Evaluation of PM_2.5 forecast using chemical data assimilation in the WRF-Chem model: A novel initiative under the Ministry of Earth Sciences Air Quality Early Warning System for Delhi, India

Air quality has become one of the most important environmental concerns for Delhi, India. In this per-spective, we have developed a high-resolution air quali-ty prediction system for Delhi based on chemical data assimilation in the chemical transport model-Weather Research and Forecasting with Chemistry (WRF-Chem). The data assimilation system was ap-plied to improve the PM_2.5 forecast via assimilation of MODIS aerosol optical depth retrievals using three-dimensional variational data analysis scheme. Near real-time MODIS fire count data were applied simul-taneously to adjust the fire-emission inputs of chemi-cal species before the assimilation cycle. Carbon monoxide (CO) emissions from biomass burning, an-thropogenic emissions, and CO inflow from the do-main boundaries were tagged to understand the contribution of local and non-local emission sources. We achieved significant improvements for surface PM_2.5 forecast with joint adjustment of initial condi-tions and fire emissions.