The impacts of assimilating meteorological observations on source emissions estimate and chemical simulations are investigated. Using 6‐hr Global Forecast System (GFS) analyses or cycling ensemble assimilation of meteorological observations have similar diurnal variations of source emissions. Compared to experiment without meteorological analyses, using 6‐hr GFS analyses provides stronger diurnal variations of SO₂ and NO emissions, and cycling ensemble assimilation of meteorological observations further strengthens the diurnal variations. When independently verified against the observed PM_2.5, SO₂, and NO₂ concentrations, simulation forced by posterior source emissions with 6‐hr GFS analyses produces smaller biases and errors than simulation forced by posterior source emissions without meteorological analyses. The biases and errors are generally further reduced with cycling ensemble assimilation of meteorological fields. Therefore, the advantages of cycling ensemble assimilation of meteorological observations to provide realistic meteorological fields and construct flow‐dependent uncertainties of meteorological fields for estimating source emissions and chemical simulations have been demonstrated.

中文翻译：

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同化气象观测对排放源估算和化学模拟的影响

研究了同化气象观测对排放源估算和化学模拟的影响。使用6小时全球预报系统（GFS）分析或气象观测的循环集合同化，源排放的日变化也相似。与没有进行气象分析的实验相比，使用6小时GFS分析可提供更强的SO ₂和NO排放的日变化，而气象观测的循环集合同化则进一步增强了日变化。根据观察到的PM _2.5，SO ₂和NO _2进行独立验证时浓度，通过6小时GFS分析由后源排放强迫进行的模拟所产生的偏差和误差比没有气象分析的由后源排放强迫进行的模拟要小。偏差和误差通常会随着气象领域的循环整体同化而进一步降低。因此，已证明了利用气象观测的循环集合同化来提供现实的气象场并构造依赖于流量的不确定性气象场以估算源排放和化学模拟的优势。