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...
| Main Authors: | , |
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| Format: | Article in Journal/Newspaper |
| Language: | English |
| Published: |
IEEE
2023
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| Subjects: | |
| Online Access: | https://dx.doi.org/10.13016/m2p1ni-dmgz https://mdsoar.org/handle/11603/30014 |
| Summary: | 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 ... |
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