A three-dimensional variational data assimilation system for a size-resolved aerosol model: Implementation and application for particulate matter and gaseous pollutant forecasts across China

摘要: A three-dimensional variational (3DVAR) data assimilation (DA) system is presented here based on a size-resolved sectional aerosol model, the Model for Simulating Aerosol Interactions and Chemistry (MOSAIC) within Weather Research Forecasting model coupled to (WRF-Chem) model. The use of this approach means that both gaseous pollutants such as SO2, NO2, CO, O3 well particulate matter (PM2.5, PM10) observational can be assimilated simultaneously. Two one-month parallel simulation experiments were conducted, one with surface hourly concentration observations above six released by China National Environmental Monitoring Centre (CNEMC) without in order verify impact initial chemical fields subsequent forecasts. Results show that, first place, DA provide more accurate field. root-mean-square error PM2.5, PM10, mass concentrations analysis field fell 29.27 μg m–3 (53.5%), 34.5 μg m–3 (50.9%), 30.36 μg m–3 (64.2%), 8.91 μg m–3 (39.5%), 0.46 mg m–3 (47.4%), 15.11 μg m–3 (51.0%), respectively, compared background assimilation. At same time, mean fraction was reduced 42.6%, 53.1%, 45.2%, 43.1%, 69.9%, 48.8%, while correlation coefficient increased 0.51, 0.55, 0.48, 0.38, 0.47, 0.65, respectively. Secondly, results reveal variable benefits from different pollutants. significantly improves CO forecasts leading positive effects last than 48 h. SO2 up 8 h but remains relatively poor NO2 Thirdly, influence varies areas. It possible PM2.5 PM10 48 h across most regions China. Indeed, over north China, much longer (48 h) are found apart east Sichuan Basin. able improve exception southwest northwest southern evident, spatial distribution perspective, remain poor.