What is already known:
What this study adds:
Knowledge of Antarctic weather and climate processes relies heavily on models due to the lack of observations over the continent. The Antarctic Mesoscale Prediction System (AMPS) is a numerical model capable of resolving finer-scale weather phenomena. The Antarctic’s unique geography, with a large ocean surrounding a circular continent containing complex terrain makes fine-scale processes potentially very important features in poleward moisture transport and the mass balance of Antarctica’s ice sheets. AMPS currently uses the 3DVAR method to produce atmospheric analyses (AMPS-3DVAR), which may not be well-suited for data-sparse regions like the Antarctic and Southern Ocean. To optimally account for flow-dependence and data sparseness unique to this region, we test the application of an ensemble adjustment Kalman Filter (EAKF) within the framework of the Data Assimilation Research Testbed (DART) and AMPS model (A-DART). We test the hypothesis that the application of A-DART improves the AMPS-3DVAR estimate of the atmosphere. We perform a test using a one-month period from 21 September - 21 October 2010 and find comparable results to both AMPS-3DVAR and GFS. In particular, we find a strong cold model bias near the surface and a warm model bias at upper-levels. Investigation of the surface bias reveals strongly biased land-surface observations while the warm bias at upper-levels is likely a circulation bias from the model warming too rapidly aloft over the continent. Increasing quality control of surface observations and assimilating polar-orbiting satellite data are expected to alleviate these issues in future tests.