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LETKF-ROMS: An improved predictability system for the Indian Ocean

Balaji, B and Banerjee, Deep Sankar and Paul, Biswamoy and Sivareddy, S and Francis, PA and Chatterjee, A and Paul, Arya (2018) LETKF-ROMS: An improved predictability system for the Indian Ocean. Technical Report. INCOIS, Hyderabad.

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We have developed the assimilation scheme Local Ensemble Transform Kalman Filter (LETKF) and interfaced with the present basin-wide operational ROMS set-up ( 1/12 degree horizontal resolution ) that assimilates in-situ temperature and salinity from RAMA moorings, NIOT buoys and Argo floats. The system also assimilate satellite track data of sea-surface temperature from AMSR-E. The speciality of this assimilation system is that it comprises of ensembles that are initialized with different model coefficients like diffusion parameters and the ensemble members also respond to two different mixing schemes - K profile parameterization and Mellor-Yamada. This aids in maintaining the spread of the ensemble intact - which has always been a challenging task. We have also employed a localization radius of ~200 km, i.e., observations influence the prognostic state variables that fall within this range. The assimilation system is also bestowed with better representative error estimates - a method developed in-house along the likes of Etherton et al. The ensemble members were forced with ensemble atmospheric fluxes provided by National Centre for Medium Range Weather Forecast (NCMRWF). Assimilation was performed every five day. We show that the assimilated system simulates the ocean state better than the present operational basin-wide ROMS. We validate it extensively against multiple observations ranging from RAMA moorings to ADCP observations across both dependent variables like temperature and salinity and independent variables like sealevel anomaly and currents. We show that assimilation improves the overall ocean state except at few isolated locations. It improves the correlation with respect to observations and reduces the root-mean-squared error. We also show that assimilation improves the estimation of mixed layer depth and 20 degree isotherm (which are diagnostic variables) thereby proving that the subsurface conditions are better simulated.

Item Type: Monograph (Technical Report)
Additional Information: Copyright of this report belongs to Indian National Centre for Ocean Information Services (INCOIS)
Uncontrolled Keywords: Data Assimilation, Local Ensemble Transform Kalman Filter, Regional Ocean Modeling System, Indian Ocean
Subjects: Oceanography > oceanography
Depositing User: INCOIS Library
Date Deposited: 13 Aug 2018 09:07
Last Modified: 19 Nov 2018 11:26

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