Prediction of Sea Ice Motion With Convolutional Long Short-Term Memory Networks

Zisis I. Petrou; Yingli Tian

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Prediction of Sea Ice Motion With Convolutional Long Short-Term Memory Networks

机译：卷积长短期记忆网络对海冰运动的预测

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摘要

Prediction of sea ice motion is important for safeguarding human activities in polar regions, such as ship navigation, fisheries, and oil and gas exploration, as well as for climate and ocean-atmosphere interaction models. Numerical prediction models used

机译：海冰运动的预测对于维护极地地区的人类活动（如船舶导航，渔业和油气勘探）以及气候和海洋-大气相互作用模型非常重要。使用的数值预测模型

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来源
《IEEE Transactions on Geoscience and Remote Sensing》 |2019年第9期|6865-6876|共12页
作者
Zisis I. Petrou; Yingli Tian;
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作者单位

Department of Electrical Engineering The City College The City University of New York New York NY USA;

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原文格式 PDF
正文语种 eng
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关键词
Sea ice; Predictive models; Arctic; Data models; Atmospheric modeling; Numerical models; Microwave radiometry;

机译：海冰;预测模型;北极;数据模型;大气模拟;数值模型;微波辐射;

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Prediction of Sea Ice Motion With Convolutional Long Short-Term Memory Networks

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