Arctic Sea Ice Concentration Prediction Using Spatial Attention Deep Learning

IF 4.7 2区地球科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Pub Date : 2024-10-24 DOI:10.1109/JSTARS.2024.3486187

Haoqi Gu;Lianchong Zhang;Mengjiao Qin;Sensen Wu;Zhenhong Du

引用次数: 0

Abstract

With the accelerating impact of global warming, the changes of Arctic sea ice has become a focal point of research. Due to the spatial heterogeneity and the complexity of its evolution, long-term prediction of Arctic sea ice remains a challenge. In this article, a spatial attention U-Net (SAU-Net) method integrated with a gated spatial attention mechanism is proposed. Extracting and enhancing the spatial features from the historical atmospheric and SIC data, this method improves the accuracy of Arctic sea ice prediction. During the test periods (2018–2020), our method can skillfully predict the Arctic sea ice up to 12 months, outperforming the naive U-Net, linear trend models, and dynamical models, especially in extreme sea ice scenarios. The importance of different atmospheric factors affecting sea ice prediction are also analyzed for further exploration.

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利用空间注意力深度学习预测北极海冰浓度

随着全球变暖影响的加速，北极海冰的变化已成为研究的焦点。由于北极海冰的空间异质性及其演变的复杂性，对其进行长期预测仍是一项挑战。本文提出了一种集成了门控空间注意力机制的空间注意力 U-Net （SAU-Net）方法。该方法从历史大气和 SIC 数据中提取并增强空间特征，提高了北极海冰预测的准确性。在测试期间（2018-2020 年），我们的方法可以熟练预测长达 12 个月的北极海冰，优于天真 U-Net、线性趋势模型和动力学模型，尤其是在极端海冰情况下。此外，还分析了不同大气因素对海冰预测的重要影响，以供进一步探讨。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 地学-成像科学与照相技术

CiteScore

9.30

自引率

10.90%

发文量

563

审稿时长

4.7 months

期刊介绍： The IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing addresses the growing field of applications in Earth observations and remote sensing, and also provides a venue for the rapidly expanding special issues that are being sponsored by the IEEE Geosciences and Remote Sensing Society. The journal draws upon the experience of the highly successful “IEEE Transactions on Geoscience and Remote Sensing” and provide a complementary medium for the wide range of topics in applied earth observations. The ‘Applications’ areas encompasses the societal benefit areas of the Global Earth Observations Systems of Systems (GEOSS) program. Through deliberations over two years, ministers from 50 countries agreed to identify nine areas where Earth observation could positively impact the quality of life and health of their respective countries. Some of these are areas not traditionally addressed in the IEEE context. These include biodiversity, health and climate. Yet it is the skill sets of IEEE members, in areas such as observations, communications, computers, signal processing, standards and ocean engineering, that form the technical underpinnings of GEOSS. Thus, the Journal attracts a broad range of interests that serves both present members in new ways and expands the IEEE visibility into new areas.