Sea ice dynamics are crucial to the global climate system, yet traditional continuum (e.g., viscous-plastic) models often fail to represent the discrete floe interactions that dominate in the marginal ice zone. Lagrangian discrete element methods (DEMs) resolve floe-scale physics more realistically, but their high particle counts make ensemble data assimilation (DA) more expensive. We consider a highly-simplified floe model and propose a scalable, domain-decomposed DA framework that couples Lagrangian particle observations with an ensemble transform Kalman filter (ETKF) to recover the underlying ocean flow field in a multiscale setting. The Eulerian domain is first partitioned into subdomains. We then impose an ETKF in each subdomain to recover the local fine-scale ocean features. A Gaussian-weighted blending step then reconstructs a globally consistent flow field across subdomain boundaries. Numerical experiments demonstrate consistently better skill scores that are characterised by normalised root mean square error (NRMSE) and pattern correlation coefficients (PCC), compared to the global and expensive DA baseline. Results suggest that the domain-decomposed DA method is an alternative, scalable approach for particle-based sea-ice floe dynamics and ocean flow recovery.

翻译：海冰动力学对全球气候系统至关重要，然而传统的连续介质（例如，粘塑性）模型通常无法表征在边缘冰区占主导地位的离散浮冰相互作用。拉格朗日离散元方法能够更真实地解析浮冰尺度的物理过程，但其高粒子数使得集合数据同化的计算成本高昂。我们考虑一个高度简化的浮冰模型，并提出一个可扩展的域分解数据同化框架，该框架将拉格朗日粒子观测与集合变换卡尔曼滤波器耦合，以在多尺度设置中恢复底层海洋流场。首先将欧拉域划分为多个子域。随后在每个子域中施加一个集合变换卡尔曼滤波器，以恢复局部的精细尺度海洋特征。接着通过一个高斯加权融合步骤，跨子域边界重建一个全局一致的流场。数值实验表明，与全局且计算昂贵的基准数据同化方法相比，该方法在归一化均方根误差和模式相关系数表征的技能评分上持续表现更优。结果表明，域分解数据同化方法是基于粒子的海冰浮冰动力学及海洋流场恢复的一种可扩展的替代方法。