Development of a three-dimensional variational assimilationsystem for lidar profile data based on a size-resolved aerosolmodel in WRF-Chem model v3.9.1 and its application in PM 2.5 forecasts across China

Abstract. For the aerosol variables in the model for simulating aerosol interactions and chemistry (MOSAIC)-4bin chemical scheme in the Weather Research and Forecasting–Chemistry (WRF–Chem) model, this study presents an observation forward aerosol extinction coefficient (AEC) and aerosol mass concentration (AMC) operator and corresponding adjoint based on the interagency monitoring of protected visual environments (IMPROVE) equation, and then a three-dimensional variational (3-DVAR) data assimilation system (DA) is developed for lidar AECs and AMCs. DA experiments are conducted based on AEC profiles measured by five light detection and ranging (lidar) systems as well as mass concentration (MC) data measured at over 1,500 ground environmental monitoring stations across China for particulate matter 2.5 µm or less in diameter (PM2.5) and PM between 2.5 and 10 µm in diameter (PM10). An experiment comparing assimilated and without assimilated measurements finds the following. While only five lidars were available within the simulation region (approximately 2.33 million km2 in size), assimilating lidar AEC data alone can effectively improve the accuracy of the initial field of the WRF–Chem as well as its forecast performance for PM2.5MCs. Compared to the without assimilated experiment, DA reduces the root mean square error of surface PM2.5MCs in the initial field of the model by 10.5 μg/m3 (17.6 %). Moreover, the positive effect resulting from the optimization of the initial field for AMCs can last for more than 24 h. By taking advantage of lidar aerosol vertical profile information and the near-surface PM MC observations, assimilating lidar AEC and surface PM2.5 (PM10) simultaneously can effectively integrate their observed information and generate a more accurate 3D aerosol analysis field.