Enhancing accuracy of air quality and temperature forecasts during paddy crop residue burning season in Delhi via chemical data assimilation

This paper examines the accuracy of Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) generated 72 hr fine particulate matter (PM2.5) forecasts in Delhi during the crop residue burning season of October-November 2017 with respect to assimilation of the Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD) retrievals, persistent fire emission assumption, and aerosol-radiation interactions.

The assimilation significantly pushes the model AOD and PM2.5 toward the observations with the largest changes below 5 km altitude in the fire source regions (northeastern Pakistan, Punjab, and Haryana) as well as the receptor New Delhi. WRF-Chem forecast with MODIS AOD assimilation, aerosol-radiation feedback turned on, and real-time fire emissions reduce the mean bias by 88-195 mu g/m(3) (70-86%) with the largest improvement during the peak air pollution episode of 6-13 November 2017.