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Ocean Subsurface Fields

Satellite data has been used to reconstruct ocean interior density and velocity anomalies in the Bay of Bengal through the “interior + surface Quasigeostrophic” (isQG) method. The inputs are sea surface height anomaly (AVISO), sea surface density anomaly; which is calculated using GHRSST sea surface temperature and SMAP sea surface salinity. One more input is the Brunt-Vaisala frequency, calculated from in-situ analysis system (ISAS) climatological data. The results show that isQG retrieved subsurface density anomalies are very promising compared to RAMA buoy data in the cold season when EKE is minimum. Validation of retrieved density has also been performed using ARGO data which reveals that isQG is more promising when the stratification is weak .

Data Access

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Data Version

  • Version 1.0 (beta)

Data Sources

  • GHRSST sea surface temperature (SST) (https://www.ghrsst.org/).
  • SMAP sea surface salinity (SSS) (https://smap.jpl.nasa.gov/data/).
  • AVISO sea surface height (SSH) (https://www.aviso.altimetry.fr/en/data.html).
  • ISAS-13 temperature/salinity profile (https://annuaire.ifremer.fr/cv/16058).

Processing Steps

  • Climatological temperature, salinity (T/S) fields are calculated from ISAS-13 data. Also Brunt-Viasala frequency(N) is calculated.
    Monthly Sea surface density anomaly(SSDA) is calculated from GHRSST, SMAP and ISAS-Climatology.
    Monthly sea surface height anomaly(SSHA) is calculated using AVISO data.
    Using interior + surface Quasi-Geostrophic(isQG) methodology, taking Barotropic and first Baroclinic modes, and taking SSHA and SSDA as boundary conditions, ocean interior density and velocity anomaly are reconstructed. 

References

  • Liu, L., S. Peng, and R. X. Huang (2017), Reconstruction of ocean’s interior from observed sea surface information, J. Geophys. Res. Oceans, 122, 1042– 1056, doi:10.1002/2016JC011927.
    Wang, J., G. Flierl, J. LaCasce, J. McClean, and A. Mahadevan (2013), Reconstructing the ocean’s interior from surface data, J. Phys. Oceanogr., 43, 1611–1626, doi:10.1175/JPO-D-12-0204.1.
    Scott, R. B., and D. G. Furnival, 2012: Assessment of traditional and new eigen-function bases applied to extrapolation of surface geostrophic current time series to below the surface in an idealized primitive equation simulation. J. Phys. Oceanogr., 42, 165–178.
    Smith, K. S., and G. K. Vallis, 2001: The scales and equilibration of mid-ocean eddies: Freely evolving flow. J. Phys. Oceanogr., 31, 554–571.
    Pedlosky, J., 1987: Geophysical Fluid Dynamics. 2nd ed. Springer Verlag, 710 pp.
    Lapeyre, G., 2009: What vertical mode does the altimeter reflect? On the decomposition in baroclinic modes and on a surface trapped mode. J. Phys. Oceanogr., 39, 2857–2874.
    P. Klein, 2006: Dynamics of the upper oceanic layers in terms of surface quasi-geostrophy theory. J. Phys. Oceanogr., 36, 165–176.

Derivation Techniques and Algorithm

  • The algorithm is called interior + surface Quasi-Geostrophic(isQG) method. User should refer doi:10.1175/JPO-D-12-0204.1.

Limitations

  • This method is only applicable in a region where the Coriolis parameter does not change abruptly. Also it is not applicable on the equator.
    Vertical movement is ignored in isQG framework.
    isQG method works well when the eddy kinetic energy (EKE) is low. If EKE is large, it will fail to generate accurate subsurface fields.
    We considered the Barotropic and the first Baroclinic modes only in our analysis. This certainly limits our approach.
    This method generates less satisfactory subsurface fields if the stratification is strong.
    The vertical resolution in our case is 10 m. This is a major drawback since in Bay of Bengal, the mixed layer can be as shallow as 15-20 m.

Known problems with data

  • Data problems due to bad weather (heavy rain) and extreme events like cyclones etc.

File Naming Convention

  • Netcdf file: isQG_yyyy.nc

MetaData

Sr. No Core Metadata Elements Definition
1 Metadata language English
2 Metadata Contact MOSDAC
3 Metadata date May, 2018
4 Data Lineage or Quality Sea surface height anomaly, Sea surface density anomaly in Bay of Bengal using isQG methodology.
5 Title
Reconstruction of Ocean interior density and horizontal velocity anomaly fields using Satellite data in Bay of Bengal
6 Abstract Satellite data has been used to reconstruct ocean interior density and velocity anomalies in the Bay of Bengal through the “interior + surface Quasigeostrophic” (isQG) method. The inputs are sea surface height anomaly (AVISO), sea surface density anomaly; which is calculated using GHRSST sea surface temperature and SMAP sea surface salinity. One more input is the Brunt-Vaisala frequency, calculated from in-situ analysis system (ISAS) climatological data. The results show that isQG retrieved subsurface density anomalies are very promising compared to RAMA buoy data in the cold season when EKE is minimum. Validation of retrieved density has also been performed using ARGO data which reveals that isQG is more promising when the stratification is weak .
7 Dataset Contact

 

Anup Kumar Mandal, OSD/AOSG/EPSA, Space Applications Centre (ISRO), Ahmedabad, 380015, anupmandal@sac.isro.gov.in

8 Update Frequency Six months.
9 Access Rights or Restriction Open Access
10 Spatial Resolution Spatial resolution is 25 km, while vertical resolution is 10m
11 Language English
12 Topic Category Ocean subsurface product (SAMUDRA Project) using satellite data.
13 Keywords Density anomaly, Velocity anomaly, subsurface fields, 3D-Ocean fields
14 Date or period January 2017 - till date
15 Responsible Party Anup Kumar Mandal,OSD/AOGG/ EPSA, Space Applications Centre (ISRO), Ahmedabad-380015, India
16 Organization Space Applications Centre (ISRO), Ahmedabad, India
16a Org. role Geophysical parameters from satellite data in the Bay of Bengal region.
16b Individual name Anup Kumar Mandal, OSD/AOSG/EPSA, SAC (ISRO), Ahmedabad-380015, India. Ph: +91 79 2691 6117. Email: anupmandal@sac.isro.gov.in
16c Position Scientist/Engineer, OSD/AOSG/EPSA, SAC (ISRO), Ahmedabad-380015
16d Vertical Extent (minimumValue, maximumValue, unitOfMeasure, vertical datum) Lat_min : 05N Lat_max : 25 N Lon_min: 75E Lon_max: 95 E
17 Geographic Extent Indian Landmass
18 Geographic name, geographic Identifier Bay of Bengal
19 Bounding box Lat_min : 05N Lat_max : 25 N Lon_min: 75E Lon_max: 95 E
20 Temporal Extent January 2017 till date
21 Access Rights or Restrictions Open Access
22 Distribution Information Online download of data files in netCDF format
23 Processing Level Level 4
4 Reference System Datum: WGS84