摘要
为提升天津空气质量数值模式精细化预报能力,基于高分辨率排放源清单,技术应用源反演技术和气溶胶三维变分同化方法开展2020年天津空气质量数值预测分析,评估不同技术对空气质量模式预报能力改进,并结合气象因素评估模式系统性误差,以期提升天津空气质量精细化预报能力,服务分区精细化大气污染防治.结果表明,基于高分辨率排放源清单、源反演技术和气溶胶三维变分同化方法,可有效改进天津空气质量模式预报能力,调整后天津PM_(2.5)、PM_(10)、SO_(2)、NO_(2)和O_(3)浓度预报平均偏差均在2μg·m^(-3)以内,其中高分辨率排放源清单应用后PM_(2.5)平均偏差为1.80μg·m^(-3),源反演技术和气溶胶三维变分同化技术应用后平均偏差分别为-1.45μg·m^(-3)和-3.98μg·m^(-3),均显著小于原模式的18.75μg·m^(-3);PM_(2.5)浓度预报和实况的相关系数,基于高分辨率排放源清单、源反演技术和气溶胶三维变分同化分别提高至0.77、0.80和0.92,相对误差分别下降至33.71%、30.62%和21.91%,空间差异有效预报天数提高至145、175和360 d,优于原空气质量模式PM_(2.5)浓度预报相关系数的0.73、相对误差的35.66%和空间差异有效预报天数的100 d.其中气溶胶三维变分同化技术将天津中-重度过程预报TS评分由0.46提升至0.72,重污染过程预报TS评分由0.60提升至0.80.方法改进后天津空气质量数值模式预报仍存在一定系统性误差,呈现低污染时预报偏高,高浓度时预报偏低;低相对湿度时预报偏高,高相对湿度时预报偏低;低风速时预报偏低,高风速时预报偏高的特征,尤其锋前低压和低压槽天气时PM_(2.5)浓度预报值比实况显著偏低,可根据上述特征进行系统性调整,进一步提升空气质量数值模式预报准确性,精细服务天津大气污染防治.
In order to improve the fine prediction ability of the Tianjin air quality numerical model,based on a high-resolution emission source list,source inversion technology and an aerosol three-dimensional variational assimilation method were applied to carry out the numerical prediction and analysis of Tianjin air quality in 2020.We evaluated the improvement of the air quality model prediction ability of different technologies,combined with meteorological factor assessment model systematic error,in order to improve the Tianjin air quality fine prediction ability.The results showed that the prediction ability of the Tianjin air quality model could be effectively improved based on a high-resolution emission source inventory,a source inversion technique,and an aerosol three-dimensional variational assimilation method.After adjustment,the average deviations in the mass concentration prediction of PM_(2.5),PM_(10),SO_(2),NO_(2),and O_(3) were all less than 2μg·m^(-3).Based on the high-resolution emission source inventory,source inversion technique,and aerosol three-dimensional variational assimilation,the correlation coefficients of PM_(2.5) mass concentration prediction and observation were increased to 0.77,0.80,and 0.92,respectively;the relative errors were reduced to 33.71%,30.62%,and 21.91%,respectively;and the effective forecast days of spatial difference were increased to 145,175,and 360 days,respectively.This was better than the correlation coefficient of the initial air quality model regarding PM_(2.5) mass concentration forecast;the relative error was 35.66%,and the effective forecast days of spatial difference was 100 days.The aerosol three-dimensional variational assimilation technique improved the TS score of the Tianjin moderate-severe process forecast from 0.46 to 0.72 and the heavy pollution process forecast TS score from 0.60 to 0.80.There were still some systematic errors in the prediction of the quality numerical model in Tianjin,such as high prediction at low pollution,low at high concentration,high a
作者
蔡子颖
唐邈
肖致美
杨旭
朱玉强
韩素芹
徐虹
邱晓滨
CAI Zi-ying;TANG Miao;XIAO Zhi-mei;YANG Xu;ZHU Yu-qiang;HAN Su-qin;XU Hong;QIU Xiao-bin(Tianjin Environmental Meteorological Center,Tianjin 300074,China;CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research,Tianjin 300074,China;Tianjin Ecological Environment Monitoring Center,Tianjin 300074,China;Tianjin Institute of Meteorology,Tianjin 300074,China)
出处
《环境科学》
EI
CAS
CSCD
北大核心
2022年第5期2415-2426,共12页
Environmental Science
基金
天津市自然科学基金项目(19JCQNJC08000)
国家自然科学基金项目(41771242)
天津市重大专项(18ZXAQSF00130,18ZXSZSF00160)
中国气象局创新发展专项(CXFZ2021Z034)
中国气象局预报员专项(CMAYBY2019-007)。
关键词
空气质量数值模式
源反演
气溶胶三维变分同化
天津
天气背景
air quality numerical model
source inversion
aerosol three-dimensional variational assimilation
Tianjin
weather background