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 h fine particulate matter (PM_2.5) forecasts in Delhi during the crop residue burning season of Oct-Nov 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 PM_2.5 towards 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 µg/m³ (70-86%) with the largest improvement during the peak air pollution episode of 6-13 November 2017. Aerosol-radiation feedback contributes ~21%, ~25%, and ~24% to reduction in mean bias of the first, second, and third day of PM_2.5forecast. Persistence fire emission assumption is found to work really well, as the accuracy of PM_2.5 forecasts driven by persistent fire emissions was only 6% lower compared to those driven by real fire emissions. Aerosol-radiation feedback extends the benefits of assimilating satellite AOD beyond PM_2.5 forecasts to surface temperature forecast with a reduction in the mean bias of 0.9^oC - 1.5^oC (17-30%). These results demonstrate that air quality forecasting can benefit substantially from satellite AOD observations particularly in developing countries that lack resources to rapidly build dense air quality monitoring networks.