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Study of statistically correcting model CMAQ-MOS for forecasting regional air quality
Based on analysis of the air pollution observational data at 8 observation sites in Beijing including outer suburbs during the period from September 2004 to March 2005, this paper reveals synchronal and in-phase characteristics in the spatial and temporal variation of air pollutants on a city-proper scale at deferent sites; describes seasonal differences of the pollutant emission influence between the heating and non-heating periods, also significantly local differences of the pollutant emission influence between the urban district and outer suburbs, i.e. the spatial and temporal distribution of air pollutant is closely related with that of the pollutant emission intensity. This study shows that due to complexity of the spatial and temporal distribution of pollution emission sources, the new generation Community Multi-scale Air Quality (CMAQ) model developed by the EPA of USA produced forecasts, as other models did, with a systematic error of significantly lower than observations, albeit the model has better capability than previous models had in predicting the spatial distribution and variation tendency of multi-sort pollutants. The reason might be that the CMAQ adopts average amount of pollutant emission inventory, so that the model is difficult to objectively and finely describe the distribution and variation of pollution emission sources intensity on different spatial and temporal scales in the areas, in which the pollution is to be forecast. In order to correct the systematic prediction error resulting from the average pollutant emission inventory in CMAQ, this study proposes a new way of combining dynamics and statistics and establishes a statistically correcting model CMAQ-MOS for forecasts of regional air quality by utilizing the relationship of CMAQ outputs with corresponding observations, and tests the forecast capability. The investigation of experiments presents that CMAQ-MOS reduces the systematic errors of CMAQ because of the uncertainty of pollution emission inventory and improves the forecast level of air quality. Also this work employed a way of combining point and area forecasting, i.e. taking the products of CMAQ for a center site to forecast air pollution for other sites in vicinity with the scheme of model products "reanalysis" and average over the "area".
作 者： XU Jianming XU Xiangde LIU Yu DING Guoan CHEN Huailiang HU Jiangkai ZHANG Jianchun WU Hao LI Weiliang HE Jinhai YANG Yuanqin WANG Jiahe 作者單位： XU Jianming,HE Jinhai(Nanjing University of Information Science & Technology, Nanjing 210044, China)XU Xiangde,LIU Yu,DING Guoan,LI Weiliang(State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081, China)
CHEN Huailiang(Meteorological Bureau of Henan Province, Institute of Meteorological Sciences, Zhengzhou 450000, China)
HU Jiangkai,ZHANG Jianchun,WU Hao,YANG Yuanqin(National Meteorological Centre of China Meteorological Administration, Beijing 100081, China)
WANG Jiahe(National Meteorological Information Centre of China Meteorological Administration, Beijing 100081, China)
刊 名： 中國科學(xué)D輯（英文版） SCI 英文刊名： SCIENCE IN CHINA SERIES D(EARTH SCIENCES) 年，卷(期)： 2005 48(z2) 分類號(hào)： P4 關(guān)鍵詞： CMAQ-MOS point-area combination emission source influence emission inventory air quality【Study of statistically correcting mo】相關(guān)文章：
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