Background error covariance with balance constraints for aerosol species and applications in data assimilation

Abstract

Abstract. Balance constraints are important for a background error covariance (BEC) in data assimilation to spread information between different variables and produce balance analysis fields. Using statistical regression, we develop the balance constraint for the BEC of aerosol variables and apply it to a data assimilation and forecasting system for the WRF/Chem model. One-month products from the WRF/Chem model are employed for BEC statistics with the NMC method. The cross-correlations among the original variables are generally high. The highest correlation between elemental carbon and organic carbon without balance constraints is approximately 0.9. However, the correlations for the unbalanced variables are less than 0.2 with the balance constraints. Data assimilation and forecasting experiments for evaluating the impact of balance constraints are performed with the observations of the surface PM2.5 concentrations and speciated concentrations along an aircraft flight track. The speciated increments of the experiment with balance constraints are more coincident than the speciated increments of the experiment without balance constraints, for the observation information can spread across variables by balance constraints in the former experiment. The forecast results of the experiment with balance constraints show significant and durable improvements from the 3rd hour to the 18th hour compared with the forecast results of the experiment without the balance constraints. However, the forecasts of these two experiments are similar during the first 3 h. The results suggest that the balance constraint is significantly positive for the aerosol assimilation and forecasting.