MT-IceNet - A Spatial and Multi-Temporal Deep Learning Model for Arctic Sea Ice Forecasting
| dc.contributor.author | Ali, Sahara | |
| dc.contributor.author | Wang, Jianwu | |
| dc.date.accessioned | 2023-10-06T18:33:36Z | |
| dc.date.available | 2023-10-06T18:33:36Z | |
| dc.date.issued | 2023-03-13 | |
| dc.description | 2022 IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT); Vancouver, WA, USA; 06-09 December 2022 | en_US |
| dc.description.abstract | 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 retrieved sea ice data from NSIDC as well as atmospheric and oceanic variables from ERA5 reanalysis product during 1979-2021, we show that our proposed model provides promising predictive performance for per-pixel SIC forecasting with up to 60% decrease in prediction error for a lead time of 6 months as compared to its state-of-the-art counterparts. | en_US |
| dc.description.sponsorship | This work is supported by NSF grants: CAREER: Big Data Climate Causality (OAC-1942714) and HDR Institute: HARP - Harnessing Data and Model Revolution in the Polar Regions (OAC-2118285). We thank Dr. Yiyi Huang (NASA Langley Research Lab) for her assistance in introducing the dataset. | en_US |
| dc.description.uri | https://ieeexplore.ieee.org/document/10062246 | en_US |
| dc.format.extent | 10 pages | en_US |
| dc.genre | conference papers and proceedings | en_US |
| dc.genre | preprints | en_US |
| dc.identifier | doi:10.13016/m2p1ni-dmgz | |
| dc.identifier.citation | Ali, Sahara, and Jianwu Wang. “MT-IceNet - A Spatial and Multi-Temporal Deep Learning Model for Arctic Sea Ice Forecasting.” In 2022 IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT), 1–10, 2022. https://doi.org/10.1109/BDCAT56447.2022.00009. | en_US |
| dc.identifier.uri | https://doi.org/10.1109/BDCAT56447.2022.00009 | |
| dc.identifier.uri | http://hdl.handle.net/11603/30014 | |
| dc.language.iso | en_US | en_US |
| dc.publisher | IEEE | en_US |
| dc.relation.isAvailableAt | The University of Maryland, Baltimore County (UMBC) | |
| dc.relation.ispartof | UMBC Information Systems Department Collection | |
| dc.relation.ispartof | UMBC Faculty Collection | |
| dc.relation.ispartof | UMBC Student Collection | |
| dc.relation.ispartof | UMBC Computer Science and Electrical Engineering Department | |
| dc.relation.ispartof | UMBC Joint Center for Earth Systems Technology (JCET) | |
| dc.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. | en_US |
| dc.title | MT-IceNet - A Spatial and Multi-Temporal Deep Learning Model for Arctic Sea Ice Forecasting | en_US |
| dc.type | Text | en_US |
| dcterms.creator | https://orcid.org/0000-0002-9933-1170 | en_US |