Updraft-Based Adaptive Assimilation of Radial Velocity Observations in a Warn-on-Forecast System

Abstract

AbstractThe Warn-on-Forecast system (WoFS) provides short-term, probabilistic forecasts of severe convective hazards including tornadoes, hail, and damaging winds. WoFS initial conditions are created through frequent assimilation of radar (reflectivity and radial velocity), satellite, and in situ observations. From 2016 to 2018, 5-km radial velocity Cressman superob analyses were created to reduce the observation counts and subsequent assimilation computational costs. The superobbing procedure smooths the radial velocity and subsequently fails to accurately depict important storm-scale features such as mesocyclones. This study retrospectively assimilates denser, 3-km radial velocity analyses in lieu of the 5-km analyses for eight case studies during the spring of 2018. Although there are forecast improvements during and shortly after convection initiation, 3-km analyses negatively impact forecasts initialized when convection is ongoing, as evidenced by model failure and initiation of spurious convection. Therefore, two additional experiments are performed using adaptive assimilation of 3-km radial velocity observations. Initially, an updraft variance mask is applied that limits radial velocity assimilation to areas where the observations are more likely to be beneficial. This experiment reduces spurious convection as well as the number of observations assimilated, in some cases even below that of the 5-km analysis experiments. The masking, however, eliminates an advantage of 3-km radial velocity assimilation for convection initiation timing. This problem is mitigated by additionally assimilating 3-km radial velocity observations in locations where large differences exist between the observed and ensemble-mean reflectivity fields, which retains the benefits of the denser radial velocity analyses while reducing the number of observations assimilated.