Observing system simulation experiment (OSSE)-quantitative evaluation of lidar observation networks to improve 3D aerosol forecasting in China

Abstract

In order to evaluate the potential impact of an aerosol lidar monitoring network in China on improving air quality prediction, an observing system simulation experiment (OSSE) was conducted using the Weather Research and Forecasting Chemistry (WRF-Chem) model and three-dimensional variational (3DVAR) data assimilation (DA) system. The impact of lidar numbers and locations on DA performance was evaluated using sensitivity analysis. Three ground-based lidar monitoring networks with 590, 310, and 99 lidars were constructed based on the distribution of existing surface air pollution monitoring stations in China to study the added value of lidar measurements. Four assimilation experiments were conducted using varying aerosol extinction coefficient data (AEXT) from three lidar monitoring networks and surface particulate matter (PM) concentration data over a four-week period. The results showed that compared with only assimilating surface PM2.5, the correlation coefficient (CORR) of PM2.5 was significantly higher, the root mean square error (RMSE) and mean fraction error (MFE) were significantly lower, and the mean mass concentration was closer to the simulated observation after adding lidar profile measurements to the 3DVAR DA system. Additionally, the evaluation of DA with 590 stations indicated spatial differences in the improvement of the positive effect of assimilation. Overall, the improvement effects of assimilation in Central and East China were better than those in other regions. Finally, it was observed that the positive effect of DA on PM2.5 prediction increased with an increase in the lidar number. However, with an increase in the number of lidars, the improvement of the positive effect of DA decreased gradually. The results indicate that it is necessary to balance the forecasting effect and economic cost while establishing a lidar observation network.