Data-driven Interpolation of Sea Level Anomalies using Analog Data Assimilation

Abstract : Despite the well-known limitations of Optimal Interpolation (OI), it remains the conventional method to interpolate Sea Level Anomalies (SLA) from altimeter-derived along-track data. In consideration of the recent developments of data-driven methods as a means to better exploit large-scale observation , simulation and reanalysis datasets for solving inverse problems, this study addresses the improvement of the reconstruction of higher-resolution SLA fields using analog strategies. The reconstruction is stated as an ana-log data assimilation issue, where the analog models rely on patch-based and EOF-based representations to circumvent the curse of dimensionality. We implement an Observation System Simulation Experiment in the South China sea. The reported results show the relevance of the proposed framework with a significant gain in terms of root mean square error for scales below 100km. We further discuss the usefulness of the proposed analog model as a means to exploit high-resolution model simulations for the processing and analysis of current and future satellite-derived altimetric data.
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Redouane Lguensat, Phi Huynh Viet, Miao Sun, Ge Chen, Tian Fenglin, et al.. Data-driven Interpolation of Sea Level Anomalies using Analog Data Assimilation. 2017. ⟨hal-01609851⟩

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