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

The differential reflectivity ( Z DR ) column is a notable polarimetric signature related to updrafts in deep moist convection. In this study, pseudo–water vapor ( q υ ) observations are retrieved from observed Z DR columns under the assumption that humidity is saturated within the convection where Z DR columns are detected, and are then assimilated within the 3DVar framework. The impacts of assimilating pseudo- q υ observations from Z DR columns on short-term severe weather prediction are first evaluated for a squall-line case. Radar data analysis indicates that the Z DR columns are mainly located on the inflow side of the high-reflectivity region. Assimilation of the pseudo- q υ observations leads to an enhancement of q υ 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- q υ from Z DR 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 Z DR 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- q υ observations from Z DR columns using the 3DVar method can improve the 0–3-h reflectivity and accumulated precipitation predictions of convective storms.