Background error statistics for aerosol variables from WRF/Chem predictions in Southern California
Background error covariance (BEC) is crucial in data assimilation. This paper addresses the multivariate BEC associated with black carbon, organic carbon, nitrates, sulfates, and other constituents of aerosol species. These aerosol species are modeled and predicted using the Model for Simulating Aerosol Interactions and Chemistry scheme (MOSAIC) in the Weather Research and Forecasting/Chemistry (WRF/Chem) model at a resolution of 4 km in Southern California. The BEC is estimated from the differences between the 36-hour and 12-hour forecasts using the NMC method. The results indicated that the maximum background error standard deviation is associated with nitrate and is larger than that of black carbon, organic carbon, and sulfate. The horizontal and vertical scale of the correlation of nitrate is much smaller than that of other species. A significant cross-correlation is found between the species of black carbon and organic carbon. The cross-correlations between nitrate and other variables are relatively smaller and exhibit a relatively smaller length scale. Single observation data assimilation experiments are performed to illustrate the effect of the BEC on analysis increments.