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Enhanced Regional Ocean Ensemble Data Assimilation Through Atmospheric Coupling in the SKRIPS Model
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  • Rui Sun,
  • sivareddy sanikommu,
  • Aneesh Subramanian,
  • Matthew R. Mazloff,
  • Bruce Cornuelle,
  • Ganesh Gopalakrishnan,
  • Arthur J Miller,
  • Ibrahim Hoteit
Rui Sun
Scripps Institution of Oceanography

Corresponding Author:[email protected]

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sivareddy sanikommu
King Abdullah University of Science and Technology
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Aneesh Subramanian
University of Colorado
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Matthew R. Mazloff
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Bruce Cornuelle
Scripps Institution of Oceanography, University of California
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Ganesh Gopalakrishnan
Climate Atmospheric Science and physical Oceanography, Scripps Institution of Oceanography
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Arthur J Miller
Scripps Institution of Oceanography
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Ibrahim Hoteit
King Abdullah Univerity of Science and Technology
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We investigate the impact of ocean data assimilation using the Ensemble Adjustment Kalman Filter (EAKF) from the Data Assimilation Research Testbed (DART) on the oceanic and atmospheric states of the Red Sea. Our study extends the ocean data assimilation experiment performed by Sanikommu et al. (2020) by utilizing the SKRIPS model coupling the MITgcm ocean model and the Weather Research and Forecasting (WRF) atmosphere model. Using a 50-member ensemble, we assimilate satellite-derived sea surface temperature and height and in-situ temperature and salinity profiles every three days for one year, starting January 01 2011. Atmospheric data are not assimilated in the experiments. To improve the ensemble realism, perturbations are added to the WRF model using several physics options and the stochastic kinetic energy backscatter (SKEB) scheme. Compared with the control experiments using uncoupled MITgcm with ECMWF ensemble forcing, the EAKF ensemble mean oceanic states from the coupled model are better or insignificantly worse (root-mean-square-errors are 30% to -2% smaller), especially when the atmospheric model uncertainties are accounted for with stochastic perturbations. We hypothesize that the ensemble spreads of the air–sea fluxes are better represented in the downscaled WRF ensembles when uncertainties are well accounted for, leading to improved representation of the ensemble oceanic states in EAKF. Although the feedback from ocean to atmosphere is included in this two-way regional coupled configuration, we find no significant effect of ocean data assimilation on the latent heat flux and 10-m wind speed, suggesting the improved skill is from downscaling the ensemble atmospheric forcings.
08 Jan 2024Submitted to ESS Open Archive
18 Jan 2024Published in ESS Open Archive