The study has been motivated by the desire to assess the performance of sea surface salinity (SSS) from the Soil Moisture and Ocean Salinity (SMOS) satellite launched by the European Space Agency. Daily Level 3 product on a 0.25 • × 0.25 • grid for the year 2010 has been used for this assessment in the Indian Ocean. Various data sets, like the in situ data sets available from the Research Moored Array for African-Asian-Australian Monsoon Analysis and Prediction (RAMA) buoys and Argo floats and also the data sets from modular ocean model version 3 simulations, have been utilized for this purpose. Comparison made at two buoy locations suggests good quality of SMOS SSS with root-mean-square error of the order of 0.36 and 0.34 psu. The triple collocation method, which explicitly takes into account the error characteristics of the SMOS, Argo, and model data sets, has been used for further validation of the SMOS data. Since the Indian Ocean exhibits characteristically different patterns of SSS in its different subregions, the study area has been divided into different such subregions. The SMOS-derived SSS appears to be of very good quality in the equatorial Indian Ocean and southern Indian Ocean, while the data are of poorer quality in the Arabian Sea and the Bay of Bengal possibly because of the errors in SSS retrieval due to the land contamination and strong winds. Index Terms-Argo floats, functional relationship (FR), modular ocean model (MOM) version 3 (MOM3) ocean model, Research Moored Array for African-Asian-Australian Monsoon Analysis and Prediction (RAMA) buoys, Soil Moisture and Ocean Salinity (SMOS) salinity, triple collocation.
The focus of the study is on the assessment of two different satellite-derived precipitation products in relation to the simulation of sea surface salinity (SSS) in the tropical Indian Ocean by an ocean general circulation model (OGCM). Precipitation derived from the National Centre for Environmental Prediction (NCEP) has also been used as a benchmark. The period of the simulations covers the years 2003-2007. A comparison with Argo floats suggests that both the satellite products are more effective than NCEP precipitation and a pure satellite product outperforms a merged product. High- and intermediate frequency variability of SSS has been found to be better captured by the satellite products. The equatorial low-salinity tongue formed during a dipole year has been found to be faithfully represented in simulation forced by either of the satellite-derived products. Possible causes for this low-salinity equatorial tongue and the high salinity near Sumatra coast during the dipole year have also been examined. Impact of satellite-derived precipitation has also been assessed in the distribution of barrier layer thickness in the tropical Indian Ocean in the winter months of 2007
Altimeter data have been assimilated in an ocean general circulation model using the water property conserving scheme. Two runs of the model have been conducted for the year 2004. In one of the runs, altimeter data have been assimilated sequentially, while in another run, assimilation has been suppressed. Assimilation has been restricted to the tropical Indian Ocean. An assessment of the strength of the scheme has been carried out by comparing the sea surface temperature (SST), simulated in the two runs, with in situ derived as well as remotely sensed observations of the same quantity. It has been found that the assimilation exhibits a significant positive impact on the simulation of SST. The subsurface effect of the assimilation could be judged by comparing the model simulated depth of the 20 • C isotherm (hereafter referred to as D20), as a proxy of the thermocline depth, with the same quantity estimated from ARGO observations. In this case also, the impact is noteworthy. Effect on the dynamics has been judged by comparison of simulated surface current with observed current at a moored buoy location, and finally the impact on model sea level forecast in a free run after assimilation has been quantified in a representative example.
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