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 PM2.5 prediction for a high-air-pollution event. The fine particulate matter (PM2.5) 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 PM2.5 profiles), were conducted for a high-air-pollution episode. The BASE simulation overestimates the wind speed, which also leads to PM2.5 underestimation. The CTRL and LDA simulations are able to improve the wind fields and enhance the PM2.5 accumulation. With a strong error correlation between the lidar-retrieved PM2.5 concentration and the wind fields, the LDA simulation effectively corrects the wind flow from the surface to the PBL top, which further adjusts the PM2.5 transport processes and leads to results that agree well with observations.
指紋深入研究「Impact of lidar data assimilation on planetary boundary layer wind and PM2.5 prediction in Taiwan」主題。共同形成了獨特的指紋。
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