三维变分在PM2.5重污染数值模拟中的应用研究

为了探讨三维变分法(3DVAR)对成渝城市群冬季PM2.5重污染模拟的改善效果,采用3DVAR对成渝城市群2017年12月至2018年1月的空气质量数值模拟结果进行资料同化,对比评估嵌套网格空气质量预报模式(NAQPMS)原始数据与同化再分析数据的准确率,并分析成渝重污染特征。研究结果显示,3DVAR在PM2.5、PM10和NO2的同化实验中均取得较好的改善效果,成渝地区检验站点各污染物相关系数(r)的平均提升比例依次为44%、90%和332%,r改善的站点占检验站点总数的比例分别为98%、100%和82%;检验站点均方根误差(RMSE)的平均下降比例分别为15%、37%和31%,RMSE改善的站点占检验站点总数的比例为65%、98%和84%。与原始模拟结果相比,同化结果能够更准确地反映成渝地区冬季重污染期间的PM2.5和PM10空间分布特征。

Application of Three-Dimensional Variational Data Assimilation on Simulation of PM2.5 Heavy Pollution

In order to explore the improvement effect of 3DVAR assimilation method on PM2.5 heavy pollution simulation in winter of Chengdu-Chongqing urban agglomeration,the results of air quality numerical simulation from December 2017 to January 2018 in Chengdu-Chongqing urban agglomeration were assimilated using 3DVAR. The accuracy of NAQPMS model raw data and assimilation reanalysis data was compared and evaluated,and the local characteristics of heavy pollution were analyzed. The results showed that 3DVAR has achieved better improvement in the assimilation tests of PM2.5,PM10 and NO2. The average increasing ratio of the correlation coefficient (r) of pollutants for the test stations in Chengdu-Chongqing urban agglomeration was 44%,90% and 332%,respectively. The promotion of stations of r accounted for 98%,100% and 82% of the total number of inspection stations. The average improving rate of root mean square error (RMSE) of inspection stations was 15%,37%,and 31%,respectively,which accounted for 65%,98% and 84% of the total number of inspection stations. Compared with the original simulation results,the assimilation results can reflect the spatial distribution characteristics of PM2.5 and PM10 during winter heavy pollution period more accurately in the Chengdu-Chongqing urban agglomeration.