Unicorn: U-Net for Sea Ice Forecasting with Convolutional Neural Ordinary Differential Equations ...

• Main Authors: Park, Jaesung, Hong, Sungchul, Cho, Yoonseo, Jeon, Jong-June
• Format: Article in Journal/Newspaper
• Language: unknown
• Published: arXiv 2024
• Subjects: Artificial Intelligence cs.AI; Atmospheric and Oceanic Physics physics.ao-ph; FOS Computer and information sciences; FOS Physical sciences; North Pole; Sea ice
• Online Access: https://dx.doi.org/10.48550/arxiv.2405.03929; https://arxiv.org/abs/2405.03929

Sea ice at the North Pole is vital to global climate dynamics. However, accurately forecasting sea ice poses a significant challenge due to the intricate interaction among multiple variables. Leveraging the capability to integrate multiple inputs and powerful performances seamlessly, many studies have turned to neural networks for sea ice forecasting. This paper introduces a novel deep architecture named Unicorn, designed to forecast weekly sea ice. Our model integrates multiple time series images within its architecture to enhance its forecasting performance. Moreover, we incorporate a bottleneck layer within the U-Net architecture, serving as neural ordinary differential equations with convolution operations, to capture the spatiotemporal dynamics of latent variables. Through real data analysis with datasets spanning from 1998 to 2021, our proposed model demonstrates significant improvements over state-of-the-art models in the sea ice concentration forecasting task. It achieves an average MAE improvement ...