摘要
建立了面向PM2.5和PM10观测资料的三维变分同化系统,并在南京地区青奥会期间进行了同化和预报试验.同化系统的控制变量为PM2.5和PM2.5-10(PM10中扣除PM2.5后剩余部分),利用南京地区2014年8月的WRF-Chem模拟结果,估计了PM2.5和PM2.5-10的背景误差协方差,发现在水平和垂直方向上PM2.5的相关系数随距离的衰减均小于PM2.5-10,这可能与PM2.5粒径小、生命史长,在大气中传播地更远有关.利用南京及周边区域的134个监测站PM2.5和PM_(10)逐时观测资料,对青奥会期间(2014年8月16-28日)进行滚动同化和预报试验,并利用模式最内层观测资料进行检验分析,结果表明同化对初始场有显著改进,PM2.5和PM10的相关系数均提高53%以上,均方根误差降低55%以上,平均偏差则降低了90%左右;同化试验对其后的预报场也有明显改进,正效应可以持续到20h以后,模式对PM10的预报效果好于PM2.5.
A 3D-VAR assimilation system was established to assimilate the observations of PM2.5 and PM_(10), and assimilation and forecast experiments were performed during Nanjing Youth Olympic Games(NYOG). The control variables of this assimilation system were PM2.5 and PM2.5-10(that was the rest of PM_(10) after taking out PM2.5). The background error covariances of PM2.5 and PM2.5-10 were estimated by using the simulated products of WRF-Chem of August 2014 in Nanjing. The results showed that the decreases of correlation coefficients of PM2.5 with the distance in the horizontal and vertical directions were less than those of PM2.5-10, with the possible reason being that the particle size of PM2.5 was smaller, the life cycle of it was longer and it spread further in the atmosphere. In addition, the WRF-Chem model was run with assimilation during NYOG(from August 16 th to August 28 th, 2014), by using the hourly data of PM2.5and PM_(10) observed from 134 measurement sites around Nanjing. Evaluated with the observations in the innermost of the model area, the experiment results suggested that the aerosol forecasts of the initial fields can be significantly improved by the assimilation. The correlation coefficients of PM2.5 and PM_(10) increased by over 53%, the root-mean-square errors of the two reduced by over 55%, and the biases reduced by about 90%. The following aerosol forecasts in positive effect can be obviously improved by the assimilation and the benefit from the assimilation of aerosol can last more than 20 hours. The forecast of PM10 was better than that of PM2.5 by the model.
出处
《中国环境科学》
EI
CAS
CSCD
北大核心
2016年第2期331-341,共11页
China Environmental Science
基金
国家自然科学基金项目(42175128)
江苏省科技支撑计划-社会发展重大研究(BE2012771)