Sensitivity Studies of Four‐Dimensional Local Ensemble Transform Kalman Filter Coupled With WRF-Chem Version 3.9.1 for Improving Particulate Matter Simulation Accuracy

Abstract. Accurately simulate severe haze events through numerical models remains challenging because of uncertainty in anthropogenic emissions and physical parameterizations of particulate matter (PM_2.5 and PM₁₀). In this study, a coupled WRF-Chem/four-dimension 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 region and surrounding provinces. The effectiveness of 4D-LETKF system and its sensitivity to ensemble member size and length of assimilation window have been investigated. The promising results show that significant improvements have been made by analysis in the simulation of particulate matter during severe haze event. The assimilation reduces root mean square errors of PM_2.5 from 69.93 to 31.19 µg m^-3 and of PM₁₀ from 106.88 to 76.83µg m^-3. Smaller RMSEs and larger correlation coefficients in the analysis of PM_2.5 and PM₁₀ are observed across nearly all verification stations, indicating that the 4D-LETKF assimilation optimizes the simulation of PM_2.5 and PM₁₀ concentration efficiently. Sensitivity experiments reveal that the combination of 48 hours of assimilation window length and 40 ensemble members shows best performance for reproducing severe haze event. In view of the performance of ensemble members, increasing ensemble member size improves divergence among each forecasting member, facilitates the spread of state vectors about PM_2.5 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 both in severe and moderate haze events. This study advances our understanding about the selection of basic parameters in the 4D-LETKF assimilation system and the performance of ensemble simulation in a particulate matter polluted environment.