摘要
大气成分数据是开展空气质量预报、认识大气污染形成机理、评估空气污染各种效应的基础,而融合了模式结果和观测资料的大气成分再分析数据则有更广泛的应用价值.本文基于WRFDA-Chem空气质量模型和三维变分同化技术,逐时同化地面站点污染物浓度观测资料(包括PM_(2.5)、PM_(10)、O_(3)、SO_(2)、NO_(2)及CO),建立了大气成分数据再分析的方法.以2019年7月和12月为例,构建空间分辨率为10 km、时间分辨率为1 h的全国大气成分再分析数据,并对该方法的性能进行了检验.结果表明,经过再分析后的大气成分数据的时间变化趋势和空间分布得到显著改善,其中,7月PM_(2.5)和O_(3)的平均偏差分别降低了55%和39%,相关系数分别提升了77%和7%,达到0.80和0.98;12月PM_(2.5)和O_(3)的平均偏差分别降低了55%和68%,相关系数分别提升了58%和13%,达到0.98和0.98.综合而言,基于WRFDA-Chem逐小时同化全国站点空气质量监测资料,能够得到高时空分辨率的大气成分再分析数据,可以为认识我国大气污染的演变特征和制定科学的管控措施提供有效支撑.
Atmospheric composition data is the basis for developing air quality forecasts,understanding the formation mechanism of air pollution,and assessing various effects of air pollution,the atmospheric composition reanalysis data integrating model results and observation data has wider application value.Based on the WRFDA-Chem numerical model and three-dimensional variational assimilation technology,after assimilating the observation data of pollutant concentration(include PM_(2.5),PM_(10),O_(3),SO_(2),NO_(2) and CO)from the ground station hour by hour,a method for reanalysis of atmospheric composition data was established.Taking July and December 2019 as an example,the nationwide atmospheric composition reanalysis data are constructed with a spatial resolution of 10 kilometers and a time resolution of 1 hour which can be used to test the method.The results show that the time trend and spatial distribution of atmospheric composition data after reanalysis are significantly improved.In July,the average deviation of PM_(2.5) and O_(3) decreased by 55% and 39%,and the correlation coefficient increased by 77%and 7%to 0.80 and 0.98,respectively;in December,the average deviation of PM_(2.5) and O_(3) decreased by 55% and 68%,and the time correlation coefficient increased by 58% and 13% both to 0.98.In general,based on WRFDA-Chem,assimilating the air quality observation data of national stations hour by hour can obtain atmospheric composition reanalysis data of China with high temporal and spatial resolution,which can provide a powerful support to understand the evolution of Chinses air pollution and formulate scientific control measures.
作者
文金科
王体健
李蒙蒙
谢旻
李树
庄炳亮
吴冬玲
李源昊
李光明
WEN Jinke;WANG Tijian;LI Mengmeng;XIE Min;LI Shu;ZHUANG Bingliang;WU Dongling;LI Yuanhao;LI Guangming(School of Atmospheric Sciences,Nanjing University,Nanjing 210023;Puyang Ecological Environment Monitoring Center,Puyang 457100)
出处
《环境科学学报》
CAS
CSCD
北大核心
2022年第10期147-159,共13页
Acta Scientiae Circumstantiae
基金
国家重点研发计划(No.2020YFA0607802,2019YFC0214603)。
