A THREE-DIMENSIONAL AEROSOL VARIATIONAL DATA ASSIMILATION SYSTEM FOR AIRCRAFT AND SURFACE OBSERVATIONS

This study use the WRF-Chem model to assess how meteorological conditions and unexpected emission reductions affected PM_2.5 in the North China Plain (NCP). It highlights regional disparities: in the Northern NCP, adverse weather negated emission reduction effects. In contrast, the Southern NCP with PM_2.5 decrease due to favorable weather and emission reductions. The research highlighted the interaction between emissions, meteorology and air quality.

This study developed a four-dimensional variational assimilation (4DVAR) system based on WRF–Chem to optimise SO₂ emissions. The 4DVAR system was applied to obtain the SO₂ emissions during the early period of the COVID-19 pandemic over China. The results showed that the 4DVAR system effectively optimised emissions to describe the actual changes in SO₂ emissions related to the COVID lockdown, and it can thus be used to improve the accuracy of forecasts.

This paper presents a 3D variational data assimilation system for aerosol optical properties, including aerosol optical thickness (AOT) retrievals and lidar-based aerosol profiles, which was developed for a size-resolved sectional model in WRF-Chem. To directly assimilate aerosol optical properties, an observation operator based on the Mie scattering theory was designed. The results show that Himawari-8 AOT assimilation can significantly improve model aerosol analyses and forecasts.

We develop a new inversion method of emission sources based on sensitivity analysis and the three-dimension variational technique. The novel explicit observation operator matrix between emission sources and the receptor’s concentrations is established. Then this method is applied to a typical heavy haze episode in North China, and spatiotemporal variations of SO₂, NO₂, and O₃ concentrations simulated using a posterior emission sources are compared with results using an a priori inventory.