State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China.
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China; School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China; Collaborative Innovation Center for Geospatial Technology, Wuhan, China.
Environ Pollut. 2020 Aug;263(Pt A):114451. doi: 10.1016/j.envpol.2020.114451. Epub 2020 Mar 25.
The new-generation geostationary satellites feature higher radiometric, spectral, and spatial resolutions, thereby making richer data available for the improvement of PM predictions. Various aerosol optical depth (AOD) data assimilation methods have been developed, but the accurate representation of the AOD-PM relationship remains challenging. Empirical statistical methods are effective in retrieving ground-level PM, but few have been evaluated in terms of whether and to what extent they can help improve PM 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 PM 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 PM predictions throughout the year and mitigated the underestimation during pollution episodes. Spatially, the performance was highly correlated with the amount of valid data.
新一代静止轨道卫星具有更高的辐射、光谱和空间分辨率，从而为改进 PM 预测提供了更丰富的数据。已经开发了各种气溶胶光学深度 (AOD) 数据同化方法，但准确表示 AOD-PM 关系仍然具有挑战性。经验统计方法在地面 PM 的反演中非常有效，但很少有研究评估它们在多大程度上有助于提高 PM 预测。因此，开发了一种基于经验和统计的方案，用于优化 WRF-Chem（天气研究和预报/化学）中气溶胶初始条件（ICs）的估计，并通过整合 Himawari-8 数据和地面观测来改进 PM 预测。通过在中国东部进行的两个为期一年的并行实验对所提出的方法进行了评估。通过为期两周的控制实验评估了卫星数据对模型性能的贡献。结果表明，该方法全年改善了 PM 预测，并减轻了污染期间的低估。从空间上看，该方法的性能与有效数据量高度相关。
