Daily Prediction of the Arctic Sea Ice Concentration Using Reanalysis Data Based on a Convolutional LSTM Network
Daily Prediction of the Arctic Sea Ice Concentration Using Reanalysis Data Based on a Convolutional LSTM Network
复制标题
使用基于卷积 LSTM 网络的再分析数据每日预测北极海冰浓度
DOI：
10.3390/jmse9030330
复制
发表时间：
2021-03
影响因子：
2.9
通讯作者：
Hong Mei
中科院分区：
文献类型：
--
作者：
Liu Quanhong;Zhang Ren;Wang Yangjun;Yan Hengqian;Hong Mei
为满足北极东北航道日益增长的航行需求，提出了一种基于卷积长短期记忆网络（ConvLSTM）算法的海冰密集度日预报模型。以前，类似的深度学习算法（如卷积神经网络; CNN）经常用于预测海冰的月度变化。为了验证该模型的有效性，基于时空尺度，通过计算空间结构相似性、均方根误差和相关系数，比较了ConvLSTM和CNN模型。结果表明，在整个测试集中，ConvLSTM的单项预测效果优于CNN。以2018年12月15日为例，ConvLSTM在模拟东北航道海冰浓度的局部变化方面上级CNN，特别是在东西伯利亚海附近。最后，采用迭代预测方法对ConvLSTM和CNN的可预测性进行了分析，结果表明ConvLSTM的可预测性优于CNN。
To meet the increasing sailing demand of the Northeast Passage of the Arctic, a daily prediction model of sea ice concentration (SIC) based on the convolutional long short-term memory network (ConvLSTM) algorithm was proposed in this study. Previously, similar deep learning algorithms (such as convolutional neural networks; CNNs) were frequently used to predict monthly changes in sea ice. To verify the validity of the model, the ConvLSTM and CNNs models were compared based on their spatiotemporal scale by calculating the spatial structure similarity, root-mean-square-error, and correlation coefficient. The results show that in the entire test set, the single prediction effect of ConvLSTM was better than that of CNNs. Taking 15 December 2018 as an example, ConvLSTM was superior to CNNs in simulating the local variations in the sea ice concentration in the Northeast Passage, particularly in the vicinity of the East Siberian Sea. Finally, the predictability of ConvLSTM and CNNs was analysed following the iteration prediction method, demonstrating that the predictability of ConvLSTM was better than that of CNNs.