Assimilating Novel Boundary Layer Observations from Dual-Polarization Radars to Improve Lower-Tropospheric Moisture and Torrential Rainfall Forecasts

The structure of the planetary boundary layer (PBL) is important for the initiation, development, and organization of convection. High-spatiotemporal-resolution networks that directly observe the PBL structure are currently unavailable. Recent studies discovered that differential reflectivity (Z_DR) observations from dual-polarization Doppler weather radars in clear-air conditions can be used to characterize the top of the daytime PBL. Compared with other observational platforms that observe the PBL, these Z_DR-derived PBL depth observations have high temporal resolution and relatively dense and uniform distributions over the CONUS. Therefore, assimilating these observations could potentially improve the estimation and forecast skill of thermodynamic structures in the lower troposphere. This study examines the impact of assimilating Z_DR-derived PBL depth observations on the forecasts of the torrential rainfall and flash flood event in eastern Kentucky on 27–28 July 2022 using a strongly coupled land–atmosphere data assimilation system. The model configuration in the experiments mimics the operational HRRR. Results show that assimilating Z_DR-derived PBL depth observations leads to considerable changes in temperature and moisture in the lower troposphere. Soil conditions, including soil moisture and associated surface heat fluxes, are also modified. The assimilation of Z_DR-derived PBL depth observations contributes to a better match between model-diagnosed PBL depth with the observations. Subsequently, rainfall forecasts are statistically significantly improved using both gridwise and neighborhood metrics, especially for the most extreme rainfall. Sensitivity experiments also show that the assimilation frequency and the observation errors assigned to Z_DR-derived PBL depth observations influence the performance of the rainfall forecasts, which deserve future study.