Tests of an Ensemble Kalman Filter for Mesoscale and Regional-scale Data Assimilation: Real Data Application in Comparison to 3DVar

The feasibility of using an ensemble Kalman filter (EnKF) for mesoscale and regional-scale data assimilation has been demonstrated in the authors' recent studies via observing system simulation experiments (OSSEs) both under a perfect-model assumption and in the presence of significant model error. This study extends the EnKF to assimilate real-data observations for a warm-season mesoscale convective vortex (MCV) event of 10-12 June 2003 and a month-long test for June 2003. Direct comparison between the EnKF and a three-dimensional variational (3DVar) data assimilation system, both implemented in the Weather Research and Forecasting model (WRF), is carried out. It is found that the EnKF performs consistently better than the 3DVar method by assimilating either individual or multiple data sources (i.e., sounding, surface and wind profiler) for the MCV event and in the month-long experiment, which is performed by assimilating 12-hourly in-situ sounding data. Proper covariance inflation and using different combinations of physical parameterization schemes in different ensemble members (the so-called “multi-scheme” ensemble) can significantly improve the EnKF performance. The benefit of using a mutli-scheme ensemble (over a single-scheme ensemble) is more pronounced in the thermodynamic variables (including temperature and moisture) than the wind fields. Results also show that the EnKF seems to benefit from using both an ensemble-based prior estimate and a flow-dependent background error covariance. On average, the EnKF analyses lead to more accurate forecasts than 3DVar analyses when they are used to initialize 60 consecutive, deterministic 72-h forecast experiments for the month. Results also show that deterministic forecasts of up to 3 days initiated from the EnKF analyses consistently outperform the forecasts initiated from the National Centers for Envionmental Prediction final analysis field of the Global Forecast System.