A comparison between EnSRF and LETKF algorithms for convective-scale radar data assimilation in the scenario of OSSEs: Effects of nonlinear observation operator
Gang Zhao1,2 and Ming Xue1,2
Center for Analysis and Prediction of Storms1 and School of Meteorology2 University of Oklahoma, Norman Oklahoma 73072
The Local Ensemble Transform Kalman filter (LETKF) is developed with the Advanced Regional Prediction System (ARPS). This ARPS-LETKF system is assessed by assimilating the simulated radar observations in a super-cell storm Observation System Simulation Experiment (OSSE). The results show that it could assimilate radar observations effectively and produce the analysis which fits the truth and observations well. Then its performance was inter-compared with the existing ARPS-EnSRF system in the same OSSE with simulated radar observations. With their optimal localization radii, the performances from ARPS-EnSRF and ARPS-LETKF are close to each other when these DA systems reach the stable stage. In the beginning spin-up stage, if only radar radial wind observations are analyzed, the performances from LETKF and EnSRF are comparable. But when the radar reflectivity observations are assimilated, EnSRF outperforms LETKF with considerable differences during the spin-up stage. The biased differences in performances are caused by the effect of nonlinear observation forward operator. This conclusion was further supported by the results from a few additional experiments.