Satellite-Based Prediction of Arctic Sea Ice Concentration Using a Deep Neural Network with Multi-Model Ensemble
Warming of the Arctic leads to a decrease in sea ice, and the decrease of sea ice, in turn, results in warming of the Arctic again. Several microwave sensors have provided continuously updated sea ice data for over 30 years. Many studies have been conducted to investigate the relationships between t...
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ftmdpi:oai:mdpi.com:/2072-4292/11/1/19/ 2023-08-20T04:03:46+02:00 Satellite-Based Prediction of Arctic Sea Ice Concentration Using a Deep Neural Network with Multi-Model Ensemble Jiwon Kim Kwangjin Kim Jaeil Cho Yong Q. Kang Hong-Joo Yoon Yang-Won Lee agris 2018-12-22 application/pdf https://doi.org/10.3390/rs11010019 EN eng Multidisciplinary Digital Publishing Institute https://dx.doi.org/10.3390/rs11010019 https://creativecommons.org/licenses/by/4.0/ Remote Sensing; Volume 11; Issue 1; Pages: 19 sea ice concentration regional climate model Bayesian model averaging deep neural network Text 2018 ftmdpi https://doi.org/10.3390/rs11010019 2023-07-31T21:55:38Z Warming of the Arctic leads to a decrease in sea ice, and the decrease of sea ice, in turn, results in warming of the Arctic again. Several microwave sensors have provided continuously updated sea ice data for over 30 years. Many studies have been conducted to investigate the relationships between the satellite-derived sea ice concentration (SIC) of the Arctic and climatic factors associated with the accelerated warming. However, linear equations using the general circulation model (GCM) data, with low spatial resolution, cannot sufficiently cope with the problem of complexity or non-linearity. Time-series techniques are effective for one-step-ahead forecasting, but are not appropriate for future prediction for about ten or twenty years because of increasing uncertainty when forecasting multiple steps ahead. This paper describes a new approach to near-future prediction of Arctic SIC by employing a deep learning method with multi-model ensemble. We used the regional climate model (RCM) data provided in higher resolution, instead of GCM. The RCM ensemble was produced by Bayesian model averaging (BMA) to minimize the uncertainty which can arise from a single RCM. The accuracies of RCM variables were much improved by the BMA2 method, which took into consideration temporal and spatial variations to minimize the uncertainty of individual RCMs. A deep neural network (DNN) method was used to deal with the non-linear relationships between SIC and climate variables, and to provide a near-future prediction for the forthcoming 10 to 20 years. We adjusted the DNN model for optimized SIC prediction by adopting best-fitted layer structure, loss function, optimizer algorithm, and activation function. The accuracy was much improved when the DNN model was combined with BMA2 ensemble, showing the correlation coefficient of 0.888. This study provides a viable option for monitoring Arctic sea ice change of the near future. Text Arctic Sea ice MDPI Open Access Publishing Arctic Remote Sensing 11 1 19 |
institution |
Open Polar |
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MDPI Open Access Publishing |
op_collection_id |
ftmdpi |
language |
English |
topic |
sea ice concentration regional climate model Bayesian model averaging deep neural network |
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sea ice concentration regional climate model Bayesian model averaging deep neural network Jiwon Kim Kwangjin Kim Jaeil Cho Yong Q. Kang Hong-Joo Yoon Yang-Won Lee Satellite-Based Prediction of Arctic Sea Ice Concentration Using a Deep Neural Network with Multi-Model Ensemble |
topic_facet |
sea ice concentration regional climate model Bayesian model averaging deep neural network |
description |
Warming of the Arctic leads to a decrease in sea ice, and the decrease of sea ice, in turn, results in warming of the Arctic again. Several microwave sensors have provided continuously updated sea ice data for over 30 years. Many studies have been conducted to investigate the relationships between the satellite-derived sea ice concentration (SIC) of the Arctic and climatic factors associated with the accelerated warming. However, linear equations using the general circulation model (GCM) data, with low spatial resolution, cannot sufficiently cope with the problem of complexity or non-linearity. Time-series techniques are effective for one-step-ahead forecasting, but are not appropriate for future prediction for about ten or twenty years because of increasing uncertainty when forecasting multiple steps ahead. This paper describes a new approach to near-future prediction of Arctic SIC by employing a deep learning method with multi-model ensemble. We used the regional climate model (RCM) data provided in higher resolution, instead of GCM. The RCM ensemble was produced by Bayesian model averaging (BMA) to minimize the uncertainty which can arise from a single RCM. The accuracies of RCM variables were much improved by the BMA2 method, which took into consideration temporal and spatial variations to minimize the uncertainty of individual RCMs. A deep neural network (DNN) method was used to deal with the non-linear relationships between SIC and climate variables, and to provide a near-future prediction for the forthcoming 10 to 20 years. We adjusted the DNN model for optimized SIC prediction by adopting best-fitted layer structure, loss function, optimizer algorithm, and activation function. The accuracy was much improved when the DNN model was combined with BMA2 ensemble, showing the correlation coefficient of 0.888. This study provides a viable option for monitoring Arctic sea ice change of the near future. |
format |
Text |
author |
Jiwon Kim Kwangjin Kim Jaeil Cho Yong Q. Kang Hong-Joo Yoon Yang-Won Lee |
author_facet |
Jiwon Kim Kwangjin Kim Jaeil Cho Yong Q. Kang Hong-Joo Yoon Yang-Won Lee |
author_sort |
Jiwon Kim |
title |
Satellite-Based Prediction of Arctic Sea Ice Concentration Using a Deep Neural Network with Multi-Model Ensemble |
title_short |
Satellite-Based Prediction of Arctic Sea Ice Concentration Using a Deep Neural Network with Multi-Model Ensemble |
title_full |
Satellite-Based Prediction of Arctic Sea Ice Concentration Using a Deep Neural Network with Multi-Model Ensemble |
title_fullStr |
Satellite-Based Prediction of Arctic Sea Ice Concentration Using a Deep Neural Network with Multi-Model Ensemble |
title_full_unstemmed |
Satellite-Based Prediction of Arctic Sea Ice Concentration Using a Deep Neural Network with Multi-Model Ensemble |
title_sort |
satellite-based prediction of arctic sea ice concentration using a deep neural network with multi-model ensemble |
publisher |
Multidisciplinary Digital Publishing Institute |
publishDate |
2018 |
url |
https://doi.org/10.3390/rs11010019 |
op_coverage |
agris |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic Sea ice |
genre_facet |
Arctic Sea ice |
op_source |
Remote Sensing; Volume 11; Issue 1; Pages: 19 |
op_relation |
https://dx.doi.org/10.3390/rs11010019 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.3390/rs11010019 |
container_title |
Remote Sensing |
container_volume |
11 |
container_issue |
1 |
container_start_page |
19 |
_version_ |
1774714207026020352 |