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dc.contributor.authorNurujjaman, M.
dc.contributor.authorApte, A.
dc.contributor.authorVinayachandran, P.
dc.identifier.citationEuropean Physical Journal: Special Topics, 2013, Vol.222, 43924, pp.875-883en_US
dc.description.abstractStudy of Oceans dynamics and forecast is crucial as it influences the regional climate and other marine activities. Forecasting oceanographic states like sea surface currents, Sea surface temperature (SST) and mixed layer depth at different time scales is extremely important for these activities. These forecasts are generated by various ocean general circulation models (OGCM). One such model is the Regional Ocean Modelling System (ROMS). Though ROMS can simulate several features of ocean, it cannot reproduce the thermocline of the ocean properly. Solution to this problem is to incorporates data assimilation (DA) in the model. DA system using Ensemble Transform Kalman Filter (ETKF) has been developed for ROMS model to improve the accuracy of the model forecast. To assimilate data temperature and salinity from ARGO data has been used as observation. Assimilated temperature and salinity without localization shows oscillations compared to the model run without assimilation for India Ocean. Same was also found for u and v-velocity fields. With localization we found that the state variables are diverging within the localization scale. 2013 EDP Sciences and Springer.en_US
dc.titleData assimilation using Ensemble Transform Kalman Filter (ETKF) in ROMS model for Indian Oceanen_US
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