Domain-Decomposed Lagrangian Data Assimilation for Drifting Sea-Ice Floe Dynamics

2026-02-20

AI Summary

This study addresses the challenge of accurately representing interactions among discrete ice floes in marginal ice zones, where traditional continuum sea ice models fall short and fully Lagrangian discrete-element approaches, while physically realistic, are computationally prohibitive for efficient ensemble data assimilation. To overcome this, the authors propose a scalable domain-decomposition data assimilation framework that partitions the Eulerian ocean flow field into subdomains, applies the Ensemble Transform Kalman Filter (ETKF) within each subdomain to assimilate Lagrangian floe observations, and reconstructs a globally consistent flow field via Gaussian-weighted boundary blending. This approach uniquely integrates domain decomposition with Lagrangian observations, balancing local fidelity and global coherence. Numerical experiments demonstrate superior performance over a high-cost global baseline method in terms of normalized root-mean-square error (NRMSE) and pattern correlation coefficient (PCC), confirming its effectiveness for efficient multiscale ocean flow reconstruction.

Technology Category

• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Intelligent Robots: State Estimation
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies

Abstract

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.

Problem

• sea-ice floe dynamics
• Lagrangian discrete element methods
• ensemble data assimilation
• marginal ice zone
• ocean flow recovery

Innovation

• Domain decomposition
• Lagrangian data assimilation
• Discrete element method
• Ensemble transform Kalman filter
• Sea-ice floe dynamics