Journal Article

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

Chen, Ge, Sun, Miao, Chapron, Bertrand, Fenglin, Tian, Lguensat, Redouane, Fablet, Ronan, Viet, Phi Huynh

2019

Overview

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.

• Publisher: MDPI AG,MDPI
• Subject: Analog data / analog data assimilation / Analog models / Anomalies / Computer Science / Computer simulation / Data assimilation / Data collection / data-driven methods / Datasets / Earth science / Empirical analysis / Experiments / General circulation models / Interpolation / Inverse problems / Methods / Neural networks / Ocean, Atmosphere / Orthogonal functions / Parameter estimation / Principal components analysis / Rain / Reconstruction / Remote sensing / Root-mean-square errors / Sciences of the Universe / Sea level / sea level anomaly / sea surface height / Signal and Image Processing / Simulation / Weather forecasting