Swain D.

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  • Article
    Estimation of ocean subsurface thermal structure from surface parameters : a neural network approach
    (American Geophysical Union, 2004-10-22) Ali, M. M. ; Swain, D. ; Weller, Robert A.
    Satellite remote sensing provides diverse and useful ocean surface observations. It is of interest to determine if such surface observations can be used to infer information about the vertical structure of the ocean's interior, like that of temperature profiles. Earlier studies used either sea surface temperature or dynamic height/sea surface height to infer the subsurface temperature profiles. In this study we have used neural network approach to estimate the temperature structure from sea surface temperature, sea surface height, wind stress, net radiation, and net heat flux, available from an Arabian Sea mooring from October 1994 to October 1995, deployed by the Woods Hole Oceanographic Institution. On the average, 50% of the estimations are within an error of ±0.5°C and 90% within ±1.0°C. The average RMS error between the estimated temperature profiles and in situ observations is 0.584°C with a depth-wise average correlation coefficient of 0.92.
  • Article
    Estimation of mixed-layer depth from surface parameters
    (Sears Foundation for Marine Research, 2006-09) Swain, D. ; Ali, M. M. ; Weller, Robert A.
    Mixed layer depth (MLD) is an important oceanographic parameter. However, the lack of direct observations of MLD hampers both specification and investigation of its spatial and temporal variability. An important alternative to direct observation would be the ability to estimate MLD from surface parameters easily available from satellites. In this study, we demonstrate estimation of MLD using Artificial Neural Network methods and surface meteorology from a surface mooring in the Arabian Sea. The estimated MLD had a root mean square error of 7.36 m and a coefficient of determination (R2) of 0.94. About 67% (91%) of the estimates lie within ± 5 m (± 10 m) of the MLD determined from temperature sensors on the mooring.