MT-IceNet - A Spatial and Multi-Temporal Deep Learning Model for Arctic Sea Ice Forecasting ...
2022 IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT); Vancouver, WA, USA; 06-09 December 2022 ... : Arctic amplification has altered the climate patterns both regionally and globally, resulting in more frequent and more intense extreme weather events in...
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ftdatacite:10.13016/m2p1ni-dmgz 2023-12-03T10:15:57+01:00 MT-IceNet - A Spatial and Multi-Temporal Deep Learning Model for Arctic Sea Ice Forecasting ... Ali, Sahara Wang, Jianwu 2023 https://dx.doi.org/10.13016/m2p1ni-dmgz https://mdsoar.org/handle/11603/30014 en eng IEEE © 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. article CreativeWork 2023 ftdatacite https://doi.org/10.13016/m2p1ni-dmgz 2023-11-03T10:37:33Z 2022 IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT); Vancouver, WA, USA; 06-09 December 2022 ... : Arctic amplification has altered the climate patterns both regionally and globally, resulting in more frequent and more intense extreme weather events in the past few decades. The essential part of Arctic amplification is the unprecedented sea ice loss as demonstrated by satellite observations. Accurately forecasting Arctic sea ice from sub-seasonal to seasonal scales has been a major research question 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 approaches to study sea ice variations, we propose MT-IceNet – a UNet-based spatial and multi-temporal (MT) deep learning model for forecasting Arctic sea ice concentration (SIC). The model uses an encoder-decoder architecture with skip connections and processes multi-temporal input streams to regenerate spatial maps at future timesteps. Using bi-monthly and monthly satellite ... Article in Journal/Newspaper Arctic 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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2022 IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT); Vancouver, WA, USA; 06-09 December 2022 ... : Arctic amplification has altered the climate patterns both regionally and globally, resulting in more frequent and more intense extreme weather events in the past few decades. The essential part of Arctic amplification is the unprecedented sea ice loss as demonstrated by satellite observations. Accurately forecasting Arctic sea ice from sub-seasonal to seasonal scales has been a major research question 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 approaches to study sea ice variations, we propose MT-IceNet – a UNet-based spatial and multi-temporal (MT) deep learning model for forecasting Arctic sea ice concentration (SIC). The model uses an encoder-decoder architecture with skip connections and processes multi-temporal input streams to regenerate spatial maps at future timesteps. Using bi-monthly and monthly satellite ... |
format |
Article in Journal/Newspaper |
author |
Ali, Sahara Wang, Jianwu |
spellingShingle |
Ali, Sahara Wang, Jianwu MT-IceNet - A Spatial and Multi-Temporal Deep Learning Model for Arctic Sea Ice Forecasting ... |
author_facet |
Ali, Sahara Wang, Jianwu |
author_sort |
Ali, Sahara |
title |
MT-IceNet - A Spatial and Multi-Temporal Deep Learning Model for Arctic Sea Ice Forecasting ... |
title_short |
MT-IceNet - A Spatial and Multi-Temporal Deep Learning Model for Arctic Sea Ice Forecasting ... |
title_full |
MT-IceNet - A Spatial and Multi-Temporal Deep Learning Model for Arctic Sea Ice Forecasting ... |
title_fullStr |
MT-IceNet - A Spatial and Multi-Temporal Deep Learning Model for Arctic Sea Ice Forecasting ... |
title_full_unstemmed |
MT-IceNet - A Spatial and Multi-Temporal Deep Learning Model for Arctic Sea Ice Forecasting ... |
title_sort |
mt-icenet - a spatial and multi-temporal deep learning model for arctic sea ice forecasting ... |
publisher |
IEEE |
publishDate |
2023 |
url |
https://dx.doi.org/10.13016/m2p1ni-dmgz https://mdsoar.org/handle/11603/30014 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic Sea ice |
genre_facet |
Arctic Sea ice |
op_rights |
© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
op_doi |
https://doi.org/10.13016/m2p1ni-dmgz |
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1784262828462440448 |