Enhanced Regional Ocean Ensemble Data Assimilation Through Atmospheric
Coupling in the SKRIPS Model
Abstract
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.