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
Jungho Im
CORRESPONDING AUTHOR
School of Urban and Environmental Engineering, Ulsan National
Institute of Science and Technology, Ulsan, South Korea
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- Long-term prediction of Arctic sea ice concentrations using deep learning: Effects of surface temperature, radiation, and wind conditions Y. Kim et al. 10.1016/j.rse.2024.114568
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- Statistical seasonal prediction of Arctic sea ice concentration based on spatiotemporal anomaly persistent method G. Lee et al. 10.1088/1748-9326/ad7d1f
- A Mid- and Long-Term Arctic Sea Ice Concentration Prediction Model Based on Deep Learning Technology Q. Zheng et al. 10.3390/rs14122889
- Forecasting of Sea Ice Concentration using CNN, PDE discovery and Bayesian Networks J. Borisova et al. 10.1016/j.procs.2023.12.019
54 citations as recorded by crossref.
- A Spatiotemporal Multiscale Deep Learning Model for Subseasonal Prediction of Arctic Sea Ice Q. Zheng et al. 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. 10.3389/fmars.2023.1077623
- 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. 10.3389/fenvs.2023.1135165
- Monthly Arctic sea ice prediction based on a data-driven deep learning model X. Huan et al. 10.1088/2515-7620/acffb2
- A Bayesian Logistic Regression for Probabilistic Forecasts of the Minimum September Arctic Sea Ice Cover S. Horvath et al. 10.1029/2020EA001176
- Improving the accuracy of global ECMWF wave height forecasts with machine learning S. Zhou et al. 10.1016/j.ocemod.2024.102450
- An Explainable Deep Learning Model for Daily Sea Ice Concentration Forecast Y. Li et al. 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. 10.1007/s00376-023-3259-3
- Advancing Arctic Sea Ice Remote Sensing with AI and Deep Learning: Opportunities and Challenges W. Li et al. 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. 10.3390/rs13173413
- STDNet: Spatio-Temporal Decompose Network for Predicting Arctic Sea Ice Concentration X. Zhu et al. 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. 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. 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. 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. 10.3390/rs13132490
- Machine Learning-Based Image Processing for Ice Concentration during Chukchi and Beaufort Sea Trials H. Kim et al. 10.3390/jmse11122281
- Probabilistic spatiotemporal seasonal sea ice presence forecasting using sequence-to-sequence learning and ERA5 data in the Hudson Bay region N. Asadi et al. 10.5194/tc-16-3753-2022
- Antarctic sea ice prediction with A convolutional long short-term memory network X. Dong et al. 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. 10.3390/app14083278
- Arctic Sea Ice Concentration Prediction Using Spatial Attention Deep Learning H. Gu et al. 10.1109/JSTARS.2024.3486187
- Data-Driven Short-Term Daily Operational Sea Ice Regional Forecasting T. Grigoryev et al. 10.3390/rs14225837
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- SICFormer: A 3D-Swin Transformer for Sea Ice Concentration Prediction Z. Jiang et al. 10.3390/jmse12081424
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- Deep Learning‐Based Seasonal Forecast of Sea Ice Considering Atmospheric Conditions Y. Zhu et al. 10.1029/2023JD039521
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- Sea Ice Extraction via Remote Sensing Imagery: Algorithms, Datasets, Applications and Challenges W. Huang et al. 10.3390/rs16050842
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- Predicting Sea Ice Concentration With Uncertainty Quantification Using Passive Microwave and Reanalysis Data: A Case Study in Baffin Bay X. Chen et al. 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. 10.1016/j.rse.2024.114568
- Subseasonal Prediction of Regional Antarctic Sea Ice by a Deep Learning Model Y. Wang et al. 10.1029/2023GL104347
- Statistical seasonal prediction of Arctic sea ice concentration based on spatiotemporal anomaly persistent method G. Lee et al. 10.1088/1748-9326/ad7d1f
- A Mid- and Long-Term Arctic Sea Ice Concentration Prediction Model Based on Deep Learning Technology Q. Zheng et al. 10.3390/rs14122889
- Forecasting of Sea Ice Concentration using CNN, PDE discovery and Bayesian Networks J. Borisova et al. 10.1016/j.procs.2023.12.019
Latest update: 06 Mar 2025
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...
