Assimilation of Radar Reflectivity Using a Time‐Lagged Ensemble Based Ensemble Kalman Filter With the “Cloud‐Dependent” Background Error Covariances

Abstract

AbstractThis study proposes a method for direct assimilation of radar reflectivity in cycled deterministic forecast systems using a time‐lagged ensemble based ensemble Kalman filter with the “cloud‐dependent” background error covariances. With this method, forecasts from multiple deterministic forecast cycles that are initialized at different times but valid at the same time, are collected as a time‐lagged ensemble to compute the background error covariances. An algorithm to compute the specified “cloud‐dependent” background error covariances was devised, in which the vertical background error covariance is computed for each of the “cloud‐feature” bins which are divided according to the column maximum radar reflectivity and cloud top height. The single‐observation experiments and the observing system simulation experiments (OSSEs) indicate that the radar reflectivity data assimilation method was able to capture the main thermodynamic and microphysical features of convective clouds and was effective in reducing the spin‐up time of convection, and generating convective cells which were missed in the background. Meanwhile, assimilating “zero‐reflectivity” observations helped suppress spurious convection. The method provided overall more accurate thermodynamic analysis, radar reflectivity and precipitation analysis and forecast of the MCS, while did not significantly increase computational cost, compared to a cloud‐analysis based latent‐heat nudging approach.