Sea Ice Forecasting using Attention-based Ensemble LSTM
Accurately forecasting Arctic sea ice from subseasonal to seasonal scales has been a major scientific effort with fundamental challenges at play. In addition to physics-based earth system models, researchers have been applying multiple statistical and machine learning models for sea ice forecasting....
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Online Access: | https://dx.doi.org/10.48550/arxiv.2108.00853 https://arxiv.org/abs/2108.00853 |
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ftdatacite:10.48550/arxiv.2108.00853 2023-05-15T14:50:49+02:00 Sea Ice Forecasting using Attention-based Ensemble LSTM Ali, Sahara Huang, Yiyi Huang, Xin Wang, Jianwu 2021 https://dx.doi.org/10.48550/arxiv.2108.00853 https://arxiv.org/abs/2108.00853 unknown arXiv Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 CC-BY Atmospheric and Oceanic Physics physics.ao-ph Artificial Intelligence cs.AI Machine Learning cs.LG FOS Physical sciences FOS Computer and information sciences Article CreativeWork article Preprint 2021 ftdatacite https://doi.org/10.48550/arxiv.2108.00853 2022-03-10T14:24:23Z Accurately forecasting Arctic sea ice from subseasonal to seasonal scales has been a major scientific effort with fundamental challenges at play. In addition to physics-based earth system models, researchers have been applying multiple statistical and machine learning models for sea ice forecasting. Looking at the potential of data-driven sea ice forecasting, we propose an attention-based Long Short Term Memory (LSTM) ensemble method to predict monthly sea ice extent up to 1 month ahead. Using daily and monthly satellite retrieved sea ice data from NSIDC and atmospheric and oceanic variables from ERA5 reanalysis product for 39 years, we show that our multi-temporal ensemble method outperforms several baseline and recently proposed deep learning models. This will substantially improve our ability in predicting future Arctic sea ice changes, which is fundamental for forecasting transporting routes, resource development, coastal erosion, threats to Arctic coastal communities and wildlife. : Accepted by the Tackling Climate Change with Machine Learning Workshop at the 2021 International Conference on Machine Learning (ICML 2021) Article in Journal/Newspaper Arctic Climate change Sea ice DataCite Metadata Store (German National Library of Science and Technology) Arctic |
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DataCite Metadata Store (German National Library of Science and Technology) |
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Atmospheric and Oceanic Physics physics.ao-ph Artificial Intelligence cs.AI Machine Learning cs.LG FOS Physical sciences FOS Computer and information sciences |
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Atmospheric and Oceanic Physics physics.ao-ph Artificial Intelligence cs.AI Machine Learning cs.LG FOS Physical sciences FOS Computer and information sciences Ali, Sahara Huang, Yiyi Huang, Xin Wang, Jianwu Sea Ice Forecasting using Attention-based Ensemble LSTM |
topic_facet |
Atmospheric and Oceanic Physics physics.ao-ph Artificial Intelligence cs.AI Machine Learning cs.LG FOS Physical sciences FOS Computer and information sciences |
description |
Accurately forecasting Arctic sea ice from subseasonal to seasonal scales has been a major scientific effort with fundamental challenges at play. In addition to physics-based earth system models, researchers have been applying multiple statistical and machine learning models for sea ice forecasting. Looking at the potential of data-driven sea ice forecasting, we propose an attention-based Long Short Term Memory (LSTM) ensemble method to predict monthly sea ice extent up to 1 month ahead. Using daily and monthly satellite retrieved sea ice data from NSIDC and atmospheric and oceanic variables from ERA5 reanalysis product for 39 years, we show that our multi-temporal ensemble method outperforms several baseline and recently proposed deep learning models. This will substantially improve our ability in predicting future Arctic sea ice changes, which is fundamental for forecasting transporting routes, resource development, coastal erosion, threats to Arctic coastal communities and wildlife. : Accepted by the Tackling Climate Change with Machine Learning Workshop at the 2021 International Conference on Machine Learning (ICML 2021) |
format |
Article in Journal/Newspaper |
author |
Ali, Sahara Huang, Yiyi Huang, Xin Wang, Jianwu |
author_facet |
Ali, Sahara Huang, Yiyi Huang, Xin Wang, Jianwu |
author_sort |
Ali, Sahara |
title |
Sea Ice Forecasting using Attention-based Ensemble LSTM |
title_short |
Sea Ice Forecasting using Attention-based Ensemble LSTM |
title_full |
Sea Ice Forecasting using Attention-based Ensemble LSTM |
title_fullStr |
Sea Ice Forecasting using Attention-based Ensemble LSTM |
title_full_unstemmed |
Sea Ice Forecasting using Attention-based Ensemble LSTM |
title_sort |
sea ice forecasting using attention-based ensemble lstm |
publisher |
arXiv |
publishDate |
2021 |
url |
https://dx.doi.org/10.48550/arxiv.2108.00853 https://arxiv.org/abs/2108.00853 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic Climate change Sea ice |
genre_facet |
Arctic Climate change Sea ice |
op_rights |
Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 |
op_rightsnorm |
CC-BY |
op_doi |
https://doi.org/10.48550/arxiv.2108.00853 |
_version_ |
1766321869321928704 |