Impact of lidar data assimilation on planetary boundary layer wind and PM_2.5 prediction in Taiwan

Accurate simulations of planetary boundary layer (PBL) processes in numerical weather models are vital for air quality predictions. Advanced remote sensing techniques, such as lidar detection and ranging (lidar), can provide aerosol information with high temporal and vertical resolutions in the PBL. In this study, a lidar data assimilation system was developed based on the Weather Research and Forecasting-Local Ensemble Transform Kalman Filter (WRF-LETKF) framework coupled with the Community Multiscale Air Quality (CMAQ) model. The objective was to investigate the impact of lidar data assimilation on PBL prediction and the subsequent influence on PM_2.5 prediction for a high-air-pollution event. The fine particulate matter (PM_2.5) profiles retrieved from two micropulse lidar observations in northern and central Taiwan were assimilated in the WRF-LETKF system. Three numerical experiments, BASE (with a nudging strategy), CTRL (with an ensemble framework), and LDA (with assimilation of lidar-retrieved PM_2.5 profiles), were conducted for a high-air-pollution episode. The BASE simulation overestimates the wind speed, which also leads to PM_2.5 underestimation. The CTRL and LDA simulations are able to improve the wind fields and enhance the PM_2.5 accumulation. With a strong error correlation between the lidar-retrieved PM_2.5 concentration and the wind fields, the LDA simulation effectively corrects the wind flow from the surface to the PBL top, which further adjusts the PM_2.5 transport processes and leads to results that agree well with observations.