Impact of CALIPSO profile data assimilation on 3-D aerosol improvement in a size-resolved aerosol model

Abstract

Based on the three-dimensional variational assimilation (3DVAR) algorithm, the extinction coefficient profiles of the CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations) satellite were assimilated for the first time on the basis of the aerosol variables from the Model for Simulating Aerosol Interactions and Chemistry (MOSAIC) mechanism of the Weather Research and Forecasting-Chemistry (WRF-Chem) model. A light aerosol pollution episode (case1) and a heavy pollution event (case2) were selected to simulate and predict using the WRF-Chem model. Two sets of data were assimilated: CALIPSO derived aerosol extinction coefficient profiles and surface PM mass concentration data obtained from ground monitoring stations across China. Then, various aerosol data sources including ground-based lidar in Beijing area, Himawari-8 (H-8) satellite aerosol optical depth (AOD) and surface PM monitors were used for comprehensive evaluate the impact of CALIPSO data assimilation on the model performance. The results showed that simultaneous assimilation of the CALIPSO extinction coefficient profiles and surface PM mass concentration data can effectively improve the model aerosol initial conditions and forecasting accuracy of PM2.5, PM10 and AOD. The positive impact on PM2.5 and PM10 forecasting by the assimilation can be maintained for more than 14 h for the case1 and 24 h for the case2, and the initial field of AOD in assimilation experiment has been greatly improved comparing the model control run (cn, without data assimilation). For the simulation of the case2, the assimilation of CALIPSO extinction coefficient profiles reduced the root mean square error (RMSE), mean fraction (MFE) and bias (BIAS) of the AOD in the initial field of the model by 0.08 (19.5%), 0.34 (55.7%) and 0.16 (48.5%) compared to the cn experiment, respectively. When the CALIPSO extinction coefficient profiles and surface PM mass concentration data were assimilated simultaneously, the reduction of the RMSE, MFE and BIAS of the AOD was 0.6 (14.6%), 0.32 (52.5%) and 0.12 (36.4%), respectively. Additionally, aerosol extinction coefficient are remarkably underestimated in the cn experiment, particularly during heavy aerosol pollution case. Assimilating surface PM data can improve the extinction coefficient distribution below 500 m. The simulated aerosol extinction coefficient distribution at 1 km to 2 km can be significantly improved in both cases by assimilating the CALIPSO extinction coefficient profiles, and the value of model simulated extinction coefficient is nearly doubled compared to the cn experiment. Finally, the evaluation with independent ground-based lidar measurements indicates that combined assimilation of surface PM mass concentration and CALIPSO extinction coefficient profile can effectively improve the aerosol distribution near the ground and in the upper layer, and thus generate a more accurate 3D aerosol analysis and forecast field.