Season-Aware Hybrid Convolutional-Transformer for Antarctic Sea Ice Concentration Forecasting

2026-08-31

AI Summary

本文针对南极海冰浓度预测问题，提出了一种结合卷积结构和Transformer的混合模型，并引入了季节先验机制以提高预测性能。

Abstract

Antarctic sea ice concentration (SIC) forecasting is an important yet challenging task due to the coexistence of complex spatial structure, long-range temporal dependencies, and strong seasonal variability. Conventional convolution-based models are effective at capturing local spatial patterns, but often have limited ability to model long-term temporal evolution. To address these challenges, we build on a hybrid Convolutional-Transformer forecasting framework for monthly Antarctic SIC forecasting. This framework combines convolutional encoding for spatial feature extraction with factorised self-attention for spatio-temporal dependency modelling. We further introduce two seasonal prior mechanisms: a month-aware positional encoding that injects calendar-month information into the token representation, and a seasonal temporal bias that encourages attention to periodically related historical states. Experimental results show that the proposed framework achieves better performance than convolutional and recurrent baselines across both classification and regression metrics. Ablation studies further indicate that the seasonal prior mechanisms provide consistent additional gains in both short- and long-horizon prediction. These results demonstrate the value of combining convolutional structures, attention mechanisms, and periodic prior information for Antarctic SIC forecasting.

Problem

• Antarctic Sea Ice Concentration
• forecasting
• spatial structure
• temporal dependencies
• seasonal variability

Innovation

• Hybrid Convolutional-Transformer
• Seasonal Prior Mechanisms
• Spatio-temporal Dependency Modelling