
