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

Abstract

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 for sea ice motion prediction often require a large number of data from diverse sources with varying uncertainties. In this paper, a deep learning approach is proposed to predict sea ice motion for several days in the future, given only a series of past motion observations. The proposed approach consists of an encoder-decoder network with convolutional long short-term memory (LSTM) units. Optical flow is calculated from satellite passive microwave and scatterometer daily images covering the entire Arctic and used in the network. The network proves able to learn long-time dependencies within the motion time series, whereas its convolutional structure effectively captures spatial correlations among neighboring motion vectors. The approach is unsupervised and end-to-end trainable, requiring no manual annotation. Experiments demonstrate that the proposed approach is effective in predicting sea ice motion of up to 10 days in the future, outperforming previous deep learning networks and being a promising alternative or complementary approach to resource-demanding numerical prediction methods.