Assimilation of Water Vapor Retrievals from ZDR Columns Using the 3DVar Method for Improving the Short-Term Prediction of Convective Storms

Abstract

Abstract The differential reflectivity (ZDR) column is a notable polarimetric signature related to updrafts in deep moist convection. In this study, pseudo–water vapor (qυ) observations are retrieved from observed ZDR columns under the assumption that humidity is saturated within the convection where ZDR columns are detected, and are then assimilated within the 3DVar framework. The impacts of assimilating pseudo-qυ observations from ZDR columns on short-term severe weather prediction are first evaluated for a squall-line case. Radar data analysis indicates that the ZDR columns are mainly located on the inflow side of the high-reflectivity region. Assimilation of the pseudo-qυ observations leads to an enhancement of qυ within the convection, while concurrently reducing humidity in no-rain areas. Sensitivity experiments indicate that a tuned smaller observation error and a shorter horizontal decorrelation scale are optimal for a better assimilation of pseudo-qυ from ZDR columns, resulting in more stable rain rates during short-term forecasts. Additionally, a 15-min cycling assimilation frequency yields the best performance, providing the most accurate reflectivity forecast in terms of both location and intensity. Analysis of thermodynamic fields reveal that assimilating ZDR columns provides more favorable initial conditions for sustaining convection, including sustainable moisture condition, a strong cold pool, and divergent winds near the surface, consequently enhancing reflectivity and precipitation. With the optimal configuration determined from the sensitivity tests, a quantitative evaluation further demonstrates that assimilating the pseudo-qυ observations from ZDR columns using the 3DVar method can improve the 0–3-h reflectivity and accumulated precipitation predictions of convective storms.