Data assimilation using ensemble transform Kalman filter (ETKF) in ROMS model for Indian Ocean

Nurujjaman, Md and Apte, A and Vinayachandran, P (2013) Data assimilation using ensemble transform Kalman filter (ETKF) in ROMS model for Indian Ocean. In: European Physical Journal Special Topics, 222 (3-4). pp. 875-883.

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Abstract

Study 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.

Item Type:	Journal Article
Publication:	European Physical Journal Special Topics
Publisher:	Springer Berlin Heidelberg
Additional Information:	Copyright of this article belongs to Springer Berlin Heidelberg.
Department/Centre:	Division of Mechanical Sciences > Centre for Atmospheric & Oceanic Sciences
Date Deposited:	19 Sep 2013 17:31
Last Modified:	19 Sep 2013 17:31
URI:	http://eprints.iisc.ac.in/id/eprint/47211

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