The Coronavirus Disease 2019 (COVID-19) outbreak caused a suspension of almost all non-essential human activities, leading to a significant reduction of anthropogenic emissions. However, the emission inventory of the chemistry transport model cannot be updated in time, resulting in large uncertainty in PM2.5 predictions. This study adopted a three-dimensional variational approach to assimilate multi-source PM2.5 data from satellite and ground observations and jointly adjusted emissions to improve PM2.5 predictions of the WRF-Chem model. Experiments were conducted to verify the method over Hubei Province, China, during the COVID-19 epidemic from Jan 21st to Mar 20th, 2020. The results showed that PM2.5 predictions were improved at almost all the validation sites, and the benefit of data assimilation (DA) can last for 48 h. However, the benefits of DA diminished quickly with the increase of the forecast time. By adjusting emissions, the PM2.5 predictions showed a much slower error accumulation along forecast time. At 48Z, the RMSE still has an 8.85 μg/m3 (19.49%) improvement, suggesting the effectiveness of emissions adjustment based on the improved initial conditions via DA.
Keywords: 3D-var; COVID-19; Data assimilation; PM(2.5) predictions; WRF-Chem.
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