Impact of Assimilating Meteorological Observations on Source Emissions Estimate and Chemical Simulations
Zhen Peng, Lili Lei, Zhiquan Liu, Hongnian Liu, Kekuan Chu, Xingxia Kou
Abstract
Abstract 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 SO 2 and NO emissions, and cycling ensemble assimilation of meteorological observations further strengthens the diurnal variations. When independently verified against the observed PM 2.5 , SO 2 , and NO 2 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.