A Comparison of 3DEnVar and 4DEnVar for Convective-Scale Direct Radar Reflectivity Data Assimilation in the Context of Filter and Smoother

Abstract

Abstract The Gridpoint Statistical Interpolation (GSI)-based four- and three-dimensional ensemble–variational (4DEnVar and 3DEnVar) methods are compared as a smoother and filter, respectively, for rapidly changing storms using the convective-scale direct radar reflectivity data assimilation (DA) framework. Two sets of experiments with varying DA window lengths (WLs; 20, 40, 100, and 160 min) and radar observation intervals (RIs; 20 and 5 min) are conducted for the 5–6 May 2019 case. The RI determines the temporal resolution of ensemble perturbations for the smoother and the DA interval for the filter spanning the WL. For experiments with a 20-min RI, evaluations suggest that filter and smoother have comparable performance with a 20-min WL; however, extending the WL results in the outperformance of filter over smoother. Diagnostics reveal that the degradation of smoother is attributed to the increased degree of nonlinearity and the issue of time-independent localization as the WL extends. Evaluations for experiments with different RIs under the same WL indicate that the outperformance of filter over smoother diminishes for most forecast hours at thresholds of 30 dBZ and above when shortening the RI. Diagnostics show that more frequent interruptions of the model introduce model imbalance for the filter, and the increased temporal resolution of ensemble perturbations enhances the degree of nonlinearity for the smoother. The impact of model imbalance on the filter overwhelms the enhanced nonlinearity on the smoother as the RI reduces.