Tests of an Ensemble Kalman Filter for Mesoscale and Regional-scale Data Assimilation. Part IV: Comparison with 3DVar in a Month-long Experiment

In previous works of this series study, an ensemble Kalman filter (EnKF) has been demonstrated to be promising for mesoscale and regional scale data assimilation in increasingly realistic environments. Parts I and II examined the performance of the EnKF by assimilating simulated observations under both perfect-and imperfect-model assumptions. Part III explored the application of the EnKF to a real-data case study in comparison to a three-dimensional variational data assimilation method (3DVar) in the Weather Research and Forecasting (WRF) model. The current study extends the single-case real-data experiments over a period of one month to examine the long-term performance and comparison of both methods at the regional scales. It is found that the EnKF systematically outperforms 3DVar for the one-month period of interest in which both methods assimilate the same standard sounding observations every 12 h over the central United States. Consistent with results from the real-data case study of Part III, the EnKF can benefit from using a mutli-scheme ensemble that partially accounts for model errors in physical parameterizations. 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. On average, the EnKF analyses lead to more accurate forecasts than the 3DVar analyses when they are used to initialize 60 consecutive, deterministic 60-h 3 forecast experiments for the month. Results also show that deterministic forecasts of up to 60 hours initiated from the EnKF analyses consistently outperform the WRF forecasts initiated from the National Centers for Environmental Prediction final analysis field of the Global Forecast System.