Lidar data assimilation method based on CRTM and WRF-Chem models and its application in PM forecasts in Beijing.

A three-dimensional variational (3DVAR) lidar data assimilation method is developed based on the Community Radiative Transfer Model (CRTM) and Weather Research and Forecasting model coupled to Chemistry (WRF-Chem) model. A 3DVAR data assimilation (DA) system using lidar extinction coefficient observation data is established, and variables from the Model for Simulating Aerosol Interactions and Chemistry (MOSAIC) mechanism of the WRF-Chem model are employed. Hourly lidar extinction coefficient data from 12:00 to 18:00 UTC on March 13, 2018 at four stations in Beijing are assimilated into the initial field of the WRF-Chem model; subsequently, a 24 h PM concentration forecast is made. Results indicate that assimilating lidar data can effectively improve the subsequent forecast. PM forecasts without using lidar DA are remarkably underestimated, particularly during heavy haze periods; in contrast, forecasts of PM concentrations with lidar DA are closer to observations, the model low bias is evidently reduced, and the vertical distribution of the PM concentration in Beijing is distinctly improved from the surface to 1200 m. Of the five aerosol species, improvements of NO are the most significant. The correlation coefficient between PM concentration forecasts with lidar DA and observations at 12 stations in Beijing is increased by 0.45, and the corresponding average RMSE is decreased by 25 μg·m, which respectively compared to those without DA.