The Impact of Assimilating Radar-estimated Rain Rates on Simulation of Precipitation in the 17--18 July 1996 Chicago Floods

Rainfall prediction remains one of the most challenging problems in weatherforecasting. In order to improve high-resolution quantitative precipitationforecasts (QPF), a new procedure for assimilating rainfall rate derived fromradar composite reflectivity has been proposed and tested in a numericalsimulation of the Chicago floods of 17--18 July 1996. The methodology isbased on the one-dimensional variation scheme (1DVAR) assimilation approachintroduced by Fillion and Errico but applied here using the Kain-Fritschconvective parameterization scheme (KF CPS). The novel feature of this workis the continuous assimilation of radar estimated rain rate over a threehour period, rather than a single assimilation at the initial (analysis)time. Most of the characteristics of this precipitation event, including thepropagation, regeneration of mesoscale convective systems, the frontalboundary across the Midwest and the evolution of the low-level jet arebetter captured in the simulation as the radar-estimated precipitation rateis assimilated. The results indicate that precipitation assimilation duringthe early stage can improve the simulated mesoscale feature of theconvection system and shorten the spin-up time significantly. Comparison ofprecipitation forecasts between the experiments with and without the 1DVARindicates that the 1DVAR scheme has a positive impact on the QPF up to 36hours in terms of the bias and bias equalized threat scores.