A three-dimensional variational data assimilation system for a size-resolved aerosol model: Implementation and application for particulate matter and gaseous pollutant forecasts across China
A three-dimensional variational data assimilation system for a size-resolved aerosol model: Implementation and application for particulate matter and gaseous pollutant forecasts across China
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尺寸分辨气溶胶模型三维变分数据同化系统：中国颗粒物和气态污染物预报的实施与应用
DOI：
10.1007/s11430-019-9601-4
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发表时间：
2020-04
期刊：
影响因子：
--
通讯作者：
Liang Yanfei
中科院分区：
文献类型：
--
作者：
Wang Daichun;You Wei;Zang Zengliang;Pan Xiaobin;He Hongrang;Liang Yanfei
本文介绍了一个基于气溶胶粒子尺度分辨模式的三维变分资料同化系统，该模式是一个与化学模式耦合的天气研究与预报模式（WRF-Chem）中的气溶胶相互作用和化学模拟模式（MOSAIC）。这种方法的使用意味着可以同时同化SO2、NO2、CO和O3等气态污染物以及颗粒物（PM2.5、PM10）观测数据。为了验证同化对初始化学场和后续预报的影响，进行了两个为期一个月的平行模拟试验，一个同化了中国环境监测总站（CNEMC）发布的上述6种污染物的地面逐时浓度观测值，另一个没有同化。结果表明，首先，使用DA系统可以提供更精确的模型初始场。分析场PM2.5、PM10、SO2、NO2、CO和O3质量浓度的均方根误差下降了29.27 μg m−3（53.5%）、34.5 μg m−3（50.9%）、30.36 μg m−3（64.2%）、8.91 μg m−3（39.5%）、0.46 mg m−3（47.4%）和15.11 μg m−3（51.0%）。平均分数误差分别降低了42.6%、53.1%、45.2%、43.1%、69.9%和48.8%，相关系数分别提高了0.51、0.55、0.48、0.38、0.47和0.65。其次，分析结果揭示了同化对不同污染物的不同效益。DA显著改善了PM2.5、PM10和CO的预测，导致持续48小时以上的积极影响。DA对SO2和O3预报的正效应可持续8 h，但对NO2预报的正效应相对较差。第三，同化的影响在不同的地区是不同的。DA对PM2.5和PM10预报的正效应可能在中国大部分地区持续48 h以上。事实上，DA显着改善SO2的预测在48小时内在中国北方，和更长的CO同化效益（48小时）被发现在大多数地区，除了中国北部和东部和整个四川盆地。除西南和西北地区外，DA能改善全国48 h内O3的预报效果，其中南方地区的O3 DA效益较为明显，而NO2 DA效益在空间分布上相对较差。
A three-dimensional variational (3DVAR) data assimilation (DA) system is presented here based on a size-resolved sectional aerosol model, the Model for Simulating Aerosol Interactions and Chemistry (MOSAIC) within the Weather Research and Forecasting model coupled to Chemistry (WRF-Chem) model. The use of this approach means that both gaseous pollutants such as SO2, NO2, CO, and O3as well as particulate matter (PM2.5, PM10) observational data can be assimilated simultaneously. Two one-month parallel simulation experiments were conducted, one with the assimilation of surface hourly concentration observations of the above six pollutants released by the China National Environmental Monitoring Centre (CNEMC) and one without assimilation in order to verify the impact of assimilation on initial chemical fields and subsequent forecasts. Results show that, in the first place, use of the DA system can provide a more accurate model initial field. The root-mean-square error of PM2.5, PM10, SO2, NO2, CO, and O3mass concentrations in analysis field fell by 29.27 μg m−3(53.5%), 34.5 μg m−3(50.9%), 30.36 μg m−3(64.2%), 8.91 μg m−3(39.5%), 0.46 mg m−3(47.4%), and 15.11 μg m−3(51.0%), respectively, compared to a background field without assimilation. At the same time, mean fraction error was reduced by 42.6%, 53.1%, 45.2%, 43.1%, 69.9%, and 48.8%, respectively, while the correlation coefficient increased by 0.51, 0.55, 0.48, 0.38, 0.47, 0.65, respectively. Secondly, the results of this analysis reveal variable benefits from assimilation on different pollutants. DA significantly improves PM2.5, PM10, and CO forecasts leading to positive effects that last more than 48 h. The positive effects of DA on SO2and O3forecasts last up to 8 h but that remains relatively poor for NO2forecasts. Thirdly, the influence of assimilation varies in different areas. It is possible that the positive effects of DA on PM2.5and PM10forecasts can last more than 48 h across most regions of China. Indeed, DA significantly improves SO2forecasts within 48 h over north China, and much longer CO assimilation benefits (48 h) are found in most regions apart from north and east China and across the Sichuan Basin. DA is able to improve O3forecasts within 48 h across China with the exception of southwest and northwest regions and the O3DA benefits in southern China are more evident, while from a spatial distribution perspective, NO2DA benefits remain relatively poor.
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