Bridging Gaps in the Climate Observation Network: A Physics-Based Nonlinear Dynamical Interpolation of Lagrangian Ice Floe Measurements via Data-Driven Stochastic Models.
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Wilhelmus, Monica M.
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Modeling and understanding sea ice dynamics in marginal ice zones rely on measurements of sea ice. Lagrangian observations of ice floes provide insight into the dynamics of sea ice, the ocean, and the atmosphere. 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 Be.....
JournalJournal of Advances In Modeling Earth Systems
Sustainable Development Goals (SDG)14.a
Maturity LevelPilot or Demonstrated
Spatial CoverageBeaufort Sea
CitationCovington, J., Chen, N. and Wilhelmus, M. M. (2022) Bridging Gaps in the Climate Observation Network: A Physics-Based Nonlinear Dynamical Interpolation of Lagrangian Ice Floe Measurements via Data-Driven Stochastic Models. Journal of Advances in Modeling Earth Systems, 14:3218, 23pp. DOI: https://doi.org/10.1029/2022MS003218
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