1. An Efficient Ensemble Data Assimilation Approach and Tests with Doppler Radar Data Jidong Gao Ming Xue Center for Analysis and Prediction of Storms, University of Oklahoma, Norman

2. Research Goals To develop an efficient ensemble Kalman filter (EnKF) method for high-resolution NWP, by using a dual resolution approach. To evaluate the efficiency and accuracy of the method through OSSEs, with simulated radar radial velocity data for a supercell storm.

3. Introduction EnKF was first introduced by Evensen (1994) and has become very popular in recent years Recently, the EnKF method has been successfully applied to the radar data assimilation problem (e.g., Snyder and Zhang 2003; Zhang et al. 2004; Dowell et al. 2004; Tong and Xue 2005). Effective assimilation of radar data is essential for initializing convective-scale NWP models

4. Radar Data Assimilation The EnKF data assimilation method is especially suitable for radar data assimilation because –Radar only observes Vr and Z, and data coverage is usually incomplete –All other variables have to be ‘retrieved’ –EnKF ‘retrieves’ the unobserved variables via background error covariance obtained through a forecast ensemble But, EnKF is expensive, because of the need for running a usually rather large ensemble of forecasts and analyses

5. In this work, we propose a dual-resolution (DR) hybrid ensemble DA strategy, with the goal of improving the EnKF efficiency With the method, an ensemble of forecasts and analyses is run at a lower resolution (LR), while a single system of analysis and forecast is performed at a higher resolution (HR) The LR forecast ensemble provides estimated background error covariance for the HR analysis The HR forecast is used to replace or partially adjust the mean of the LR analysis ensemble The Methodology

6. LR EnKF Analysis HR EnKF Single higher-resolution analysis and forecast Lower-resolution analysis and forecast ensemble covariance replace mean covariance replace mean HR EnKF

7. OSSEs with a Simulated Supercell Storm A truth simulation is created using ARPS with the Del City supercell sounding, at  x = 2 km The model domain: 92 x 92 x 16 km 3. LR has  x=4 km, HR has  x=2 km  z = 500 m. Vr data collected at grid point locations are assimilated, at 5 min intervals 20 ensemble members are used

8. List of EnKF OSSEs ExperimentDescription EXP1Single-reslution EnKF at HR (2 km) EXP2Single-resolution EnKF at LR (4 km) EXP3Dual-resolution hybrid EnKF (2 & 4 km)

9. RMS Errors of the Analyses for the Three Experiments HR EnKF (EXP1) LR EnKF (EXP2) DR EnKF (EXP3)

10.  ’ (contours), Z(color shades) and V h (vectors) at Surface Truth EXP2 LR-EnKF EXP1 HR-EnKF EXP3 DR-EnKF

11.  ’, Z and V h at Surface after 80 min assimilation Truth EXP1 HR-EnKF EXP2 LR-EnKF EXP3 DR-EnKF

12. W at 6 km AGL after 80 min assimilation Truth EXP2 LR- EnKF EXP1 HR- EnKF EXP3 DR-EnKF

13. 2-h Forecasts of  ’, Z and V h at surface Truth EXP2 LR EXP1 HR EXP3 DR

14. 2-h Forecasts of w at 6 km AGL Truth EXP2 LR EXP1 HR EXP3 DR

15. Summary and Discussion A new efficient dual-resolution (DR) approach for EnKF is proposed and tested with simulated radar data for a supercell storm. It is shown that the EnKF analysis using DR is almost as good as the HR analysis, but is much better than the LR analysis. For this case, we save CPU 3-4 times. However, depending on the resolution one choose, the method have the potential to save CPU 10-50 times more than Original EnKF methods.

16. Summary and Discussion My new experiments: using Dx =Dy= 4km with model EnKF run, to provide error structure for Dx =Dy= 1km, single model run. The result is also very positive.