Comparisons of Hybrid En3DVar with 3DVar and EnKF for Radar Data Assimilation: Tests with the 10 May 2010 Oklahoma Tornado Outbreak

Abstract In this study, a hybrid En3DVar data assimilation (DA) scheme is compared with 3DVar, EnKF, and pure for the of radar in real tornadic storm case. Results using hydrometeor mixing ratios (CVq) or logarithmic (CVlogq) as control variables are variational DA framework. To address lack radial velocity impact issues when CVq, procedure that assimilates reflectivity two separate analysis passes adopted. Comparisons made terms root-mean-square innovations (RMSIs) well intensity structure analyzed forecast storms. For uses 100% ensemble covariance, CVlogq CVq have similar RMSIs analyses, but errors grow faster during forecasts CVlogq. Introducing static background error covariance at 5% (with CVlogq) significantly reduces growth. Pure produces more intense analyses than EnKF closely match observations. Hybrid 50% outperforms other weights updraft helicity thus used final comparison 3DVar EnKF. The found to outperform better capturing storms 3DVAR evolution rotating updraft.