Assimilation of Radar Reflectivity Using a Time‐Lagged Ensemble Based Ensemble Kalman Filter With the "Cloud‐Dependent" Background Error Covariances. Issue 10 (19th May 2022)

• Record Type: Journal Article
• Authors: Wang, Haoliang; Liu, Yubao; Duan, Jing; Shi, Yueqin; Lou, Xiaofeng; Li, Jiming
• Is Part Of: Journal of geophysical research. Volume 127:Issue 10(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-05-19
• Subjects: Atmospheric physics -- Periodicals; Geophysics -- Periodicals; 551.5
• DOI: 10.1029/2021JD036207
• Languages: English
• ISSNs: 2169-897X

Abstract

Abstract: This 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. Plain Language Summary: With a cycled forecast system, for a given time, several forecasts are produced by the model initialized at different times. Such forecasts can serve as a time‐lagged ensemble, in which the differences in initial conditions among different forecast cycles are used in lieu of uncertainty estimates of the initial atmospheric state. In this study, we propose a method for direct assimilation of radar reflectivity data in cycled deterministic forecast systems, using a time‐lagged ensemble based ensemble Kalman filter. 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. We devised an algorithm to compute the specified "cloud‐dependent" background error covariances, 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 assimilation experiments indicate that the radar reflectivity data assimilation scheme can improve the analysis and short‐term forecast of the mesoscale convective system without significantly increasing computational cost compared to a cloud‐analysis based latent nudging method. Key Points: This paper describe a scheme to directly assimilate radar reflectivity data for cycled deterministic forecast systems Time‐lagged ensemble is used to compute the "cloud‐dependent" background error covariances This new radar reflectivity data assimilation scheme can improve the analysis and short‐term forecast of mesoscale convective system