A B S T R A C TObserving system experiments are presented to characterise impacts of surface and vertical profile measurements on aerosol analysis and forecast skill. A three-dimensional (3D) variational data assimilation system is implemented within the Weather Research and Forecasting/Chemistry model, and the control variables consist of eight species of the Model for Simulation Aerosol Interactions and Chemistry scheme. In the experiments, the 3D profiles of aircraft speciated observations and surface concentration observations acquired during the California Research at the Nexus of Air Quality and Climate Change field campaign are assimilated. The data assimilation experiments are performed at 02:00 local time 2 June 2010, and surface observations at 02:00 and aircraft observations from 01:30 to 02:30 local time are assimilated. The results show that the assimilation of both aircraft and surface observations improves the subsequent forecasts. The improved forecast skill resulting from the assimilation of the aircraft profiles persists a time longer than the assimilation of the surface observations, which suggests the necessity of vertical profile observations for extending aerosol forecasting time.

Abstract. Balance constraints are important for background error covariance (BEC) in data assimilation to spread information between different variables and produce balance analysis fields. Using statistical regression, we develop a balance constraint for the BEC of aerosol variables and apply it to a three-dimensional variational data assimilation system in the WRF/Chem model; 1-month forecasts from the WRF/Chem model are employed for BEC statistics. The cross-correlations between the different species are generally high. The largest correlation occurs between elemental carbon and organic carbon with as large as 0.9. After using the balance constraints, the correlations between the unbalanced variables reduce to less than 0.2. A set of data assimilation and forecasting experiments is performed. In these experiments, surface PM 2.5 concentrations and speciated concentrations along aircraft flight tracks are assimilated. The analysis increments with the balance constraints show spatial distributions more complex than those without the balance constraints, which is a consequence of the spreading of observation information across variables due to the balance constraints. The forecast skills with the balance constraints show substantial and durable improvements from the 2nd hour to the 16th hour compared with the forecast skills without the balance constraints. The results suggest that the developed balance constraints are important for the aerosol assimilation and forecasting.

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.

Abstract. We develop a new inversion method which is suitable for linear and nonlinear emission source (ES) modeling, based on the three-dimensional decoupled direct (DDM-3D) sensitivity analysis module in the Community Multiscale Air Quality (CMAQ) model and the three-dimensional variational (3DVAR) data assimilation technique. We established the explicit observation operator matrix between the ES and receptor concentrations and the background error covariance (BEC) matrix of the ES, which can reflect the impacts of uncertainties of the ES on assimilation. Then we constructed the inversion model of the ES by combining the sensitivity analysis with 3DVAR techniques. We performed the simulation experiment using the inversion model for a heavy haze case study in the Beijing–Tianjin–Hebei (BTH) region during 27–30 December 2016. Results show that the spatial distribution of sensitivities of SO2 and NOx ESs to their concentrations, as well as the BEC matrix of ES, is reasonable. Using an a posteriori inversed ES, underestimations of SO2 and NO2 during the heavy haze period are remarkably improved, especially for NO2. Spatial distributions of SO2 and NO2 concentrations simulated by the constrained ES were more accurate compared with an a priori ES in the BTH region. The temporal variations in regionally averaged SO2, NO2, and O3 modeled concentrations using an a posteriori inversed ES are consistent with in situ observations at 45 stations over the BTH region, and simulation errors decrease significantly. These results are of great significance for studies on the formation mechanism of heavy haze, the reduction of uncertainties of the ES and its dynamic updating, and the provision of accurate “virtual” emission inventories for air-quality forecasts and decision-making services for optimization control of air pollution.

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.