Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.5194/acp-17-4837-2017 |
Improving PM2.5 forecast over China by the joint adjustment of initial conditions and source emissions with an ensemble Kalman filter | |
Peng, Zhen1,2; Liu, Zhiquan2; Chen, Dan2,3; Ban, Junmei2 | |
2017-04-13 | |
发表期刊 | ATMOSPHERIC CHEMISTRY AND PHYSICS |
ISSN | 1680-7316 |
EISSN | 1680-7324 |
出版年 | 2017 |
卷号 | 17期号:7 |
文章类型 | Article |
语种 | 英语 |
国家 | Peoples R China; USA |
英文摘要 | In an attempt to improve the forecasting of atmospheric aerosols, the ensemble square root filter algorithm was extended to simultaneously optimize the chemical initial conditions (ICs) and emission input. The forecast model, which was expanded by combining the Weather Research and Forecasting with Chemistry (WRF-Chem) model and a forecast model of emission scaling factors, generated both chemical concentration fields and emission scaling factors. The forecast model of emission scaling factors was developed by using the ensemble concentration ratios of the WRF-Chem forecast chemical concentrations and also the time smoothing operator. Hourly surface fine particulate matter (PM2.5) observations were assimilated in this system over China from 5 to 16 October 2014. A series of 48 h forecasts was then carried out with the optimized initial conditions and emissions on each day at 00:00UTC and a control experiment was performed without data assimilation. In addition, we also performed an experiment of pure assimilation chemical ICs and the corresponding 48 h forecasts experiment for comparison. The results showed that the forecasts with the optimized initial conditions and emissions typically outperformed those from the control experiment. In the Yangtze River delta (YRD) and the Pearl River delta (PRD) regions, large reduction of the root-mean-square errors (RMSEs) was obtained for almost the entire 48 h forecast range attributed to assimilation. In particular, the relative reduction in RMSE due to assimilation was about 37.5% at nighttime when WRF-Chem performed comparatively worse. In the Beijing-Tianjin-Hebei (JJJ) region, relatively smaller improvements were achieved in the first 24 h forecast but then no improvements were achieved afterwards. Comparing to the forecasts with only the optimized ICs, the forecasts with the joint adjustment were always much better during the night in the PRD and YRD regions. However, they were very similar during daytime in both regions. Also, they performed similarly for almost the entire 48 h forecast range in the JJJ region. |
领域 | 地球科学 |
收录类别 | SCI-E |
WOS记录号 | WOS:000403956400001 |
WOS关键词 | DATA ASSIMILATION SYSTEM ; AEROSOL OPTICAL DEPTH ; VARIATIONAL DATA ASSIMILATION ; ATMOSPHERIC DATA ASSIMILATION ; OPTIMAL INTERPOLATION METHOD ; MODIS AOD ASSIMILATION ; PM10 DATA ASSIMILATION ; TRANSPORT MODEL ; GOCART MODEL ; GLOBAL-MODEL |
WOS类目 | Environmental Sciences ; Meteorology & Atmospheric Sciences |
WOS研究方向 | Environmental Sciences & Ecology ; Meteorology & Atmospheric Sciences |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/30569 |
专题 | 地球科学 |
作者单位 | 1.Nanjing Univ, Sch Atmospher Sci, Nanjing, Jiangsu, Peoples R China; 2.Natl Ctr Atmospher Res, POB 3000, Boulder, CO 80307 USA; 3.CMA, Inst Urban Meteorol, Beijing, Peoples R China |
推荐引用方式 GB/T 7714 | Peng, Zhen,Liu, Zhiquan,Chen, Dan,et al. Improving PM2.5 forecast over China by the joint adjustment of initial conditions and source emissions with an ensemble Kalman filter[J]. ATMOSPHERIC CHEMISTRY AND PHYSICS,2017,17(7). |
APA | Peng, Zhen,Liu, Zhiquan,Chen, Dan,&Ban, Junmei.(2017).Improving PM2.5 forecast over China by the joint adjustment of initial conditions and source emissions with an ensemble Kalman filter.ATMOSPHERIC CHEMISTRY AND PHYSICS,17(7). |
MLA | Peng, Zhen,et al."Improving PM2.5 forecast over China by the joint adjustment of initial conditions and source emissions with an ensemble Kalman filter".ATMOSPHERIC CHEMISTRY AND PHYSICS 17.7(2017). |
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