Towards the improvements of simulating the chemical and optical properties of Chinese aerosols using an online coupled model – CUACE/Aero
Towards the improvements of simulating the chemical and optical properties of Chinese aerosols using an online coupled model – CUACE/Aero
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DOI：
10.3402/tellusb.v64i0.18965
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发表时间：
2012-01
期刊：
影响因子：
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通讯作者：
Chunhong Zhou;S. Gong;Xiaoye Zhang;Hongli Liu;M. Xue;G. Cao;X. An;H. Che;Yangmei Zhang
中科院分区：
文献类型：
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作者：
Chunhong Zhou;S. Gong;Xiaoye Zhang;Hongli Liu;M. Xue;G. Cao;X. An;H. Che;Yangmei Zhang
CUACE/Aero是中国气象局统一的气溶胶大气化学环境，是一个综合的气溶胶数值模拟模块，包括排放、气体化学和粒度分离的多组分气溶胶算法。在线耦合到中尺度天气预报模式（MM 5），其性能和改进的气溶胶化学和光学模拟使用的观测数据的气溶胶/气体从密集的观测和从CMA大气监视网络，加上气溶胶光学厚度（AOD）从CMA气溶胶遥感网络（CARSNET）和中分辨率成像光谱仪（MODIS）的数据进行了评估。针对2008年7月13日至31日北京和华北地区，当一个严重的灰霾天气系统发生时，该模型捕捉到的PM10的一般变化与观测的大部分数据在2的因子内，合并的相关系数（r）为0.38（显著性水平=0.05）。相关系数在农村比在城市网站，更好地在白天比在夜间。在化学上，每日平均建模和观测浓度之间的相关系数范围从0.34（黑碳）到0.09（硝酸盐），硫酸盐，铵和有机碳（OC）之间。与PM10一样，化学物种的值白天高于夜间。平均而言，硫酸盐、铵、硝酸盐和有机碳分别被低估了约60%、70%、96.0%和10.8%。黑碳被高估了约120%。根据北京地区大气气溶胶的观测数据，对北京地区的主要人为气溶胶（如BC、OC、硫酸盐、硝酸盐和铵）的一次粒子排放量谱进行了重新构建。这不仅改善了模拟的气溶胶光学厚度和观测的气溶胶光学厚度之间的相关性，而且减少了由原始模式的一次气溶胶粒径分布模拟的气溶胶光学厚度的高估。归一化的平均误差已减少到62%与CARSNET观测和76%与MODIS，从原来的111%和143%，分别。探讨了导致模型中气溶胶浓度低估和其他差异的因素，并从分析中提出了提高模型性能的改进措施。研究发现，气象预报的准确性对重污染事件的发生和积累的模拟起着至关重要的作用，尤其是环流风场和边界层的处理。
ABSTRACT CUACE/Aero, the China Meteorological Administration (CMA) Unified Atmospheric Chemistry Environment for aerosols, is a comprehensive numerical aerosol module incorporating emissions, gaseous chemistry and size-segregated multi-component aerosol algorithm. On-line coupled into a meso-scale weather forecast model (MM5), its performance and improvements for aerosol chemical and optical simulations have been evaluated using the observations data of aerosols/gases from the intensive observations and from the CMA Atmosphere Watch network, plus aerosol optical depth (AOD) data from CMA Aerosol Remote Sensing network (CARSNET) and from Moderate Resolution Imaging Spectroradiometer (MODIS). Targeting Beijing and North China region from July 13 to 31, 2008, when a heavy hazy weather system occurred, the model captured the general variations of PM10 with most of the data within a factor of 2 from the observations and a combined correlation coefficient (r) of 0.38 (significance level=0.05). The correlation coefficients are better at rural than at urban sites, and better at daytime than at nighttime. Chemically, the correlation coefficients between the daily-averaged modelled and observed concentrations range from 0.34 for black carbon (BC) to 0.09 for nitrates with sulphate, ammonium and organic carbon (OC) in between. Like the PM10, the values of chemical species are higher for the daytime than those for the nighttime. On average, the sulphate, ammonium, nitrate and OC are underestimated by about 60, 70, 96.0 and 10.8%, respectively. Black carbon is overestimated by about 120%. A new size distribution for the primary particle emissions was constructed for most of the anthropogenic aerosols such as BC, OC, sulphate, nitrate and ammonium from the observed size distribution of atmospheric aerosols in Beijing. This not only improves the correlation between the modelled and observed AOD, but also reduces the overestimation of AOD simulated by the original model size distributions of primary aerosols. The normalised mean error has been reduced to 62% with the CARSNET observations and 76% with MODIS, from the original 111% and 143%, respectively. The factors resulting in the underestimation of aerosol concentrations and other discrepancies in the model are explored, and improvements in enhancing the model performance are proposed from the analysis. It is found that the accuracy in meteorological predictions plays a critical role on the simulation of the occurrence and accumulation of heavy pollution episode, especially the circulation winds and the treatment of Planetary Boundary Layer (PBL).