面向南极海冰密集度预测的季节感知混合卷积-Transformer模型

中文摘要 AI 辅助

南极海冰密集度（SIC）预测是一项重要但极具挑战性的任务，原因在于其同时存在复杂的空间结构、长程时间依赖关系以及强烈的季节变异性。传统基于卷积的模型能有效捕捉局部空间模式，但对长期时间演化的建模能力往往有限。为应对这些挑战，我们构建了一种混合卷积-Transformer预测框架，用于南极SIC的月度预测。该框架结合卷积编码进行空间特征提取，与因子化自注意力机制用于时空依赖关系建模。我们进一步引入两种季节先验机制：一种是感知月份的位置编码，将日历月份信息注入令牌表示；另一种是季节时间偏置，鼓励模型关注具有周期性关联的历史状态。实验结果表明，所提出的框架在分类和回归指标上均优于卷积和循环基线模型。消融研究进一步显示，季节先验机制在短期和长期预测中均能提供持续的额外增益。这些结果证明了将卷积结构、注意力机制和周期性先验信息相结合对南极SIC预测的价值。

英文摘要

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 space-time factorised self-attention for SIC modelling. We further introduce two season-based 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 as well as ECMWF's physics-based dynamical model SEAS5 under both classification and regression metrics. Ablation studies further indicate that the seasonality-aware components 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.

发表机构

• School of Computer Science, Australian National University（澳大利亚国立大学计算机科学学院）
• Yau Mathematical Sciences Center, Tsinghua University（清华大学丘成桐数学科学中心）

机构由 AI 辅助整理，请以论文原文为准。