KOPRI Repository

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

Cited 4 time in wos
Cited 4 time in scopus
Metadata Downloads
Title
Prediction of monthly Arctic sea ice concentrations using satellite and reanalysis data based on convolutional neural networks
Other Titles
CNN기반 위성과 재분석 자료를 이용한 북극해빙 월별 예측
Authors
Kim, Young Jun
Kim, Hyun-cheol
Han, Daehyeon
Lee, Sanggyun
Im, Jungho
Subject
Physical Geography; Geology
Keywords
Arctic Sea Ice; Convolution Neural Network; Prediction; Reanalysis Data; Satellite Data
Issue Date
2020-03
Citation
Kim, Young Jun, et al. 2020. "Prediction of monthly Arctic sea ice concentrations using satellite and reanalysis data based on convolutional neural networks". CRYOSPHERE, 14(1): 1083-1104.
Abstract
Changes in Arctic sea ice affect atmospheric circulation, ocean current, and polar ecosystems. There have been unprecedented decreases in the amount of Arctic sea ice due to global warming. In this study, a novel 1-month sea ice concentration (SIC) prediction model is proposed, with eight predictors using a deep-learning approach, convolutional neural networks (CNNs). This monthly SIC prediction model based on CNNs is shown to perform better predictions (mean absolute error MAE of 2.28 %, anomaly correlation coefficient ACC of 0.98, root-mean-square error RMSE of 5.76 %, normalized RMSE nRMSE of 16.15 %, and NSE NashSutcliffe efficiency of 0.97) than a random-forest-based (RF-based) model (MAE of 2.45 %, ACC of 0.98, RMSE of 6.61 %, nRMSE of 18.64 %, and NSE of 0.96) and the persistence model based on the monthly trend (MAE of 4.31 %, ACC of 0.95, RMSE of 10.54 %, nRMSE of 29.17 %, and NSE of 0.89) through hindcast validations. The spatio-temporal analysis also confirmed the superiority of the CNN model. The CNN model showed good SIC prediction results in extreme cases that recorded unforeseen sea ice plummets in 2007 and 2012 with RMSEs of less than 5.0 %. This study also examined the importance of the input variables through a sensitivity analysis. In both the CNN and RF models, the variables of past SICs were identified as the most sensitive factor in predicting SICs. For both models, the SIC-related variables generally contributed more to predict SICs over ice-covered areas, while other meteorological and oceanographic variables were more sensitive to the prediction of SICs in marginal ice zones. The proposed 1-month SIC prediction model provides valuable information which can be used in various applications, such as Arctic shipping-route planning, management of the fishing industry, and long-term sea ice forecasting and dynamics
URI
https://repository.kopri.re.kr/handle/201206/11979
DOI
http://dx.doi.org/10.5194/tc-14-1083-2020
Appears in Collections  
2020-2020, Study on remote sensing for quantitative analysis of changes in the Arctic cryosphere (20-20) / Kim, Hyun-cheol (PE20080)
Files in This Item

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

Browse