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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49 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
- 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
- Data-Driven Short-Term Daily Operational Sea Ice Regional Forecasting T. Grigoryev et al. 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 10.1109/TGRS.2023.3279089
- SICFormer: A 3D-Swin Transformer for Sea Ice Concentration Prediction Z. Jiang et al. 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. 10.3390/rs15235437
- ConvLSTM-Based Wave Forecasts in the South and East China Seas S. Zhou et al. 10.3389/fmars.2021.680079
- Deep Learning‐Based Seasonal Forecast of Sea Ice Considering Atmospheric Conditions Y. Zhu et al. 10.1029/2023JD039521
- Daily-Scale Prediction of Arctic Sea Ice Concentration Based on Recurrent Neural Network Models J. Feng et al. 10.3390/jmse11122319
- Data-driven surrogate modeling of high-resolution sea-ice thickness in the Arctic C. Durand et al. 10.5194/tc-18-1791-2024
- Calibration of sea ice drift forecasts using random forest algorithms C. Palerme & M. Müller 10.5194/tc-15-3989-2021
- Incorporating physical constraints in a deep learning framework for short-term daily prediction of sea ice concentration Q. Liu et al. 10.1016/j.apor.2024.104007
- Neural Network Prediction for Ice Shapes on Airfoils Using iceFoam Simulations S. Strijhak et al. 10.3390/aerospace9020096
- Key factors for quantitative precipitation nowcasting using ground weather radar data based on deep learning D. Han et al. 10.5194/gmd-16-5895-2023
- Semantic image segmentation for sea ice parameters recognition using deep convolutional neural networks C. Zhang et al. 10.1016/j.jag.2022.102885
- Optimization of the k-nearest-neighbors model for summer Arctic Sea ice prediction Y. Lin et al. 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. 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. 10.3390/rs13122283
- Short-Term Daily Prediction of Sea Ice Concentration Based on Deep Learning of Gradient Loss Function Q. Liu et al. 10.3389/fmars.2021.736429
- Improving short-term sea ice concentration forecasts using deep learning C. Palerme et al. 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. 10.3390/jmse9030330
- Prediction of Pan-Arctic Sea Ice Using Attention-Based LSTM Neural Networks J. Wei et al. 10.3389/fmars.2022.860403
- Seasonal Arctic sea ice forecasting with probabilistic deep learning T. Andersson et al. 10.1038/s41467-021-25257-4
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- Neural Network Approaches to Reconstruct Phytoplankton Time-Series in the Global Ocean E. Martinez et al. 10.3390/rs12244156
- Sea Ice Extraction via Remote Sensing Imagery: Algorithms, Datasets, Applications and Challenges W. Huang et al. 10.3390/rs16050842
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- 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: 20 Nov 2024
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...
