三维空气质量模型初始场VOCs同化对臭氧预报的影响  

The Influence of VOCs Assimilation in Initial Conditions in a 3D Air Quality Model on Ozone Prediction

作　　者：沈劲 叶钰洁 黎柏良 林玉君 蔡日东 刘军 廖彤 陈多宏 卢清 赵志远 SHEN Jin;YE Yujie;LI Boliang;LIN Yujun;CAI Ridong;LIU Jun;LIAO Tong;CHEN Duohong;LU Qing;ZHAO Zhiyuan(State Environmental Key Laboratory of Regional Air Quality Monitoring,Guangdong Environmental Protection Key Laboratory of Secondary Air Pollution Research,Guangdong Ecological Environmental Monitoring Center,Guangzhou,Guangdong 510308,China;Guangzhou Hexin Instrument Co.,Ltd.,Guangzhou,Guangdong 510535,China;Anhui Landun Photoelectron Co.,Ltd.,Tongling,Anhui 244000,China;Guangdong Provincial Key Laboratory of Water and Air Pollution Control,South China Institute of Environmental Science,Ministry of Ecology and Environment,Guangzhou,Guangdong 510655,China;3 Clear Technology Co.,Ltd.,Beijing 100029,China)

机构地区：[1]广东省生态环境监测中心,国家环境保护区域空气质量监测重点实验室,广东省环境保护大气二次污染研究重点实验室,广东广州510308 [2]广州禾信仪器股份有限公司,广东广州510535 [3]安徽蓝盾光电子股份有限公司,安徽铜陵244000 [4]生态环境部华南环境科学研究所,广东省水与大气污染防治重点实验室,广东广州510655 [5]中科三清科技有限公司,北京100029

出　　处：《环境监控与预警》2023年第5期24-29,共6页Environmental Monitoring and Forewarning

基　　金：广东省重点领域研发计划(2020B1111360003);2022年度国家环境保护区域空气质量监测重点实验室开放基金项目。

摘　　要：于2023年2月15日—3月8日,采用中尺度数值预报模式/嵌套网格空气质量模式系统(WRF/NAQPMS),分析了初始场同化6项常规大气污染物及挥发性有机物(VOCs)对广东省臭氧(O_(3))预报的改进效果。结果表明,同化6项常规污染物可显著降低O_(3)预报的标准化平均偏差(NMB)和均方根误差(RMSE),NMB从-26%改善为-8%,RMSE从50.6μg/m^(3)下降到35.0μg/m^(3)。但对相关系数(r)的改善效果不佳,从0.51下降到0.49。相比于只同化常规6项污染物,同时同化VOCs对O_(3)的预报效果改善较为明显,r从0.49提高到0.63。此外,对NMB和RMSE的改善效果也较好,NMB从-8%改善为-3%,RMSE从35.0μg/m^(3)下降到30.1μg/m^(3)。相比于不同化,同化6项常规污染物的改善效果显著,空气质量指数(AQI)等级预报准确率可提升10%以上,AQI范围预报准确率可提升40%以上。相比于仅同化6项常规污染物,再增加同化VOCs,AQI等级预报准确率和范围预报准确率均提升5%左右,改善程度不高。The improvement effect of assimilating six conventional atmospheric pollutants and VOCs in the initial conditions on ozone prediction in Guangdong was analyzed using WRF/NAQPMS.The evaluation period was from February 15,2023 to March 8,2023.Assimilation of six conventional pollutants could significantly reduce the normalized mean bias(NMB)and root-mean-square error(RMSE)of ozone prediction,improving NMB from-26%to-8%,and reducing RMSE from 50.6μg/m^(3)to 35.0μg/m^(3).However,the improvement on the correlation coefficient was poor,decreasing from 0.51 to 0.49.Compared with only assimilating six conventional pollutants,assimilating VOCs simultaneously improved the prediction of ozone significantly,with a correlation coefficient increased from 0.49 to 0.63.In addition,the improvement on NMB and RMSE was also good.NMB was improved from-8%to-3%,and RMSE was reduced from^(3)5.0μg/m^(3)to 30.1μg/m^(3).Compared with non-assimilation,the improvement of the assimilation of six conventional parameters was significant.The accuracy of AQI level was increased by more than 10%,and the accuracy of AQI range was increased by more than 40%.Compared with only assimilating six conventional parameters,the improvement of adding assimilating VOCs was not significant,with an AQI level and range accuracy increase of about 5%.

关 键 词：空气质量模型 初始场 挥发性有机物同化 臭氧预报 广东省 

分 类 号：X51[环境科学与工程—环境工程]