Impact of reduced non-Gaussianity on analysis and forecast accuracy by assimilating every-30 s radar observation with ensemble Kalman filter: idealized experiments of deep convection

Abstract. This study investigates the impact of very high frequency data assimilation on analysis and forecast accuracy with the local ensemble transform Kalman filter for idealized deep convection. Previous studies showed that assimilating every 30 s data from Phased Array Weather Radar (PAWR) alleviates the problem of strongly non-Gaussian error probability distribution due to rapid nonlinear evolution of deep convection in real-world cases. This study performs perfect model observing system simulation experiments to understand better the impact of assimilating radar reflectivity every 30 s focusing on non-Gaussianity. The idealized experimental settings have unique advantage in verifications for unobserved variables since it was unclear in the previous studies with real-world data. The results show that every 30 s data assimilation contributes to a significant improvement of the analysis accuracy, particularly for vertical velocity associated with strong convection, although the impact on the forecast accuracy is limited. We also find a significant reduction in the non-Gaussianity of first guess ensemble. The impact of assimilation frequency on reducing non-Gaussianity is enhanced when the uncertainty in background wind or stability is included in the initial ensemble perturbation.