1 8A.1 The Relative Importance of Assimilating Radial Velocity and Reflectivity Data to Storm-Scale Analysis and Forecast Jidong Gao 1* , Guoqing Ge 1,3 , David J. Stensrud 2 , and Ming Xue 1,3 1 Center for Analysis and Prediction of Storms, University of Oklahoma, 2 National Severe Storm Laboratory/NOAA, 3 School of Meteorology, University of Oklahoma, Norman, OK 1. Introduction The NEXRAD (WSR-88D) Doppler radar network allows meteorologists to track severe weather events and provide better warning information to the public, ultimately saving lives and reducing property damage. However, the assimilation of such data into NWP models to provide physically consistent three-dimensional analyses and short-term forecasts has not been extensively explored. Since Doppler radar is the only operational instrument capable of providing observations of sufficient spatial and temporal resolution to capture convective-scale phenomena, the assimilation of reflectivity and velocity data from Doppler radars is vital to predicting ongoing convection and is part of the “warn on forecast” vision of the National Weather Service described in Stensrud et al. (2009). Several methods exist for the assimilation of * corresponding author address: Jidong Gao, 1 Center for Analysis and Prediction of Storms, University of Oklahoma, e-mail: jdgao@ou.edu radar data. Sun et al (1991) and Sun and Crook (1997, 1998) have shown that four-dimensional variation analysis (4DVAR) is an idealized approach to assimilate radar data. However, To assimilate radar data, 4DVAR has so far been limited to relatively simple model configurations, usually with warm-rain microphysics only (Sun 2005). Computational cost and strong nonlinearity with model physics, including ice microphysics, often causes difficulties in 4DVAR assimilation of radar data. Ensemble Kalman filter (EnKF) is another advanced method for assimilating radar data (Snyder and Zhang 2003; Zhang et al. 2004; Dowell et al. 2004; Tong and Xue 2005; Gao and Xue 2008). Caya et al. (2005) showed that EnKF and 4DVAR produce analyses of generally similar quality and computational cost. Though these two methods are advanced methods theoretically, they are rather expensive computationally, especially at the convection-resolving resolution. For realtime analysis and forecasting for convective weather, the three-dimensional (3DVAR)