每日北极海冰浓度演化的结构化神经建模：物理轨迹驱动学习与预报域自适应

中文摘要 AI 辅助

准确模拟海冰浓度（SIC）的每日演化对于提高基于深度学习的海冰预测的可信度和业务预报能力至关重要。然而，现有的深度学习方法往往将潜在的海冰演化关系和数据误差耦合在高维非线性映射中，难以构建稳定且可验证的演化核心，并将其可靠地应用于实际预报。为解决这一问题，本研究提出了一种再分析-预报双域解耦框架，用于学习海冰演化算子。该框架基于轻量级物理基线生成每日演化轨迹，采用时间约束的联合多步补偿网络补偿未解析过程，并引入冰质量感知输运机制抑制数值耗散。在预报阶段，基础演化核心的参数被固定，而轻量级变量语义自适应机制校准跨域分布和演化响应，从而将预报域误差与基础演化误差分离。实验表明，所构建的基础演化核心能够在再分析强迫下，在短期和年度尺度上准确且稳定地模拟每日海冰演化，并可通过轻量级自适应有效迁移到预报域，在保持基础演化结构的同时实现稳定的实际预报能力。源代码将在稿件被接收后于该https URL公开提供。

英文摘要

Accurate modeling of the daily evolution of sea ice concentration (SIC) is central to improving the credibility and operational forecasting capability of deep learning-based sea ice prediction. However, existing deep learning methods often couple the underlying sea ice evolution relationships and data errors within high-dimensional nonlinear mappings, making it difficult to construct a stable and verifiable evolution core and apply it reliably to practical forecasting. To address this issue, this study proposes a reanalysis-forecast dual-domain decoupled framework for learning sea ice evolution operators. The framework builds upon a lightweight physical baseline to generate daily evolution trajectories, employs a temporally constrained joint multi-lead compensation network to com?pensate for unresolved processes, and introduces an ice-mass?aware transport mechanism to suppress numerical dissipation. In the forecasting stage, the parameters of the base evolution core are fixed, while a lightweight variable-semantic adaptation mechanism calibrates inter-domain distributions and evolution responses, thereby separating forecast-domain errors from base evolution errors. Experiments show that the constructed base evolution core can accurately and stably simulate daily sea ice evolution at both short-term and annual scales under reanal?ysis forcing, and can be effectively transferred to the forecast domain through lightweight adaptation, achieving stable prac?tical forecasting capability while preserving the base evolution structure. The source code will be made publicly available at https://github.com/zhangmaqun65535/SNM upon acceptance of this manuscript.

发表机构

• Ocean University of China（中国海洋大学）
• University of Glasgow（格拉斯哥大学）

机构由 AI 辅助整理，请以论文原文为准。