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Abstract In this study, the acquisition method of meteorological observation data of surface, such as cloud cover, wind speed, wind direction and temperature, was investigated to explore how to regulate the standardized application of meteorological observation data of surface in the model. Combined with the demand for meteorological data of AERMOD model, recommended in the “Guidelines for Environmental Impact Assessment-Atmospheric Environment” of HJ2.2-2008 promulgated by China, we set up four scenarios. We used measured SO2 data of Shangdu Power Plant as the verification data. In the case of other model input parameters constant, the four scenarios used 10minutes and 1hour meteorological data of surface of experiment station, and replaced opaque sky cover with total cloud amount and low cloud amount respectively. Scene One used 10minutes meteorological data of surface and replaced opaque sky cover with low cloud amount. Compared to Scene One, Scene Two replaced opaque sky cover with total cloud amount, Scene Three used 1hour meteorological data of surface, Scene Four used 1hour meteorological data of surface and replaced opaque sky cover with total cloud amount. In addition to the differences mentioned above, other meteorological data of surface were the same in the four scenarios. The results indicated that FB was closer to 0in Scene Two, Scene Three and Scene Four, less than that in Scene One. The RHCR in Scene Three and Scene Four was 1.33 and 1.41respectively, closer to 1, showing that Scene Three and Scene Four were better than other scenes in the prediction of high value. The RHCR of Scene Two exceeded Scene Three, indicating that wind had a greater effect on the simulation results in model than cloud. Composite analysis of FB and RHCR and Q-Q plots showed that the simulation of Scene Four was closer to the observation, the meteorological data were fully consistent with the standardized data application method recommanded in this study. This scene regulated standardized application of data in model and improved accuracy of the prediction of atmospheric environmental impact assessment.
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Received: 26 March 2015
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