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

The structure of the planetary boundary layer (PBL) is important for initiation, development, and organization of convection. High-spatiotemporal-resolution network that directly observe the PBL structure is unavailable now. However, recent studies discovered that differential reflectivity (Z_DR) observations from dual-polarization Doppler weather radars in clear-sky conditions can be used to characterize the top of the PBL during the daytime. Compared with other observational platforms that observe the PBL, such as rawinsondes, ground-based or space-borne infrared or microwave sounders, lidars, or radio occultations, these Z_DR-derived PBL depth observations have high temporal resolution and relatively dense and uniform distributions over CONUS. Therefore, assimilating these observations could potentially improve the estimation of thermodynamic structures in the lower troposphere, and therefore, subsequent forecasts.

This study examines the impact of assimilating Z_DR-derived PBL depth observations on the forecasts of the flash flooding event happened in east Kentucky in July 2022. The model configuration in the experiments mimics the operational HRRR. Results show that assimilating Z_DR-derived PBL depth observations led to considerable changes in temperature and moisture in the lower troposphere. Soil conditions, including soil moisture, and associated surface heat fluxes, are also modified. These impacts of assimilating Z_DR-derived PBL depth observations contribute to a better match between model-diagnosed PBL depth with the observations. Subsequently, rainfall forecasts are statistically significantly improved for multiple different thresholds using both grid-wise and neighborhood metrices, 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 also influence the performance of the rainfall forecasts, which deserves future studies to explore more adaptive methods.