A Data Assimilation Case Study Using a Limited-Area Ensemble Kalman Filter

Abstract

Ensemble Kalman filter (EnKF) data assimilation experiments are conducted on a limited-area domain over the Pacific Northwest region of the United States, using the Weather Research and Forecasting model. Idealized surface pressure, radiosoundings, and aircraft observations are assimilated every 6 h for a 7-day period in January 2004. The objectives here are to study the performance of the filter in constraining analysis errors with a relatively inhomogeneous, sparse-observation network and to explore the potential for such a network to serve as the basis for a real-time EnKF system dedicated to the Pacific Northwest region of the United States. When only a single observation type is assimilated, results show that the ensemble-mean analysis error and ensemble spread (standard deviation) are significantly reduced compared to a control ensemble without assimilation for both observed and unobserved variables. Analysis errors are smaller than background errors over nearly the entire domain when averaged over the 7-day period. Moreover, comparisons of background errors and observation increments at each assimilation step suggest that the flow-dependent filter corrections are accurate in both scale and amplitude. An illustrative example concerns a misspecified mesoscale 500-hPa short-wave trough moving along the British Columbia coast, which is corrected by surface pressure observations alone. The relative impact of each observation type upon different variables and vertical levels is also discussed.