Abstract
The algorithms based on Surface Quasi-Geostrophic (SQG) dynamics have been developed and validated by many researchers through model products, however it is still doubtful whether these SQG-based algorithms are worth using in terms of observed data. This paper analyzes the factors impeding the practical application of SQG and makes amends by a simple “first-guess (FG) framework”. The proposed framework includes the correction of satellite salinity and the estimation of the FG background, making the SQG-based algorithms applicable in realistic circumstances. The dynamical-statistical method SQG-mEOF-R is thereafter applied to satellite data for the first time. The results are compared with two dynamical algorithms, SQG and isQG, and three empirical algorithms, multivariate linear regression (MLR), random forest (RF), and mEOF-R. The validation against Argo profiles showed that the SQG-mEOF-R presents a robust performance in mesoscale reconstruction and outperforms the other five algorithms in the upper layers. It is promising that the SQG-mEOF-R and the FG framework are applicable to operational reconstruction.
Highlights
The satellite can provide a broad view on the ocean, while only the sea surface can be observed
The NWP is characterized by the Kuroshio current, where mesoscale eddies are ubiquitous, while the South-East Pacific (SEP) is dominated by the Peru–Chile upwelling where there exist abundant subsurface-intensified eddies [44]
This paper proposed an FG scheme to carry out Surface Quasi-Geostrophic (SQG)-based algorithms in the practical application, based on which the dynamical-statistical algorithm SQG-mEOF-R was validated by observed data for the first time
Summary
The satellite can provide a broad view on the ocean, while only the sea surface can be observed. Argo profiles can reach as deep as 2000 m but are sparsely and loosely distributed. It is an important issue to project the satellite data into the interior of the ocean. This projection extends the conventional remote sensing and can be nominated as subsurface and deeper ocean remote sensing [1]. The empirical modes can represent multi-scale signals, the modes at one exact grid cannot be computed since Argo profiles are irregularly distributed. The other approach is to perform surface-subsurface regression
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