Greg Hakim

The performance of a pseudo-operational limited-area ensemble Kalman filter (EnKF) system, based on the Weather Research and Forecasting model over a two year period is described here. This system assimilates conventional observations from surface stations, rawinsondes, ACARS and cloud motion vectors each six hours on a domain that includes the eastern Pacific Ocean and western North America. Ensemble forecasts from this system and deterministic output from other operational numerical weather prediction models during this same period are verified against rawinsonde and surface observation data in the domain. In comparison to operational forecasts, the ensemble-mean from this system has slightly higher errors in wind and temperature, but lower errors in moisture, even though satellite data is not considered. Time-average correlations computed from the ensemble members indicate that assimilating ACARS and cloud wind data with flow-dependent error statistics can correct the moisture field. Comparison of the EnKF forecasts with a WRF forecast that is cycled without observation assimilation and the GFS analysis indicate that the skill in the EnKF's forecasts results from assimilating observations and not from lateral boundary conditions or the model formulation. In addition, the ensemble variance is generally in good agreement with the ensemble-mean error and unlike other EnKF systems, the spread increases with forecast hour.