  The Ensemble Adjustment Kalman Filter (EAKF) of the Data Assimilation
Research Testbed (DART) is implemented to assimilate observations of satellite
sea surface temperature, altimeter sea surface height and in situ ocean
temperature and salinity profiles into an eddy-resolving 4 km Massachusetts
Institute of Technology general circulation model (MITgcm) of the Red Sea. We
investigate the impact of three different ensemble generation strategies (1)
Iexp, uses ensemble of ocean states to initialize the model on 1st January,
2011 and inflates filter error covariance by 10 percent, (2) IAexp, adds
ensemble of atmospheric forcing to Iexp, and (3) IAPexp, adds perturbed model
physics to IAexp. The assimilation experiments are run for one year, starting
from the same initial ensemble and assimilating data every three days. Results
demonstrate that the Iexp mainly improved the model outputs with respect to
assimilation free MITgcm run in the first few months, before showing signs of
dynamical imbalances in the ocean estimates, particularly in the data-sparse
subsurface layers. The IAexp yielded substantial improvements throughout the
assimilation period with almost no signs of imbalances, including the
subsurface layers. It further well preserved the model mesoscale features
resulting in an improved forecasts for eddies, both in terms of intensity and
location. Perturbing model physics in IAPexp slightly improved the forecast
statistics and also the placement of basin scale eddies. Increasing
hydrographic coverage further improved the results of IAPexp compared to IAexp
in the subsurface layers. Switching off multiplicative inflation in IAexp and
IAPexp leads to further improvements, especially in the subsurface layers.
