Sub-Surface Ocean Structure from Satellite Surface Observations in the North Indian Ocean

In this study interior plus Surface Quasi-Geostrophic (isQG) methodology is used to reconstruct subsurface density anomaly using sea surface temperature (SST), sea surface salinity (SSS) and sea surface height (SSH). The study is carried out in the Arabian Sea and Bay of Bengal of the Northern India...

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Bibliographic Details
Published in:Marine geodesy 2021-11, Vol.44 (6), p.573-592
Main Authors: Mandal, Anup Kumar, Chaudhary, Aditya, Agarwal, Neeraj, Sharma, Rashmi
Format: Article
Language:English
Subjects:
Anomalies
Buoys
Density
Density anomaly profile
Density profiles
isQG
Ocean models
ocean-interior
Oceans
Root-mean-square errors
Salinity
Satellite observation
Satellite surfaces
Satellites
Sea surface
Sea surface temperature
Stability
Surface salinity
Surface stability
Surface temperature
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Summary:In this study interior plus Surface Quasi-Geostrophic (isQG) methodology is used to reconstruct subsurface density anomaly using sea surface temperature (SST), sea surface salinity (SSS) and sea surface height (SSH). The study is carried out in the Arabian Sea and Bay of Bengal of the Northern Indian ocean region. isQG is first tested for identical twin experiments, where the surface data and the stability profile (N 2 ) were taken from a numerical ocean model. The root mean square error (RMSE) between isQG and model density anomalies lie within the error bars of model density anomaly in most of the levels. The impact of expected errors in synthetic observations of SST and SSS on isQG retrieved density anomalies was studied and it is found that error in SSS results in greater RMSE in isQG density anomaly profile in the Arabian sea than in the Bay of Bengal. isQG method is then applied to the satellite observations of SST, SSS and SSH and retrieved density profiles are then compared with model outputs and in-situ observations from RAMA buoy for the year 2019. The comparison indicates seasonal dependency and effect of N 2 on the performance of isQG method.
ISSN:0149-0419
1521-060X
DOI:10.1080/01490419.2021.1974132