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• W2915130817 abstract "Aerosol optical depth (AOD) is an important parameter characterizing the optical properties of atmospheric aerosols and can be used to indicate aerosol loading and evaluate air quality. In this study, a FY-3/medium-resolution spectral imager (MERSI) AOD data assimilation (DA) system was developed using a three-dimensional variational DA method to assess the impact of Chinese FY-3/MERSI AOD data assimilation on air quality forecasts. Two typical sand-dust weather events occurred during the spring season of years 2010 and 2011 were selected as case study. The DA system and Weather Research and Forecasting model coupled with a chemical model (WRF-Chem) were used to evaluate the impacts of FY-3/MERSI AOD DA on air quality forecasts. This was based on comparisons between modeled AOD data and AOD data acquired by the Aerosol Robotic Network (AERONET) and Moderate Resolution Imaging Spectroradiometer (MODIS) satellites. Results from both case studies revealed that FY-3/MERSI AOD DA apparently improved the air quality forecasts. Key findings of the FY-3/MERSI AOD DA experiments included: (1) FY-3/MERSI AOD DA adjusted the simulated aerosol particle content of the WRF-Chem model and efficiently improved the extinction coefficient fields below 500 hPa. Moreover, AOD DA had the strongest effect on adjusting the extinction coefficients at 750 hPa (approximately 2 km). Compared with the AOD background field, the AOD analysis field was similar to the satellite observation field. (2) Compared with the control experiments without DA, the AOD DA experiment produced more accurate 24-h AOD forecasts, more consistent with the AERONET and satellite observations. (3) Due to the spatial distribution and intensity difference of satellite AOD data, satellite AOD data assimilation has obvious individual characteristics for the improvement of particle concentration prediction. Our study findings suggest that the developed DA system can facilitate the effective use of AOD data acquired by Chinese satellites in air quality forecasting models and can improve dust forecasting results." @default.
• W2915130817 created "2019-03-02" @default.
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• W2915130817 date "2019-05-01" @default.
• W2915130817 modified "2023-10-15" @default.
• W2915130817 title "Assessing the impact of Chinese FY-3/MERSI AOD data assimilation on air quality forecasts: Sand dust events in northeast China" @default.
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• W2915130817 doi "https://doi.org/10.1016/j.atmosenv.2019.02.026" @default.
• W2915130817 hasPublicationYear "2019" @default.
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