Short-term Forecasting of Sea Ice Thickness Based on PredRNN++

• Published in: Journal of Physics: Conference Series
• Main Authors: Liu, Quanhong, Zhang, Ren, Wang, Yangjun, Yan, Hengqian, Xu, Jing, Guo, Yutong
• Format: Article in Journal/Newspaper
• Language: unknown
• Published: IOP Publishing 2023
• Subjects: Arctic; Sea ice
• Online Access: http://dx.doi.org/10.1088/1742-6596/2486/1/012017; https://iopscience.iop.org/article/10.1088/1742-6596/2486/1/012017; https://iopscience.iop.org/article/10.1088/1742-6596/2486/1/012017/pdf

Abstract The navigational potential of the Arctic shipping routes is gradually emerging under the trend of melting Arctic sea ice. However, the opening of the Arctic shipping routes still faces many difficulties, especially the complexity of sea ice changes and the navigational safety risks caused by the uncertainty of the sea ice forecast. In recent years, the deep learning method has emerged in sea ice forecasting due to its powerful non-linear fitting capability. In this paper, from the perspective of combining deep learning methods with expertise in meteorology and oceanography, an improved predictive recurrent neural network (PredRNN++) model is applied to sea ice thickness (SIT) forecasting for the first time. In this study, the short-term forecast (1-3 days) of SIT was realized, and the predictability was tested, confirming the effect of reasonable factor selection and screening on SIT forecasting.