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

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

A study of forecast sensitivity to observations in the Bay of Bengal using LETKF

IntroductionAssimilating all available observations in numerical models may lead to deterioration of the analysis. Ensemble Forecast Sensitivity to Observations (EFSO) is a method that helps to identify all such observations which benefit the analyses. EFSO has never been tested in an ocean data assimilation system because of a lack of robust formulation of a squared norm against which beneficiality of observations can be estimated.MethodsHere, we explore the efficacy of EFSO in the ocean data assimilation system that comprises the ocean model, Regional Ocean Modeling System (ROMS), coupled to the assimilation system Local Ensemble Transform Kalman Filter (LETKF), collectively called LETKF- ROMS, in the Bay of Bengal by envisaging a novel squared norm. The Bay of Bengal is known for its higher stratification and shallow mixed layer depth. In view of baroclinicity representing the stratification of the ocean, we use the modulus of the baroclinic vector as the squared norm to evaluate forecast errors in EFSO.ResultsUsing this approach, we identify beneficial observations. Assimilating only the beneficial observations greatly improves the ocean state. We also show that the improvements are more pronounced in the head of the Bay of Bengal where stratification is much higher compared to the rest of the basin.DiscussionThough this approach doesn’t degrade the ocean state in other regions of the Indian Ocean, a universal squared norm is needed that can be extended beyond the Bay of Bengal basin