Assimilation of Lidar Profilers to Improve Bore Forecasts during the PECAN Campaign 2015

This study focuses on enhancing atmospheric bore forecasts associated with convection initiation during the 2015 PECAN field campaign by assimilating radar reflectivity, radial velocity, and lidar water vapor observations. Utilizing the Penn State University Weather Research & Forecasting Model Ensemble Kalman Filter data assimilation system (PSU-WRF-EnKF), we assessed the impact of these assimilations on bore forecasting on 14 July, examined deterministic forecasts with varying lead times, and elucidated the underlying physical processes. The high-resolution model results reveal that the assimilation of conventional observations notably improves meteorological fields, particularly air temperature, leading to an intensified cold pool as validated by the time evolution and spatial distribution of MESONET observations. Assimilating radar reflectivity and radial velocity effectively removes spurious clouds, with statistical metrics indicating a reduction in reflectivity RMSE from 20 dBZ to 8 dBZ over time. Lidar water vapor profile assimilation enhances water vapor forecasts, showing better alignment with radiosonde profiles. Deterministic forecasts initiated from the EnKF analysis at 0430Z show better agreement with observations in predicting bore intensity, despite some discrepancies in the bore's exact shape. The physical processes presented provide a comprehensive view of bore evolution. Overall, assimilating conventional and radar observations enhances mesoscale forecasts, while lidar assimilation refines the water vapor details within the planetary boundary layer, as evidenced by comparisons with independent observations of radiosonde.