An Evaluation of Applying Ensemble Data Assimilation to an Antarctic Mesoscale Model

Knowledge of Antarctic weather and climate processes relies heavily on models because of the lack of observations over the continent and Southern Ocean. The Antarctic Mesoscale Prediction System (AMPS) is a numerical model capable of resolving finer-scale weather phenomena in the Antarctic area. The Antarctic's unique geography, with a large ocean surrounding a circular continent containing complex terrain, makes fine-scale processes potentially important features in poleward moisture transport and in 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 such as 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 the Global Forecasting System (GFS). In particular, we find a strong cold model bias near the surface and a warm model bias at upper-levels, which is shown from the analysis increment. Investigation of the surface bias reveals strongly biased land-surface observations while the warm bias at upper-levels is a potential 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.