Sea Ice Forecasting using Attention-based Ensemble LSTM
Sea Ice Forecasting using Attention-based Ensemble LSTM
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DOI：
10.13016/m2gbqc-q97l
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发表时间：
2021-07
期刊：
影响因子：
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通讯作者：
Sahara Ali;Yiyi Huang;Xin Huang;Jianwu Wang
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文献类型：
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作者：
Sahara Ali;Yiyi Huang;Xin Huang;Jianwu Wang
准确预测北极海冰从亚季节到季节尺度一直是一项重大的科学工作，面临着根本性的挑战。除了基于物理学的地球系统模型外，研究人员还将多种统计和机器学习模型用于海冰预测。考虑到数据驱动的海冰预测的潜力，我们提出了一种基于注意力的长短期记忆（LSTM）集成方法来预测未来1个月的月海冰范围。使用来自NSIDC的每日和每月卫星检索海冰数据以及来自ERA5再分析产品的大气和海洋变量39年，我们表明我们的多时相集成方法优于几个基线和最近提出的深度学习模型。这将大大提高我们预测未来北极海冰变化的能力，这对于预测运输路线、资源开发、海岸侵蚀、对北极沿海社区和野生动物的威胁至关重要。
Accurately forecasting Arctic sea ice from subseasonal to seasonal scales has been a major scientific effort 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 sea ice forecasting, we propose an attention-based Long Short Term Memory (LSTM) ensemble method to predict monthly sea ice extent up to 1 month ahead. Using daily and monthly satellite retrieved sea ice data from NSIDC and atmospheric and oceanic variables from ERA5 reanalysis product for 39 years, we show that our multi-temporal ensemble method outperforms several baseline and recently proposed deep learning models. This will substantially improve our ability in predicting future Arctic sea ice changes, which is fundamental for forecasting transporting routes, resource development, coastal erosion, threats to Arctic coastal communities and wildlife.