Improved characterization of the spatiotemporal extent, intensity, and causes of extreme aerosol optical depth events is critical to quantifying their regional climate forcing and the link to near‐surface air quality. An analysis of regional‐scale extreme aerosol events over the eastern United States is undertaken using output from the Modern‐Era Retrospective analysis for Research and Applications, version 2 (MERRA‐2) reanalysis and observations from the MODerate resolution Imaging Spectroradiometers (MODIS). Six extreme aerosol optical depth (AOD) events during 2003–2007, dominated by anthropogenic emissions and characterized by a regional scale extent, are identified and simulated using the Weather Research and Forecasting model coupled with Chemistry (WRF‐Chem) applied at 12 km resolution. Statistical analyses show output from WRF‐Chem during these events is generally negatively biased in terms of the mean AOD and PM2.5, but WRF‐Chem exhibits skill in capturing the peak AOD. WRF‐Chem also exhibits fidelity in reproducing the spatiotemporal characteristics of the extreme AOD events in intensity, location of centroid, propagation, duration, and their spatial extension. Considerable event‐to‐event variability in model skill in simulating spatial patterns of extreme events is observed, with the highest spatial correlation with MERRA‐2 AOD noted for events centered in the Midwest. Mean fractional bias in modeled peak AOD is minimized for the most intense events and for events centered over the southeastern USA. WRF‐Chem output is also negatively biased in downwelling shortwave radiation at the ground and specific humidity consistent with a positive bias in simulated precipitation relative to MERRA‐2.
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 PM2.5toward 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 days of PM2.5forecast. Persistence fire emission assumption is found to work really well, as the accuracy of PM2.5forecasts 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 PM2.5forecasts 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.
more » « less- NSF-PAR ID:
- 10379072
- Publisher / Repository:
- DOI PREFIX: 10.1029
- Date Published:
- Journal Name:
- Journal of Geophysical Research: Atmospheres
- Volume:
- 125
- Issue:
- 17
- ISSN:
- 2169-897X
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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