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[1] Ensemble optimal interpolation technique has been used to blend winds for the years 2011-2012, derived by OSCAT, a scatterometer onboard the satellite Oceansat-2, with winds from two different numerical weather prediction (NWP) centers, namely, National Center for Environmental Prediction (NCEP) and European Centre for Medium Range Weather Forecast (ECMWF). These blended winds, when compared with buoy winds, show higher correlations and lower root-mean-square errors (RMSE) than the standalone NWP winds. Analysis of divergence and curl of wind stresses also suggests that the blending is physically realistic. Comparisons of blended winds with buoy winds and analysis of divergence and curls as also the subsequent spectral analysis show that the blended ECMWF wind is the best among all the wind products. Consequent assessment of the impact of blended ECMWF wind product has been done using an ocean general circulation model (OGCM). The technique of empirical orthogonal function (EOF) analysis has been used to assess the impact on sea level and surface currents. The dominant modes of simulated variability in the case of forcing by blended winds are found to be more faithfully reproducing the well-known features of global ocean circulation. Validation of the depth of the 20 C isotherm (D20) also establishes the relative superiority of the blended wind product.

Singular Evolutive Extended Kalman (SEEK) filter has been used to assimilate Aquarius-derived sea surface salinity (SSS) in a near-global ocean general circulation model (OGCM). Advanced Very High Resolution Radiometer (AVHRR)-derived sea surface temperature (SST) has also been assimilated in conjunction. The primary aim of the study is to investigate the improvement in simulation of global ocean surface currents as a result of this assimilation. The route of empirical orthogonal function (EOF) analysis has been taken for an efficient assessment of this impact separately in the space and time domains and satellitederived surface current has been used as a benchmark. As expected, the assimilation has been found to impart significant positive impact in both the domains. Also, joint assimilation of SSS and SST has been found to be better than standalone SSS assimilation. These results have been further corroborated by a comparison with buoy-derived surface currents. Further emphasis has been laid on the simulation of Wyrtki and monsoon jets in the equatorial Indian Ocean, because of their importance in the climate of this region and again it has been found that assimilation guides the simulation toward realism in both the cases. Finally, impact on the SSS and SST fronts and their zonal displacements in the western Pacific has been investigated and here again the assimilation has led to an improvement in simulation of these features.

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Sea surface salinity (SSS) from Aquarius mission and sea surface temperature (SST) from Advanced Very High Resolution Radiometer (AVHRR) for the years 2012–2014 are assimilated into the global Massachusetts Institute of Technology General Circulation Model (MITGCM). Investigation of the impact of assimilation of these two data sets on simulated mixed layer depth (MLD) and barrier layer thickness (BLT) forms the core of our study. The method of assimilation is the Singular Evolutive Extended Kalman (SEEK) filter. Several assimilation runs are performed. Single‐parameter assimilation, as well as joint assimilation, is conducted. To begin with, the model simulated SST and SSS are compared with independent Argo observations of these two parameters. Use of latitudinally varying error variances, which is a novel feature of our study, gives rise to the significant improvement in the simulation of SSS and SST. The best result occurs when joint assimilation is performed. Afterward, simulated MLD and BLT are compared with the same parameters derived from Argo observations forming an independent validation data set. Comparisons are performed both in temporal and spatial domains. Significant positive impact of assimilation is found in all the cases studied, and joint assimilation is found to outperform single‐parameter assimilation in each of the cases considered. It is found that simulations of MLD and BLT improve up to 24% and 29%, respectively, when a joint assimilation of SSS and SST is carried out.