Ensemble based regional ocean data assimilation system for the Indian Ocean: Implementation and evaluation
Online Publication Date2019-09-19
Print Publication Date2019-11
Embargo End Date2021-09-19
Permanent link to this recordhttp://hdl.handle.net/10754/658628
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AbstractA high-resolution ocean circulation model for the Indian Ocean (IO) using Regional Ocean Modeling System (ROMS) is operational at Indian National Centre for Ocean Information Services (INCOIS) which provides ocean state forecasts for the Bay of Bengal (BoB) and the Arabian Sea (AS) to the Indian Ocean rim countries. To provide an improved estimate of ocean state, a variant of Ensemble Kalman Filter (EnKF), viz., the Local Ensemble Transform Kalman Filter (LETKF) has been developed and interfaced with the present basin-wide operational ROMS. This system assimilates in-situ temperature and salinity profiles and satellite track data of sea-surface temperature (SST). The ensemble members of the assimilation system are initialized with different parameters like diffusion and viscosity coefficients and are subjected to an ensemble of atmospheric fluxes. In addition, one half of the ensemble members respond to K profile parameterization mixing scheme while the other half is subjected to Mellor–Yamada mixing scheme. This strategy aids in arresting the filter divergence which has always been a challenging task. The assimilated system simulates the ocean state better than the present operational ROMS. Improvements permeate to deeper ocean depths with better correlation and reduced root-mean-squared deviation (RMSD) with respect to observations particularly in the northern Indian Ocean which is data rich in density. Analysis shows domain averaged RMSD reduction of about 0.2–0.4 °C in sea surface temperature and 2–4 cm in sea level anomaly. The assimilated system also manages to significantly improve the thickness of the temperature inversion layers and the duration of its occurrence in northern Bay of Bengal. The most profound improvements are seen in currents, with an error reduction of 15 cm/s in zonal currents of central Bay of Bengal.
CitationBaduru, B., Paul, B., Banerjee, D. S., Sanikommu, S., & Paul, A. (2019). Ensemble based regional ocean data assimilation system for the Indian Ocean: Implementation and evaluation. Ocean Modelling, 143, 101470. doi:10.1016/j.ocemod.2019.101470
SponsorsFunded by Ministry of Earth Sciences (MoES). SSR and AP acknowledge the training on LETKF-MOM by Prof. Eugenia Kalnay and her team Dr. Travis Sluka and Dr. Steve Penny at the University of Maryland under the Monsoon Mission-I project funded by Ministry of Earth Sciences (MoES), Govt. of India. The data assimilation system is configured as a part of the Ocean - Modeling, Data Assimilation and Process SpeCific ObservaTions (O-MASCOT) programme coordinated by the Indian National Centre for Ocean Information Services (INCOIS) and funded by the Ministry of Earth Sciences (MoES), Govt. of India (Ref: MoES/36/OOIS/O-MASCOT/2017). The authors wish to thank Dr. Kunal Chakraborty for providing the boundary condition for ROMS and Dr. Francis Pavanathara and Dr. Abhisek Chatterjee for setting up the operational model O-ROMS. Authors also thank the OSF team, INCOIS for providing the ocean state forecasts from the present operational ROMS against which LETKF-ROMS is compared with. The authors would also like to thank Dr. Munmun Das Gupta for providing the ocean observations from NCMRWF and the data management team from INCOIS for providing the data. All the experiments were conducted on the high performance computer Aditya, IITM, Pune, India. The support from Aditya-HPC team is highly appreciated. MATLAB was used for generating the figures. This is INCOIS contribution number 355.