Bridging Gaps in the Climate Observation Network: A Physics-based Nonlinear Dynamical Interpolation of Lagrangian Ice Floe Measurements via Data-Driven Stochastic Models

TL;DR

This paper introduces a physics-informed nonlinear stochastic modeling approach to accurately interpolate missing Lagrangian ice floe data, improving the understanding of Arctic sea ice dynamics despite observational noise.

Contribution

It develops a combined physics-based and data-driven nonlinear dynamical interpolation framework for recovering missing sea ice measurements with uncertainty quantification.

Findings

Successfully recovers floe locations and shapes in noisy satellite data

Accurately estimates floe thickness and underlying ocean fields

Provides a robust method for Arctic climate analysis

Abstract

Modeling and understanding sea ice dynamics in marginal ice zones relies on acquiring Lagrangian ice floe measurements. However, optical satellite images are susceptible to atmospheric noise, leading to gaps in the retrieved time series of floe positions. This paper presents an efficient and statistically accurate nonlinear dynamical interpolation framework for recovering missing floe observations. It exploits a balanced physics-based and data-driven construction to address the challenges posed by the high-dimensional and nonlinear nature of the coupled atmosphere-ice-ocean system, where effective reduced-order stochastic models, nonlinear data assimilation, and simultaneous parameter estimation are systematically integrated. The new method succeeds in recovering the locations, curvatures, angular displacements, and the associated strong non-Gaussian distributions of the missing floes in the Beaufort Sea. It also accurately estimates floe thickness and recovers the unobserved underlying ocean field with an appropriate uncertainty quantification, advancing our understanding of Arctic climate.