Enhancing Accuracy of Air Quality and Temperature Forecasts During Paddy Crop Residue Burning Season in Delhi Via Chemical Data Assimilation. Issue 17 (24th August 2020)

• Record Type: Journal Article
• Authors: Kumar, Rajesh; Ghude, Sachin D.; Biswas, Mrinal; Jena, Chinmay; Alessandrini, Stefano; Debnath, Sreyashi; Kulkarni, Santosh; Sperati, Simone; Soni, Vijay K.; Nanjundiah, Ravi S.; Rajeevan, M.
• Is Part Of: Journal of geophysical research. Volume 125:Issue 17(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-08-24
• Subjects: assimilation -- satellite AOD -- air quality forecast -- New Delhi -- PM2.5; Atmospheric physics -- Periodicals; Geophysics -- Periodicals; 551.5
• DOI: 10.1029/2020JD033019
• Languages: English
• ISSNs: 2169-897X

Abstract

Abstract: 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 μg/m 3 (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 days of PM2.5 forecast. Persistence fire emission assumption is found to work really well, as the accuracy of PM2.5 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 PM2.5 forecasts to surface temperature forecast with a reduction in the mean bias of 0.9–1.5°C (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. Key Points: MODIS AOD assimilation and aerosol‐radiation feedback improved accuracy of 72 hr PM2.5 forecasts in New Delhi by 70–86% Persistent fire emission assumption works really well for Delhi air quality forecasting system and introduces uncertainty of less than 6% Improved air quality forecasts also lead to improved surface temperature forecast through aerosol‐radiation feedback