Assimilation of Doppler Lidar–Derived Planetary Boundary Layer Height Measurements Using an Ensemble Kalman Filter: Case Studies during the PECAN Field Campaign

This study investigates the assimilation of planetary boundary layer height (PBLH) data into a convective-permitting Weather Research and Forecasting (WRF) Model using the ensemble-based Data Assimilation Research Testbed (DART). We conduct experiments to (i) identify effective approaches for assimilating Doppler lidar–retrieved PBLH and (ii) assess its impact on analysis and forecast performance during four intensive observation periods (IOPs) of low-level jets (LLJs) from the Plains Elevated Convection at Night (PECAN) field campaign (June–July 2015). Using a multiphysics ensemble with a rank histogram filter and Boxcar–Ramp covariance localization (1 km vertical and 50 km hor-izontal half-width) and observation error of 20% of observed PBLH, our results show that PBLH assimilation improves the analysis and 6-h forecast of potential temperature (θ), water vapor mixing ratio (q), and especially wind (u, y) profiles, including LLJs, relative to no assimilation. These findings demonstrate the value of PBLH assimilation despite its lack of direct constraints on thermodynamic profiles. We suggest that assimilating PBLH within the standard 12-hourly sounding intervals could serve as a complement to radiosonde assimilation, which provides direct thermodynamic constraints. Further examining PBLH sensitivity to thermodynamic variables, optimizing covariance localization, and considering surface– atmosphere interactions could strengthen observational constraints, retain information, and enhance forecast performance. SIGNIFICANCE STATEMENT: The study of data assimilation for planetary boundary layer datasets into numerical weather prediction models is still in its early stages of development. Our study tested the aforementioned procedure with various parameters and strategies. The results indicate that the data assimilation of planetary boundary layer height can improve model performance in both the analysis and forecast stages for temperature, water vapor, and wind profiles. We suggest that the data assimilation of planetary boundary layer–related measurements should also include thermodynamic measurements to optimize performance.