Sensitivity studies of a four-dimensional local ensemble transform Kalman filter coupled with WRF-Chem version 3.9.1 for improving particulate matter simulation accuracy

Abstract

Accurately simulating severe haze events through numerical models remains a challenge because of uncertainty in anthropogenic emissions and physical parameterizations of particulate matter (PM2.5 and PM10). In this study, a coupled Weather Research and Forecasting with Chemistry (WRF-Chem)–four-dimensional local ensemble transform Kalman filter (4D-LETKF) data assimilation system has been successfully developed to optimize particulate matter concentration by assimilating hourly ground-based observations in winter over the Beijing–Tianjin–Hebei (BTH) region and surrounding provinces. The effectiveness of the 4D-LETKF system and its sensitivity to the ensemble member size and length of the assimilation window are investigated. The promising results show that significant improvements have been made by analysis in the simulation of particulate matter during a severe haze event. The assimilation reduces root mean square errors in PM2.5 from 69.93 to 31.19 µg m−3 and of PM10 from 106.88 to 76.83 µg m−3. Smaller root mean square errors and larger correlation coefficients in the analysis of PM2.5 and PM10 are observed across nearly all verification stations, indicating that the 4D-LETKF assimilation optimizes the simulation of PM2.5 and PM10 concentration efficiently. Sensitivity experiments reveal that the combination of 48 h of assimilation window length and 40 ensemble members shows the best performance for reproducing the severe haze event. In view of the performance of ensemble members, an increasing ensemble member size improves ensemble spread among each forecasting member, facilitates the spread of state vectors about PM2.5 and PM10 information in the first guess, favors the variances between each initial condition in the next assimilation cycle, and leads to better simulation performance in both severe and moderate haze events. This study advances our understanding of the selection of basic parameters in the 4D-LETKF assimilation system and the performance of ensemble simulations in a particulate-matter-polluted environment.