Improved PM2.5 predictions of WRF-Chem via the integration of Himawari-8 satellite data and ground observations

Hong, Jia; Mao, Feiyue; Min, Qilong; Pan, Zengxin; Wang, Wei; Zhang, Tianhao; Gong, Wei

Improved PM2.5 predictions of WRF-Chem via the integration of Himawari-8 satellite data and ground observations

2020

The new-generation geostationary satellites feature higher radiometric, spectral, and spatial resolutions, thereby making richer data available for the improvement of PM₂.₅ predictions. Various aerosol optical depth (AOD) data assimilation methods have been developed, but the accurate representation of the AOD-PM₂.₅ relationship remains challenging. Empirical statistical methods are effective in retrieving ground-level PM₂.₅, but few have been evaluated in terms of whether and to what extent they can help improve PM₂.₅ predictions. Therefore, an empirical and statistics-based scheme was developed for optimizing the estimation of the initial conditions (ICs) of aerosol in WRF-Chem (Weather Research and Forecasting/Chemistry) and for improving the PM₂.₅ predictions by integrating Himawari-8 data and ground observations. The proposed method was evaluated via two one-year experiments that were conducted in parallel over eastern China. The contribution of the satellite data to the model performance was evaluated via a 2-week control experiment. The results demonstrate that the proposed method improved the PM₂.₅ predictions throughout the year and mitigated the underestimation during pollution episodes. Spatially, the performance was highly correlated with the amount of valid data.

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