The Tropospheric Biennial Oscillation (TBO), a major interannual
variation phenomenon in the Indo-Pacific region,
is the result of strong ocean-atmosphere coupling over the
Asian-Australian monsoon area. Along with other meteorological
and oceanographic parameters, the tropical circulation
also exhibits interannual oscillations. Even though the
TBO is the result of strong air–sea interaction, the circulation
cells during TBO years are, as yet, not well understood. In
the present study, an attempt has been made to understand
the interannual variability of the mean meridional circulation
and local monsoon circulation over south Asia in connection
with the TBO. The stream function computed from the zonal
mean meridional wind component of NCEP=NCAR reanalysis
data for the years 1950–2003 is used to represent the
meanmeridional circulation. Mean meridional mass transport
in the topics reverses from a weak monsoon to a strong monsoon
in the presence of ENSO, but in normal TBO yearsmean
transport remains weak across the Northern Hemisphere.
The meridional temperature gradient, which drives the mean
meridional circulation, also shows no reversal during the
normal TBO cycle. The local Hadley circulation over the
monsoon area follows the TBO cycle with anomalous ascent
(descent) in strong (weak) monsoon years. During normal
TBO years, the Equatorial region and Indian monsoon areas
exhibit opposite local Hadley circulation anomalies
Mohan Kumar, K; Supriya, M H; Saseendran Pillai, P R(IEEE, 2009)
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Abstract:
The paper investigates the feasibility of implementing an intelligent
classifier for noise sources in the ocean, with the help of artificial neural networks,
using higher order spectral features. Non-linear interactions between the component
frequencies of the noise data can give rise to certain phase relations called Quadratic
Phase Coupling (QPC), which cannot be characterized by power spectral analysis.
However, bispectral analysis, which is a higher order estimation technique, can
reveal the presence of such phase couplings and provide a measure to quantify such
couplings. A feed forward neural network has been trained and validated with higher
order spectral features