Multi-task Deep Convolutional Network to Predict Sea Ice Concentration and Drift in the Arctic Ocean ...

Forecasting sea ice concentration (SIC) and sea ice drift (SID) in the Arctic Ocean is of great significance as the Arctic environment has been changed by the recent warming climate. Given that physical sea ice models require high computational costs with complex parameterization, deep learning tech...

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Bibliographic Details
Main Authors: Koo, Younghyun, Rahnemoonfar, Maryam
Format: Report
Language:unknown
Published: arXiv 2023
Subjects:
Machine Learning cs.LG
Computer Vision and Pattern Recognition cs.CV
Atmospheric and Oceanic Physics physics.ao-ph
FOS Computer and information sciences
FOS Physical sciences
Arctic
Arctic Ocean
Sea ice
Online Access:https://dx.doi.org/10.48550/arxiv.2311.00167
https://arxiv.org/abs/2311.00167
Description
Summary:Forecasting sea ice concentration (SIC) and sea ice drift (SID) in the Arctic Ocean is of great significance as the Arctic environment has been changed by the recent warming climate. Given that physical sea ice models require high computational costs with complex parameterization, deep learning techniques can effectively replace the physical model and improve the performance of sea ice prediction. This study proposes a novel multi-task fully conventional network architecture named hierarchical information-sharing U-net (HIS-Unet) to predict daily SIC and SID. Instead of learning SIC and SID separately at each branch, we allow the SIC and SID layers to share their information and assist each other's prediction through the weighting attention modules (WAMs). Consequently, our HIS-Unet outperforms other statistical approaches, sea ice physical models, and neural networks without such information-sharing units. The improvement of HIS-Unet is obvious both for SIC and SID prediction when and where sea ice ...

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