Multiconstituent Data Assimilation With WRF-Chem/DART: Potential for Adjusting Anthropogenic Emissions and Improving Air Quality Forecasts Over Eastern China

We use the Weather Research and Forecasting Model with the chemistry/Data Assimilation Research Testbed (WRF-Chem/DART) chemical weather forecasting/data assimilation system with multiconstituent data assimilation to investigate the improvement of air quality forecasts over eastern China. We assimilate surface in situ observations of sulfur dioxide (SO2), nitrogen dioxide (NO2), ozone (O-3), carbon monoxide (CO), particulate matter with diameters less than 2.5 mu m (PM2.5) and 10 mu m (PM10), and satellite aerosol optical depth to adjust the related anthropogenic emissions as well as the chemical initial conditions. We validate our forecast results out to 72 hr by comparison with the in situ observations. Results show that updated emissions improve the model performance between 10% and 65% root mean square error reduction for the assimilated species except particulate matter with a diameter between 2.5 and 10 mu m (PM2.5-10), which is slightly improved due to the limited anthropogenic contribution to it. In a sensitivity experiment with a different update interval, the CO improvement is found to be sensitive to the cycling time used to update the CO emissions. In another sensitivity experiment when NO2 observations are not assimilated and nitrogen oxides (NOx) emission are adjusted by only O-3, NO2 forecasts show similar root mean square error improvement but have lower spatial correlation, indicating the value and limitation of the O-3-NOx cross-variable relationship.