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Open AccessArticle

Data-Driven Interpolation of Sea Level Anomalies Using Analog Data Assimilation

1
IGE, Université Grenoble Alpes, CNRS, IRD, Grenoble INP, 38000 Grenoble, France
2
IMT Atlantique, Lab-STICC UMR CNRS 6285, UBL, 29200 Brest, France
3
Key Laboratory of Digital Ocean, National Marine Data and Information Service, Tianjin 300171, China
4
Department of Marine Information Technology, Ocean University of China, Qingdao 266100, China
5
Laboratoire d’Océanographie Physique et Spatiale, IFREMER, 29200 Brest, France
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(7), 858; https://doi.org/10.3390/rs11070858
Received: 21 January 2019 / Revised: 4 April 2019 / Accepted: 4 April 2019 / Published: 9 April 2019
(This article belongs to the Special Issue Assimilation of Remote Sensing Data into Earth System Models)
From 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 Sea Level Anomaly (SLA) fields using analog strategies. This reconstruction is stated as an analog data assimilation issue, where the analog models rely on patch-based and Empirical Orthogonal Functions (EOF)-based representations to circumvent the curse of dimensionality. We implement an Observation System Simulation Experiment (OSSE) 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 (RMSE) for scales below 100 km. 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 with regard to conventional interpolation schemes, especially optimal interpolation. View Full-Text
Keywords: analog data assimilation; sea level anomaly; sea surface height; interpolation; data-driven methods analog data assimilation; sea level anomaly; sea surface height; interpolation; data-driven methods
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MDPI and ACS Style

Lguensat, R.; Viet, P.H.; Sun, M.; Chen, G.; Fenglin, T.; Chapron, B.; Fablet, R. Data-Driven Interpolation of Sea Level Anomalies Using Analog Data Assimilation. Remote Sens. 2019, 11, 858.

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