Improvement of Particulate Matter Forecasts in South Korea using the 3D-Var Aerosol Data Assimilation

Although air quality forecast is very important on human health, the accuracy of air quality forecast from chemical transport model is limited due to the deficiency of chemistry, the uncertainties of emission and initial conditions. Data Assimilation (DA) can effectively reduce the uncertainty of the chemical initial conditions and leads to improvements in the aerosol predictions.

To improve PM₁₀ and PM_2.5 forecast accuracy, the Weather Research and Forecasting model with Chemistry (WRF-Chem) and the Gridpoint Statistical Interpolation (GSI) assimilation system are used in this research. The WRF-Chem covers East Asia in 27km resolution as mother domain and Korea in 9km resolution as nested domain. The model adopts the Model for Ozone And Related Tracers (MOZART) chemistry and the Global Ozone Chemistry Aerosol Radiation and Transport (GOCART) aerosol schemes. In our data assimilation system, we assimilate data every 6 hours based on the three-dimensional variational (3D-Var) method. The assimilated data includes satellite-derived aerosol optical depth (AOD) from the Geostationary Ocean Color Imager (GOCI) and the Moderate Resolution Imaging Spectroradiometer (MODIS) as well as in-situ PM₁₀ and PM_2.5 from China and Korea.

The WRF-Chem simulation without DA initialization shows large underestimation and low correlation with the surface PM₁₀ and PM_2.5 observation in April, 2017. However, the simulations with DA initialization show significant improvements in the air quality forecast skill. The correlation of PM₁₀ in analysis field is improved from 0.15 to 0.84 in South Korea and the correlation of PM_2.5 is improved from 0.30 to 0.87. This study evaluates the quality of the DA analysis fields and the forecasting skill.