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

TL;DR

This paper introduces a scalable, domain-decomposed data assimilation framework using ensemble transform Kalman filters for Lagrangian sea-ice floe models, improving ocean flow recovery accuracy.

Contribution

It presents a novel domain-decomposed DA method coupling Lagrangian observations with ETKF, enhancing scalability and accuracy in sea-ice floe dynamics modeling.

Findings

Improved skill scores over baseline methods.

Better NRMSE and PCC metrics.

Scalable approach for particle-based sea-ice modeling.

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.