黄丛吾,陈报章,马超群,王体健. 2018. 基于极端随机树方法的WRF-CMAQ-MOS模型研究[J]. 气象学报, 76(5):779-789, doi:10.11676/qxxb2018.036
基于极端随机树方法的WRF-CMAQ-MOS模型研究
WRF-CMAQ-MOS studies based on extremely randomized trees
投稿时间:2017-10-25  修订日期:2018-04-10
DOI:10.11676/qxxb2018.036
中文关键词:  多尺度空气质量模型(CMAQ)  WRF  极端随机树  CMAQ-MOS模型
英文关键词:CMAQ  WRF  Extremely randomized trees  CMAQ-MOS model
基金项目:国家重大科技研发项目(2017YFC0209803、2016YFC0208504)和国家自然科学基金项目(91544230、41575145)。
作者单位E-mail
黄丛吾 南京大学大气科学学院, 南京, 210023  
陈报章 中国矿业大学环境与测绘学院, 徐州, 221116  
马超群 南京大学大气科学学院, 南京, 210023  
王体健 南京大学大气科学学院, 南京, 210023 tjwang@nju.edu.cn 
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中文摘要:
      随着城市化、工业化的快速发展,空气污染已经成为了公众最关注的问题之一。为了提高空气质量预报的准确度,以多尺度空气质量模型(Community Multi-Scale Air Quality,CMAQ)为工具,结合中尺度WRF(Weather Research and Forecast Model)气象预报数据、气象观测数据、污染物浓度观测数据,基于极端随机树方法建立了WRF-CMAQ-MOS(Weather Research and Forecast Model-Community Multi-Scale Air Quality-Model Output Statistics)统计修正模型。结果表明,结合WRF气象预报的CMAQ-MOS方法明显修正了由于模型非客观性产生的模式预报偏差,提高了预报效果。使用线性回归方法不能获得较好的优化效果,选取极端随机树方法和梯度提升回归树方法对模型进行改进和比较,发现极端随机树方法对结合WRF气象要素的CMAQ-MOS模型有较大的提升。针对徐州地区空气质量预报,进一步使用基于极端随机树方法的WRF-CMAQ-MOS模型对2016年1、2、3月的空气质量指数(AQI)及PM2.5、PM10、NO2、SO2、O3、CO六种污染物优化试验进行验证,发现优化效果最为明显的两种污染物分别是NO2及O3,2016年1、2、3月整体相关系数NO2由0.35升至0.63,O3由0.39升至0.79,均方根误差NO2由0.0346减至0.0243 mg/m3,O3由0.0447减至0.0367 mg/m3。文中发展的WRF-CMAQ-MOS统计修正模型可以有效提升预报精度,在空气质量预报中具有很好的应用前景。
英文摘要:
      With the rapid development of urbanization and industrialization, air pollution has become one of the problems which are most concerned with. In this paper, the CMAQ (Community Multi-Scale Air Quality) model is combined with the WRF (Weather Research and Forecast Model) forecast of meteorological data, observed meteorological data and pollutants concentration data to build the WRF-CMAQ-MOS statistical correction model based on the method of Extremely Randomized Trees. The output of CMAQ model like AQI and pollutants concentrations of PM2.5, PM10, NO2, SO2, O3 and CO etc. are optimized. In the experiment that combines WRF meteorological elements with the CMAQ-MOS model, it is found that the linear regression method cannot well reflect the effect of optimization. Thereby the Extremely Randomized Trees method and Gradient Boosted Regression Trees were selected to improve the model performance. The investigations show that the Extremely Randomized Trees method has greatly improved the performance of CMAQ-MOS model combined with WRF meteorological elements. Finally, the model and method are evaluated using the data of January, February and March 2016. The results show that the most obvious optimization effect can be found in two pollutants, i.e., NO2 and O3. The correlation coefficient of NO2 in January, February and March 2016 is increased from 0.3542 to 0.6274, the correlation coefficient of O3 is increased from 0.3883 to 0.7886, the root-mean-square error of NO2 is decreased from 0.0346 to 0.0243 mg/m3, and the root-mean-square error of O3 is decreased from 0.0447 to 0.0367 mg/m3. The WRF-CMAQ-MOS statistical correction model developed in this study can effectively improve the prediction accuracy of various pollutants, and thus has a great application prospect in operational air quality prediction.
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