Abstract 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 (a) identify effective approaches for assimilating Doppler lidar-retrieved PBLH and (b) 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 multi-physics ensemble with a rank histogram filter and Boxcar-Ramp covariance localization (1 km vertical and 50 km horizontal half-width) and observation error of 20% of observed PBLH, our results show that PBLH assimilation improves the analysis and 6-hour forecast of potential temperature ( θ ), water vapor mixing ratio ( q ), and especially wind ( u , v ) 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.
Pan et al. (Thu,) studied this question.