Articles | Volume 14, issue 3
https://doi.org/10.5194/tc-14-1083-2020
https://doi.org/10.5194/tc-14-1083-2020
Research article
 | 
25 Mar 2020
Research article |  | 25 Mar 2020

Prediction of monthly Arctic sea ice concentrations using satellite and reanalysis data based on convolutional neural networks

Young Jun Kim, Hyun-Cheol Kim, Daehyeon Han, Sanggyun Lee, and Jungho Im

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Latest update: 18 May 2024
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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.