Impact of Lidar Data Assimilation on Planetary Boundary Layer Wind and PM2.5 Prediction over Taiwan

The planetary boundary layer (PBL) structures strongly affect the air pollution diffusion and dispersion processes. The accurate simulation of the PBL physical processes in the numerical weather models is vital for air quality prediction. Advanced remote sensing techniques such as lidar can provide aerosol information with high resolution both in vertical and temporal, having widely used to monitor the PBL air quality. However, how the densely aerosol data can be refined into a modeling system and improves air quality forecast skill, still remains a challenge. In order to gain a better understanding of the interactions between aerosol and meteorological fields and the following feedback processes, we implemented a lidar data assimilation system based on the Weather Research and Forecasting - Local Ensemble Transform Kalman Filter (WRF-LETKF) framework coupled with the Community Multiscale Air Quality Model (CMAQ).

A high air pollution episode occurred during February 14-16, 2017 was studied in Taiwan. The fine particulate matter (PM2.5) profile retrieved from Micro-Pulse lidar (MPL) were assimilated in WRF-LETKF system. The lidar retrieved PM2.5 profile is able to reflect the dynamic and thermodynamic structures of the PBL. The simulation results indicate that the overestimated wind speed at the surface layer was corrected by assimilating the lidar observation not only during the DA step but also the forecast period. The correction can further improve the vertical structure of PM2.5 concentration during the 12-h forecast. In general, the transportation process of PM2.5 was well-simulated by the lidar data assimilation as well.