Evaluation of the WRF-CMAQ Model Performances on Air Quality in China with the Impacts of the Observation Nudging on Meteorology

Accurate meteorological fields are imperative for correct air quality modeling through their influence on the various chemical species in the atmosphere. In this study, the simulations from the Community Multiscale Air Quality (CMAQ) model were conducted with meteorological fields generated by the Weather Research and Forecasting (WRF) using the original and observation nudging approaches to investigate if the better model performances for PM2.5, O-3, and their related precursors in China were produced from the latter. Two pollution episodes (one for PM2.5 and another for O-3) in 2018 were selected on the basis of the observations at the monitoring supersites in Xianghe and Taizhou cities. The results showed that the Nudging cases had better model performances on all meteorological parameters with higher values of index of agreement (IOA) and lower values of mean bias (MB). It was found that the Nudging case improved model performances for PM2.5 and its chemical components at the Xianghe site with lower values of normalized mean bias (NMB) and higher values of correlation coefficient (R) than the base case. The results for the regional PM(2.5 )over China indicated that the Nudging case reproduced the spatial patterns of mean PM2.5 concentrations in the 367 cities with the NMB value of -31%, much better than -42.0% in the base case. During the O-3 pollution episode, the Nudging case improved the model performances for O-3, CO, NO2, and VOCs with lower NMB values and higher R values than the base case. The results of regional O-3 over China revealed that the Nudging case reproduced the spatial patterns of observed mean O-3 concentrations in the 367 cities very well with the NMB value of 0.97%, much lower than -5.67% in the base case. The results of this study have great implications for better simulations of meteorology and air quality.