Benefits of Smoothing Backgrounds and Radar Reflectivity Observations for Multiscale Data Assimilation with an Ensemble Kalman Filter at Convective Scales: A Proof-of-Concept Study

Abstract

Abstract In the ensemble Kalman filter (EnKF), the covariance localization radius is usually small when assimilating radar observations because of high density of the radar observations. This makes the region away from precipitation difficult to correct using only radar data stating “no echo” if no other observations are available, as there is no reason to correct the background. To correct errors away from innovating radar observations, a multiscale localization (MLoc) method adapted to dense observations like those from radar is proposed. In this method, different scales are corrected successively by using the same reflectivity observations, but with a different degree of smoothing and localization radius at each step. In the context of observing system simulation experiments, single and multiple assimilation experiments are conducted with the MLoc method. Results show that the MLoc assimilation updates areas that are away from the innovative observations and improves on average the analysis and forecast quality in single cycle and cycling assimilation experiments. The forecast gains are maintained until the end of the forecast period, illustrating the benefits of correcting different scales.