%0 Journal Article
%T The Development of a Hybrid EnKF-3DVAR Algorithm for Storm-Scale Data Assimilation
%A Jidong Gao
%A Ming Xue
%A David J. Stensrud
%J Advances in Meteorology
%D 2013
%I Hindawi Publishing Corporation
%R 10.1155/2013/512656
%X A hybrid 3DVAR-EnKF data assimilation algorithm is developed based on 3DVAR and ensemble Kalman filter (EnKF) programs within the Advanced Regional Prediction System (ARPS). The hybrid algorithm uses the extended alpha control variable approach to combine the static and ensemble-derived flow-dependent forecast error covariances. The hybrid variational analysis is performed using an equal weighting of static and flow-dependent error covariance as derived from ensemble forecasts. The method is first applied to the assimilation of simulated radar data for a supercell storm. Results obtained using 3DVAR (with static covariance entirely), hybrid 3DVAR-EnKF, and the EnKF are compared. When data from a single radar are used, the EnKF method provides the best results for the model dynamic variables, while the hybrid method provides the best results for hydrometeor related variables in term of rms errors. Although storm structures can be established reasonably well using 3DVAR, the rms errors are generally worse than seen from the other two methods. With two radars, the results from 3DVAR are closer to those from EnKF. Our tests indicate that the hybrid scheme can reduce the storm spin-up time because it fits the observations, especially the reflectivity observations, better than the EnKF and the 3DVAR at the beginning of the assimilation cycles. 1. Introduction The effective assimilation of radar data into a numerical weather prediction (NWP) model requires advanced data assimilation (DA) techniques, such as variational and ensemble Kalman filter methods. A three-dimensional variational (3DVAR) system, which includes a mass continuity equation and other appropriate model equations as weak constraints, has been developed in recent years [1¨C5]. This system was designed with special considerations for assimilating radar data into a convective-scale nonhydrostatic model¡ªthe Advanced Regional Prediction System (ARPS)¡ªand has been used to provide initial conditions for numerous real-time convective-scale data forecasts. These forecasts have been produced since 2008 using grid spacing that varied from 4 to 1£¿km for domains covering the entire continental United States as part of the NOAA Hazardous Weather Testbed (HWT) Spring Experiments [6, 7]. For the HWT Spring Experiments, Level-II radial velocity and reflectivity data from over 120 operational Weather Surveillance Radar-1988 Doppler (WSR-88D) radars were analyzed using the 3DVAR system, and ensemble forecasts were produced by adding additional initial condition perturbations to this 3DVAR analysis. The ARPS 3DVAR
%U http://www.hindawi.com/journals/amete/2013/512656/