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The Ensemble Adjustment Kalman Filter (EAKF) of the Data AssimilationResearch Testbed (DART) is implemented to assimilate observations of satellitesea surface temperature, altimeter sea surface height and in situ oceantemperature and salinity profiles into an eddy-resolving 4 km MassachusettsInstitute of Technology general circulation model (MITgcm) of the Red Sea. Weinvestigate 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, addsensemble of atmospheric forcing to Iexp, and (3) IAPexp, adds perturbed modelphysics to IAexp. The assimilation experiments are run for one year, startingfrom the same initial ensemble and assimilating data every three days. Resultsdemonstrate that the Iexp mainly improved the model outputs with respect toassimilation free MITgcm run in the first few months, before showing signs ofdynamical imbalances in the ocean estimates, particularly in the data-sparsesubsurface layers. The IAexp yielded substantial improvements throughout theassimilation period with almost no signs of imbalances, including thesubsurface layers. It further well preserved the model mesoscale featuresresulting in an improved forecasts for eddies, both in terms of intensity andlocation. Perturbing model physics in IAPexp slightly improved the forecaststatistics and also the placement of basin scale eddies. Increasinghydrographic coverage further improved the results of IAPexp compared to IAexpin the subsurface layers. Switching off multiplicative inflation in IAexp andIAPexp leads to further improvements, especially in the subsurface layers.

Impact of Atmospheric and Model Physics Perturbations On a High-Resolution Ensemble Data Assimilation System of the Red Sea