[1] A 3-dimensional Princeton Ocean Model has been configured for the east coast of India, which is applied to study the transformation of the upper ocean's response in the near-coastal waters to the 1999 Orissa super cyclone in the Bay of Bengal at the time of landfall. It is well known that the sea surface temperature (SST) cooling is more towards the right of the storm track, because of the dominant wind stress forcing there. Consequently, the model simulated SSTs showed more cooling to the right of the storm track. However, it is found to be true only in the open ocean. The coastal dynamics transformed the scenario as the cyclone translated over the coastal waters owing to the impedance of the coast-line to the circulation and shifted the region of maximum surface cooling to the left of the cyclone track. The satellite imageries during the period also endorsed the model simulations. Citation:
Sea surface salinity (SSS) is one of the key components of the Earth's global water cycle. Reliable information on SSS is very important for ocean modelling, data assimilation, and ocean and climate research applications. In this study, SSS variability in the tropical Indian Ocean (TIO) was analysed using the Aquarius instrument on board the SAC-D satellite and in situ observations from the Research Moored Array for African-Asian-Australian Monsoon Analysis and Prediction (RAMA) buoys and Array for Real-Time Geostrophic Oceanography (ARGO) data sets for the period 2012-2013. Comparison of two recent versions (V2 and V3) of Aquarius-based SSS estimates to nine RAMA buoys on a daily timescale showed excellent mutual agreement. The systematic underestimation of SSS by satellite-based V2 products over the TIO shows a clear advantage for the new version product (V3). A larger root-meansquare error of the order of 0.50 psu in the satellite-based SSS was observed over the highly variable (larger standard deviation) Bay of Bengal region as compared with ARGO data sets. In the eastern equatorial Indian Ocean region, satellite-based SSS overestimated SSS below 34 psu and underestimated SSS of 34-35 psu as compared with ARGO data. However, the V3 SSS from Aquarius showed marginal improvement over V2 SSS. Monthly variation and fast Fourier analysis of the satellite-based SSS estimates are in reasonably good agreement with in situ observations which suggest that Aquarius is able to capture SSS variability in the TIO. The Aquarius-based V3 SSS showed a temporal autocorrelation of 0.6 over most parts of the TIO up to day 10, and decreased gradually with time. Overall analysis suggests that Aquarius-derived V3 SSS can detect variability in SSS satisfactorily in the TIO and is in reasonably good agreement with in situ observations.
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